# Capy > Capy is the best AI software engineer. Delegate tasks to parallel coding agents that plan, build, and ship — from task to merged PR. Capy uses a dual-agent architecture: Captain (the planner) creates detailed task specifications by analyzing your codebase, and Build (the executor) implements them in isolated cloud VMs. Each task gets its own branch and PR. Teams can run concurrent AI coding agents across supported Claude/GPT/Gemini/Grok/Kimi/GLM/Qwen models. Pricing: Pro is $20/month per org with 3 seats and $20 of usage credits included, then $10/seat beyond that. Usage beyond included credits is pay-as-you-go. Founded in 2024. Backed by Y Combinator (F24). Based in San Francisco. ## Core Pages - [Homepage](https://capy.ai): Main landing page with product overview, features, and pricing - [Pricing](https://capy.ai/pricing): Pro ($20/mo, 3 seats included) and Enterprise plans with feature comparison - [Enterprise](https://capy.ai/enterprise): Enterprise features — SSO, audit logging, custom VMs - [Security](https://capy.ai/security): SOC 2 compliance, encryption, isolated environments, data handling - [About](https://capy.ai/about): Team and company background - [Open Source Program](https://capy.ai/open-source): Free Capy for OSS projects with 1000+ stars and OSI-approved license - [Careers](https://capy.ai/careers): Open positions ## Documentation - [Docs home](https://docs.capy.ai/welcome): Welcome guide and product overview - [Quickstart](https://docs.capy.ai/quickstart): Connect GitHub, launch a thread, review the change, open a pull request - [Models & pricing](https://docs.capy.ai/models-and-pricing): Supported models, token rates, plans, BYOK, and machine time - [Threads](https://docs.capy.ai/threads), [Tasks](https://docs.capy.ai/tasks), [Pull requests](https://docs.capy.ai/pull-requests), [Reviews](https://docs.capy.ai/review), [Automations](https://docs.capy.ai/automations) - [Instructions](https://docs.capy.ai/instructions) and [Skills](https://docs.capy.ai/skills): Teach Capy repository conventions and repeatable workflows - [Integrations](https://docs.capy.ai/integrations/github): GitHub, Slack, Linear, Vercel, Tailscale, and MCP - [API reference](https://docs.capy.ai/api-reference/overview): Create threads, drive agents, manage automations, start reviews - [Docs index for LLMs](https://docs.capy.ai/llms.txt): Every documentation page with a one-line summary ## Product Concepts - Captain: AI planning agent that reads codebases, asks clarifying questions, and writes exhaustive implementation specs. Never writes production code. - Build: AI execution agent that receives specs and implements them in dedicated Ubuntu VMs with full access to Docker, runtimes, git, and terminal. - Review Agent: Automated code review on every PR — finds bugs, security issues, and quality problems. On AI-generated PRs, routes fixes back to Build automatically. - Jams: Individual coding tasks. Each jam runs in its own isolated cloud VM with its own git branch. - Parallel Development: Run multiple concurrent jams, each in isolated cloud environments with their own branches. ## Articles - [What Is Agentic Coding? A Practical Guide for 2026](https://capy.ai/articles/agentic-coding): Learn how agentic coding works, how it differs from autocomplete and chat, where autonomous coding agents fit, and how to use them safely in a real software workflow. Agentic coding is a software development workflow in which an AI agent works toward an engineering outcome through a sequence of actions. The agent does more than generate a snippet: it can inspect a repository, decide what to change, edit several files, run commands, interpret failures, and package the result for review. The important word is **workflow**. A capable model alone does not make development agentic. The surrounding system needs repository context, tools, an execution environment, durable task state, verification, and a clear boundary where a human can inspect the result. ## Agentic coding vs. autocomplete, chat, and background agents These interfaces overlap, but they place responsibility in different parts of the loop. | Mode | Developer supplies | AI supplies | Typical handoff | | ------------------------ | ------------------------------------------- | -------------------------------------------- | ----------------------------------------- | | Autocomplete | Current file and cursor context | A short code continuation | Inline suggestion | | Coding chat | Questions, context, and follow-up direction | Explanations and proposed edits | Conversation or local diff | | Interactive coding agent | A task plus active supervision | Repository exploration, edits, and commands | Local branch or commit | | Background coding agent | Outcome, constraints, and acceptance checks | Multi-step execution in a remote environment | Branch, pull request, logs, and artifacts | Agentic coding can happen in an editor, terminal, or cloud service. A foreground agent is useful when the problem is exploratory and the developer wants to steer each step. A [background coding agent](/articles/background-coding-agents-guide) is useful when the task can be delegated and reviewed asynchronously. ## The agentic coding loop A reliable workflow usually follows six stages. ### 1. Define the outcome A task should describe the behavior that must change, the repository or service involved, constraints that must remain true, and how the result will be checked. “Improve authentication” leaves too many product and security decisions unstated. “Reject expired session tokens in the API middleware, preserve service-token behavior, and run the existing auth integration suite” gives an agent and reviewer a concrete contract. ### 2. Inspect before editing The agent searches the repository, reads nearby conventions, traces call sites, and identifies the smallest safe change. This is where repository documentation, issue context, and prior pull requests matter. An agent that starts generating code before understanding the existing design is still autocomplete with shell access. ### 3. Plan the change For a small fix, the plan may be one edit and one targeted check. Larger work may need a dependency graph: separate tasks for independent packages, sequencing for shared interfaces, and an integration step. Planning should reduce uncertainty, not produce a ceremonial checklist that ignores what the repository reveals. ### 4. Execute in a controlled environment The agent edits files, installs approved dependencies, runs commands, and iterates on failures. An isolated container, worktree, or VM gives the task its own filesystem and process state. That prevents unrelated tasks from overwriting one another and limits the effect of package scripts or shell commands. ### 5. Verify the requested behavior Compilation is evidence, not completion. The agent should run checks close to the changed surface and exercise the actual interface when possible. Depending on the task, that may include tests, type checks, linters, builds, API calls, browser flows, screenshots, or a review of generated artifacts. ### 6. Return a reviewable change The result should arrive on a focused branch or pull request with the relevant diff and verification evidence. A human or review agent can then compare the implementation with the original request, identify regressions, request repairs, and decide whether the change is safe to merge. ## What makes an agent autonomous? Autonomy is not a binary label. It is the amount of useful work an agent can complete between human decisions. A practical coding agent needs several capabilities: - **Repository understanding:** search, file reading, dependency tracing, and awareness of local conventions - **Tool use:** shell commands, package managers, Git, documentation, APIs, and browser automation where needed - **State management:** a durable record of the goal, progress, failures, and next actions - **Error recovery:** the ability to interpret a failed command, revise the approach, and re-run the relevant check - **Environment control:** isolated filesystem and process state with appropriately scoped credentials - **Delivery integration:** branches, commits, pull requests, review findings, and human approval boundaries For the system design behind those capabilities, see our [guide to coding agent architecture](/articles/coding-agent-architecture). ## When agentic coding works well Agentic workflows are strongest when correctness is observable and the work can be reviewed as a bounded change. Good candidates include: - a bug with reproducible steps and a known failing interface - a contained feature with explicit acceptance criteria - a dependency update with established checks - a refactor protected by meaningful integration coverage - a migration with a documented sequence and rollback boundary - documentation or content work with a defined audience and format The common property is not task size. It is **specifiability**: the outcome, constraints, and evidence can be stated well enough for someone else to execute and review. ## When to keep a human in the foreground Do not delegate unresolved judgment and call the result autonomy. Keep the loop interactive when the team is deciding product behavior, diagnosing an active incident, exploring an unfamiliar architecture, handling sensitive production systems, or iterating on visual details that require rapid feedback. Agents also struggle when the repository cannot be reproduced outside one developer laptop, tests do not reflect the important behavior, credentials are overly broad, or the task depends on context that exists only in someone's head. Improving those conditions helps human contributors and CI as much as it helps agents. ## Production guardrails Agentic coding expands the number of actions software can take on a developer's behalf. The controls should expand with it. 1. **Use least privilege.** Grant only the repositories, tools, secrets, and network access required by the task. 2. **Isolate independent work.** Give each task a separate environment and branch; sequence work that touches the same interfaces. 3. **Make setup reproducible.** Document runtime versions, install commands, checks, and safe test credentials. 4. **Require evidence.** Preserve command output, test results, screenshots, or other artifacts that support the completion claim. 5. **Review the diff.** Passing checks do not prove that the implementation matches the product requirement. 6. **Keep consequential actions human-owned.** Merges, deployments, production mutations, and permission changes need explicit approval. These controls are not friction around agentic development. They are what make delegation trustworthy. ## A practical agentic workflow with Capy [Capy](https://capy.ai) separates orchestration from execution. Captain investigates a request, identifies dependencies, and prepares tasks. Build agents implement those tasks in isolated Ubuntu VMs, while Review analyzes pull request diffs and returns structured findings. Independent tasks can run concurrently; dependent work can be sequenced on top of an earlier branch. The workflow is designed around a reviewable unit: 1. describe the desired outcome and connect the repository 2. let Captain investigate and define implementation tasks 3. run independent Build tasks in separate environments 4. verify each change against the repository and requested behavior 5. create focused pull requests and review the diffs 6. keep the merge decision with the team That is one implementation of agentic coding, not the definition of the category. Editor agents, terminal agents, and other cloud platforms make different trade-offs. Evaluate them by how reliably they turn a well-specified task into a verified, reviewable change. --- - [What Are Background Coding Agents?](https://capy.ai/articles/background-coding-agents-guide): Learn how background coding agents differ from editor and CLI agents, how isolated cloud environments work, and when async development is the right fit. A background coding agent is an AI software agent that continues working asynchronously after you delegate a development task. It typically operates in a remote environment, inspects a repository, edits code, runs checks, and returns a branch or pull request for review while you focus on other work. ## Foreground agents and background agents solve different problems AI coding tools started as foreground assistants: autocomplete in an editor, chat beside an open file, or a terminal agent that edits your current checkout. These tools are useful because the feedback loop is immediate. You can answer a question, inspect a diff, change direction, or stop a risky command while you are actively working. A background or cloud agent changes the interaction model. Instead of pairing continuously, you hand off a task and let the agent work in a separate environment. The agent can read files, install dependencies, run tests, iterate on failures, and prepare a branch while your laptop is closed or while you work on something else. Neither model is inherently better. A foreground editor or CLI agent is usually a good fit when the problem is ambiguous and your judgment is part of the loop. A background agent becomes useful when the result can be described clearly enough to review after the work is done. | Workflow | Foreground editor or CLI agent | Background or cloud agent | | --- | --- | --- | | Interaction | Synchronous, turn-by-turn pairing | Asynchronous delegation and handoff | | Typical environment | Your local checkout and tools | Remote sandbox, container, or VM | | Best fit | Exploration, debugging, rapid iteration | Defined tasks with acceptance checks | | Parallel work | Usually limited by local attention and state | Often supports multiple independent runs | | Handoff | Local diff or commit | Branch, pull request, logs, or artifacts | The boundary is not fixed. Some products offer both modes. Cursor, for example, describes its [Cloud Agents](https://cursor.com/docs/cloud-agent) as the cloud counterpart to local agents and notes that they were formerly called Background Agents. OpenAI's [Codex cloud](https://developers.openai.com/codex/cloud) can also receive background tasks, including parallel tasks, in its own cloud environments. ## How isolated environments change the workflow The remote environment is the most important architectural difference. A useful coding agent needs more than repository text: it needs a working checkout, the right runtime, dependencies, setup commands, and a way to verify the result. Isolation makes it possible to give each task its own filesystem and process space instead of asking several agents to modify the same local directory. Different vendors implement that isolation differently. Cursor says its Cloud Agents run in isolated cloud VMs with cloned repositories, dependencies, secrets, startup commands, and network access. Google's [Jules FAQ](https://jules.google/docs/faq/) says each task runs in a fresh VM where Jules clones the repository, installs dependencies, and makes changes from the prompt. GitHub's [Copilot cloud agent](https://docs.github.com/en/copilot/concepts/agents/coding-agent/about-coding-agent) uses an ephemeral GitHub Actions-powered environment and caps a session at 59 minutes. Isolation reduces accidental interference between tasks, but it does not remove security questions. A cloud environment may execute package scripts, call external services, and receive scoped secrets. Teams should decide which repositories are eligible, which credentials can be exposed to a task, whether outbound network access is necessary, and what logs or artifacts are retained. A sandbox is a boundary to configure, not a substitute for review. ## Repository setup determines agent quality Background agents are only as effective as the development environment they can reproduce. A repository that builds only on one engineer's laptop is difficult for an agent for the same reason it is difficult for a new teammate or CI runner. Start with a deterministic setup path: - document the package manager and runtime versions - keep install, type-check, lint, test, and build commands discoverable - provide setup scripts for required system packages - separate safe test credentials from production credentials - record repository-specific conventions near the code - make acceptance checks narrow enough to run during a task Setup should also be economical. If every run spends twenty minutes installing unnecessary tools, background execution will feel slower and cost more than it should. Snapshots, cached dependencies, containers, and explicit setup scripts can help, but the right mechanism depends on the vendor and repository. ## Async handoff works best with reviewable tasks A useful background task describes an outcome, constraints, and a verification path. "Improve the billing system" is too broad. "Reject expired checkout sessions in the webhook handler, preserve idempotency, add the existing integration coverage, and run the billing test suite" gives the agent a bounded target and gives the reviewer a way to evaluate the result. The handoff should include more than a code diff. Review the task transcript or summary, changed files, test output, and pull request description when available. Check whether the agent made assumptions that were not in the request. If a service offers screenshots, browser artifacts, or logs, use them as evidence, not as a replacement for reading the code. Pull request review remains a human responsibility. Background agents can produce plausible changes that compile while missing a product edge case, widening permissions, or silently weakening error handling. The review process should be the same disciplined process used for human-authored changes: inspect the diff, verify the checks, request revisions, and merge only when the code meets the team's standards. ## Task decomposition is a throughput skill Running several tasks concurrently is valuable only when the tasks are independent enough to review and merge. Good decomposition avoids overlapping edits and gives each agent a crisp acceptance check. | Better parallel task | Risky parallel task | | --- | --- | | Add an API endpoint behind an existing service interface | Redesign the entire API surface | | Fix three unrelated bugs in separate modules | Ask three agents to refactor the same shared module | | Update docs while another agent handles a contained bug | Combine product discovery, schema design, and implementation | | Add tests for an established contract | Invent a new architecture without review checkpoints | For large projects, planning and execution may be separate activities. [Devin's advanced capabilities](https://docs.devin.ai/work-with-devin/advanced-capabilities) describe managed sessions that break large tasks into parallel workstreams, with each session in its own isolated VM. [Kiro Web autonomous mode](https://kiro.dev/docs/web/autonomous-mode/) is in preview and describes a flow that clarifies requirements, plans work, delegates to specialized sub-agents, and can open pull requests from an isolated sandbox. Parallelism has a coordination cost. More branches mean more review load, more merge sequencing, and a greater chance that two reasonable changes conflict. Start with a few independent tasks, measure how often they need intervention, and expand only when the review process can absorb the output. ## Current background agent options The category now includes products with different assumptions about where work starts and how it returns to the team: | Product | Current documented approach | | --- | --- | | [Capy](https://docs.capy.ai/welcome.md) | Captain can write task-level specs; Build edits code and runs commands in isolated Ubuntu VMs; Review supports the pull request workflow | | [Cursor Cloud Agents](https://cursor.com/docs/cloud-agent) | Isolated VMs, parallel tasks, multi-repo environments, and entry points from Cursor Web, desktop, Slack, GitHub, Linear, and an API | | [GitHub Copilot cloud agent](https://docs.github.com/en/copilot/concepts/agents/coding-agent/about-coding-agent) | GitHub-native delegation in ephemeral GitHub Actions environments, with a 59-minute maximum per session | | [Codex cloud](https://developers.openai.com/codex/cloud) | Background work, including parallel work, in cloud environments connected to GitHub | | [Jules](https://jules.google/docs/faq/) | GitHub-connected autonomous tasks in fresh VMs with setup scripts | | [Devin](https://docs.devin.ai/work-with-devin/advanced-capabilities) | Managed parallel sessions in isolated VMs, plus playbooks and knowledge workflows | | [Kiro Web autonomous mode](https://kiro.dev/docs/web/autonomous-mode/) | Preview workflow for clarification, planning, specialized sub-agent execution, and pull request handoff | This is not a ranking. Each option makes different trade-offs around editor integration, Git hosting, environment control, models, runtime limits, orchestration, and cost. Read the current vendor documentation before standardizing on a workflow because capabilities and previews change. ## Where Capy fits [Capy](https://docs.capy.ai/welcome.md) is one orchestration-first option in this market. Captain is the planning mode: it reads the codebase and prepares detailed specs. Build is the execution mode: it edits files, runs commands, installs packages, and commits changes inside an isolated Ubuntu VM. Review supports inspecting agent output before merge, and concurrent threads let teams delegate independent work without sharing one mutable environment. Capy also offers model choice, which can matter when teams want to balance quality, latency, and cost by task. Its [pricing documentation](https://docs.capy.ai/pricing.md) separates AI usage, VM runtime, and auxiliary services such as the Review Agent, so the cost model reflects the resources a task consumes. That design is useful for teams that want planning, execution, and review to be visible parts of one asynchronous workflow. It is not the only valid approach. A team already centered on GitHub may prefer Copilot cloud agent, an editor-heavy team may value Cursor's mix of local and cloud workflows, and other teams may prefer Codex, Jules, Devin, or Kiro based on their environment and integration needs. ## Risks to manage explicitly The main risks are operational, not theoretical: - **Over-broad credentials:** a test task should not receive production database access. - **Supply-chain exposure:** install scripts and third-party packages execute inside a real environment. - **Weak specifications:** an agent can complete the wrong interpretation of an underspecified request. - **Review overload:** parallel output is not useful if nobody can inspect it carefully. - **Merge conflicts:** tasks that touch the same files can erase the time saved by concurrency. - **Hidden environment drift:** a cloud build that differs from CI can create false confidence. - **Cost drift:** long runs, large contexts, and repeated retries can consume more budget than expected. Mitigate these risks with scoped repository permissions, scoped secrets, explicit network policies where available, reproducible setup, budget controls, and required pull request review. Treat task logs as audit material. Keep an engineer accountable for the merge decision. ## When not to use a background coding agent Do not delegate every task simply because the workflow is available. Keep work in the foreground when you are diagnosing an active production incident, deciding product behavior, exploring a poorly understood architecture, performing sensitive infrastructure changes, or iterating on visual details that need rapid human feedback. Background execution is also a poor fit when the repository cannot be built or tested outside a specific laptop, when the task requires unrestricted access to production systems, or when the expected change is so small that writing and reviewing the delegation takes longer than doing it directly. The practical rule is straightforward: use a background agent when the task can be stated clearly, executed in an isolated environment, verified with concrete checks, and reviewed as a normal pull request. Use a foreground editor or CLI agent when the important work is the conversation itself. --- - [Best Cloud Coding Agent Platforms in 2026](https://capy.ai/articles/best-cloud-coding-agents-2026): Compare the best cloud coding agents in 2026 across environments, parallelism, PR workflows, repo support, time limits, entry points, and pricing. The best cloud coding agents are Capy, Devin, GitHub Copilot cloud agent, OpenAI Codex, Cursor Cloud Agents, Google Jules, and Kiro Web autonomous mode; Conductor Cloud is an early-access option to watch. The right choice depends less on benchmark claims than on environment setup, parallel execution, pull-request workflow, repository scope, runtime limits, entry points, and billing. Cloud coding agents are becoming a distinct category from local coding assistants. Instead of waiting inside your editor for the next prompt, they clone code into a remote environment, work while you do something else, run validation commands, and return a branch, diff, or pull request. That makes them useful for well-scoped implementation work, backlogs, refactors, CI fixes, and maintenance tasks. The category is also broader than it first appears. Some products are browser-first delegation systems. Some are GitHub-native agents. Others extend an existing editor, desktop app, or local worktree workflow with cloud execution. Several products now support parallel tasks, so an honest comparison should not claim that any one vendor is the only parallel cloud agent. ## How to choose a cloud coding agent Evaluate the workflow around the model, not just the model itself: - **Environment model:** Does each task receive a fresh sandbox, a configurable VM, or a local worktree? Can it install dependencies, use secrets safely, reach required services, and run your real test suite? - **Parallelism:** Can you start independent tasks concurrently? Can the product coordinate child agents, generate multiple candidates, or isolate branches cleanly when the work overlaps? - **PR and review workflow:** Does the agent open pull requests, respond to comments, repair CI failures, summarize diffs, or run a dedicated review pass? - **Repository breadth:** Can one run change multiple repositories, or is each task restricted to one repo and one pull request? - **Time limits:** Are long migrations realistic, or must work be split into shorter sessions? Published limits and preview-stage constraints matter. - **Workflow entry points:** Can work start from a browser, GitHub issue, pull-request comment, editor, Slack message, Linear issue, CLI, API, or schedule? - **Pricing model:** Compare subscription tiers, included usage, model-token charges, VM runtime, Actions minutes, and early-access uncertainty. A cheap seat can still produce variable execution costs. ## Quick comparison | Product | Environment model | Parallelism | PR and review workflow | Repo breadth | Useful entry points | Pricing model | | --- | --- | --- | --- | --- | --- | --- | | Capy | Isolated Ubuntu VM per task | Multiple concurrent jams | Branches, PRs, summaries, structured review findings, Captain-managed triage and fix loop | Best assessed against your connected project setup | Web task workflow | Credit-based plans; AI, VM runtime, and auxiliary services consume credits | | Devin | Isolated VM per managed Devin session | Coordinator can delegate parallel workstreams | Designed to plan, code, test, and ship; managed sessions can open separate PRs | Advanced docs describe work spanning repositories | Web plus integrations such as Slack, Linear, Jira, and MCP on paid plans | Free, Pro, Max, Teams, and Enterprise plans with quotas and pay-as-you-go options | | GitHub Copilot cloud agent | Ephemeral GitHub Actions-powered environment | Background tasks, with one branch and one PR per assigned task | Branch work, iteration, optional PR creation, PR-comment changes, automations | One repository per task | GitHub.com, issues, PR comments, VS Code, and integrations | Paid Copilot plans; uses AI credits and GitHub Actions minutes | | OpenAI Codex cloud/web | Own cloud environment per task | Background tasks can run in parallel | Connect GitHub and create PRs; delegate from GitHub with `@codex` | Choose a repo for cloud tasks | Web, editor extension, GitHub, plus Codex app and terminal surfaces | Included with eligible ChatGPT plans; check current usage allowances | | Cursor Cloud Agents | Isolated cloud VMs with full development environments | Run as many agents as needed in parallel | Separate branch, push changes, merge-ready PRs, artifacts, remote desktop control | Explicit multi-repo environments | Cursor Web, desktop, Slack, GitHub, Linear, API, mobile PWA | Paid Cursor plan required; cloud work billed at selected-model API pricing | | Google Jules | Fresh cloud VM per task | Supports multiple tasks; CLI also documents parallel candidates | Autonomous GitHub work, PRs, CI-failure fixes on Jules-created PRs | GitHub-connected repository tasks; API also supports repoless sessions | Web, API, CLI, schedules | Multiple plans, including a no-cost plan | | Kiro Web autonomous mode | Isolated sandbox | Specialized sub-agents execute a planned task | Clarifies, plans, executes, opens PRs, and addresses PR comments | Documents tasks across one or more repositories | Kiro Web and GitHub feedback | Preview feature; verify current availability and plan terms | | Conductor | Desktop product uses local Mac worktrees; Cloud is early access | Desktop product runs parallel Codex and Claude Code agents | Local reviewable diffs and merge workflow; Cloud details remain limited | Local repo workspaces today | macOS desktop; Cloud waitlist | Desktop product available; Cloud pricing not yet published | ## The best cloud coding agents in 2026 ### 1. Capy — Best for a planned task-to-review workflow [Capy](https://docs.capy.ai/welcome.md) separates planning from execution: Captain reads the codebase and writes detailed specs, while Build edits files, runs commands, installs packages, and implements tasks in its own VM. That division is useful when you want to queue more than isolated one-line fixes. You can run multiple concurrent jams without pretending that parallelism is unique to Capy. The review workflow is the more meaningful differentiator. Capy's [PR review documentation](https://docs.capy.ai/review.md) describes summaries, structured findings with categories and severities, inline GitHub comments for medium-or-higher findings, and a Captain-managed triage-and-fix loop for tasks it owns. That can reduce the manual work between an agent finishing an implementation and a reviewer seeing a cleaner PR. Capy uses a credit model rather than a simple per-seat promise. Its [pricing documentation](https://docs.capy.ai/pricing.md) says credits cover AI usage, isolated Ubuntu VM runtime, and auxiliary services such as the Review Agent; published plans start at $20 per month. This makes costs legible, but teams should still budget for model choice, runtime, and task complexity. **Best for:** Teams that want planning, implementation, and review stages in one cloud workflow. ### 2. Devin — Best for coordinated large workstreams [Devin's advanced capabilities](https://docs.devin.ai/work-with-devin/advanced-capabilities) go beyond launching independent sessions. A coordinator can break a large task into work packages, start managed Devins in parallel, monitor consumption, message child sessions, and compile results. The documentation specifically frames this for migrations, bulk test coverage, parallel research, and work spanning modules or repositories. That orchestration layer is valuable when a large job can be decomposed safely. It also requires judgment: parallel agents can create coordination overhead or conflicting diffs if the work packages are poorly scoped. Devin's [pricing page](https://devin.ai/pricing) lists Free, Pro, Max, Teams, and Enterprise offerings; paid individual plans describe up to 10 concurrent sessions, while Teams and Enterprise describe unlimited concurrent sessions. **Best for:** Organizations delegating migrations, repetitive refactors, or reusable playbook-driven work. ### 3. GitHub Copilot cloud agent — Best for GitHub-native delegation [GitHub Copilot cloud agent](https://docs.github.com/en/copilot/concepts/agents/coding-agent/about-coding-agent) is the natural shortlist candidate when issues, pull requests, and repository policy already live in GitHub. It works in an ephemeral GitHub Actions-powered environment, can research a repository, plan, change code on a branch, run tests and linters, iterate, and optionally open a pull request. GitHub also documents issue assignment, PR-comment requests, VS Code entry points, integrations, and automations. Its boundaries are unusually clear. GitHub documents one repository, one branch, and exactly one pull request per assigned task, plus a hard maximum session time of 59 minutes. That is a reasonable fit for incremental backlog items, but a poor fit for one-shot cross-repo migrations or tasks that cannot be split into focused units. Costs use paid Copilot access, AI credits, and GitHub Actions minutes. **Best for:** Teams that want low-friction delegation inside an existing GitHub workflow. ### 4. OpenAI Codex cloud/web — Best for OpenAI-centered multi-surface work [Codex cloud](https://developers.openai.com/codex/cloud) can read, edit, and run code in its own cloud environment, including background tasks that run in parallel. After connecting GitHub, you can configure environments, delegate work from the editor extension, create pull requests, and tag `@codex` on GitHub issues or pull requests to propose changes. OpenAI's broader [Codex product page](https://openai.com/codex/) positions the agent across app, editor, terminal, worktree, cloud-environment, automation, and review workflows. That breadth is attractive if your team already uses ChatGPT and wants the same agent across multiple surfaces. Compare current plan allowances carefully: the cloud docs state that eligible Plus, Pro, Business, Edu, and Enterprise plans include Codex, but actual usage needs vary by team. **Best for:** Teams invested in OpenAI models and a connected app, web, editor, terminal, and GitHub workflow. ### 5. Cursor Cloud Agents — Best for environment depth and entry-point breadth [Cursor Cloud Agents](https://cursor.com/docs/cloud-agent), formerly called Background Agents, run in isolated cloud VMs with cloned repositories, installed dependencies, secrets, startup commands, and network access. Cursor documents parallel agents, multi-repo environments, MCP servers, hooks, artifacts such as screenshots and videos, and remote desktop control. These are practical capabilities when verification requires more than compiling a patch. Cursor also offers one of the broadest lists of starting points: web, desktop, Slack, GitHub, Linear, API, and a mobile PWA. Cloud Agents require a paid Cursor plan and are billed at API pricing for the selected model. For teams already using Cursor as an editor, the cloud mode can be a gradual extension rather than a wholesale workflow change. **Best for:** Teams that value full VM setup, multi-repo work, visual artifacts, and many delegation surfaces. ### 6. Google Jules — Best for Google-native autonomous maintenance [Google Jules](https://jules.google/docs/faq/) is a GitHub-integrated autonomous coding agent. Its FAQ says each task runs in a fresh cloud VM where Jules clones the repository, installs dependencies, and makes changes from your prompt; it also offers a plan available without cost. This makes it approachable for trying background delegation without committing to an enterprise rollout. The [Jules changelog](https://jules.google/docs/changelog/) shows a widening maintenance workflow: scheduled tasks, suggested tasks, an API, CLI support, selected MCP integrations, and automatic fixes for CI failures on pull requests Jules creates. The CLI changelog also documents a `--parallel` option for generating multiple suggestions, with a maximum of five candidates. Jules is worth evaluating if proactive maintenance and Google model access are more important than a broad vendor-neutral model menu. **Best for:** Teams exploring scheduled maintenance, CI repair, and GitHub automation in Google's ecosystem. ### 7. Kiro Web autonomous mode — Best preview for structured autonomous execution [Kiro Web autonomous mode](https://kiro.dev/docs/web/autonomous-mode/) is explicitly a preview feature. When enabled, the agent asks clarifying questions, builds a plan and acceptance criteria, delegates steps to specialized sub-agents, works in an isolated sandbox, and can open one or more pull requests when complete. The documentation also describes tasks across one or more repositories and a feedback loop where PR comments can trigger updates. The preview label matters. Kiro's structured clarification and planning flow is promising for well-defined features and refactors, but buyers should verify current access, pricing, stability, and limits before standardizing a production workflow around it. In collaborative mode, Kiro is also positioned for tasks where you want more step-by-step interaction. **Best for:** Teams willing to test a preview workflow with explicit clarification, planning, and sub-agent execution. ### 8. Conductor — Best local desktop option to watch as Cloud matures [Conductor](https://www.conductor.build/) needs careful framing. Its generally available product is a Mac desktop app that runs Codex and Claude Code agents in parallel using isolated local git worktrees, with reviewable diffs and a merge workflow. That can be useful, but local worktrees are not the same environment model as remote cloud VMs. [Conductor Cloud](https://www.conductor.build/cloud) is a separate early-access offering described as a way to run a team of coding agents in the cloud. The public Cloud page is currently a waitlist, not a detailed generally available platform with published pricing and operational limits. Include Conductor when comparing the direction of the category, but do not represent its Cloud product as fully GA. **Best for:** Mac users who want parallel local agents today and are interested in Conductor's early-access Cloud direction. ## Which one should you pick? Start with the work you actually want to delegate. For a queue of scoped features with a review loop, evaluate Capy. For a migration that decomposes into coordinated work packages, evaluate Devin. For routine issues owned by a GitHub-centric team, try Copilot cloud agent. For OpenAI-centered workflows across several surfaces, test Codex. For multi-repo environments, rich verification artifacts, and many entry points, look closely at Cursor Cloud Agents. Jules is a credible option for teams drawn to Google's ecosystem and maintenance automation. Kiro Web autonomous mode is worth a preview evaluation if its clarification-first flow matches your development culture. Conductor is useful today as a local Mac worktree orchestrator, while its cloud offering should be treated as early access until more product detail is public. Run the same representative tasks through two or three finalists: one incremental feature, one bug with a reliable reproduction, and one change that exercises your actual tests or preview environment. Then compare not only whether code was produced, but how much setup, correction, review, and cost it took to reach a mergeable result. --- - [Codex Cloud vs Capy](https://capy.ai/articles/codex-cloud-vs-capy): Codex Cloud and Capy both run coding agents in isolated environments. Compare cloud execution, agent orchestration, model choice, reviews, integrations, automations, and pricing. Codex Cloud and Capy both delegate real coding work to background agents in isolated environments. Choose Codex for an OpenAI- and ChatGPT-native experience with a polished multi-agent app and automations. Choose Capy for cross-provider model choice and an explicit Captain → Build → Review orchestration loop with structured PR triage. ## TL;DR - Codex Cloud reads, edits, and runs code in its own cloud environments. It works in the background, supports parallel delegation, connects to GitHub, and can turn completed work into pull requests. - The Codex app expands that workflow with multi-agent project management, built-in worktrees, skills, and scheduled automations. Codex is not a single-task tool. - Capy runs Build agents in isolated Ubuntu VMs and separates planning, implementation, and review into distinct roles: Captain → Build → Review. - Codex is the best fit for teams centered on OpenAI and ChatGPT-connected workflows. Capy is the better fit when provider choice and an explicit review triage-and-fix loop matter most. ## What is Codex Cloud? [Codex Cloud](https://developers.openai.com/codex/cloud) is OpenAI's hosted environment for its coding agent. Codex can read, edit, and run code, and cloud tasks continue in the background while you work on something else. OpenAI explicitly supports running tasks in parallel, so the right mental model is delegated cloud engineering work rather than a single synchronous coding chat. The GitHub connection is central to the workflow. After connecting an account, Codex can work with code from your repositories and create pull requests from completed work. Teams can also delegate from GitHub by tagging `@codex` on issues and pull requests, or start a cloud task from the IDE extension and monitor the result before applying its diff locally. [Codex cloud environments](https://developers.openai.com/codex/cloud/environments) are configurable. A task starts in a container with your repository checked out at a selected branch or commit, runs your setup script, applies your internet-access policy, then lets the agent edit files and run terminal commands in a validation loop. You can pin runtime versions, install dependencies automatically or through a custom setup script, provide environment variables, and use secrets during setup. Cached container state can be reused for up to 12 hours, with an optional maintenance script for resumed environments. Internet access is also a deliberate control rather than an all-or-nothing assumption. Setup scripts can reach the internet to install dependencies. During the agent phase, access is off by default, but it can be configured as limited or unrestricted when a task genuinely needs network access. ## What is Capy? [Capy](https://capy.ai) is an AI development platform for delegating coding work to agents that run in isolated Ubuntu VMs. Each Build task has its own branch, conversation history, and environment, so agents can edit files, install packages, run commands, and validate changes without interfering with your local checkout or another task. Capy's defining design choice is role separation. **Captain** reads the codebase, plans work, and writes detailed task specifications. **Build** executes the coding work in a VM. **Review** analyzes pull request diffs, produces structured findings with categories and severity, and feeds real issues back into the workflow. When Captain manages a task, it can triage findings, mark false positives as irrelevant, route confirmed issues back to Build, and re-review updated code. Capy also provides broad model selection rather than centering the product on one provider. Teams can choose among models including Claude Opus 4.7 and 4.6, GPT-5.5 and GPT-5.3-Codex, Gemini 3.1 Pro, and Grok 4.1 Fast. That matters when different repositories, budgets, or task types benefit from different tradeoffs in reasoning quality, speed, context size, and price. ## Codex Cloud is more than cloud execution It would be inaccurate to describe Codex as limited or single-task. The [Codex app](https://openai.com/codex/) is designed as a command center for multi-agent workflows. OpenAI highlights parallel agents across projects, built-in worktrees, cloud environments, and skills that adapt the agent to a team's standards and recurring work. [Codex automations](https://developers.openai.com/codex/app/automations) extend that model into recurring background work. An automation can report findings into a triage inbox or archive a run when there is nothing to report. In Git repositories, it can run in the local project or on a dedicated worktree, which keeps automated changes separate from unfinished local work. Automations can use skills, plugins, schedules, sandbox controls, and thread-based wake-ups for ongoing work such as monitoring a deployment, checking PR status, or addressing new review feedback. This is a meaningful advantage for teams already invested in OpenAI's ecosystem. Codex spans connected surfaces: the app, cloud delegation, editor workflows, terminal usage, and ChatGPT-account-based access. If your team wants one OpenAI coding agent that follows work across those surfaces, Codex has the more native experience. ## Head-to-head comparison | Feature | Codex Cloud | Capy | |---------|-------------|------| | Primary experience | OpenAI coding agent across cloud and ChatGPT-connected surfaces | AI development platform with explicit agent roles | | Background execution | Yes, including parallel cloud tasks | Yes, with parallel Build tasks | | Environment | Configurable cloud containers | Isolated Ubuntu VM per Build task | | Repository workflow | GitHub connection, diffs, PR creation, `@codex` delegation | Task branches, diffs, PR creation, Captain-managed handoffs | | Environment setup | Setup scripts, optional maintenance scripts, cached state | Full VM access for installs, commands, runtimes, and tools | | Internet controls | Configurable; off by default during the agent phase | VM-based execution with task tooling and web access | | Multi-agent workspace | Codex app with parallel agents and built-in worktrees | Captain delegates parallel Build tasks | | Skills | Yes | Yes | | Automations | Codex app automations with schedules, inbox triage, and worktree options | Slack and Linear integrations for team workflows | | Model selection | OpenAI coding models | Cross-provider selection across Anthropic, OpenAI, Google, xAI, and more | | Review workflow | Codex supports code review workflows | Dedicated Review role with structured findings, triage, fix routing, and re-review | | Pricing | Included with eligible ChatGPT plans | Usage tiers from $20/month | ## Where Codex wins **OpenAI- and ChatGPT-native workflows.** Codex is the clear choice when a team wants an OpenAI coding agent that feels continuous across its app, cloud environments, editor, terminal, and GitHub delegation. The product is designed around that connected experience rather than around selecting among many providers. **A dedicated multi-agent desktop app.** The Codex app gives engineers a command center for agents working across projects. Built-in worktrees are especially useful when several agents or automation runs need to make changes without colliding with unfinished local work. **Automations as a first-class feature.** Codex automations are unusually concrete: recurring work can run on schedules, keep context through thread automations, report findings into an inbox, use skills and plugins, and run against isolated worktrees. For routine monitoring, triage, or follow-up loops, that is a strong reason to prefer Codex. **Fine-grained cloud environment configuration.** Setup scripts, maintenance scripts, runtime pinning, cached state, secrets limited to setup, and configurable agent internet access give teams practical controls for hosted execution. ## Where Capy wins **Cross-provider model choice.** Capy lets teams choose models from Anthropic, OpenAI, Google, xAI, and other providers instead of committing the entire workflow to one model family. You can use a fast, economical option for routine edits, a stronger reasoning model for architecture-heavy changes, or compare approaches by running the same task with different models. **Explicit orchestration roles.** Capy makes the handoffs visible and deliberate. Captain plans, Build implements, and Review checks the result. This separation is useful for larger work where a detailed implementation spec, an isolated coding run, and a structured review pass should be treated as distinct stages rather than blended into one thread. **Structured review triage and fixing.** Capy's Review Agent assigns categories, severity, and code locations to findings. When Captain manages the task, it can distinguish false positives from real issues, route confirmed issues to Build, and re-review the updated pull request. That closed loop is valuable when your priority is moving from finding a problem to resolving it with minimal manual coordination. **Team integrations and an accessible entry point.** Capy connects to Slack and Linear, which helps teams delegate work from the tools where planning and discussion already happen. Capy Pro tiers start at $20 per month, with usage charged through credits for AI tokens, VM runtime, and auxiliary services such as review. ## Which should you choose? Choose Codex when your team wants OpenAI's coding agent as the center of its workflow. Its cloud tasks are capable background workers, its GitHub integration covers practical pull request workflows, and the Codex app adds a thoughtful multi-agent workspace with worktrees, skills, and automations. It is particularly compelling for teams already using ChatGPT plans that include Codex and for engineers who want scheduled background work as a native part of the coding-agent experience. Choose Capy when you want an orchestrated development workflow that stays model-flexible. The Captain → Build → Review structure is easy to reason about: planning is separate from execution, execution happens in isolated Ubuntu VMs, and review produces structured findings that can return to Build for fixes. Capy is also the stronger option when your team wants to select Claude, GPT, Gemini, Grok, or other models according to the task rather than standardizing on one provider. Neither product should be reduced to a simple coding chatbot. Both can take real engineering work into hosted environments, run commands, change code, and support pull request workflows. The decision is about emphasis: Codex offers the more cohesive OpenAI-native app and automation story, while Capy offers broader provider choice and a more explicit orchestration-and-review system. --- - [Agent Architecture for Software Development](https://capy.ai/articles/coding-agent-architecture): A technical guide to coding agent architecture: orchestration, isolated execution environments, tools, context management, verification, PR review, and parallel scheduling. Coding agent architecture for software development is the system design that turns an engineering request into a verified code change. It coordinates planning, model reasoning, isolated execution, tools, context management, Git operations, automated checks, review, and human approval so an agent can complete real repository work without treating code generation as the whole job. ## Why architecture matters more than code generation A coding agent is not just a language model with permission to write files. Useful software work is a stateful control problem. The system must understand a request, inspect a repository, choose a sequence of actions, execute commands, interpret failures, preserve progress, and produce an artifact that can enter a team's normal delivery process. That is why model benchmarks alone do not tell you whether an agent platform will work for your team. Current models such as Claude Opus 4.7 or 4.6, GPT-5.5 or GPT-5.3-Codex, Gemini 3.1 Pro, and Grok 4.1 Fast may each be suitable for different workloads. The surrounding architecture determines whether the model receives the right context, whether it can safely use tools, and whether its output is verified before merge. A robust design should make failures legible. If a test fails, the execution worker needs the command output. If two tasks overlap, the scheduler needs dependency information. If a pull request contains a subtle regression, the review layer needs the intended behavior as well as the diff. If a task runs long, the context layer needs to compact history without discarding the implementation state. ## The layers of a coding agent system The exact product boundary varies, but production systems generally need the following layers. | Layer | Primary responsibility | Failure mode it should prevent | | --- | --- | --- | | Request intake | Capture scope, repository, constraints, and desired outcome | Starting with an ambiguous or unauthorized task | | Planner or orchestrator | Decompose work, identify dependencies, delegate, and monitor progress | Asking one session to improvise a large project end to end | | Execution workers | Inspect code, edit files, run commands, and iterate | Producing untested snippets instead of repository changes | | Isolated environment | Provide a controlled filesystem, branch, dependencies, and process space | Cross-task interference or damage to a developer machine | | Tools, MCP, and browser access | Connect the agent to terminals, code search, APIs, documentation, and web interfaces | Reasoning without the evidence or capabilities the task requires | | State, context, and compaction | Preserve goals, progress, observations, and handoffs | Losing the plot during long-running work | | Verification | Run tests, builds, type checks, linters, and targeted acceptance checks | Treating plausible code as correct code | | Git and PR boundary | Package changes into reviewable branches and diffs | Mixing unrelated edits or bypassing normal delivery controls | | Review and triage | Analyze diffs, emit findings, prioritize fixes, and re-check changes | Merging defects that automated tests miss | | Human approval | Keep consequential decisions with accountable people | Allowing autonomy to silently become merge authority | These layers are separable even when one user interface hides the transitions. Separating them makes the system easier to reason about: a worker should not invent product scope while deep in a failing build, and a reviewer should not assume that passing tests prove the diff matches the request. ## Request intake and planning Request intake should capture more than a prompt string. At minimum, the system needs the target repository, base branch, explicit constraints, acceptance checks, relevant issue or pull request context, and the permissions available to the run. High-quality intake also records which actions require approval: opening a pull request may be routine, while changing infrastructure or merging code may not be. The planner or orchestrator turns that request into an execution graph. For a small fix, the graph may contain one worker and one verification pass. For a larger initiative, it may contain several independent tasks, a dependency chain, and a final integration step. The planner should distinguish parallelizable work from coupled work. Two workers can often update separate packages safely; two workers rewriting the same shared interface need coordination or sequencing. Capy uses this separation concretely. Its [documentation](https://docs.capy.ai/using-capy.md) describes **Captain** as the planning mode: Captain reads the codebase, creates detailed task specifications, and delegates work. Captain does not edit files or run commands. **Build** is the execution mode: it edits files, runs commands, installs packages, browses the web, and commits code inside an isolated Ubuntu VM. This is a useful boundary because planning quality and implementation quality are different concerns. ## Execution workers and isolated environments An execution worker needs a real development environment, not only a patch-generation API. It should be able to search the repository, read nearby conventions, edit multiple files, install dependencies, invoke the compiler, run tests, and inspect failures. The worker's loop is empirical: form a hypothesis, change the repository, run the most relevant check, and update the plan from evidence. Isolation is the safety and concurrency primitive behind that loop. A dedicated cloud VM gives the task its own filesystem and processes. It can install packages or run a container without polluting a developer laptop. More importantly, separate tasks do not fight over uncommitted edits, ports, generated artifacts, or dependency versions. Capy's [welcome guide](https://docs.capy.ai/welcome.md) states the basic contract plainly: describe the task, an agent builds it in its own VM, then review the diff and create a pull request. Capy's Build agent runs in an isolated Ubuntu VM with common languages and tools available. That architecture supports independent task branches while keeping the developer's main branch clean. Cloud VMs are not free. They add startup cost, resource management, and environment-configuration work. For a fast, interactive one-file edit, a local session can feel more direct. For concurrent tasks, dependency installation, Docker-based services, or work that should continue in the background, the VM boundary usually earns its cost. ## Tools, MCP, and browser access The worker is only as effective as its tool surface. Filesystem reads and writes, code search, Git, and shell execution cover many repository tasks. Real workflows often also need documentation lookup, issue and pull request context, database inspection, screenshots, or interactions with a browser-based product. Model Context Protocol (MCP) servers provide one way to expose external systems through typed tools. MCP can connect an agent to project-management systems, monitoring data, design tools, or internal services without stuffing every possible fact into the initial prompt. Browser automation fills a different gap: it can validate user-facing behavior, reproduce a UI bug, or operate systems that lack a convenient API. Tool access should follow least privilege. A worker that only needs read-only documentation should not receive production mutation credentials. Shell commands should run inside the task environment. Sensitive actions should be logged and, where appropriate, gated by human approval. Architecture is not merely about giving an agent more tools; it is about assigning capabilities at the narrowest useful boundary. ## State, context, and compaction A repository, an issue, tool outputs, diffs, and chat history can exceed the useful attention span of a model long before the task is complete. A durable coding-agent system therefore separates operational state from conversational context. Operational state includes the current branch, changed files, command results, open findings, task status, and dependency graph. Context is the bounded information passed into the next model turn. The context layer should retain the specification, key observations, decisions, verification status, and next steps while removing redundant logs and stale exploration. Capy documents a handoff mechanism for long tasks: Build and Captain can continue work in a fresh context with a concise summary, progress, and next steps. As context grows, the system provides progressive reminders before a handoff becomes required. This is a practical form of compaction. It treats context as a managed resource rather than relying on an indefinitely growing transcript. ## Verification, Git boundaries, and review Verification should happen continuously, not as a ceremonial final step. A worker should begin with targeted checks close to the edited surface, then run the broader build or test suite required by the repository. Static analysis, tests, formatting, build output, and an exercised user flow answer different questions. No single check substitutes for the rest. Git creates an important boundary between autonomous execution and team delivery. Each task should land on a dedicated branch with a focused diff. The pull request becomes a reviewable artifact: humans and automated systems can inspect exactly what changed, compare it with the request, and decide whether it is ready to merge. The worker can prepare the change without owning the final decision. Review deserves its own agent role. Capy's [PR review documentation](https://docs.capy.ai/review.md) describes a **Review** agent that reads pull request diffs and emits structured findings with a title, rationale, category, severity, and code location. Captain can then triage the findings, mark false positives as irrelevant, recognize resolved issues, and delegate real fixes back to Build. This creates a controlled loop: implementation, verification, review, triage, repair, and re-review. Human approval remains necessary. Tests cannot decide whether a product compromise is acceptable. A reviewer cannot infer every operational risk. Merge authority, permission-sensitive actions, and high-impact changes should stay with accountable humans even when agents do most of the mechanical work. ## Parallel scheduling and dependency handling Parallel agents increase throughput only when the scheduler understands dependencies. A backlog is not automatically a set of safe concurrent jobs. The orchestrator should identify shared files, schema dependencies, generated artifacts, ordering constraints, and integration risks before launching workers. The cleanest fanout pattern is one independent deliverable per environment and branch. Workers can progress concurrently, while the scheduler monitors status and routes blockers. If task B depends on an interface introduced by task A, the scheduler can wait, rebase onto A's branch, or define a stable contract before either worker begins. If two tasks unexpectedly collide, the system should surface the conflict rather than silently combine edits. Parallelism also applies inside a task. Read-only research, independent code searches, or unrelated verification commands may run concurrently. Editing the same file from multiple workers is a different matter: local speedups can create integration debt. The architecture should maximize useful concurrency, not worker count. ## Architectural trade-offs in practice No coding-agent architecture is universally best. The right choice depends on task size, coupling, environment needs, review requirements, and how much workflow automation a team wants. | Pattern | Strengths | Trade-offs | Best fit | | --- | --- | --- | --- | | Single session | Fast feedback, low orchestration overhead, direct developer control | Context can become noisy; limited throughput; shared local state | Small fixes and interactive exploration | | Planner → worker | Clear specifications, cleaner execution focus, easier delegation | Additional coordination step; plan can become stale if reality differs | Multi-step features and well-scoped backlog work | | Multi-agent fanout | Concurrent progress across independent tasks | Conflict resolution, dependency tracking, and integration overhead | Batches of separable work across modules or repositories | | Local worktrees | Lightweight branch isolation with familiar local tools | Shared machine resources; setup drift; developer machine remains involved | Desktop workflows with moderate parallelism | | Cloud VMs | Strong task isolation, background execution, reproducible toolchains | Provisioning latency, cost, and secrets management | Autonomous work, CI-like environments, and higher concurrency | Public products illustrate these choices without proving that one pattern wins everywhere. [Devin's managed Devins documentation](https://docs.devin.ai/work-with-devin/advanced-capabilities) describes a coordinator session that scopes work, launches child sessions in isolated VMs, monitors progress, resolves conflicts, and compiles results. That is a multi-agent fanout design suited to work that can be split into packages. [GitHub Copilot cloud agent](https://docs.github.com/en/copilot/concepts/agents/coding-agent/about-coding-agent) takes a GitHub-centered approach. It can research a repository, plan, make changes on a branch, execute checks in its own ephemeral GitHub Actions-powered development environment, and optionally open a pull request. Its documented constraints, including one branch and one pull request per assigned task, are meaningful architectural boundaries rather than minor implementation details. [Codex cloud](https://developers.openai.com/codex/cloud) runs background tasks, including parallel tasks, in its own cloud environments and can create pull requests from connected GitHub repositories. The emphasis is delegated execution with configurable environments and tools. [Conductor](https://www.conductor.build/docs/core/parallel-agents) exposes a local-workspace choice: separate isolated workspaces for separate branches, or multiple agent workflows inside one workspace when the work belongs on the same branch. Capy's design combines a planner → worker split with isolated task VMs, branch-based delivery, and a dedicated review-and-triage loop. That is a strong fit when the goal is not merely to accelerate one coding conversation, but to move several reviewable units of software work through a consistent pipeline. ## How to evaluate an architecture Start with your failure modes, not a feature checklist. Ask whether the system can preserve a clean branch per task, reproduce your toolchain, limit credentials, verify the requested behavior, compact long-running context, and expose an auditable diff. Then ask how it behaves when work is parallel: whether dependencies are modeled, whether conflicts are surfaced, and whether reviewers can understand the origin and status of each change. Finally, measure outcomes at the pull-request boundary. Useful metrics include acceptance-check pass rate, reviewer correction rate, time from request to reviewable diff, rework after review, conflict frequency, and the proportion of tasks that require human rescue. The best architecture for a team is the one that produces trustworthy, mergeable work with a clear operational model—not the one that simply launches the largest number of agents. --- - [Running Coding Agents in Slack](https://capy.ai/articles/coding-agents-in-slack): Learn how coding agent Slack integrations work, compare Capy, Devin, GitHub Copilot, and Cursor, and set up a governed workflow for engineering tasks. A coding agent Slack integration lets an engineering team turn a conversation into an actionable software task without copying context into another tool. The useful versions do more than accept a bot mention: they preserve the right thread, route work to the right repository, respect permissions, and return a reviewable handoff. ## Why coding agents belong in Slack Many engineering tasks begin before anyone opens an issue tracker. A support engineer shares a screenshot of a broken state. A product manager asks why a workflow behaves differently for one customer. An engineer notices a missing guard while discussing a deploy. By the time someone creates a ticket, the original message, file, and follow-up details may already be scattered across a long channel. A Slack integration shortens that gap. Instead of restating the problem in a separate coding-agent UI, a teammate can invoke an agent where the discussion is already happening. The channel and thread provide immediate social context: who reported the problem, what evidence is attached, what teammates have clarified, and where the agent should post progress. That convenience does not make every Slack message an implementation spec. The best workflow treats Slack as an intake surface and a collaboration layer. The agent still needs a clear task, an appropriate repository and branch, a configured development environment, and a review handoff that humans can inspect before code reaches production. ## How Capy works in Slack With the [Capy Slack integration](https://docs.capy.ai/integrations/slack.md), an organization admin installs the app, selects a default project, and can map individual Slack channels to specific Capy projects. Mention `@Capy` in a channel where the app is installed and Capy creates a coding task from the message. It replies in a Slack thread, and that thread stays synced with the task so the team can continue the conversation in place. The initial message can include images and files. That is useful when the task starts with a screenshot, a log excerpt, a design asset, or another artifact that would otherwise be lost when someone manually rewrites the request. The integration also supports a default branch in Slack settings. If no Slack-specific default branch is configured, Capy uses the project’s default branch. For exceptions, the initial mention can use `project=...`, such as `@Capy project="Payments platform" fix the login bug`. Capy first matches by project ID or exact name, accepts one clear fuzzy match, and otherwise asks the requester to choose from the projects they can access. Repository and branch instructions remain part of the task itself—for example, “use the backend repository on `branch=staging`”—so the agent can interpret them in the selected project instead of maintaining a second routing system. Capy also includes controls for context hygiene inside the synced thread. Prefix a message with `aside` when teammates need to discuss something without sending it to the agent or adding it to the agent’s later thread history. Type `mute` or `@Capy mute` to stop regular thread replies from triggering responses, then type `unmute` or `@Capy unmute` to resume them. A direct `@Capy` mention still goes through while a thread is muted. ## What a linked personal connection enables A workspace-level installation is only one part of Slack access. In Capy, each teammate can optionally link a personal Slack connection for richer context. This lets Capy search messages on that person’s behalf and read permitted Slack content according to the access levels they grant. Search and public-channel access are enabled for the linked connection. Private-channel reads are optional and apply only to private channels the user belongs to. Direct-message and group-DM reads are also optional. This model matters because “connect Slack” should not be interpreted as “give an agent unrestricted access to every conversation in the company.” When permissions allow it, Capy agents can read messages from channels or threads, search workspace messages, post messages to channels or threads, react with emoji, and list channels to find the relevant conversation. They can also send file attachments with messages. Every action remains subject to the connected user’s permitted scope, so teams should start with the minimum context required for their normal workflow and expand access deliberately. ## Comparison: Capy, Devin, GitHub Copilot, and Cursor Several coding-agent products now use Slack as an entry point, but the documented workflows are not identical. A fair evaluation should focus on how a team starts work, what context is captured, where results appear, and which controls are available. | Product | Slack trigger | Documented context and collaboration model | Documented routing or handoff details | | ------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | | [Capy](https://docs.capy.ai/integrations/slack.md) | Mention `@Capy` in an installed channel | Creates a task and synced reply thread; accepts images and files; supports `aside`, `mute`, and `unmute`; linked personal connections enable permitted Slack search and reads | Default project, channel-to-project mappings, default branch, and an initial-message `project` directive with fuzzy matching and a choice fallback | | [Devin](https://docs.devin.ai/integrations/slack) | Tag `@Devin` in a channel | Starts an in-thread session, accepts attachments, and documents controls including `!ask`, `!deep`, mute/unmute, aside, sleep, archive, and exit | Session continues in the Slack thread; its docs emphasize installation and linking each individual user | | [GitHub Copilot cloud agent](https://docs.github.com/en/copilot/how-tos/use-copilot-agents/coding-agent/integrate-coding-agent-with-slack) | Mention `@GitHub Copilot` in a thread or message the GitHub App directly | Public preview; captures the full thread as context and stores that context in the pull request; a direct message can limit thread context | Uses a configured default repository and supports repository or branch parameters; creates a pull request and returns its link | | [Cursor Cloud Agents](https://cursor.com/docs/cloud-agent) | Use the `@cursor` command | Slack is one documented trigger for starting a Cloud Agent | Cloud Agents work in isolated cloud VMs and hand off changes through pushed branches and pull requests | The right choice depends on the workflow around the trigger. Capy is useful when Slack intake needs permission-aware project routing, synced threads, and Slack context tools. Devin’s official docs show a mature in-thread collaboration model with attachments and several inline controls. GitHub Copilot cloud agent is notable for a direct pull-request handoff and for explicitly warning that a mentioned thread is captured as context and stored in the PR. Cursor documents Slack among the places teams can start Cloud Agents, alongside web, desktop, GitHub, Linear, and API entry points. None of these differences should be turned into an unsupported exclusivity claim. Product capabilities change, preview features evolve, and the best fit depends on how your team already manages repositories, conversations, and pull requests. Read the official documentation, test with a limited pilot, and validate the exact permissions shown during installation. ## Set governance before broad rollout The ease of mentioning an agent can hide the fact that software changes are still being requested. Treat a Slack invocation like any other engineering intake path. Define which channels are appropriate for agent tasks, which repositories each project may access, what the default branch should be, and who is expected to review the final output. Channel-to-project mappings help make common cases boring. A frontend channel can map to the frontend project, an infrastructure channel can map to infrastructure, and unmapped channels can fall back to a carefully selected default project. The `project` directive handles exceptions without changing repository or branch semantics; those stay in the task request for the agent to interpret. If people constantly override project routing manually, the project configuration probably needs attention. Permissions deserve the same care. Start with public-channel search if that meets the use case. Enable private-channel or DM reads only for teammates who need those sources and understand the sensitivity of the conversations involved. Review app scopes with security stakeholders, and use normal repository protections, CI requirements, and branch policies after the agent produces code. ## Practice context hygiene in threads Slack context is valuable because it is close to the problem. It is also noisy. A single thread may contain speculation, unrelated follow-ups, customer details, pasted credentials that should never have been shared, or a side conversation between teammates. More context is not automatically better context. Start agent tasks with a concise statement of the problem, the intended behavior, and the verification target. Attach the screenshot or file that materially helps. Use an `aside` message in Capy when a human-only discussion should not reach the agent, and mute a busy thread when automatic replies would create distraction. If the task grows into a larger project, move durable requirements into the appropriate issue, design document, or task description rather than relying on a Slack transcript as the only source of truth. GitHub’s documentation makes the same general risk visible from another angle: when Copilot cloud agent is mentioned in a thread, it captures the entire thread and stores that context in the resulting pull request. That can be convenient for review, but it is also a reason to choose a fresh thread or direct message when the existing discussion contains irrelevant or sensitive material. ## Design the review handoff The goal of a Slack coding agent is not to merge code invisibly from chat. It is to reduce the distance between an observed problem and a reviewable implementation. A healthy handoff includes the target repository, base branch, code diff, test results, unresolved questions, and a pull request or equivalent review surface where normal engineering controls still apply. Reviewers should evaluate agent-authored work the same way they evaluate human-authored work. Check whether the implementation matches the request, inspect security-sensitive changes carefully, read the tests, and confirm that CI passes. If the task started from a loosely worded Slack message, pay extra attention to unstated assumptions. The convenience of the trigger increases the importance of a clear review boundary. Slack is useful during execution because teammates can add clarifications while the context is fresh. It is less suitable as the permanent record of why a change was accepted. Keep merge decisions, requested changes, approvals, and release evidence in your source-control and deployment systems. ## What Slack should not replace A coding agent Slack integration should not replace your issue tracker for planned roadmap work. It should not replace a design document for architectural decisions, a runbook during an incident, or a pull request for code review. It should never become a reason to paste secrets, customer data, or unrestricted internal context into channels where they do not belong. Slack is strongest at capture and coordination. It gives teams a low-friction way to convert a live discussion into a task, preserve the relevant thread, and keep humans close enough to clarify the work. Durable systems remain necessary for prioritization, ownership, review, compliance, and historical understanding. ## A practical rollout checklist Start with one or two engineering channels and a small group of teammates. Map those channels to the correct projects, confirm the default project for DMs and unmapped channels, and set an intentional default branch. Test a normal task, a task with an image or file, and a task with an explicit `project` directive. Also test an uncertain project name so the requester sees the access-filtered choice fallback. Then test the collaboration controls. Send an `aside` message that should remain human-only, mute and unmute a thread, and confirm the team understands when direct mentions still trigger the agent. Link a personal Slack connection only for users who need workspace search, then verify public, private-channel, and DM access against the permission levels you intended to grant. Finally, rehearse the review handoff. Make sure reviewers know where to find the resulting work, which checks must pass, and how to request revisions. A Slack integration succeeds when it makes intake faster while leaving governance, code quality, and human judgment intact. --- - [Conductor Build vs Capy](https://capy.ai/articles/conductor-build-vs-capy): Conductor Build is a local Mac app for parallel Claude Code and Codex agents. Capy is a hosted AI development platform. Here is how they compare. Conductor Build vs Capy is mainly a choice between local Mac orchestration and hosted browser orchestration. Conductor Build organizes parallel Claude Code and Codex agents in worktree-based local workspaces. Capy runs planned coding tasks in cloud VMs, keeps work team-visible, and connects execution to GitHub pull requests and review. ## TL;DR - Choose Conductor Build if you want parallel Claude Code and Codex sessions on your Mac, prefer local control, and already have agent subscriptions you want to keep using. - Choose Capy if you want hosted execution that does not depend on a developer laptop, a shared browser workflow, and explicit Captain, Build, and Review roles. - Both products support parallel AI coding, but they draw the boundary differently: Conductor coordinates local workspaces, while Capy coordinates cloud tasks and GitHub pull requests. - [Conductor Cloud](https://www.conductor.build/cloud) is currently early access. It should not be treated as a generally available replacement for Conductor's Mac app. ## What is Conductor Build? [Conductor Build](https://www.conductor.build/) is a Mac app for running coding agents in parallel. Its public homepage describes parallel Claude Code and Codex agents in isolated workspaces, with a view for seeing what agents are doing and reviewing and merging their changes. The product works on top of a developer's local repository workflow rather than moving the entire coding environment into a hosted service. The isolation model is Git-native. Conductor's FAQ says that each workspace is a new git worktree, and its [parallel agents documentation](https://www.conductor.build/docs/core/parallel-agents) explains that a new workspace gets its own branch, files, and clean place for an agent to work. That makes separate workspaces appropriate for features, bug fixes, pull requests, or issues that should move independently through review. Conductor also supports a second form of parallelism: multiple tabs or agent flows within one workspace. That is useful when agents should share the same branch and code state, such as when a developer wants to compare approaches before creating another branch. The distinction matters because not every parallel task needs a separate environment. Conductor is particularly attractive when a developer already uses Claude Code or Codex and wants a better operational surface around those tools. Its public FAQ says Claude Code uses the login or API key the developer already has, including Claude Pro or Max plans. The trade-off is that the established product is tied to a Mac and its local environment. Conductor Cloud exists, but its [official page](https://www.conductor.build/cloud) labels it as early access. ## What is Capy? [Capy](https://capy.ai) is a browser-based platform for orchestrating coding work in the cloud. Its [documentation](https://docs.capy.ai/welcome.md) describes a task workflow where an agent builds changes in its own VM, then the developer reviews the diff and creates a pull request. A Build agent can edit files, install packages, run commands, and commit code inside that isolated Ubuntu VM. Capy separates planning from implementation. **Captain** reads a codebase and writes detailed specifications so that **Build** agents can execute focused tasks. This structure is useful when the work starts as a broad request rather than a ready-to-run agent prompt. Instead of asking a developer to manually divide a larger effort across local sessions, the platform gives planning an explicit place in the workflow. Capy also has an explicit **Review** role connected to GitHub pull requests. The [review documentation](https://docs.capy.ai/review.md) says the Review agent reads pull request diffs, produces structured findings with categories and severities, and posts medium-or-higher findings as inline GitHub comments. When Captain manages a task, it can triage findings and send real issues back to Build for fixing. Capy is therefore designed around a PR lifecycle, not only around generating a local diff. Capy's [public pricing](https://capy.ai/pricing) starts at $20 per month. Credits are shared at the organization level and pay for AI usage, VM runtime, and auxiliary services such as Review. Capy's public product pricing says plans include unlimited concurrent threads, so the platform is intended for running multiple work streams without forcing a team to serialize them behind a single developer machine. ## Head-to-head comparison | Feature | Conductor Build | Capy | | ------------------------- | ---------------------------------------------------------------------- | ------------------------------------------------------------- | | Primary interface | Mac desktop app | Browser-based cloud platform | | Execution environment | Developer's Mac | Hosted isolated Ubuntu VM per Build task | | Supported agent direction | Claude Code and Codex agents | Broad cross-provider model choice | | Parallelism | Multiple isolated workspaces and multiple agent flows in one workspace | Unlimited concurrent threads with cloud task execution | | Isolation model | Git worktree and new branch per workspace | Separate VM per Build task | | Planning | Developer directs local agent work | Captain creates task specifications for Build | | Code changes | Review and merge workspace changes | Review diffs and create GitHub pull requests | | Review workflow | Local diff review and merge flow | Review agent with GitHub findings and Captain triage | | Team visibility | Centered on the developer running the Mac app | Shared browser workflow for project work | | Laptop dependency | Local Mac remains part of the active workflow | Hosted tasks continue without depending on a developer laptop | | Cloud option | Conductor Cloud is early access | Cloud execution is the core product | | Pricing approach | Uses existing underlying agent access | Shared usage tiers from $20 per month | ## Where Conductor Build is the better fit **You want local Mac control.** Conductor's core workflow is straightforward: keep the repository and coding environment on a developer's Mac, start workspaces, and inspect changes before merging them. For developers who prefer to keep their terminals, local services, and existing setup close at hand, that is a feature rather than a limitation. There is less conceptual distance between the agent and the environment the developer already understands. **You already pay for Claude Code or Codex.** Conductor coordinates tools developers may already use directly. If a team is satisfied with those agent experiences and primarily needs a way to run several of them without juggling terminal windows, Conductor addresses the orchestration problem without introducing a separate hosted execution model. Existing Claude Code access can continue to work through the developer's current login or API key. **Git worktrees match how you want to isolate changes.** A worktree and branch per workspace is a familiar Git abstraction. It is well suited to parallel local features and bug fixes that should remain separate until review. Conductor also avoids over-isolating related experiments by allowing multiple agent flows in one workspace when sharing code state is the desired behavior. **You want an active developer in the loop.** Conductor's local workflow makes sense for engineers who want to watch agent progress, inspect the workspace, and intervene using their usual Mac tools. Capy's delegation model can remove laptop dependency, but not every developer wants that separation for every task. For hands-on local iteration, Conductor is a credible default. ## Where Capy is the better fit **Execution should not depend on a laptop.** Capy runs Build tasks in hosted VMs. That is useful for tasks that take time, need a clean Linux environment, or should continue while a developer closes a laptop or switches context. It also gives the team a consistent execution boundary instead of inheriting the state of whichever local Mac launched the task. **Work needs a team-visible home.** Capy's browser workflow gives project work a shared surface. A teammate can follow tasks and GitHub pull requests without requiring access to another developer's desktop. This is particularly useful when the goal is not merely to accelerate one developer's local session, but to make delegated engineering work visible and reviewable across a team. **You want planning, building, and review to be explicit stages.** Capy's Captain, Build, and Review roles make the handoffs visible. Captain turns larger requests into specifications, Build executes inside an isolated VM, and Review analyzes pull request diffs with structured findings. Developers can still review the result, but the workflow is designed for delegation rather than requiring them to manually coordinate every local agent session. **You want cross-provider model choice.** Conductor's published workflow centers on Claude Code and Codex. Capy supports a broader selection of models, which can matter for teams that want to select models by task type, compare provider behavior, or avoid tying the whole workflow to two agent products. More choice is not automatically better, but it can be valuable when a team already has preferences across providers. **You want cloud parallelism as the current product, not an early-access add-on.** Conductor Cloud is worth watching, but its official page is clear that access is early. Capy's hosted model is already the core workflow. Teams choosing a platform for laptop-independent execution today should evaluate that difference directly rather than assuming both cloud offerings have the same maturity or availability. ## How to choose Start with the environment you want to own. If developers should retain direct control of local repositories and already work comfortably with Claude Code or Codex on Macs, Conductor Build is the more natural choice. It adds an organized workspace layer without asking the team to adopt a hosted task system. Its worktree model is understandable, and its two kinds of parallelism cover both independent branches and shared-state experiments. Choose Capy when the operational goal is different: work should run away from the laptop, stay visible to teammates, and flow through GitHub pull requests with explicit planning and review stages. Capy asks the team to adopt a hosted orchestration layer, and its credit model includes VM runtime as well as model usage. In return, the execution environment is not limited by the initiating developer's Mac, and model selection is not limited to Claude Code and Codex. The products overlap on parallel coding agents, but neither is universally better. Conductor Build is a practical local control plane for developers who want to orchestrate existing agent tools. Capy is a hosted delegation system for teams that want planning, execution, review, and PR workflow collected in one browser-based service. --- - [Cursor alternative for cloud agents](https://capy.ai/articles/cursor-alternative): Looking for a Cursor alternative? Compare Cursor and Capy honestly across cloud agents, editor workflows, orchestration, models, review, and pricing. If you are searching for a Cursor alternative, start with the workflow you want rather than a feature checklist. Cursor now combines an AI-native desktop editor with capable cloud agents. Capy is the stronger alternative when you want a browser-based orchestration dashboard with distinct planning, implementation, and review roles across parallel coding tasks. ## Cursor is no longer just an editor It would be inaccurate to compare Cursor only with code completion tools or desktop editor extensions. Cursor still provides the editor-first experience that made it popular: developers can stay close to the code, ask for changes, and keep continuity between local development and agent-assisted work. But Cursor has expanded into remote execution through [Cloud Agents](https://cursor.com/docs/cloud-agent), formerly called Background Agents. According to Cursor's official documentation, Cloud Agents run in isolated cloud VMs with development environments rather than on a developer's local machine. They clone repositories, install dependencies, receive secrets, run startup commands, access networks, build software, execute tests, and verify changes. You can run as many agents as you want in parallel without keeping your laptop connected to the internet. Cursor also supports multi-repo environments for work that crosses frontend, backend, infrastructure, or shared-library repositories. Cloud Agents can connect to GitHub or GitLab, work on separate branches, and hand off changes through repository workflows. Teams can configure environments with agent-led setup, saved snapshots, or a Dockerfile through `.cursor/environment.json`, making repeated tasks less dependent on manual bootstrapping. The remote workflow is substantial. Cursor Cloud Agents can be started from Cursor Web, Cursor Desktop, Slack, GitHub comments, Linear, or an API. They support MCP servers, command-based hooks, screenshots, videos, logs, computer use, and remote desktop control, so a developer can inspect or operate the modified application inside the agent's environment before releasing control back to the agent. Cursor's docs say Cloud Agents use a curated model selection that always runs in Max Mode. That matters because the choice between Cursor and Capy is no longer “editor versus cloud agents.” Both products can execute coding work in isolated remote environments. The meaningful question is whether you want cloud agency to extend an editor-centered workflow or whether you want the browser dashboard to be the primary place where work is planned, dispatched, reviewed, and compared. ## What makes Capy a different Cursor alternative? [Capy](https://capy.ai) is built around browser-based orchestration. Instead of treating a cloud agent as a remote extension of an editor session, Capy makes the task pipeline visible from the start. A **Captain** role helps turn high-level goals into implementation work. **Build** agents execute tasks in isolated Ubuntu VMs. A **Review** role evaluates the resulting changes and supports structured triage of review findings before a pull request is considered ready. The role separation is the key product difference. Planning, implementation, and review are not compressed into one undifferentiated agent interaction. A user can hand Captain a broader objective, let Build agents work on scoped tasks, and inspect review feedback in a structured workflow. This is useful when the work resembles managing a development queue more than pair programming inside a single file. Capy uses GitHub pull request workflows as the handoff boundary. Build tasks can operate independently in their own Ubuntu environments while the dashboard gives the team a shared view of progress. Slack and Linear integrations help bring work in from the places where teams already discuss issues and priorities. Capy also offers a broad model choice, so teams can experiment with different model strengths rather than standardizing every task on one curated set. Capy Pro starts at $20 per month and uses shared organization credits. Cursor [Individual pricing](https://cursor.com/pricing) also starts at $20 per month. Cursor's documentation says Cloud Agents are billed at API pricing for the selected model, so teams comparing the products should consider actual usage patterns as well as the subscription price. A team dispatching many long-running agents will care about task mix, selected models, and budget controls in either product. ## Cursor vs Capy comparison | Feature | Cursor | Capy | |---------|--------|------| | Primary workflow | AI-native desktop editor plus Cursor Web and Cloud Agents | Browser-based orchestration dashboard | | Remote execution | Cloud Agents in isolated VMs | Build agents in isolated Ubuntu VMs | | Parallel work | As many Cloud Agents as wanted in parallel | Parallel Build tasks across isolated environments | | Workflow structure | Editor continuity with local and cloud agent handoff | Captain → Build → Review role separation | | Repository support | GitHub and GitLab; multi-repo Cloud Agent environments | GitHub PR workflows | | Triggers and integrations | Cursor Web, Desktop, Slack, GitHub, Linear, API | Dashboard, Slack, Linear | | Environment setup | Agent-led setup, saved snapshots, or Dockerfile configuration | Isolated Ubuntu VM environments | | Model approach | Curated Cloud Agent models, always in Max Mode | Broad model choice for experimentation | | Tooling | MCP, hooks, artifacts, computer use, remote desktop control | Agent tools inside isolated task VMs | | Review workflow | Agent output and PR handoff; Teams includes Bugbot code review | Structured Review triage for findings | | Entry pricing | Individual starts at $20/mo; Cloud Agents billed at selected-model API pricing | Pro starts at $20/mo with shared org credits | ## Where Cursor wins **Editor-first continuity.** Cursor remains the better choice for developers who want AI coding to feel like an extension of their everyday desktop editor. The path from reading code locally to asking for help and then dispatching a Cloud Agent is coherent. Teams do not have to adopt a separate orchestration mental model before getting value. **Rich remote-desktop cloud agents.** Cursor's Cloud Agent surface is more capable than a simplistic background-worker comparison suggests. The documented artifacts, computer use, and remote desktop control give developers a practical way to inspect work, test visual changes, and take over the environment when needed. That is valuable for UI work, debugging, and tasks where the agent's branch must be exercised interactively. **Multi-repo and GitLab support.** Cursor explicitly documents multi-repo Cloud Agent environments and GitLab connections alongside GitHub. If your work routinely spans repositories, or your organization uses GitLab, those are meaningful reasons to prefer Cursor. **Flexible entry points.** Cursor lets teams launch cloud work from the desktop editor, web dashboard, Slack, GitHub comments, Linear, or an API. That makes the remote agent layer available without forcing everyone to begin from the same screen. ## Where Capy wins **Orchestration-first design.** Capy is a better fit when the unit of work is not an editor conversation but a queue of engineering outcomes. The dashboard is designed for dispatching work, monitoring parallel execution, and understanding where each task stands. Users looking to delegate rather than continuously steer will find that orientation clearer. **Explicit planning and review roles.** Captain, Build, and Review give teams a visible separation of responsibilities. The system is designed to move from a broader request into implementation and then through structured review triage. That makes Capy useful when you want planning and review to be productized stages, not optional steps a developer must remember to request. **Model experimentation.** Capy's broad model choice is a practical advantage for teams that want to compare model behavior, route different work to different models, or avoid committing their workflow to a smaller curated selection. Cursor's Max Mode policy for Cloud Agents is intentionally opinionated; Capy offers more room for experimentation. **Shared organization workflow.** Shared org credits, GitHub PR workflows, and a browser-based task view make Capy well suited to teams treating agents as a coordinated pool of engineering capacity. Slack and Linear integrations help tasks enter that system without reducing the dashboard to an editor companion. ## Which Cursor alternative should you choose? Choose Cursor if your developers want an editor-first product that also has serious cloud execution. Its Cloud Agents are not a side feature: they offer isolated VMs, parallel execution, multi-repo environments, environment snapshots or Dockerfile setup, MCP, hooks, artifacts, remote desktop control, and multiple trigger surfaces. Cursor is especially compelling when developers want to move fluidly between local editing and rich remote-agent environments. Choose Capy if you want the control plane to come first. Its browser dashboard, Captain → Build → Review role separation, isolated Ubuntu VMs, GitHub PR workflow, broad model choice, shared organization credits, Slack and Linear connections, and structured review triage are designed for teams managing several delegated outcomes at once. The value is not that Capy replaces an editor feature-for-feature. The value is that it gives agent work an orchestration layer with planning and review as first-class stages. The honest answer is that there is no universal winner. Cursor has grown beyond its original editor identity and deserves to be evaluated as a capable cloud-agent platform. Capy is the better Cursor alternative when you specifically want an orchestration-first dashboard and more deliberate role separation around coding work. --- - [Devin vs Capy: Autonomous Agent Architecture Compared](https://capy.ai/articles/devin-vs-capy): Devin and Capy both run software tasks autonomously. Compare managed agents, planning, isolated VMs, PR review, integrations, model choice, and pricing. Devin vs Capy is a choice between two capable autonomous coding workflows. Devin is stronger when teams want managed parallel agents, reusable playbooks, and organizational knowledge. Capy is compelling when teams want model-agnostic Captain planning, isolated Ubuntu Build execution, GitHub PR automation, and structured Review-agent triage in one workflow. ## TL;DR - [Devin](https://devin.ai) can plan, code, test, and ship. Its advanced capabilities include parallel managed Devins, with each child session running in its own isolated VM, plus playbooks, knowledge management, session analysis, and schedules. - [Capy](https://capy.ai) separates the work into **Captain**, **Build**, and **Review**. Captain plans against the repository, Build executes inside isolated Ubuntu VMs, and Review turns PR analysis into structured findings that can be triaged and fixed. - Both products support parallel work, GitHub-centered development, and collaboration integrations. The practical decision is not “autonomous agent versus copilot”; it is which operating model fits your team. ## What is Capy? [Capy](https://docs.capy.ai/welcome) is an AI software development platform built around a staged workflow. You describe a bug fix, feature, or refactor. **Captain** reads the codebase and prepares detailed specs, while **Build** does the implementation work: editing files, installing packages, running commands, and preparing code changes inside an isolated Ubuntu VM. That separation matters when a repository task requires more than generating a patch. Planning and execution are explicit phases rather than a single opaque conversation. Teams can use different models for different tasks, making Capy a model-agnostic workflow with broad model choice instead of a product tied to one default model path. Capy also has a dedicated [Review agent](https://docs.capy.ai/review). It reads pull request diffs, generates functional summaries, and reports findings with a category, severity, and code location. When Captain manages a task, it can triage findings as open, resolved, or irrelevant, send valid issues back to Build, and re-review updated code. Medium-and-higher findings can be posted as inline GitHub comments, while lower-severity findings remain visible in the dashboard. The result is a legible software-delivery loop: plan the work, execute it in an isolated environment, open or update the GitHub PR, inspect structured review findings, and iterate where necessary. Slack and Linear integrations let teams connect that workflow to the places where engineering requests already arrive. ## What is Devin? [Devin](https://devin.ai) is an AI software engineering product that can plan, code, test, and ship. It is not limited to a single agent working through one queue. Devin’s [advanced capabilities](https://docs.devin.ai/work-with-devin/advanced-capabilities) include managed Devins: a coordinator can split a large task into workstreams and delegate them to child sessions running in parallel, with each child in its own isolated VM. The coordinator can monitor progress, send follow-up instructions, manage compute usage, stop sessions that are no longer needed, and compile results. That makes Devin a credible option for large migrations, repeated tasks across modules, and work that benefits from centralized delegation. Parallelism is a real Devin feature, not a gap that separates it from Capy. Devin also offers a mature layer for operational memory. Teams can analyze previous sessions, extract patterns into reusable playbooks, refine existing playbooks from feedback, maintain organizational knowledge, and schedule recurring or one-time sessions. Its integration surface includes Slack, Linear, and MCP, alongside repository workflows. For teams that want an agent platform to accumulate and reuse working practices over time, these capabilities are substantial. ## Head-to-head comparison | Feature | Devin | Capy | |---------|-------|------| | Core workflow | Agent can plan, code, test, and ship | Captain plans → Build executes → Review triages | | Parallel execution | Managed Devins run in parallel; each child has an isolated VM | Concurrent threads run independently; Build executes in isolated Ubuntu VMs | | Planning | Available within Devin, including coordinator-driven delegation | Captain is an explicit planning layer before Build execution | | Execution environment | Isolated VM per managed Devin child session | Isolated Ubuntu VM per Build task run | | Review | Includes Devin Review | Dedicated Review agent with categorized, severity-ranked PR findings and triage statuses | | Knowledge reuse | Playbooks, session analysis, knowledge management, schedules | Repository-aware Captain specs and project instructions within the delivery workflow | | Model approach | Devin-powered product tiers | Model-agnostic workflow with broad model choice | | Integrations | Slack, Linear, MCP, and repository integrations | GitHub PR workflows plus Slack and Linear integrations | | Entry pricing | Free tier; Pro at $20/mo | Pro tiers from $20/mo | | Usage model | Included quota with pay-as-you-go available on listed paid tiers | Credits for AI usage, VM runtime, and auxiliary services | ## The architectural trade-off The clearest difference is not whether either tool can edit code autonomously. Both can. It is how each product makes multi-step engineering work visible and reusable. Devin’s managed-agent layer is attractive when a team wants one coordinator to break a broad assignment into parallel subprojects. Its playbooks and knowledge features make sense for repeated operational patterns: migrations that recur across services, incident procedures, or domain-specific rules that should improve with accumulated experience. Scheduled sessions add another useful axis for routine maintenance. If your evaluation is heavily weighted toward managed orchestration and institutional memory, Devin deserves serious consideration. Capy makes a different bet: separate the delivery lifecycle into specialized, inspectable stages. Captain creates the plan. Build works against that plan in an isolated Ubuntu environment. Review reports specific findings and triage state around the PR. This structure is useful when engineers want to understand what the system intends to do, review the resulting code through standard GitHub mechanics, and see which review issues are open, fixed, or intentionally dismissed. Neither approach removes the need for engineering judgment. A parallel agent fleet can still produce overlapping changes if a task is poorly decomposed. A detailed spec can still miss a product requirement. Isolated VMs reduce environment collisions, but they do not guarantee that a patch is correct. In both products, teams should define repository instructions, keep tasks scoped, run the relevant test suite, and review the final PR before merging. ## Where Devin stands out **Managed parallel orchestration.** Devin can delegate to managed child sessions and coordinate the results. This is a meaningful capability for work that can be partitioned into independent packages, such as a migration across modules or a repeated code-quality pass. **Playbooks and knowledge.** Devin can create and improve reusable playbooks from past sessions, analyze outcomes, and maintain organization knowledge. Teams that want the agent platform to become a durable operational memory system may value this more than a rigid delivery pipeline. **Scheduling and MCP.** Scheduled sessions support recurring maintenance, while MCP extends the integration surface. Those capabilities make Devin useful beyond ad hoc implementation requests. **Clear packaged tiers.** Devin lists a Free tier, Pro at $20 per month, Max at $200 per month, Teams from an $80 monthly minimum, and Enterprise. That provides a recognizable starting point for individuals and organizations comparing plans. ## Where Capy stands out **Explicit Captain → Build → Review stages.** Capy’s workflow is easy to inspect. The planning agent writes specs, Build performs implementation work, and Review evaluates the PR diff with structured findings. The roles are distinct without forcing engineers to assemble separate tools. **Broad model choice.** Capy is designed as a model-agnostic workflow. Teams can choose among available models based on task difficulty, cost sensitivity, or preferred behavior instead of treating model selection as an implementation detail hidden behind one agent product. **Transparent PR review triage.** Capy’s Review agent does more than produce a general comment. Findings have categories, severity, locations, and triage statuses. Re-reviews understand prior findings, and Captain-managed flows can route real issues back to Build for fixes. **Usage-scaled pricing.** [Capy pricing](https://capy.ai/pricing) starts with Pro tiers from $20 per month. Credits pay for actual AI usage, isolated VM runtime, and auxiliary services such as Review; higher tiers include bonus credits, and overage can be controlled with auto-reload and an organization-wide spending cap. Capy’s public pricing currently lists unlimited concurrent threads, which is useful for teams that want to run independent workstreams without a small fixed concurrency allowance. ## Pricing considerations Price comparisons need more context than the cheapest monthly number. Devin’s [pricing page](https://devin.ai/pricing) lists Free, Pro at $20 per month, Max at $200 per month, Teams from an $80 monthly minimum, and custom Enterprise pricing. Listed paid tiers include usage quotas, with pay-as-you-go available beyond quota where specified. Devin’s packaged tiers are straightforward if you want to select a product level and then monitor usage inside it. Capy also starts at $20 per month, but its [credit model](https://docs.capy.ai/pricing) is intentionally usage-scaled. Credits cover model tokens, Build VM runtime, and auxiliary services such as Review. More capable models can cost more per token, and larger VMs cost more per hour, so teams can tune both quality and spend. The right comparison is your real workload: task size, model selection, VM runtime, review frequency, and the number of active workstreams. ## Which one should you choose? Choose Devin when your highest-priority requirements are managed parallel sessions, playbooks, knowledge management, session analysis, or scheduled work. It has a mature orchestration surface for teams that want agents to coordinate work and preserve reusable operating knowledge. Choose Capy when you want a model-agnostic workflow with an explicit boundary between planning, implementation, and PR review. Captain → Build → Review is especially useful for teams that care about isolated Ubuntu execution, broad model choice, GitHub PR workflows, transparent review triage, and usage-scaled tiers. Both products are serious tools for delegated software engineering. The best evaluation is a representative repository task: give each product a scoped feature or refactor, inspect the plan, watch how it handles tests and environment setup, read the resulting PR, and compare the review experience. The operational details will tell you more than a feature checklist alone. --- - [GitHub Copilot Coding Agent vs Capy](https://capy.ai/articles/github-copilot-coding-agent-vs-capy): GitHub Copilot cloud agent (formerly called Copilot Coding Agent) is a GitHub-native agent. Capy adds planning, isolated Build VMs, and a Review Agent loop. For teams seeking a **Copilot Coding Agent alternative**, Capy is the stronger fit when work needs explicit planning, parallel isolated VMs, and an integrated review-and-fix loop. GitHub Copilot cloud agent (formerly called Copilot Coding Agent) remains a practical choice for focused repository tasks where GitHub-native simplicity matters more than orchestration depth. ## TL;DR - GitHub Copilot cloud agent is a cloud coding agent for focused repository tasks. It researches, plans, changes a branch, runs checks, and can open a pull request from a GitHub Actions-powered ephemeral environment. - Capy uses **Captain** for planning, **Build** agents in isolated Ubuntu VMs for execution, and a **Review Agent** for triage and fixes. Its public pricing page says Pro starts at $20 and includes unlimited concurrent threads. - Copilot cloud agent wins when a team wants a direct GitHub-native task-to-PR path. Capy wins when the work benefits from orchestration, parallel threads, longer-running VM-backed execution, and a built-in review-fix loop. ## What is GitHub Copilot cloud agent? [GitHub Copilot cloud agent](https://docs.github.com/en/copilot/concepts/agents/coding-agent/about-coding-agent) (formerly called Copilot Coding Agent) is GitHub's cloud agent for completing development tasks asynchronously. You can assign it work, let it research the repository and plan an approach, and have it modify code on its own branch. It can run tests and linters, then optionally open a pull request for human review. The environment is intentionally GitHub-native. Each session runs in an ephemeral environment powered by GitHub Actions. That makes the operational model familiar for teams already using GitHub, but it also means agent work consumes GitHub Actions minutes and AI credits. Copilot cloud agent is available with paid Copilot plans. The task boundary is deliberately narrow: one repository per run, one branch, and exactly one pull request per task. GitHub also documents a hard 59-minute maximum session length. Those constraints are not necessarily drawbacks. They provide a predictable unit of work and make Copilot a sensible option for well-scoped issues that fit comfortably inside one repository and one pull request. Copilot cloud agent is not limited to starting from github.com. GitHub documents entry points from GitHub, GitHub Mobile, Visual Studio Code, and integrations including Slack, Microsoft Teams, Linear, and Jira. For example, the official [Slack integration guide](https://docs.github.com/en/copilot/how-tos/use-copilot-agents/coding-agent/integrate-coding-agent-with-slack) explains how to mention Copilot in a Slack thread, select a repository, and create a task from the conversation. Copilot also supports customization through MCP servers, custom agents, hooks, and skills. ## What is Capy? [Capy](https://capy.ai) is an AI development platform organized around planning, implementation, and review. Instead of treating a coding request as a single agent session, Capy gives the workflow distinct stages. **Captain** helps break down work and coordinate tasks. **Build** agents carry out those tasks inside isolated Ubuntu VMs, where they can inspect the repository, install dependencies, run project commands, and prepare code changes without sharing a mutable environment with another task. Capy keeps the resulting work in a GitHub-native pull request flow. Branches and PRs remain the reviewable artifacts developers already understand. The difference is that Capy adds orchestration around that flow: teams can keep multiple threads moving, and Capy's public [pricing page](https://capy.ai/pricing) says the Pro plan starts at $20 with unlimited concurrent threads. Capy also includes a **Review Agent** workflow. Review findings can be triaged, and issues that need changes can return to a fix loop rather than stopping at a list of comments. Capy has Slack and Linear integrations, so work can begin outside the browser while still ending in the repository's normal GitHub workflow. It also offers a broad current model selection, which matters for teams that want to choose among models rather than standardize every coding task on one provider. ## Head-to-head comparison | Feature | GitHub Copilot cloud agent | Capy | |---------|-----------------------------|------| | Primary workflow | Focused GitHub task → branch → optional PR | Captain planning → Build execution → Review Agent loop → GitHub PR | | Execution environment | GitHub Actions-powered ephemeral environment | Isolated Ubuntu VM per Build task | | Repository scope | One repository per run | Project-oriented workflow with parallel threads | | Branch and PR scope | One branch and exactly one PR per task | GitHub-native branch and PR flow | | Session duration | Hard 59-minute maximum | Better suited to longer-running VM-backed workflows | | Parallel work | Multiple tasks can be assigned; each task has its own constrained run | Public pricing says unlimited concurrent threads | | Review workflow | Opens a PR for review | Review Agent triage and fix loop | | Integrations | GitHub, GitHub Mobile, VS Code, Slack, Teams, Linear, Jira | GitHub, Slack, Linear | | Customization | MCP servers, custom agents, hooks, skills | Broad current model selection and project-level orchestration | | Pricing | Included with paid Copilot plans; uses Actions minutes and AI credits | Pro from $20 | ## Where GitHub-native simplicity wins **A clear unit of work.** One repository, one branch, and one pull request per task is easy to reason about. If your backlog is already expressed as tightly scoped GitHub issues, the constraint can be useful rather than limiting. Reviewers know where to look, and the task has a defined end state. **A familiar operational surface.** Copilot cloud agent lives close to GitHub's existing issue, branch, pull request, and Actions concepts. Teams already operating inside GitHub may value having fewer systems to configure and fewer workflow layers to explain. Starting work from Slack, Teams, Linear, or Jira broadens the entry points without changing the GitHub-centered output. **Rich customization inside the Copilot ecosystem.** MCP servers, custom agents, hooks, and skills give teams ways to tailor Copilot cloud agent. If a team already pays for Copilot and most tasks fit under 59 minutes, GitHub's agent can be the straightforward choice. **Predictable boundaries.** The 59-minute session maximum, one-repository scope, and one-PR scope force teams to keep work bounded. That is a real trade-off, but it can encourage smaller pull requests and clearer delegation. ## Where Capy's orchestration wins **Planning is a first-class stage.** Captain is useful when the hard part is not typing code but deciding how work should be split. A broad feature, migration, or multi-step cleanup can be decomposed before implementation begins, instead of asking one agent session to research, plan, execute, and review within a fixed window. **Isolated Ubuntu VMs support deeper execution.** Build agents work in VM-backed environments rather than a short-lived GitHub Actions-powered session capped at 59 minutes. That is valuable for repositories with heavier setup, slower checks, or tasks that require more sustained investigation. It is also useful when several tasks need to run without colliding through a shared local workspace. **Parallel threads increase throughput.** Capy's public pricing says unlimited concurrent threads. That does not remove the need to scope work carefully or review every pull request, but it changes how a team can approach a backlog: independent tasks can progress simultaneously instead of waiting for a single thread to finish. **Review connects back to implementation.** Capy's Review Agent does not exist only to produce comments. Findings can be triaged and fed into a fix loop. This is particularly helpful when a team wants an agent workflow that continues through review remediation rather than handing every finding back to a human. **Model choice stays open.** Copilot cloud agent offers a cohesive GitHub product experience. Capy is a better fit for teams that value broad current model selection and want to choose a model for the job while keeping the same planning, Build, and review workflow around it. ## Which should you choose? Choose **GitHub Copilot cloud agent** when your tasks are focused, repository-local, and likely to fit inside its 59-minute maximum. It is especially attractive when your team already pays for Copilot, lives in GitHub, and wants a direct assignment-to-pull-request workflow. Its one-repository, one-branch, one-PR structure is restrictive for some work, but it is also legible and easy to govern. Choose **Capy** when coordination is part of the problem. Captain planning, isolated Ubuntu Build VMs, unlimited concurrent threads on the public Pro plan, and the Review Agent triage-and-fix loop give teams a more orchestrated workflow. Capy adds moving parts because it is solving for a broader development process, not only a single bounded agent session. Neither product removes the need for engineering judgment. Small, well-defined changes often benefit from GitHub-native simplicity. Larger backlogs, heavier repositories, and work that benefits from parallel execution or a longer-running VM-backed workflow are where Capy's architecture becomes more valuable. --- - [Long-Running Coding Tasks](https://capy.ai/articles/long-running-coding-agents): A practical guide to long-running coding agents: persistent environments, context management, checkpoints, acceptance criteria, cost controls, and product tradeoffs. A **long running coding agent** is an AI development agent that can continue a bounded software task across setup, implementation, testing, and review without requiring a developer to supervise every step. Reliability comes from persistent execution state, managed context, checkpoints, and a clear stop condition — not from keeping a VM alive forever. ## Why coding tasks become long-running Many useful engineering tasks take longer than a chat window or a single edit-and-test loop. A dependency upgrade may require installing packages, finding breaking API changes, updating several modules, running a slow test suite, and repairing failures. A frontend change may need a browser check after the build. A migration may require planning before any code changes are safe. The difficult part is not merely elapsed time. Long-running work creates state that must survive each iteration: - **Environment state:** installed dependencies, build artifacts, containers, and generated files. - **Repository state:** partial edits, Git branches, commits, and uncommitted diagnostics. - **Reasoning state:** the original request, discovered constraints, rejected approaches, and the next action. - **Verification state:** which checks passed, which failed, and what still needs evidence. - **Budget state:** how much runtime and model usage remain before the task should pause or ask for input. An agent that can write a patch but cannot preserve or recover these states will struggle as the work expands. An agent that can stay active for hours but lacks a finish condition may simply spend more time wandering. Duration is a systems-design problem, not a model benchmark. ## The reliability stack for long-running work A practical long-running coding agent needs several layers working together. ### Persistent but reproducible environments A durable workspace avoids reinstalling packages and losing partial progress between iterations. It also lets the agent inspect the real effects of its changes: compiler errors, test output, running services, and generated diffs. But persistence alone is not enough. The setup should remain deterministic through lockfiles, bootstrap commands, environment configuration, and repository instructions. Otherwise, an old workspace can hide missing setup steps that will fail in CI or for another developer. The right principle is: preserve the active workspace, but make it possible to rebuild that workspace from declared inputs. ### Context management and compaction Long tasks accumulate more information than a model should replay verbatim forever. Raw terminal logs, full file contents, and earlier dead ends can crowd out the current objective. A reliable agent periodically compacts that history into a concise working state: completed work, unresolved failures, important file paths, explicit constraints, and the next checks to run. [Capy's handoff model](https://docs.capy.ai/using-capy.md#handoffs) is designed for this problem. For long tasks, Build and Captain can continue work in a fresh context through a handoff that carries forward a summary, progress, and next steps. The workspace can remain useful while the reasoning context is refreshed. That is materially different from pretending one ever-growing prompt will remain efficient indefinitely. ### Checkpoints and human decision points A checkpoint is a recoverable point with enough evidence to continue or stop safely. Useful checkpoints include a clean dependency install, a passing focused test, a completed migration step, a build result, or a reviewable diff. Human checkpoints matter when a task reaches an architectural fork, requests a destructive operation, needs new credentials, or would spend materially more budget than expected. This is not a failure of autonomy. The agent should execute routine steps independently and surface the small set of decisions where human judgment is valuable. A good long-running workflow alternates autonomous stretches with intentional review points. ### Verifiable acceptance criteria “Improve the checkout flow” is hard to finish reliably. “Handle network failures and invalid-card responses in `src/payments/checkout.ts`, follow the existing order-service pattern, and run the checkout integration suite” gives the agent an observable completion boundary. Acceptance criteria should name behavior, scope, and the strongest relevant check. The agent can then stop when it has evidence, rather than when it has generated a plausible-looking diff. For large tasks, intermediate checks reduce the cost of discovering a mistake at the end. ### Branch isolation Long work should not mutate a developer's main checkout or collide with another task. One branch or worktree per task makes partial progress reviewable and disposable. It also creates a clear handoff artifact: a diff or pull request that can be reviewed without importing all of the agent's internal reasoning. Isolation becomes more important when several agents run concurrently. Parallelism without branch discipline creates merge conflicts and ambiguous ownership. Parallelism with scoped branches turns independent tasks into an understandable queue of reviewable changes. ### Credit and timeout controls Long-running does not mean unlimited. Teams need a stop policy for sessions that are blocked, looping, or simply too expensive relative to the value of the change. That policy can include maximum execution windows, credit balances, spend caps, model choices, and manual checkpoints before high-cost follow-up work. Capy's [pricing documentation](https://docs.capy.ai/pricing.md) makes the budget behavior explicit. Credits cover AI usage, VM runtime, and auxiliary services such as the Review Agent. When the balance runs out, active tasks pause. Auto-reload can purchase more credits to keep work moving, but an organization-wide monthly spend cap remains a hard ceiling; teams can also disable overage entirely. Pause semantics are safer than implying a VM runs forever. ## How Capy handles long-running coding tasks In [Capy](https://capy.ai), a task is a coding session with its own branch and its own isolated Ubuntu VM. The Build agent works inside that VM with common development tools available: it can edit files, install packages, run commands, use Docker, test the application, and inspect the result. The main branch stays clean while work is in progress. Capy separates planning from implementation. **Captain** reads the codebase, turns complex requests into task specifications, and delegates work. **Build** executes a scoped implementation in the VM. That split is useful for long work because planning and coding have different failure modes: the planner should decompose scope and define evidence, while the implementation agent should stay focused on editing and verification. For tasks that outgrow one reasoning context, Capy uses context compaction through handoffs. A fresh context receives the concise state needed to continue instead of replaying every earlier token. This preserves continuity without treating prompt length as infinite. After implementation, Capy's **Review Agent** adds a triage loop. It reviews PR findings, classifies them as open, resolved, or irrelevant, and can route actionable findings on Capy-generated work back to Build for fixes and re-review. Humans still decide what to merge. Review triage is especially useful after long implementations because the final diff may contain interactions that were not obvious during any one edit. ## Competitor tradeoffs “Background agent” is not one uniform product shape. Each tool makes a different tradeoff among maximum session duration, environment isolation, parallelism, automation, and developer control. | Tool | Long-running or asynchronous model | Useful tradeoff to understand | | --- | --- | --- | | [Capy](https://docs.capy.ai/using-capy.md) | One isolated Ubuntu VM and branch per task, Captain planning, Build implementation, context-compacted handoffs, and Review triage | Designed for bounded tasks that may need extended implementation and review; credits, auto-reload, and spend caps control continuation | | [GitHub Copilot cloud agent](https://docs.github.com/en/copilot/concepts/agents/coding-agent/about-coding-agent#limitations-of-copilot-cloud-agent) | Ephemeral GitHub Actions-powered development environment with branch-based work | Each session has a hard 59-minute maximum that cannot be extended or bypassed, so larger work needs decomposition | | [Codex cloud](https://developers.openai.com/codex/cloud) | Background tasks can run in parallel in their own cloud environments | Strong fit for OpenAI-native cloud delegation; [Codex app automations](https://developers.openai.com/codex/app/automations) also support scheduled background runs and dedicated worktrees | | [Cursor Cloud Agents](https://cursor.com/docs/cloud-agent) | Isolated cloud VMs with cloned repos, dependencies, secrets, startup commands, network access, parallel runs, and remote desktop control | Environment configuration is central; Cursor supports saved snapshots, Dockerfile-based setup, MCP servers, and artifacts for verification | | [Devin](https://docs.devin.ai/work-with-devin/advanced-capabilities) | Managed sessions can run in parallel in isolated VMs, with a coordinator that scopes work and monitors progress | Adds session orchestration, ACU monitoring, playbooks, and one-time or recurring schedules for workflows that benefit from managed execution | | [Kiro autonomous agent](https://kiro.dev/blog/introducing-kiro-autonomous-agent/) | Preview product with asynchronous tasks in isolated sandboxes and up to 10 concurrent tasks for paid users during rollout | Emphasizes cross-task context, sandbox configuration, sub-agent coordination, and review-driven learning; preview availability and limits matter | A fixed timeout is not automatically bad. GitHub Copilot's 59-minute cap makes the boundary easy to reason about, but it pushes teams to split work that may need longer verification. Persistent cloud environments are not automatically better either: they require careful setup, secrets handling, budget controls, and a way to detect stalled work. Codex's automation model illustrates another distinction. A scheduled automation can start fresh or return to a thread, and Git repositories can use a dedicated background worktree so automation changes do not collide with unfinished local edits. That is useful for recurring triage, but it is a different problem from carrying one large implementation across multiple checkpoints. ## Practical checklist Before delegating a task that may run for a while, use this checklist: 1. **Bound the task.** Describe one coherent feature, fix, migration, or refactor. Split independent outcomes into separate tasks. 2. **State the acceptance criteria.** Name expected behavior, files or modules in scope, and the strongest relevant test, build, lint, or browser check. 3. **Make setup deterministic.** Commit lockfiles, document bootstrap commands, and provide only the environment variables and secrets the task needs. 4. **Isolate the branch.** Give each task its own branch, worktree, or VM-backed checkout. Do not let background work modify an active local branch unexpectedly. 5. **Choose checkpoints.** Require progress to become observable after setup, after a focused implementation step, before expensive verification, and before merge. 6. **Plan context recovery.** Ensure the agent can summarize completed work, preserve the workspace, and continue with a compact next-step state when the conversation grows large. 7. **Set cost boundaries.** Decide whether to use a fixed timeout, a credit limit, auto-reload, a monthly spend cap, or a human approval before additional runtime. 8. **Review the final artifact.** Inspect the diff, the command output, and any review findings. Merge only after the acceptance check has passed. ## What to optimize for The best long-running agent is not the one with the longest advertised runtime. It is the one that can turn an extended task into a sequence of recoverable, observable steps and stop with evidence. Persistent VMs, context compaction, branch isolation, and background execution matter because they support that workflow. They are not substitutes for a precise task definition. For teams evaluating tools, start with a real task that includes setup, a multi-file change, and a meaningful verification command. Measure how often the agent needs help, whether it preserves useful state, how clearly it reports checkpoints, and whether the final diff is reviewable. A reliable coding agent should make long work easier to supervise, not harder to understand. --- - [From database filters to agent search: how AI is reinventing lead discovery](https://capy.ai/articles/database-filters-to-agent-search): Lead discovery is shifting from static database filters to agent-based live search. Here is where each model performs best and how to evaluate fit for your ICP. Most lead generation stacks were built around filtering pre-indexed records. That model is efficient when your target market matches common categories, but it gets fragile when your ICP is local, niche, or constantly changing. This is why AI-native discovery is gaining traction. Instead of asking which existing rows match fixed filters, you can ask an agent to find who matches your profile right now across live sources. ## TL;DR - Traditional databases like Apollo and ZoomInfo are filter interfaces over static indexes. - Agent-based tools like Origami run live discovery across maps, the open web, directories, and business data APIs. - The difference matters most for under-indexed ICPs: small companies, local segments, niche operators, and recently founded teams. - Origami uses parallel enrichment and verification rather than slow one-by-one lookups. - In practice, many teams should test both approaches per ICP instead of treating this as a binary replacement. ## The core constraint with traditional databases Most database platforms optimize for one operation: filtering a static index. You select criteria such as industry, headcount, title, and geography. The platform returns records already collected and normalized on its own crawl and refresh cadence. That is often effective for mainstream ICPs, including mid-market SaaS companies, common executive titles, and relatively stable taxonomies. But performance drops when your targets are: - local or regional businesses - sub-50-employee companies - recently founded teams - non-standard operator roles - niche verticals with inconsistent categorization When coverage is weak, better sequencing does not fix top-of-funnel data quality. ## What agent-based discovery changes Agent-based discovery flips the workflow from passive filtering to active search. With Origami, you describe your ICP in plain language. The system then performs live discovery across Google Maps, company sites, directory pages, public business sources, and structured APIs for enrichment. The operative question changes from: - “Which records in this database match my filters?” to: - “Who actually matches this profile now?” That shift is especially relevant for segments where static indexes are incomplete or stale. ## How the pipeline works | Step | What happens | | --- | --- | | Input | Plain-language ICP description | | Discovery | Agent searches Maps, web pages, directories, and APIs | | Enrichment | Parallel waterfall across providers for email, phone, and LinkedIn data | | Verification | Bounce checks, deduplication, and confidence scoring | | Output | Verified contact list ready for outbound tooling | The key design choice is parallelism. Instead of serial lookups that fail slowly provider by provider, the system gathers signals concurrently and resolves conflicts during verification. ## Where outputs differ in practice | ICP scenario | Static database filtering | Agent-based live discovery | | --- | --- | --- | | VP Sales at 200+ employee SaaS | Usually strong coverage | Also performs well | | Pediatric dentists in suburban markets | Often spotty coverage | Often stronger via Maps + web discovery | | Recently founded startups | Frequently incomplete and delayed | Typically fresher through live search | | Independent consultants with weak LinkedIn presence | Inconsistent match quality | Better hit rate from web and directory sources | | Regional niche operators | Sensitive to taxonomy quality | More resilient via direct-source discovery | Trade-off: static filtering is usually faster at query time. Agent discovery is often slower per run, but can be broader and fresher for under-indexed segments. ## Quick self-test for ICP fit Run this test with your current database on one real ICP: - 500 results and low bounce rate: likely well covered. - 500 results and high bounce rate: breadth exists, freshness is weak. - Fewer than 200 results for a large TAM: likely under-indexed. - Many results but weak relevance: likely taxonomy mismatch. This gives you a fast signal on whether your constraint is sequencing execution or list-quality coverage. ## Architecture pattern: plan, parallelize, synthesize Messy real-world ICP discovery is rarely a single query problem. Ambiguous targets usually require decomposition into constraints such as: - geography and proximity logic - company-level qualification signals - role inference from sparse public data - recency and freshness checks - contactability confidence The pattern that works is planning plus parallel execution, followed by synthesis and scoring. Conceptually, this mirrors modern AI engineering workflows where planning and execution are separated. Capy applies that planning/execution split to software development workflows. Discovery systems like Origami apply a similar architectural pattern to lead generation. ## Where Origami fits in the GTM stack Origami sits in the discovery layer: “who should we contact?” Your existing sequencing and CRM systems still handle outreach orchestration, responses, and pipeline management. For teams selling to startup operators and smaller technical organizations, broader discovery can improve top-of-funnel coverage before sequencing starts. This includes many 5–25-person engineering teams that tools like Capy frequently support. ## Final take For many teams, the practical move is not choosing ideology. It is running the same ICP through both models and comparing output quality directly. If your segment is already well indexed, database filters may be sufficient. If your segment is under-indexed, live agent discovery can materially improve coverage and freshness. Try both on a single ICP slice, compare bounce and relevance, then scale what wins. You can evaluate Origami at [origami.chat](https://origami.chat). --- - [Best AI Code Review Tools in 2026](https://capy.ai/articles/best-ai-code-review-tools-2026): AI code review has evolved from noisy bots to genuine quality gates. Here are the 7 best tools for automated PR review and code quality. AI code review tools use large language models to automatically analyze pull requests for bugs, security vulnerabilities, and code quality issues. Early AI review tools had a reputation for spamming PRs with obvious or incorrect comments. The current generation is different — [GitClear's 2025 code quality report](https://www.gitclear.com/coding_on_copilot_data_shows_ais_downward_pressure_on_code_quality) found that AI-assisted code is reverted 39% more often than human-written code, making automated review more critical than ever. Here are the 7 best AI code review tools in 2026, ranked by what actually matters: signal-to-noise ratio, actionability, context depth, and cost at scale. ## What to look for in AI code review tools Five criteria separate the useful from the noisy: - **Signal-to-noise ratio** — Does it find real issues, or flood PRs with nitpicks? - **Actionability** — Can it fix issues, or only point them out? - **Context depth** — Does it understand your codebase, or just the diff? - **Integration** — How well does it fit into your existing PR workflow? - **Cost at scale** — Per-developer pricing adds up. What's the real cost for your team? ## The 7 best AI code review tools ### 1. Capy Review Agent — Best overall AI code review [Capy's](https://capy.ai) Review Agent works on any PR in your GitHub repo — whether written by humans, AI, or a mix of both. It examines diffs for bugs, security issues, and code quality problems, and posts line-by-line findings directly on the PR. What makes Capy different from every other tool on this list: the review-fix cycle is fully automated. When the Review Agent finds an issue on a Capy-generated PR, it routes findings back to the Build agent, which fixes them and pushes updates — then the Review Agent re-checks. Findings get classified as open, resolved, or irrelevant. No human in the loop unless you want one. For human-written PRs, you still get the same review depth — just without the automated fix loop. Review is included in every Capy plan — no separate add-on or per-reviewer fee. **Best for:** Any team that wants AI code review with automated fixing on AI-generated PRs **Integration:** GitHub **Pricing:** Included in Capy plans (Pro from $20/mo) ### 2. CodeRabbit — Best for multi-platform support [CodeRabbit](https://www.coderabbit.ai) is a widely used AI code review tool, reporting 2 million+ repositories and 13 million PRs reviewed. It integrates with GitHub and GitLab via a simple install, and starts reviewing every PR automatically. CodeRabbit runs 40+ linters and SAST tools alongside LLM-based analysis. The interactive chat lets you discuss findings and generate tests directly on the PR. The main limitation: CodeRabbit can only comment on issues — it can't fix them. That feedback loop is still on you or another tool. **Best for:** Teams needing GitHub + GitLab support with static analysis depth **Integration:** GitHub, GitLab **Pricing:** Free (basic) / $24/mo per dev (Pro) ### 3. GitHub Copilot Code Review — Best for GitHub-native teams GitHub's built-in AI review appears natively in the PR interface, making it the lowest-friction option for teams already on GitHub. Review comments look and behave like human review comments, and the AI learns from your repository's patterns. The trade-off is depth — it's not as specialized as CodeRabbit and doesn't include static analysis tools. For teams where "good enough" review with zero additional setup is the goal, it works. But you're not getting much. **Best for:** Teams already on GitHub Copilot who want no-setup AI review **Integration:** GitHub (native) **Pricing:** Included in Copilot plans ### 4. Qodo (formerly CodiumAI) — Best for test-driven quality [Qodo](https://www.qodo.ai) flips the script on code review by focusing on testing rather than commenting. It generates test cases, identifies untested code paths, and analyzes behavior to ensure changes work as intended. Traditional review tools tell you "this function might have a bug." Qodo generates a test that proves it. For teams where test coverage is the bottleneck in code quality, Qodo is more effective than comment-based review. **Best for:** Teams wanting test generation and behavior analysis alongside review **Integration:** VS Code, JetBrains, CI/CD pipelines **Pricing:** Free tier / paid for teams ### 5. Sourcery — Best for Python and code quality metrics [Sourcery](https://www.sourcery.ai) combines AI code review with quantitative code quality metrics. It measures complexity, duplication, and code health, and provides refactoring suggestions that go beyond surface-level comments. For Python teams, Sourcery's language-specific knowledge is a genuine advantage — it understands Pythonic patterns and anti-patterns in a way generic LLM-based tools don't. **Best for:** Python teams who want metrics-driven code quality alongside review **Integration:** GitHub, VS Code, CLI **Pricing:** Free for open source / paid for teams ### 6. Graphite Reviewer — Best for fast merge workflows [Graphite](https://graphite.com) integrates AI review into its PR stacking and merging workflow. The reviewer is designed for speed — it flags critical issues quickly without blocking the fast merge cycles that Graphite users expect. Not as deep as CodeRabbit, but tightly integrated into a workflow optimized for shipping fast. **Best for:** Teams using Graphite's stacked PR workflow **Integration:** GitHub (via Graphite) **Pricing:** Included in Graphite plans ### 7. Ellipsis — Best for configurable review rules [Ellipsis](https://www.ellipsis.dev) offers AI code review with a strong emphasis on customizable rules. You can define exactly what the AI should look for — from security patterns to style guidelines to business logic rules. For teams with specific coding standards or regulatory requirements, the ability to configure review rules in detail is a significant advantage. **Best for:** Teams needing customizable review rules and compliance checks **Integration:** GitHub **Pricing:** Free tier / paid for teams ## Quick comparison | Tool | Standalone | Fix loop | Static analysis | Per-dev pricing | |------|------------|----------|-----------------|-----------------| | Capy Review | ✅ Any PR | ✅ Auto-fix | LLM-based | Included | | CodeRabbit | ✅ | ❌ | ✅ 40+ tools | $24/mo | | Copilot Review | Part of Copilot | ❌ | Basic | Included | | Qodo | ✅ | Generates tests | Behavior analysis | Free / paid | | Sourcery | ✅ | ❌ | Metrics-based | Free / paid | | Graphite | Part of Graphite | ❌ | Basic | Included | | Ellipsis | ✅ | ❌ | Rule-based | Free / paid | ## Our pick **Capy's Review Agent** is the strongest option for most teams — it reviews any PR, and for AI-generated code it closes the loop by automatically fixing what it finds. Review is included in every plan, not charged separately per reviewer. If you need GitLab support, CodeRabbit is an option. If you're on GitHub Copilot or Graphite already, their built-in reviewers exist but are limited. --- - [Best AI Coding Assistants in 2026: 10 Tools Compared](https://capy.ai/articles/best-ai-coding-agents-2026): Compare the best AI coding assistants and autonomous coding agents in 2026 by workflow, autonomy, environment, model support, review, and pricing. AI coding assistants now cover several different workflows: autocomplete inside an editor, interactive terminal agents, and autonomous cloud agents that work from a task and return a branch or pull request. Comparing them as if they were interchangeable produces a bad shortlist. This guide compares 10 widely used options by the work they are designed to do. We reviewed their documented interfaces, execution environments, model support, parallel workflows, Git and review features, and pricing structure. Product capabilities and plans change quickly, so verify current limits with each vendor before standardizing on a tool. ## Quick picks by workflow | If you need… | Start with… | Why | | ---------------------------------------------- | ----------- | ---------------------------------------------------------------------- | | Delegated tasks in isolated cloud environments | Capy | Planning, execution, review, and pull request workflows in one product | | An autonomous terminal session | Claude Code | Strong repository and command-line workflow | | AI integrated into daily editing | Cursor | Autocomplete, chat, and multi-file edits inside an editor | | An open-source terminal assistant | Aider | Broad model support, Git integration, and local control | | OpenAI-native cloud and CLI workflows | Codex | Tight integration with OpenAI models and cloud tasks | | A free, customizable VS Code agent | Roo Code | Role-based modes and bring-your-own-model support | ## What makes a great AI coding assistant in 2026? The useful criteria depend on whether you want assistance or delegation: - **Interaction model** — Is it editor autocomplete, a live agent session, or an asynchronous task? - **Autonomy** — Can it inspect the repository, plan changes, run commands, and recover from failures? - **Environment** — Does it share your local checkout or run in a separate sandbox, worktree, or VM? - **Verification** — Can it run the repository's tests and checks rather than stopping at code generation? - **Git and review** — Does it create a focused branch or pull request, and where does review happen? - **Parallelism** — Can independent tasks run concurrently without sharing mutable state? - **Model flexibility** — Can you choose a model or provider based on quality, latency, and cost? For a deeper explanation of delegated workflows, see [what agentic coding means in practice](/articles/agentic-coding) and our [guide to background coding agents](/articles/background-coding-agents-guide). ## The 10 best AI coding assistants and agents ### 1. Capy — Best for parallel development [Capy](https://capy.ai) is the only AI IDE designed around concurrent task execution. Each task runs in a dedicated cloud VM, and a two-agent architecture separates planning (Captain) from implementation (Build) — so the AI handles both deciding what to build and doing the work. What sets Capy apart is throughput. While other tools help you code faster on one thing, Capy lets you clear a backlog in parallel. Automated branching, PR creation, and a built-in review step mean you go from task description to merge-ready code without leaving the platform. **Best for:** Teams managing backlogs, developers who want to ship multiple features simultaneously **Models:** 30+ (Claude, GPT, Gemini, Grok, GLM, Kimi, Qwen) **Parallel:** ✅ Unlimited **Pricing:** Pro from $20/mo ### 2. Claude Code — Best autonomous CLI agent [Claude Code](https://code.claude.com/docs) is Anthropic's official terminal agent. It can explore codebases, run commands, fix its own errors, and commit changes with minimal guidance. It appeals to developers who want a more autonomous terminal experience. The trade-off: Claude models only, higher token costs, and less control over what the agent does. **Best for:** Terminal developers who want maximum autonomy **Models:** Claude only **Parallel:** ❌ **Pricing:** API usage (Claude models) ### 3. Cursor — Best AI-enhanced editor [Cursor](https://cursor.com) is a VS Code fork with AI integrated into the editing experience. Tab completion, multi-file refactoring, semantic search, and inline chat are built in. Cursor has a large user base in the "AI editor" category. It enhances rather than replaces your coding workflow, but it's still a single-session editor — no parallelism, no planning, no automated review. **Best for:** Developers who want AI-enhanced editing, not AI-automated development **Models:** Multiple (GPT, Claude, built-in) **Parallel:** ❌ **Pricing:** Free / $20/mo Pro / $40/mo Business ### 4. Aider — Best open-source CLI tool [Aider](https://aider.chat) is a popular open-source AI pair programming CLI. 41,000+ GitHub stars, 100+ model support, solid Git integration, and a terminal-first design. Aider won't plan your work or review your code, and it's limited to one task at a time. Useful for focused, interactive coding sessions where you want cost control. **Best for:** Terminal developers who want control and transparency **Models:** 100+ (BYOK) **Parallel:** ❌ **Pricing:** Free (you pay API costs) ### 5. Amp Code — Best for team collaboration [Amp](https://ampcode.com) (Sourcegraph) is a CLI-based coding agent that combines code intelligence with AI in a team-friendly package. Shared threads, reusable workflows, and integration with Sourcegraph's code search are its differentiators — though it's CLI-only now after killing its VS Code extension in March 2026. **Best for:** Teams already on Sourcegraph, or teams wanting shared AI workflows **Models:** Frontier (Claude, GPT) **Parallel:** Limited **Pricing:** Pay-as-you-go ### 6. Codex — Best for OpenAI-native workflows [Codex](https://openai.com/codex/) is OpenAI's coding agent, running each task in a cloud sandbox. It's tightly integrated with the OpenAI ecosystem and handles multi-file changes well. **Best for:** Developers invested in OpenAI's ecosystem **Models:** GPT models **Parallel:** Limited (via cloud sandboxes) **Pricing:** Included in ChatGPT Pro, API pricing ### 7. Roo Code — Best free VS Code agent [Roo Code](https://roocode.com) gives you role-based AI modes inside VS Code for free. Architect, Coder, and Debugger modes provide structured AI assistance with any model via BYOK. **Best for:** VS Code users who want free, customizable AI modes **Models:** Any (BYOK) **Parallel:** Cloud only **Pricing:** Free / Cloud from $5/hr ### 8. Kilo Code — Best for scale [Kilo Code](https://kilo.ai) is the largest open-source coding agent by usage — 1.5 million+ users, 25 trillion tokens processed, 500+ model support. It works across VS Code, JetBrains, and CLI. **Best for:** Developers wanting the largest model ecosystem and community **Models:** 500+ (BYOK) **Parallel:** ❌ **Pricing:** Free with optional credits ### 9. 1Code — Best for Claude Code power users [1Code](https://github.com/21st-dev/1code) wraps CLI agents (primarily Claude Code) in a desktop UI with parallel worktrees, real-time diffs, and background execution. The best interface for people who want Claude Code with a GUI. **Best for:** Claude Code users who want a visual interface and parallel sessions **Models:** Depends on CLI agent **Parallel:** ✅ Via worktrees **Pricing:** Free / $20/mo Pro ### 10. Goose — Best emerging open-source agent [Goose](https://github.com/block/goose) (Block/Square) is an ambitious open-source autonomous agent that can plan, code, and iterate independently. Less mature but strong backing and an extensible plugin architecture. **Best for:** Developers who want an open-source autonomous agent with room to grow **Models:** Multiple **Parallel:** ❌ **Pricing:** Free (you pay API costs) ## Feature comparison | Tool | Parallel | Planning agent | Code review | Open source | Interface | | ----------- | ------------ | -------------- | ----------- | ----------- | ----------------- | | Capy | ✅ Unlimited | ✅ | ✅ | No | Browser IDE | | Claude Code | ❌ | Partial | ❌ | No | Terminal | | Cursor | ❌ | ❌ | ❌ | No | Desktop IDE | | Aider | ❌ | ❌ | ❌ | Yes | Terminal | | Amp Code | Limited | ❌ | ✅ Basic | No | CLI | | Codex | Limited | ❌ | ❌ | No | Web + CLI | | Roo Code | Cloud | Mode-based | ❌ | Yes | VS Code | | Kilo Code | ❌ | ❌ | ❌ | Yes | VS Code/JetBrains | | 1Code | ✅ Worktrees | ❌ | ❌ | Yes | Desktop | | Goose | ❌ | Partial | ❌ | Yes | Terminal | ## How to choose Start with the work you want to hand off. For AI-enhanced editing without changing your workflow, **Cursor** is the most direct category fit. For a terminal session that you actively steer, compare **Claude Code** and **Aider**. For asynchronous tasks that need their own environments, branches, verification, and review, compare cloud agents such as **Capy**, **Codex**, and the cloud modes offered by other vendors. Run the same representative task in two or three tools. Record setup time, interventions, test results, review corrections, and total cost. A product that looks fast in a demo may be a poor fit if it cannot reproduce your environment or return a reviewable change. --- - [Best Open-Source AI Coding Agents in 2026](https://capy.ai/articles/best-open-source-ai-coding-agents): Don't want vendor lock-in? Here are the best open-source AI coding agents in 2026 — plus how they compare to cloud platforms like Capy for parallel development. Open-source coding agents give you three things proprietary tools can't: full visibility into what the AI is doing with your code, the ability to modify behavior, and zero vendor lock-in. According to [GitHub's 2025 Octoverse report](https://github.blog/news-insights/octoverse/octoverse-a-new-developer-joins-github-every-second-as-ai-leads-typescript-to-1/), open-source AI coding projects saw 2x year-over-year growth in contributors. For developers who work with sensitive codebases, need custom workflows, or just philosophically prefer open tools, the open-source AI coding ecosystem is thriving in 2026. Here's a curated list of the best options, organized by how you'd actually use them. ## TL;DR - **CLI:** Aider (established), Goose (autonomous), OpenCode (fast-growing, multi-platform), Forge Code (agentic). - **IDE:** Roo Code (VS Code modes), Kilo Code (largest community), Continue (privacy-first). - **Desktop:** 1Code (GUI for CLI agents). - **Cloud gap:** None offer true parallel VMs, planning agents, or automated PR workflows — that's where [Capy](https://capy.ai) fills in. ## CLI agents ### 1. Aider — The standard-bearer [Aider](https://aider.chat) (41K+ stars) is one of the earlier open-source AI coding CLIs and has a loyal user base. It connects to 100+ models, has clean Git integration (every AI change gets its own commit), and gives you control over context management. **Why use it:** You want a CLI AI coding experience with model flexibility and Git integration. **Limitations:** Single-session only, no planning agent, you manage everything yourself. ### 2. Goose — The autonomous agent [Goose](https://github.com/block/goose) (Block/Square) is more autonomous than Aider — it can plan, execute, and iterate on code with less hand-holding. It supports plugins and custom tools, making it extensible for specialized workflows. **Why use it:** You want an agent that can operate more independently, with a plugin system for custom capabilities. **Limitations:** Smaller community, less mature, behavior can be unpredictable. ### 3. OpenCode — The fast-growing terminal agent [OpenCode](https://github.com/opencode-ai/opencode) is an open-source AI coding agent available as a terminal CLI, desktop app, and IDE extension. With 120K+ GitHub stars and 5M+ monthly developers, it's grown into one of the largest open-source coding agents — offering a privacy-first approach where no code or context data is stored. **Why use it:** You want a widely adopted, privacy-first coding agent with terminal, desktop, and IDE options. **Limitations:** Newer entrant compared to Aider, still maturing its agentic capabilities. ### 4. Forge Code — The agentic CLI [Forge](https://github.com/antinomyhq/forge) is a CLI coding agent with agentic features — it can browse files, understand context, and make multi-file changes. More structured than Aider but less opinionated than Claude Code. **Why use it:** You want an agentic CLI that falls between Aider's interactivity and Claude Code's autonomy. **Limitations:** Tiny community, limited documentation. ## IDE extensions ### 5. Roo Code — Best for VS Code with custom modes [Roo Code](https://roocode.com) (Cline fork) adds role-based AI modes to VS Code — Architect, Coder, Debugger, and custom modes you define. BYOK model support means you use whatever provider you want. **Why use it:** You want free, customizable AI assistance inside VS Code with specialized modes. **Limitations:** VS Code only, single-agent at a time (in free tier), no integrated review. ### 6. Kilo Code — Largest by usage [Kilo Code](https://kilo.ai) has 1.5M+ users and supports 500+ models across VS Code, JetBrains, and CLI. It includes inline autocomplete, browser automation, and automated refactoring. **Why use it:** You want the largest ecosystem and the most model options in an open-source extension. **Limitations:** Still an editor extension with the same fundamental constraints. ### 7. Continue — Best for privacy-focused BYOK [Continue](https://www.continue.dev) is an open-source AI code assistant for VS Code and JetBrains that emphasizes privacy. It supports local models via Ollama and any cloud provider via BYOK. **Why use it:** Privacy is your top priority and you want to run models locally. **Limitations:** Less agentic than Roo Code or Kilo Code, more copilot than autonomous agent. ## Desktop / GUI tools ### 8. 1Code — Best GUI for CLI agents [1Code](https://github.com/21st-dev/1code) (21st.dev, YC W26) wraps CLI agents like Claude Code and Codex in a desktop UI with parallel worktrees, real-time diffs, and background execution. **Why use it:** You want a visual interface for Claude Code with parallel session management. **Limitations:** Depends on proprietary CLI agents underneath, macOS/Linux only (Windows experimental). ## How they compare to cloud platforms Open-source tools excel at flexibility, privacy, and cost control. But they share common limitations: parallelism maxes out at local worktrees, planning is manual, and the Git lifecycle (branching, PRs, review) is on you. Cloud platforms like [Capy](https://capy.ai) trade local control for automation: | Capability | Open-source tools | Capy | | --------------------- | --------------------------- | -------------------------------- | | Parallel execution | ❌ (except 1Code worktrees) | ✅ Unlimited, separate cloud VMs | | Planning intelligence | ❌ | ✅ Dedicated planning agent | | Code review | ❌ | ✅ Automated review step | | Automated PRs | ❌ | ✅ | | Data stays local | ✅ | ❌ (cloud VMs) | | Customizable | ✅ (fork/modify) | ❌ | | Cost transparency | ✅ (API costs only) | Usage-based | ## Our recommendation Start with an open-source tool that matches your workflow — Aider for terminal, Roo Code for VS Code, 1Code for a GUI. When you hit the ceiling on parallelism, planning, or automation, try [Capy](https://capy.ai) alongside your open-source tools. They complement each other well. --- - [Best Aider Alternatives in 2026](https://capy.ai/articles/best-aider-alternatives): Aider is great for terminal-based AI pair programming. But if you need parallelism, a GUI, or more automation, here are 5 alternatives worth trying. Aider is a popular open-source CLI AI coding tool with 41,000+ GitHub stars, solid Git integration, 100+ model support, and an interactive pair programming workflow that appeals to terminal-native developers. It consistently ranks among the top AI coding tools on [SWE-bench](https://www.swebench.com/), the industry benchmark for code generation quality. So why would anyone switch? A few reasons come up repeatedly: Aider is inherently single-threaded — one conversation, one task. It has no built-in planning intelligence — you need to know what to build and which files to include. There's no GUI for developers who prefer visual workflows. And each session is independent, with no persistent memory across tasks. If you hit any of those walls, here are five alternatives that solve different parts of the problem. ## TL;DR - **Capy** for parallel automated workflows. **Claude Code** for more autonomous terminal sessions. **Amp Code** for team collaboration. **Roo Code** for visual, mode-based workflows. **Goose** for an open-source autonomous agent. ## Best Aider alternatives ### 1. Capy — Best for parallel, automated workflows [Capy](https://capy.ai) is the opposite end of the spectrum from Aider. Where Aider is a real-time conversation about code, Capy is a dispatch system — you describe work at a high level, and AI handles planning, coding, reviewing, and PR creation across parallel cloud environments. The gap it fills for Aider users is everything you do manually between coding sessions: deciding what to build next, figuring out which files are involved, managing branches, opening PRs, and reviewing the result. Capy's planning agent handles the first two. Its execution agents handle the rest, each working in a separate sandboxed VM. The trade-off is interactivity. Aider lets you steer every change in real time. Capy is closer to delegation — you check in on progress and review the output. **Pros:** Concurrent task execution, AI-driven planning, sandboxed environments, automated Git workflow, built-in review **Cons:** No terminal/CLI interface, cloud-only, less interactive than Aider, not open source **Pricing:** Pro from $20/mo ### 2. Claude Code — Best for Aider users who want more power [Claude Code](https://code.claude.com/docs) is Anthropic's official CLI coding agent. Like Aider, it runs in your terminal — but it's more autonomous. Claude Code can explore your codebase, run commands, fix its own errors, and commit changes with less hand-holding. It's the natural upgrade for Aider users who want a more agentic experience while staying in the terminal. The trade-off: it only works with Claude models, and it's more expensive due to higher token usage. **Pros:** More autonomous than Aider, good terminal UX, codebase understanding, Git integration **Cons:** Claude models only, higher cost, less user control over context **Pricing:** Pay per API usage (Claude models) ### 3. Amp Code — Best for teams [Amp](https://ampcode.com) brings Sourcegraph's code intelligence to a CLI-based AI coding agent. It offers shared threads and workflows that let teams build institutional knowledge around AI-assisted coding. For Aider users working in teams, Amp's collaboration features fill a real gap. When one person discovers a good prompt or workflow, the whole team benefits. **Pros:** Team collaboration, Sourcegraph code intelligence, CLI-first, pay-as-you-go **Cons:** Less model flexibility than Aider, newer product, limited parallelism **Pricing:** Pay-as-you-go + free daily grants ### 4. Roo Code — Best for visual, mode-based workflows [Roo Code](https://roocode.com) is an open-source VS Code extension with role-based AI modes (Architect, Coder, Debugger). For Aider users who want the multi-model flexibility but prefer a GUI, Roo Code is the closest match — BYOK, open-source, and free. The custom mode system is unique: you can create specialized AI personalities for security, documentation, or domain-specific tasks. It's more structured than Aider's free-form conversation. **Pros:** Free, open source, BYOK, custom AI modes, VS Code integration **Cons:** VS Code only, no parallelism (in free tier), less polished Git integration than Aider **Pricing:** Free extension / Cloud from $5/hr ### 5. Goose — Best for open-source autonomous agents [Goose](https://github.com/block/goose) is Block's (formerly Square) open-source AI coding agent. It's more autonomous than Aider — closer to a junior developer that can plan, execute, and iterate independently. Goose supports multiple LLMs and can be extended with plugins. For Aider users who want more autonomy from their AI without leaving the open-source ecosystem, Goose is worth a look. Less mature but more ambitious in scope. **Pros:** Open source, more autonomous than Aider, extensible with plugins, multi-model **Cons:** Smaller community, less mature, less predictable behavior, no GUI **Pricing:** Free (you pay API costs) ## Quick comparison | Tool | Interface | Parallel | Planning | Models | Open source | |------|-----------|----------|----------|--------|-------------| | Capy | Browser IDE | ✅ Unlimited | ✅ Built-in | 30+ built-in | No | | Claude Code | Terminal | ❌ | Partial (autonomous) | Claude only | No | | Amp Code | CLI | Limited | ❌ | Frontier models | No | | Roo Code | VS Code | Cloud only | Mode-based | Any (BYOK) | Yes | | Goose | Terminal | ❌ | Partial (autonomous) | Multiple | Yes | ## Bottom line Aider sets the bar for terminal AI pair programming. If you're looking for something different, it's usually because you need parallelism (Capy), more autonomy (Claude Code), team features (Amp), a GUI (Roo Code), or a different open-source approach (Goose). Know what you're optimizing for, and the choice is straightforward. --- - [Best CodeRabbit Alternatives in 2026](https://capy.ai/articles/best-coderabbit-alternatives): CodeRabbit is the go-to AI code review tool, but it's not the only option. Here are 5 alternatives for automated PR review in 2026. CodeRabbit is a widely used AI code review tool — 2 million repos, 13 million PRs reviewed, and GitHub/GitLab integration. For many teams, it's the first tool they try. But it's not perfect for everyone. The Pro plan at $24/dev/month gets expensive for larger teams. The reviews are purely reactive — CodeRabbit reviews code but can't fix it. And some teams want review integrated into their development workflow, not bolted on as a separate tool. If you're exploring alternatives, here are five tools that approach AI code review from different angles. ## TL;DR - **Capy** for integrated development + review with automated fixing. **GitHub Copilot** for zero-setup GitHub-native review. **Sourcery** for Python teams. **Qodo** for test-focused quality. **Graphite** for fast merge workflows. ## Best CodeRabbit alternatives ### 1. Capy — Best for integrated development + review [Capy's](https://capy.ai) Review Agent works on any PR in your GitHub repo — human-written or AI-generated. It examines diffs for bugs, security issues, and code quality problems with line-by-line findings. The real differentiator is what happens next. On Capy-generated PRs, open findings get sent directly to the Build agent, which fixes them and pushes updates. Then the Review Agent re-checks. This closed loop means issues get resolved automatically — no "AI comments, human fixes" pattern. Review is built into every Capy plan — no separate add-on or per-reviewer charge. **Pros:** Closed-loop review → fix → re-review, no separate per-dev pricing, integrated with development workflow **Cons:** GitHub only (no GitLab), cloud-only **Pricing:** Included in Capy plans (Pro from $20/mo) ### 2. GitHub Copilot Code Review — Best for GitHub-native teams GitHub Copilot now includes AI-powered code review for PRs. Review comments appear natively alongside human reviews in the GitHub UI. If your team is already paying for GitHub Copilot, it's the lowest-friction option. No additional tool to install, no new billing. The trade-off is that review depth is limited compared to dedicated tools. **Pros:** Native GitHub integration, no separate tool needed, included in Copilot subscription **Cons:** GitHub only (no GitLab/Bitbucket), less specialized than CodeRabbit, review quality varies **Pricing:** Included in GitHub Copilot plans ### 3. Sourcery — Best for Python teams [Sourcery](https://www.sourcery.ai) started as a Python code quality tool and has expanded into general AI code review. It's particularly strong for Python codebases, with opinionated rules about code complexity, duplication, and Pythonic patterns. For teams working primarily in Python who want review that goes deeper than generic LLM analysis, Sourcery brings domain-specific expertise. **Pros:** Excellent Python-specific analysis, code quality metrics, refactoring suggestions **Cons:** Strongest in Python (weaker for other languages), smaller community **Pricing:** Free for open source, paid plans for teams ### 4. Qodo (formerly CodiumAI) — Best for test-focused review [Qodo](https://www.qodo.ai) focuses on AI-powered testing and code quality rather than traditional line-by-line PR review. It generates test cases, analyzes code behavior, and identifies untested paths — a complementary approach to comment-based review. If your code quality problem is more about test coverage than code style, Qodo addresses the root cause rather than the symptoms. **Pros:** Test generation and coverage analysis, behavior-focused review, IDE integration **Cons:** Not a traditional PR review tool, learning curve for test-focused workflow **Pricing:** Free tier available, enterprise plans for teams ### 5. Graphite Reviewer — Best for fast review cycles [Graphite](https://graphite.com) is primarily a stacking and merging tool for GitHub, but its AI reviewer adds automated PR review tightly integrated with fast PR workflows. If you're already using Graphite for stacked PRs, the AI reviewer fits naturally. The focus is speed — Graphite is built for teams that want to merge fast, and the AI reviewer supports that by flagging critical issues quickly. **Pros:** Integrated with stacked PR workflows, fast review turnaround, clean GitHub integration **Cons:** Best value if you're already on Graphite, narrower feature set than CodeRabbit **Pricing:** Included in Graphite plans ## Quick comparison | Tool | Best for | Standalone | Fix loop | Pricing | |------|----------|------------|----------|---------| | Capy | Integrated dev + review | ✅ Any PR | ✅ Auto-fix | Included in usage | | Copilot Review | GitHub-native teams | Part of Copilot | ❌ | Included in Copilot | | Sourcery | Python teams | Yes | ❌ | Free / paid | | Qodo | Test-focused quality | Yes | Partial (generates tests) | Free / paid | | Graphite | Fast PR pipelines | Part of Graphite | ❌ | Included in Graphite | ## Bottom line CodeRabbit can only comment, not fix, and the $24/dev/month pricing scales poorly. **Capy** reviews any PR and automatically fixes what it finds on AI-generated code — with review included in every plan, not charged separately. If you need GitLab support, CodeRabbit is an option. For everything else, Capy does more for less. --- - [Amp Code vs Capy: AI Coding Agent Comparison](https://capy.ai/articles/amp-code-vs-capy): Amp is Sourcegraph's CLI-based AI coding agent. Capy is a parallel AI development platform. Here's how they compare. Amp (by Sourcegraph) is a CLI-based AI coding agent powered by frontier models with Sourcegraph's code intelligence. Capy is a cloud IDE for running multiple AI agents in parallel with full VM isolation. Amp brings team collaboration and code search depth. Capy brings parallel execution and end-to-end automation. ## TL;DR - Amp leverages Sourcegraph's code intelligence and adds team collaboration features (shared threads, workflows, leaderboards). - Capy runs unlimited parallel agents in isolated VMs with a two-agent architecture (Captain + Build) and automated PR workflows. - Amp is strongest for teams already on Sourcegraph. Capy is strongest for parallel throughput. ## What is Capy? [Capy](https://capy.ai) solves a different problem than code intelligence. It's a browser-based platform where you spin up as many coding agents as you need, each working in its own sandboxed cloud environment. A **Captain** agent reads your codebase and decomposes work into specs. **Build** agents pick up those specs and execute them — installing dependencies, running tests, committing code — in separate VMs that can't interfere with each other. A review step checks the output before PRs land on GitHub. ## What is Amp? [Amp](https://ampcode.com) is an AI coding agent built by Sourcegraph, the company behind the code intelligence platform. It's a terminal CLI that uses frontier AI models to generate code changes. (Amp previously had a VS Code extension, but [killed it in March 2026](https://ampcode.com/news/the-coding-agent-is-dead) to go all-in on the CLI.) Amp has team collaboration features — shared threads, workflows, and context that let teams build on each other's patterns. It also benefits from Sourcegraph's code search capabilities. Amp uses a pay-as-you-go pricing model, with a free tier that offers daily grants. It supports multiple models including Claude and GPT variants, and recently added a review agent. ## Head-to-head comparison | Feature | Amp | Capy | |---------|-----|------| | Architecture | CLI (previously also VS Code) | Cloud IDE with isolated VMs | | Code intelligence | Sourcegraph's code search engine | Captain's full codebase analysis | | Parallel agents | Limited (threads, not true parallel VMs) | Unlimited concurrent tasks in isolated VMs | | Team features | Shared threads, workflows, leaderboards | Task dashboard, shared project view | | Model support | Frontier models (Claude, GPT) | 30+ models (Claude, GPT, Gemini, Grok, etc.) | | Code review | Extensible review agent (new) | Built-in Review Agent with triage workflow | | Git workflow | Manual | Automated: task → branch → PR | | Environment | Local machine | Full Ubuntu VM per task | | Pricing | Pay-as-you-go + free tier | Pro from $20/mo | ## Where Capy wins **True parallel execution.** Amp's threads are sequential conversations. Capy's tasks are genuinely parallel — five agents working in five separate cloud VMs at the same time. When you have a backlog to clear, the throughput difference compounds fast. **No local environment constraints.** Amp runs on your machine, inheriting whatever toolchain you have installed. Capy's Build agents run in fresh cloud VMs with Docker, system packages, and common runtimes available out of the box. **Planning as a first-class step.** Captain produces multi-step implementation plans that Build agents execute autonomously. Amp leverages Sourcegraph's code graph for understanding, but the strategic decomposition of work still falls on you. **Tighter review loop.** Capy's review step triages findings as open, resolved, or irrelevant, and routes open issues back for automated fixing. Amp's review capabilities are newer and don't yet have the same automated fix pipeline. **Broader model selection.** 30+ models including Gemini, Grok, GLM, and Kimi alongside Claude and GPT. You can compare outputs on the same task, or match model strengths to task types. ## Where Amp wins **Sourcegraph's code intelligence.** If you're already using Sourcegraph, Amp benefits from its code search, cross-repository references, and dependency analysis. That context helps for large codebases. **Terminal workflow.** Amp runs in your terminal — no browser tab to manage. **Team thread sharing.** Shared threads and workflows let teams reuse each other's patterns. **Enterprise contracts.** If your organization already trusts Sourcegraph with code access, Amp inherits that trust. ## Who should use what? **Use Capy if:** - You need true parallel development — multiple agents, multiple tasks, isolated VMs - You want AI-assisted planning, not just AI-assisted coding - Automated PR creation and integrated code review matter to your workflow - You want to compare models easily or use non-standard models (Gemini, Grok, etc.) **Use Amp if:** - You're already on Sourcegraph and want code intelligence in your AI agent - You prefer terminal workflows over browser-based platforms - Team collaboration features (shared threads, workflows) are a priority - You're in an enterprise with existing Sourcegraph contracts --- - [Best Roo Code Alternatives in 2026](https://capy.ai/articles/best-roo-code-alternatives): Looking for a Roo Code alternative? Here are 5 tools for AI-assisted coding — from parallel cloud IDEs to CLI agents — with honest pros and cons for each. Roo Code is free, open-source, and the custom mode system (Architect, Coder, Debugger) has its fans. But there are real limits. That said, there are common reasons developers look elsewhere. Roo Code is a VS Code extension, so you're locked to that editor. Its parallel capabilities require the Cloud service, which adds cost. Custom modes are powerful but require manual configuration. And because it's a Cline fork, it inherits some of Cline's limitations around context management and multi-file operations. If any of those friction points resonate, here are five alternatives worth trying. ## TL;DR - **Capy** for parallel development with AI planning. **Kilo Code** for a more polished VS Code extension. **Aider** for terminal workflows. **Amp Code** for teams on Sourcegraph. **Continue** for privacy-focused BYOK. ## Best Roo Code alternatives ### 1. Capy — Best for parallel development [Capy](https://capy.ai) isn't an editor extension — it's a standalone browser platform where AI agents run concurrently, each in their own cloud VM. If you're outgrowing Roo Code, it's probably because of one of two things: you want to run multiple tasks at once, or you want AI to handle more than just the coding step. Capy does both — it pairs a planning agent with execution agents that work independently in sandboxed environments, producing branches and PRs without manual Git operations. You can assign different models to different tasks based on complexity, with 30+ options built in and no API key management. **Pros:** Concurrent execution, sandboxed cloud VMs, automated Git and PR workflow, 30+ built-in models, integrated review **Cons:** Cloud-only (no local option), requires learning a new platform, not open source **Pricing:** Pro from $20/mo ### 2. Kilo Code — Best free VS Code alternative [Kilo Code](https://kilo.ai) is another Cline-derived VS Code extension, but it's evolved significantly. With 1.5 million+ users and 25 trillion tokens processed, it's the largest open-source coding agent by usage. It supports 500+ AI models, includes inline autocomplete, browser automation, and automated refactoring. If you like Roo Code's approach but want a more polished extension with a bigger community and more model options, Kilo Code is the natural next step. **Pros:** Massive model support, large community, cross-IDE support, free and open source **Cons:** Still an editor extension (same limitations), no planning agent **Pricing:** Free with optional API credits ### 3. Aider — Best for terminal developers [Aider](https://aider.chat) is a popular CLI-based AI pair programming tool. It connects to 100+ LLMs, maps your codebase for context, and automatically commits every AI change to Git with descriptive messages. Aider is more interactive than Roo Code — it's a real-time conversation, not a task executor. The trade-off: no GUI, no parallelism, and a steeper learning curve for complex projects. **Pros:** Solid CLI experience, 100+ models, good Git integration, cost-efficient **Cons:** No parallelism, no GUI, steep learning curve for complex projects, no built-in review **Pricing:** Free (you pay API costs directly) ### 4. Amp Code — Best for teams on Sourcegraph [Amp](https://ampcode.com) is Sourcegraph's CLI-based AI coding agent. Its edge is team collaboration: shared threads, reusable workflows, and integration with Sourcegraph's code intelligence platform. If your team already uses Sourcegraph for code search and navigation, Amp is the most natural AI coding tool. The pay-as-you-go model is transparent, and the free tier is generous. **Pros:** Sourcegraph code intelligence, team collaboration features, terminal-first workflow **Cons:** Best value requires existing Sourcegraph, limited parallelism, newer review features **Pricing:** Pay-as-you-go + free daily grants ### 5. Continue — Best for BYOK privacy-focused devs [Continue](https://www.continue.dev) is an open-source AI code assistant for VS Code and JetBrains that emphasizes privacy and flexibility. It supports any LLM provider (including local models), offers tab autocomplete, inline editing, and chat, all while keeping your data local. Continue is less "agentic" than Roo Code — it's more of a copilot than an autonomous agent. But if your main reason for using Roo Code is the BYOK model flexibility and you want a cleaner, more focused experience, Continue delivers. **Pros:** Multi-IDE support, truly BYOK including local models, privacy-focused, clean UX **Cons:** Less agentic than Roo Code, no autonomous task execution, no parallelism **Pricing:** Free and open source ## Quick comparison | Tool | Parallel | Open source | IDE required | Models | Code review | |------|----------|-------------|--------------|--------|-------------| | Capy | ✅ Unlimited | No | No (browser) | 30+ built-in | ✅ Built-in | | Kilo Code | ❌ | Yes | VS Code/JetBrains/CLI | 500+ BYOK | ❌ | | Aider | ❌ | Yes | No (terminal) | 100+ BYOK | ❌ | | Amp Code | Limited | No | CLI | Frontier models | ✅ Basic | | Continue | ❌ | Yes | VS Code/JetBrains | Any (BYOK) | ❌ | ## Bottom line Roo Code fills a specific niche well: free, open-source, mode-based AI assistance inside VS Code. If you're outgrowing that niche — because you need parallelism, a different editor, or a more automated workflow — the right alternative depends on what you're optimizing for. For raw throughput and automation, [Capy](https://capy.ai). For terminal workflows, Aider. For staying in VS Code with more polish, Kilo Code. --- - [Aider vs Capy: CLI Tool or Parallel AI Platform?](https://capy.ai/articles/aider-vs-capy): Aider is a CLI AI coding tool. Capy is a parallel AI development platform. An honest comparison of two very different approaches. Aider is an open-source CLI tool for interactive AI pair programming in your terminal. Capy is a cloud IDE for orchestrating multiple AI agents in parallel. One helps you write better code in a single session. The other lets you ship an entire sprint while you review PRs. They're almost complementary rather than competitive — but if you're choosing one workflow, here's how to decide. ## TL;DR - Aider is a well-known terminal AI pair programmer: interactive, precise, cost-efficient. - Capy is a parallel development platform: Captain plans, Build agents execute in isolated VMs, Review Agent catches issues. - Aider excels at focused, single-task collaboration. Capy excels at throughput. ## What is Capy? [Capy](https://capy.ai) is an AI-native IDE built for parallel development. Instead of chatting with one AI in a terminal, you dispatch multiple AI agents — each working in its own isolated Ubuntu VM — from a single dashboard. Capy's architecture splits AI into **Captain** (the planner) and **Build** (the coder). Captain reads your codebase, creates detailed task specifications, and coordinates work. Build agents execute those specs with full VM access — they can install packages, run Docker, execute tests, and browse the web. Each task automatically creates its own branch and produces a PR when done. ## What is Aider? [Aider](https://aider.chat) is an open-source, terminal-based AI pair programming tool created by Paul Gauthier. It connects to cloud or local LLMs (Claude, GPT, Gemini, and 100+ others) to help you write and edit code through natural language conversation. Aider automatically commits each AI change with clear messages, letting you review and revert easily. It maps your entire codebase to give the LLM context, supports 100+ programming languages, and works within any development environment since it's just a terminal command. With 41,000+ GitHub stars, Aider has a large following in the CLI AI coding space. It's free, lightweight, and gives you control over what the AI touches. ## Head-to-head comparison | Feature | Aider | Capy | |---------|-------|------| | Interface | Terminal CLI | Browser-based cloud IDE | | Workflow | Interactive pair programming | Task-based parallel execution | | Parallel agents | No (one session at a time) | Unlimited concurrent tasks | | Model support | 100+ via API keys | 30+ built-in (no API key management) | | Git integration | Auto-commits each change | Auto-branches and creates PRs | | Planning | You plan, AI codes | Captain plans, Build codes | | Code review | None built-in | Built-in Review Agent | | Environment | Your local machine | Full Ubuntu VM per task (Docker, all runtimes) | | Context management | Repo map + file selection | Full codebase analysis by Captain | | Pricing | Free (you pay LLM API costs) | Pro from $20/mo | | Open source | Yes (Apache 2.0) | No | ## Where Capy wins **Parallel execution.** This is the fundamental difference. While you're pair-programming one feature with Aider, Capy can have five agents building five features simultaneously. For anyone managing a backlog, the throughput difference is massive. **Planning is handled for you.** Captain analyzes your entire codebase before any code is written — understanding architecture, patterns, and integration points. With Aider, the planning burden is entirely on you. You need to know which files to add to context and what approach to take. **Rich environments out of the box.** Each Capy task gets a fresh cloud VM. Need to test a database migration? Docker's already there. Need to build a Rust project alongside a Node app? All runtimes are pre-installed. Aider runs in your terminal and is limited to whatever's on your machine. **Everything after coding is automated.** Capy handles branching, PR creation, and review without you touching Git. With Aider, you get code editing, but the surrounding workflow — branching strategy, PRs, code review — is still manual. **Review is built in.** Capy automatically reviews PRs for bugs, security issues, and style problems before they reach you. With Aider, you're either self-reviewing or wiring up separate tools. ## Where Aider wins **Interactive, real-time collaboration.** Aider is a conversation. You talk, the AI responds, you iterate. You're in the loop on every change. Capy's model is more "delegate and review." **Minimal overhead.** `pip install aider-chat`, set an API key, and you're coding. No account, no cloud dependency, no browser tab. Aider adds AI to your existing workflow without changing it. **Cost control.** You bring your own API key and see exactly what every session costs. Aider is designed to minimize token usage, keeping costs predictable. **Model flexibility.** Aider supports 100+ models including local ones via Ollama. You control which model handles your code. **Transparent Git history.** Every AI change gets its own commit with a descriptive message. You can revert any individual change without losing the rest. ## Who should use what? **Use Capy if:** - You have multiple tasks to ship and want them done in parallel - You want AI to help with planning, not just coding - You need complex environments (Docker, multiple runtimes, system packages) - You want automated branching, PR creation, and code review - You're managing a team or project where throughput matters more than real-time interaction **Use Aider if:** - You love terminal workflows and want AI that fits into them - You work on one focused task at a time with high iteration - You want maximum control over AI behavior, context, and costs - You prefer local-first, open-source, no-account-required tools --- - [CodeRabbit vs Capy: AI Code Review Compared](https://capy.ai/articles/coderabbit-vs-capy): CodeRabbit is a standalone AI code review tool. Capy has review built into its AI development platform. Here's how they compare for automated PR reviews. CodeRabbit is a dedicated AI code review service that integrates with GitHub and GitLab to review pull requests automatically. Capy is a full AI development platform with a built-in Review Agent. They solve different problems — one reviews code written by anyone, the other writes code *and* reviews it in one closed loop. ## TL;DR - CodeRabbit reviews all PRs in your repo regardless of who wrote them. 40+ linters, 13 million PRs reviewed, deep static analysis. - Capy's Review Agent works on any PR — and for Capy-generated PRs, it routes findings directly to Build agents for automated fixing. - CodeRabbit is a standalone review tool. Capy is review + automated fixing in one platform. ## What is Capy? [Capy](https://capy.ai) is an AI-native IDE for parallel development. It includes a **Review Agent** that works on any PR in your GitHub repo — human-written or AI-generated. Capy's Review Agent examines every PR for bugs, security vulnerabilities, and code quality issues. On Capy-generated PRs, it goes further: it routes findings directly to the Build agent for automated fixes, then re-reviews the updated code. This closed loop means issues get resolved without manual intervention. ## What is CodeRabbit? [CodeRabbit](https://www.coderabbit.ai) is a standalone AI code review tool that automates PR reviews on GitHub and GitLab. Install it, and it posts line-by-line review comments on every pull request — identifying bugs, security issues, and style problems. CodeRabbit integrates 40+ linters and SAST tools alongside LLM-based analysis, and offers an interactive chat for discussing changes. It reports 2 million+ repositories and 13 million PRs reviewed. Pricing starts free for basic summaries, with Pro at $24/month per developer. The core limitation: CodeRabbit can only comment on problems — it can't fix them. ## Head-to-head comparison | Feature | CodeRabbit | Capy Review Agent | |---------|------------|-------------------| | Primary function | Standalone code review | Review built into AI dev platform | | PR integration | GitHub, GitLab | GitHub (via platform) | | Review scope | All PRs from any source | All PRs + auto-fix on Capy PRs | | Line-by-line comments | Yes | Yes, with finding triage system | | Static analysis | 40+ linters and SAST tools | LLM-based analysis with context | | Interactive chat | Yes (on PRs) | Yes (feedback loop with Build agent) | | PR summaries | Yes | Yes | | Fix implementation | Suggests fixes | Routes findings to Build agent, auto-fixes | | Also writes code | No | Yes (Build agent) | | Also plans work | No | Yes (Captain agent) | | Pricing | Free / $24/mo per dev | Included in Capy plans (Pro from $20/mo) | ## Where Capy wins **Closed-loop fixing.** When Capy's Review Agent finds an issue on AI-generated code, it routes findings directly to the Build agent for a fix — then re-reviews the updated code. CodeRabbit can suggest fixes, but the actual implementation still requires human action or a separate tool. **Full context.** Capy's Review Agent reviews code written by an agent it shares context with. It knows the task spec, the planning decisions, and the intended behavior. CodeRabbit sees the diff and the repo — it doesn't know *why* changes were made. **Review included in every plan.** Capy's Review Agent comes built into the platform — no separate per-reviewer fee on top of your seat cost. CodeRabbit's Pro plan at $24/dev/month is a separate charge just for review. **Write and review in one place.** If you're using Capy for development, the review step is seamless. No separate tool to configure, no additional GitHub app to install. ## Where CodeRabbit wins **Multi-platform support.** CodeRabbit works on GitHub and GitLab. Capy's Review Agent currently supports GitHub only. **Static analysis integration.** 40+ linters and SAST tools run alongside LLM-based review. Useful if your team relies on specific static analysis rules. **Low setup cost.** Install CodeRabbit on your repo and it starts reviewing. No workflow changes required — though you're limited to comments, not fixes. ## Who should use what? **Use Capy if:** - You want AI review on any PR with automated fixing on AI-generated code - You want code review included in your development platform, not charged as a separate per-reviewer add-on - You're on GitHub - You value the closed loop: review → fix → re-review without context switching **Use CodeRabbit if:** - You need GitLab or Bitbucket support (Capy is GitHub only) - You specifically want 40+ linter integrations alongside LLM review - You're committed to a review-only tool and don't need the fix loop --- - [1Code vs Capy: Parallel AI Agents, Two Different Approaches](https://capy.ai/articles/1code-vs-capy): Comparing 1Code and Capy for parallel AI coding — one wraps CLI agents in a desktop UI, the other is a cloud IDE. Here's how they differ. 1Code wraps CLI agents like Claude Code in a desktop UI with parallel worktrees. Capy is a cloud IDE built from scratch for parallel development with isolated VMs and a two-agent architecture. They both solve the "run multiple AI agents at once" problem, but the approaches are fundamentally different. If you want a better interface for tools you already use locally, 1Code is great. If you want a complete platform that handles planning, execution, review, and deployment, Capy is the move. ## TL;DR - 1Code is an open-source desktop GUI that wraps CLI agents (primarily Claude Code) with parallel worktrees. - Capy is a cloud IDE with isolated VMs per task, a dedicated planning agent (Captain), and automated PR workflows. - 1Code enhances your existing local tools. Capy replaces the workflow entirely. ## What is Capy? [Capy](https://capy.ai) takes a clean-room approach to parallel AI development: everything runs in the cloud from scratch. When you create a task, Capy spins up a fresh Linux VM — fully isolated, with Docker and common runtimes pre-installed — and assigns a **Build** agent to work independently inside it. The bigger architectural difference is the planning layer. Capy's **Captain** agent analyzes your repository before any work begins, producing structured implementation specs that Build agents follow. You don't need to prompt each agent individually — Captain decomposes the work. The result is branches and PRs, created automatically, with a review step built into the pipeline. ## What is 1Code? [1Code](https://github.com/21st-dev/1code) is an open-source desktop application built by 21st.dev (YC W26). It gives you a graphical interface for running AI coding agents — primarily Claude Code, but also OpenCode and Codex — in parallel. The core idea: instead of juggling multiple terminal windows running separate Claude Code sessions, 1Code gives you a unified UI with isolated Git worktrees, real-time diff previews, and background execution. 1Code runs locally on macOS and Linux, with experimental Windows support. It has over 5,000 GitHub stars and a growing community. There's a free tier and a Pro plan at $20/month that adds hosted previews. ## Head-to-head comparison | Feature | 1Code | Capy | |---------|-------|------| | Architecture | Desktop app wrapping CLI agents | Cloud IDE with isolated VMs | | Parallel agents | Yes, via separate worktrees | Yes, each task gets its own VM | | Agent types | Single agent (whatever CLI you run) | Two agents: Captain (planning) + Build (coding) | | Model support | Depends on CLI agent (Claude Code, Codex) | 30+ models — Claude, GPT, Gemini, Grok, and more | | Isolation | Git worktrees (shared filesystem) | Full VM isolation per task | | Code review | None built-in | Built-in Review Agent | | Git workflow | Manual (you manage branches) | Automated: task → branch → PR | | Environment | Local machine | Full cloud Ubuntu VM (Docker, all runtimes) | | Pricing | Free / $20/mo Pro | Pro from $20/mo | | Open source | Yes (Apache 2.0) | No | ## Where Capy wins **True isolation.** Each Capy task runs in its own VM. One agent can't corrupt another's work — there's no shared filesystem, no shared state. 1Code uses Git worktrees on your local disk, which means a bad `npm install` or a stray `rm -rf` in one worktree can bleed into others. **Built-in planning layer.** 1Code expects you to direct each agent session yourself — you type a prompt, the CLI agent executes. Captain analyzes your repo structure, dependencies, and patterns first, then writes implementation specs that Build agents follow. The planning happens once, not per-agent. **Automated Git lifecycle.** Capy creates branches, commits work, opens PRs, and runs review automatically. 1Code stops at "run the agent and show you the diff" — branching, review, and PR creation are on you. **Model choice per task.** Want Claude for a complex refactor and a lighter model for a config change? Capy lets you pick per task from 30+ options. 1Code is limited to whatever CLI agents it wraps. ## Where 1Code wins **Local-first development.** Everything runs on your machine. No data leaves your environment, no cloud dependencies, no latency. If you're working with sensitive code or in an air-gapped environment, this matters. **Works with existing tools.** If you already use Claude Code's CLI, 1Code gives you a wrapper around it. You keep your existing workflows and model configurations. **Open source and free.** You can fork it, modify it, and self-host entirely. The Pro tier is optional. ## Who should use what? **Use Capy if:** - You want a complete platform, not just a UI wrapper - You run multiple coding tasks in parallel and want full isolation - You want AI-assisted planning, not just AI-assisted coding - You care about automated code review and PR workflows - You want to choose from 30+ AI models per task **Use 1Code if:** - You're already productive with Claude Code or Codex CLI - You want a local-first, open-source solution - You mainly need a better UI for managing multiple agent sessions - Privacy and data locality are your top priorities --- - [Roo Code vs Capy: VS Code Extension or Parallel Cloud IDE?](https://capy.ai/articles/roo-code-vs-capy): Roo Code adds AI modes to your editor. Capy gives you a full parallel development platform. An honest comparison for developers choosing between them. Roo Code is a free, open-source VS Code extension that adds role-based AI assistants to your editor. Capy is a standalone cloud IDE where multiple AI agents work in parallel across isolated VMs. One enhances your current workflow inside VS Code. The other replaces the workflow entirely with an AI-native one. ## TL;DR - Roo Code gives you specialized AI modes (Architect, Coder, Debugger) inside VS Code with BYOK model support. - Capy gives you dedicated agents (Captain for planning, Build for execution) running in isolated cloud VMs. - Roo Code is a free AI extension for VS Code with limitations. Capy is a different category entirely — a parallel development platform. ## What is Capy? [Capy](https://capy.ai) doesn't live inside an editor at all. It's a browser-based platform where AI agents operate in their own cloud environments — separate from your local machine, separate from each other. The architecture is agent-based rather than mode-based. **Captain** reads your full repository and produces task-level implementation specs. **Build** agents execute those specs in dedicated VMs where they can install packages, run containers, and use whatever toolchain the project needs. Each task gets its own branch, and Capy opens a PR when the work is done. ## What is Roo Code? [Roo Code](https://roocode.com) is an open-source VS Code extension forked from Cline that adds role-based AI modes to your editor — Architect for planning, Coder for implementation, Debugger for fixing issues — each with its own prompt configuration. It's model-agnostic via BYOK (bring your own key). The extension is free, with an optional Roo Code Cloud service for running autonomous agents at $5/hour. Roo Code has SOC 2 compliance, which matters for enterprise teams. ## Head-to-head comparison | Feature | Roo Code | Capy | |---------|----------|------| | Architecture | VS Code extension | Cloud IDE with isolated VMs | | AI roles | Mode-based (Architect, Coder, Debugger) | Agent-based (Captain plans, Build executes) | | Parallel agents | Limited (Cloud only, sequential in editor) | Unlimited parallel tasks, each in its own VM | | Model support | BYOK — any provider via API key | 30+ built-in (Claude, GPT, Gemini, Grok, etc.) | | Environment | Your local machine / Cloud sandbox | Full Ubuntu VM per task (Docker, all runtimes) | | Code review | None built-in | Built-in Review Agent | | Git workflow | Manual | Automated: task → branch → PR | | IDE dependency | Requires VS Code | Standalone (browser-based) | | Pricing | Free extension / Cloud from $5/hr | Pro from $20/mo | | Open source | Yes (Apache 2.0) | No | ## Where Capy wins **Concurrent execution, not sequential.** In Roo Code, you finish one task before starting the next. Capy runs tasks concurrently — ten features on ten separate branches, each in a sandboxed cloud environment that can't interfere with the others. **Planning stays separate from coding.** Roo Code's modes share a single conversation — you switch from Architect to Coder in the same session, and context bleeds between phases. Captain produces specs from a full repository analysis, then Build agents execute against those specs independently. The planner never gets distracted by implementation details. **No editor constraints.** Roo Code runs inside VS Code's process model, which limits what the agent can do — no native Docker, restricted filesystem access, shared state between tasks. Build agents run in full Linux VMs where they can install anything, run containers, and use the same toolchains your CI pipeline uses. **Git operations are automatic.** Creating branches, committing work, opening PRs, running review — Capy handles all of it. With Roo Code, you still manage the Git lifecycle around the AI's code changes. ## Where Roo Code wins **Free and open source.** The extension costs nothing. You only pay for API calls to your chosen provider (or Cloud runtime if you opt in). **BYOK model support.** Use whatever provider and model you want, including local models via Ollama. You control costs directly. **Custom modes.** You can create specialized AI personas for specific task types. Useful if you want different behavior for security reviews vs. feature work. ## Who should use what? **Use Capy if:** - You want to run multiple coding tasks simultaneously - You need full VM isolation — Docker, system packages, complex build environments - You want AI to handle planning separately from coding - Automated PR workflows matter to your team's velocity - You want to try different models per task without managing API keys **Use Roo Code if:** - VS Code is your IDE and you don't want to leave it - You want free, open-source AI assistance with your own API keys - You work on one thing at a time and want an AI copilot, not a fleet ## Blog - [Connect Capy to your Tailscale tailnet](https://capy.ai/blog/tailscale-integration): Capy machines can now join your Tailscale tailnet as ephemeral, tagged nodes, so agents can reach private databases and internal services without public exposure or static IP allow lists. Teams using Capy often ask us if we have static outbound IPs they can allow-list, or if there is some way to expose internal services to Capy. Since every Capy thread runs in its own isolated sandbox machine with dynamic addresses, it's hard for us to provide a static list of IP addresses. When working on real production codebases, an agent that can only see the code and reach GitHub only sees half of the story. It can't access internal logs and traces, reproduce a bug against staging, hit an internal API to verify a fix, or pull from a private registry to run the tests. Some other solutions exist here, such as building an MCP or exposing an API with secure environment variables, but they all have something in common: they require opening up firewalls and exposing internal infrastructure to the public. The solution: Tailscale, a mesh networking tool built on WireGuard. With Tailscale, Capy machines can access your infrastructure securely. Each Capy machine gets its own tagged identity on your tailnet, and your ACLs decide what that identity can reach. ## How it works **1. Define a tag.** In your tailnet policy file, add a tag for Capy machines under `tagOwners`. We recommend `tag:capy`. ```json "tagOwners": { "tag:capy": ["autogroup:admin"], } ``` **2. Create an OAuth client.** On the Trust credentials page, create an OAuth credential with **Write** on **Keys › Auth Keys**, scoped to the tag from step one. Copy the client ID and secret. **3. Connect Capy.** In Settings › Integrations › Tailscale, click Connect and paste in the client ID, secret, and tag. Capy checks the credentials against your tailnet before saving anything. Then turn on each project whose machines should join. Nothing joins until you do. That's it. From then on, whenever a machine in one of those projects boots or wakes, Capy mints a single-use auth key and the machine joins your tailnet as `capy-` with your tag. When the machine pauses or is destroyed, Tailscale drops the node, and a resumed machine rejoins with a fresh key. ## Granting access Capy machines can reach exactly what your ACLs allow their tag, and nothing else. To let agents reach a database on your tailnet: ```json "acls": [ { "action": "accept", "src": ["tag:capy"], "dst": ["staging-db:5432"] }, ] ``` The destination can be a host alias, a tailnet IP or CIDR, or another tag. If you run a subnet router, the same rule shape opens up a whole VPC or on-prem subnet: ```json "acls": [ { "action": "accept", "src": ["tag:capy"], "dst": ["10.20.0.0/16:*"] }, ] ``` You probably don't want to hand agents the whole subnet though, so keep the rules to the services they need. ## How we handle your credentials Your OAuth client secret is stored encrypted and never leaves Capy. Machines never see it either: every time a machine boots or wakes, Capy uses the secret to mint a fresh single-use auth key just for that join. The key expires five minutes after it's minted, and once the machine is on the tailnet it doesn't need one, as Tailscale keeps the node connected with its own node key. To stop joins, turn a project off or disconnect the integration in Settings; Capy deletes the credentials on disconnect. Machines already on the tailnet stay until they pause or stop, and you can remove them from the Tailscale admin console any time. Full setup details, more ACL examples, and troubleshooting are in the [docs](https://docs.capy.ai/integrations/tailscale). If you hit anything odd, let us know in Slack. --- - [New Capy Pro Tiers: Subscriptions That Scale With You](https://capy.ai/blog/new-capy-pro-tiers): Capy Pro now scales from $20/mo to $2,000/mo with bonus credits, a flat 20% off when you pay yearly, and one shared balance for your whole org. Capy Pro now scales with you. Starting today, you can subscribe at any commitment level from **$20/mo** to **$2,000/mo**, with bonus credits at every step, a flat 20% off when you pay yearly, and one shared balance for your whole team. If $20/mo Capy Pro covered your work, nothing changes. If you've been hitting overage every month, you can finally lock in a tier that matches your real usage and keep the difference as bonus credits. ## TL;DR - **Capy Pro is now tiered** from $20 to $2,000 per month. - Higher tiers come with **+5% to +15% bonus credits** baked in. - **Yearly billing** is a flat **20% off** any tier. - Overage is still simple: **$1 of overage = $1 of credits**, with optional auto-reload and a monthly spend cap. - Credits stay **org-level**: one shared balance for your whole team. ## Why we changed it The old model was simple but blunt: $20/mo Capy Pro plus pay-as-you-go overage on top of your included credits. That worked for solo developers, but anyone running real production work bumped into the same problem. The moment you crossed the $20 line, the rest of the month was full-rate metered billing with no way to commit and save. Most teams using Capy heavily wanted exactly the opposite: **predictable monthly spend with a discount for committing**. The new tiers do that without changing the parts that already work. ## Capy Pro tiers | Plan | Monthly | Yearly | Credits included | Bonus | | ------------------- | --------- | --------- | ---------------- | ----- | | **Capy Pro** | $20/mo | $16/mo | $20 | | | **Capy Pro $100** | $100/mo | $80/mo | $105 | +5% | | **Capy Pro $500** | $500/mo | $400/mo | $550 | +10% | | **Capy Pro $2,000** | $2,000/mo | $1,600/mo | $2,300 | +15% | | **Enterprise** | Custom | Custom | Custom | | The bigger the tier you commit to, the more credits land in your balance every month. At $2,000/mo you walk in with **$2,300 worth of credits**, a built-in 15% boost on top of whatever you save by paying yearly. Yearly billing takes a flat 20% off any tier. If you already trust your monthly burn, that's the easiest discount in the building. ## Overage stays simple If you push past your monthly credits, top-ups still work the way they always have: - **$1 of overage buys $1 of credits.** No multipliers, no fine print. - **Auto-reload** keeps long-running tasks from stalling at midnight when your balance hits zero. - **Monthly spend cap** is an org-wide hard ceiling. Capy pauses before it crosses the limit you set, even if auto-reload is on. If you'd rather not deal with overage at all, you can disable it entirely and Capy will simply stop when your monthly credits run out. ## Enterprise is still on the table If you need more than $2,000/mo of credits, custom contracts, BYOK keys, SSO, audit logs, or a dedicated success engineer, **Enterprise** still exists with custom pricing and the same generous credit ratios as the public tiers. We've sized the standard Enterprise packages ($50,000/mo and $100,000/mo) so they line up cleanly with the rest of the curve. You should never have to negotiate the basics. ## What to do next - **Already on Capy Pro?** Nothing breaks. Stay on $20/mo, or hop up a tier when you feel the overage line. - **Hitting overage every month?** Switch to the smallest tier that covers your real burn. Bonus credits and the yearly discount mean you're almost always better off subscribing than paying pure overage. - **Running a team?** Move your org onto a tier that matches its monthly burn. Everyone shares the balance, and you keep the bonus credits. You can pick a tier from the billing page in your settings, or start fresh on the [pricing page](/pricing). --- - [GPT-5.5 Is the First OpenAI Model We'd Actually Run as Captain.](https://capy.ai/blog/gpt-5-5-captain-eval): GPT-5.5 is the fastest, most Captain-ready OpenAI model we have run in production. We've made it our new default for Captain. GPT-5.5 is OpenAI’s newest model, and it’s now live in Capy. We had early access to the model, and ran internal testing; mainly having it run as the model for our orchestration agent (Captain Capy). To summarise how we felt from ten days of production testing: GPT-5.5 feels very much like a smart, more Opus-coded OpenAI model. Earlier models like GPT-5.4 are insanely good at narrow coding tasks, but often feel like talking to a brick wall the moment you need to discuss implementation or actually work through a plan. We found GPT-5.5 was nicer to talk with, took much bigger end-to-end swings, and worked better than any previous OpenAI model we have tested in our Captain harness. OpenAI told us this model would be more token efficient than its predecessors. We found that to be true. GPT-5.5 showed better tail latency in Captain than every production model we have been running against it, and it did it while still taking on noticeably more ambitious work. Here is the data from our testing (GPT-5.5 data was surfaced from 495 production sessions and 56,587 model calls). ## Where GPT-5.5 wins Three things stood out in real Captain work. The first is speed you actually feel. GPT-5.5’s p95 sits at 16.8 seconds and p99 at 35.4 seconds. Opus 4.6 is at 57.7 and 137. GPT-5.4 is at 46.5 and 85.2. For an orchestrator that is constantly reasoning over long threads and firing tool calls, that tail behaviour is what makes the whole product feel responsive. The second is ambition. Compared to earlier OpenAI models, GPT-5.5 stops scope-minimising. It takes much bigger end-to-end swings, and it does not need the usual “please stop being conservative” prompting to actually commit to a direction. The third is tone. GPT-5.5’s debugging updates are short, high-signal, and easy to follow in long threads. That sounds minor on paper, but it matters a lot when Captain is producing most of the text the user actually reads. ## Where GPT-5.5 still stalls Those upsides held through ten days. The downsides got sharper. The biggest one is that review-and-fix loops still do not converge. GPT-5.5 will fix a reviewer comment in a way that introduces new problems, then re-enter the same loop without catching them. That compounds fast on any PR with more than a couple of review rounds. Triage judgement is the next problem. On bug-heavy threads, we repeatedly caught GPT-5.5 delegating bug-finding to subagents prematurely instead of investigating inline as Captain. Motion is not the same thing as progress. A Captain that outsources the thing it should be doing is effectively skipping the actual job. Task stacking and scope control are also still weak. When multiple fixes compete for attention in one thread, GPT-5.5 tends to creep into adjacent work instead of holding the line on the original ask. Operators repeatedly flagged that GPT-5.5 “doesn’t really know how to stack”. ## How it looked in real work Two recent sessions made the tradeoff concrete. On our Live2D and native TTS avatar pipeline, GPT-5.5 put together one of the strongest “ambitious execution” traces we have seen from an OpenAI model: broad scope, cross-system surface area, and real delivery momentum all the way through. Across a 24 minute active span, it ran 117 assistant events and 174 tool calls, with roughly 18M input and 32K output tokens. The build touched 95 files (86 added, 9 modified), largely due to bundled runtime and assets. On a Clerk auth migration incident, GPT-5.5 found the root cause fast and moved to a workable fix path quickly. But the first-pass implementation still showed the recurring GPT-5.5 pattern: take the expedient shortcut first, clean up after review pressure. Useful energy, still needs Captain-level discipline on top. On review-heavy cleanup threads, operators hit the churn repeatedly: GPT-5.5 fixing comments in ways that introduced new issues, then re-entering the same loop. This is exactly where we still prefer Opus today. ## What this means for Capy users Up until now, most of our users have been running a combination of Opus and Sonnet for Captain and GPT models for Build tasks. After ten days of production testing, we finally feel that OpenAI has a model that can seriously be used for Captain-style orchestration. GPT-5.5 is a very viable replacement for Opus as Captain. On review-heavy, convergence-sensitive threads, Opus still wins. But if your work is fast-moving debugging, broad task orchestration, or “get me from issue to PR quickly”, GPT-5.5 is the first OpenAI model we would actually reach for as Captain, especially because of how token efficient it is. It’s available in Capy today — and for the next week, it’s served at 50% off. --- - [The April Update: Captain Takes the Helm](https://capy.ai/blog/april-2026-update): Build mode is gone. Captain now orchestrates all tasks with thread numbers, smarter model selection, and CI awareness. The April update simplifies how work happens in Capy. **Build mode** is deprecated, and **Captain** now handles every task from one conversation. Captain now plans, delegates, monitors, and ships without asking you to switch modes. ## Captain-only workflow Build mode is deprecated. All tasks now start in Captain, where the conversation, plan, execution, and PR stay connected. Captain plans the work, delegates implementation to Build agents, monitors progress, and creates pull requests when the task is ready. You describe the outcome once, then Captain keeps the workflow moving. Tasks are now **thread-local**. Instead of global task numbers, each thread gets its own Task 1, Task 2, Task 3 sequence. The result is simpler: one conversation, one workflow, and task numbers that make sense in context. ## Smarter model configuration Model selection is now **thread-scoped**. You can pick the Captain model and Build model for each thread instead of changing a global setting before every task. Speed and reasoning toggles give you finer control over how a thread behaves. Use faster settings for small edits, deeper reasoning for complex planning, and change them without affecting other work. **Claude Opus 4.7** is now the default Captain model. Every thread also gets a unique identifier like SCO-1234. You will see it in the sidebar, dashboard, and Slack so it is easy to connect a conversation to the work it produced. ## CI awareness Captain is now **CI-aware**. When Captain opens a pull request, it subscribes to the PR checks and watches them as they run. If a check fails, Captain detects the failure, reads the output, and can send the fix back through the workflow. You no longer need to copy CI logs into chat or explain which job failed. This makes PR follow-up feel much closer to a real teammate. Captain sees the signal as it happens and can react before the failure gets stale. ## Try it now Start a new thread and describe what you want to build. Captain will plan the work, choose the right execution path, track the result, and react when CI needs attention. The new workflow is available now. One conversation is enough. --- - [$1,000 in Capy Credits for Every YC Company](https://capy.ai/blog/yc-deal): Every Y Combinator company - current batches and alumni - gets $1,000 in Capy credits plus a free Review Agent. Every Y Combinator company - current batch and alumni - now gets **$1,000 in Capy credits** plus a **free Review Agent**. If you haven't used Capy before: Capy is an AI software engineer for real codebases. You give it a task, and it plans the work, makes the changes, opens the PR, and reviews the result. ## What you actually get If you're a YC company, you get: - **$1,000 in Capy credits** - **A free Review Agent** - Credits that stay on your org balance until they're used This is a real credit grant, not temporary promo access with a countdown timer. Capy Pro includes $20 in credits per month. This deal gives you **50x that amount upfront**, which means you can use it for real work - not just a quick test drive. ## What $1,000 in credits is enough to do ### Ship features end-to-end Give Capy a task like "add a team invitation flow with email notifications." Capy reads the codebase, writes a plan, implements the changes in an isolated environment, runs tests, opens a PR, and reviews the result before you ever look at the diff. ### Clear bug backlogs in parallel If you have 20 bugs sitting in GitHub Issues or Linear, Capy can work them in parallel - one task per bug, one isolated environment per task, one clean PR per fix. The goal is simple: fewer weeks spent triaging, more mornings where the PRs are already ready for review. ### Run migrations and refactors without losing a week Framework upgrades. ORM migrations. TypeScript conversions. Test backfills. The kind of work every team knows it needs to do, but nobody wants to babysit. Capy is especially good at scoped, tedious, multi-file work like upgrading frameworks across large codebases, migrating from one library or pattern to another, writing tests around fragile systems, and breaking large projects into ordered steps and stacked PRs. ### Use it from Slack If your team works in Slack, Capy can start there too. Tag it in a channel, create the task in a thread, and let teammates hand off work without bouncing across tools. ## Why Capy works differently Most AI coding tools are one model in a loop: read some context, generate some code, hope it works. Capy splits the work across specialized agents. ### Captain plans Captain reads the codebase, understands the existing patterns, asks clarifying questions when needed, and writes a detailed spec for the task. ### Build executes Build takes that spec, works inside an isolated cloud VM, uses the full toolchain, makes the changes, runs tests, and opens a PR. ### Review Agent catches what matters Once the PR is open, the Review Agent reads the diff, flags bugs and real quality issues, and filters out low-value noise. The result: fewer wasted iterations, better first-pass code, and review cycles that feel more like engineering and less like cleanup. ## Who this is for This deal is especially useful if your team is shipping fast with a small engineering org, buried under bugs, migrations, and backlog cleanup, trying to increase output without hiring a huge team, or already using AI tools but frustrated by how much supervision they still need. If that sounds like your company, this is worth activating. ## Claim the YC deal If you're a YC company, you can claim the deal at [capy.ai/yc](https://capy.ai/yc). You'll get $1,000 in Capy credits, a free Review Agent, and enough runway to see where Capy fits in your actual workflow. --- - [Capy Is SOC 2 Type II Certified](https://capy.ai/blog/capy-achieves-soc-2-type-ii): Capy has achieved SOC 2 Type II certification. For AI agents that read your code and push commits on your behalf, independent security verification isn't a nice-to-have. Capy is SOC 2 Type II certified. Most SaaS tools store your data passively. Capy's agents read your private codebase, execute code, and push commits on your behalf. The security bar for that kind of access has to be higher — and now it's independently verified. ## Type I vs. Type II SOC 2 (System and Organization Controls 2) is an auditing standard developed by the American Institute of CPAs. It evaluates how a company manages and protects customer data across trust service criteria — security, availability, processing integrity, confidentiality, and privacy — with security being the mandatory baseline. **SOC 2 Type I** is a point-in-time assessment. Auditors verify that the right controls are in place and properly designed — a snapshot. **SOC 2 Type II** evaluates whether those controls operate effectively over an extended observation period, typically six to twelve months. Not "do you have the right locks?" but "did you lock the door, every day, for the last six months?" We went straight for Type II. Type I tells you we set things up correctly. Type II proves we run them correctly. ## Why the bar is higher for AI agents When an AI agent reads your repository, it sees everything: proprietary business logic, internal APIs, environment variables, credentials, commit history. When it executes code, it's running arbitrary commands inside an environment connected to your stack. When it pushes a PR, it's making changes to production-bound code. That's a qualitatively different trust surface than a tool that stores a document or syncs a calendar. The auditors knew it, and so did we. The controls SOC 2 Type II required us to prove, over six months of evidence, included: **Code execution isolation**: Each Build agent task runs in an ephemeral, isolated VM. Environments are created fresh for each task and torn down on completion. Auditors verified that no data leaks between tasks or between customers, and that agents can't traverse into broader infrastructure. **Credential and secret handling**: Auditors examined how we scope, store, and expire GitHub OAuth tokens, workspace credentials, and API keys. They confirmed secrets are never written to logs, never persisted beyond their intended use, and that access revocation is immediate. **Access controls and least privilege**: Every integration — GitHub, Slack, third-party tools — is scoped to the minimum permissions needed. Auditors reviewed months of access logs to verify continuous enforcement, not just policy documentation. **Change management**: Capy proposes code changes; humans approve before anything merges. Auditors examined our own internal deployment pipeline — every code change to Capy itself going through the same review gates we ask our customers to use. **Incident detection and response**: Every security event over the six-month observation period was reviewed. Detection times, escalation procedures, customer notification processes, post-incident documentation. The question wasn't whether we had a runbook — it was whether we followed it. ## What this means for you **For enterprises**: SOC 2 Type II is a standard vendor requirement in healthcare, finance, legal, and government procurement. Our report is available on request. Email [security@capy.ai](mailto:security@capy.ai) and we'll share it under NDA. **For everyone**: Whether your team is three engineers or three hundred, you're using an AI agent with elevated access to your codebase. Those controls protect your code, your secrets, and your customers — regardless of your size or industry. **Going forward**: SOC 2 Type II is the foundation, not the ceiling. We'll maintain continuous compliance, run annual audits, and expand our security posture as Capy's capabilities grow. Trusting Capy to work in your codebase means trusting it with your most sensitive engineering assets. SOC 2 Type II gives that trust an independent foundation — not our word, but six months of auditor verification. --- - [We Stress-Tested GPT-5.4 Before Launch. Here's What Happened.](https://capy.ai/blog/gpt-5-4-early-access): OpenAI gave us early access to GPT-5.4. We ran 65 sessions, 2,256 LLM calls, and our own AI agent wrote the evaluation report. OpenAI gave us early access to GPT-5.4 before it launched. We did what any reasonable team would do: threw it at our hardest production workflows and watched what happened. 65 sessions. 2,256 LLM calls. Five team members independently testing. Three pre-release snapshots over two days. Then our own AI agent, Captain Capy, crawled through every Slack message, pulled production traces from ClickHouse, and auto-generated a 12-page evaluation report that we sent straight to OpenAI. This is what we found. ## TL;DR - We tested 3 pre-release GPT-5.4 snapshots inside Capy's dual-agent system over 48 hours - **Speed is real**: ~2x faster at p95 compared to our Claude Opus 4.6 baseline - **Strengths**: system prompt adherence, clean code style on isolated problems, concise output - **Weaknesses**: codebase grounding in large repos, long-horizon planning, knowing when to stop iterating - GPT-5.4 is now available in Capy, try it yourself ## How we tested Capy uses a dual-agent architecture: **Captain** plans and orchestrates, **Build** writes code. Both are powered by LLMs with 50+ tools each. We tested GPT-5.4 in both roles against our production baseline across the same system prompts, tool definitions, and harness. Tasks ranged from routine (rename a button, fix a merge panel) to ambitious: implement tiling window management, reskin an entire UI, debug OG image rendering. This matches our real daily workload. Five team members tested independently: CEO, CTO, and three engineers, each bringing different workflows and expectations. ## The AI that evaluated the AI Here is the part we did not expect to become the story. After two days of testing, we needed to compile all the feedback (Slack threads, production traces, team impressions) into a structured report for OpenAI. So we asked Captain Capy to do it. One prompt. Captain searched every Slack channel for mentions of GPT-5.4, pulled aggregate statistics from our ClickHouse traces (latency distributions, token counts, tool call patterns), cross-referenced team sentiment across channels, and produced a 12-page PDF with tables, direct quotes, and specific session traces. An AI, writing a report about an AI, for the team that builds AI agents. We sent it to OpenAI with zero human edits. ## Where GPT-5.4 shines **It is fast.** At p95, GPT-5.4 clocked in at ~22 seconds versus ~50 seconds for our baseline. For an orchestration agent making dozens of tool calls per session, that speed compounds quickly. Median latency is comparable (~5.5s vs ~5.7s), but the tail latency improvement means fewer sessions where users are left waiting. **System prompt adherence is excellent.** Our prompts are long and detailed, thousands of tokens covering formatting, tool usage, communication style, and guardrails. GPT-5.4 follows them more faithfully than any model we have tested. Where other models drift from instructions over long conversations, GPT-5.4 stays locked in. **Clean code on focused problems.** Given a well-scoped, isolated task (implementing Effect.ts Schedule combinators, fixing a specific component) the output is clean, concise, and correct. | Metric | GPT-5.4 | Baseline (Opus 4.6) | | --- | --- | --- | | p95 latency | ~22s | ~50s | | p50 latency | ~5.5s | ~5.7s | | Avg output tokens/call | 330 | 587 | | Total sessions tested | 65 | 1,806 | | Total LLM calls | 2,256 | 40,106 | ## Where it fell short ### The review fixing loop This was the most dramatic finding. Capy's workflow includes a review step: after Build finishes a task and Captain creates a PR, a Review Agent checks the code. If it finds issues, Captain triages, marking false positives as irrelevant and sending real issues back to Build for fixes. Our baseline model exercises judgment here. It knows when findings are borderline, when to stop iterating, and when good enough is the right call. GPT-5.4 Captain took every finding at face value and dutifully sent each one back to Build. Each fix sometimes introduced new findings. This created a loop. On one task, a UI reskin, GPT-5.4 Captain entered **six consecutive review-fix cycles**: That loop consumed 164,000 tokens and ~43 minutes. Our baseline handles the same workflow in a single pass because it triages aggressively and bundles remaining fixes together. ### Codebase grounding In a 250K+ line monorepo, you need a model that finds existing patterns and extends them. GPT-5.4 tends to rewrite from scratch rather than integrate with what is already there. It reads extensively (one session logged 104 file reads for just 6 edits, a 17:1 ratio versus the typical 3:1) but struggles to synthesize what it reads into targeted changes. ### The conservatism problem We ran the same creative prompt through both models: "reskin our UI with a Warcraft theme." Our baseline produced an ambitious, cohesive overhaul touching design tokens, component primitives, and layout globals. GPT-5.4 did the bare minimum that technically qualifies, and used the X (Twitter) logo as a close button icon instead of building a proper one. That captures the difference. GPT-5.4 treats creative tasks as mechanical checklists. It does what you ask, but does not bring the initiative that makes an AI agent feel like a collaborator rather than a command executor. ### The planner problem Captain's job is to be a proactive technical architect: explore the codebase, ask clarifying questions, write exhaustive specs, make creative decisions. GPT-5.4 Captain skips clarifying questions, writes thinner specs, and sometimes delegates decisions to Build that Captain should own. In one session, instead of specifying how to fix an issue, it told the Build agent to "figure out how to fix it using a previous commit." That is not planning, that is punting. ## The scorecard Five team members tested independently and converged on the same assessment: | Area | Rating | | --- | --- | | Speed | ✅ Excellent, ~2x faster at p95 | | System prompt adherence | ✅ Strong, best we have seen | | Code style (isolated tasks) | ✅ Clean and concise | | Codebase grounding | ❌ Rewrites instead of integrating | | Long-horizon orchestration | ❌ Review fixing loops | | Creative planning | ❌ Mechanical, not thoughtful | | Proactive communication | ⚠️ Too quiet, follows "be concise" too literally | The speed advantage is real and meaningful. The intelligence on isolated problems is competitive. But for the multi-file, multi-step, judgment-heavy workflows that define AI-native development, there is a gap. ## What this means for you GPT-5.4 is available in Capy today. If your work is speed-sensitive and well-scoped (focused coding tasks, quick edits, problems with clear boundaries) it is a strong option. For complex orchestration, long-horizon planning, and tasks that need creative judgment, our default model remains the better choice. Every new model we test makes Capy better. The testing process surfaces edge cases in our own agent architecture, and the feedback loop with model providers pushes the whole ecosystem forward. And the 12-page evaluation report that Captain Capy auto-generated? It was good enough to send directly to OpenAI with zero human edits. If that is not a testament to what AI agents can do today, we do not know what is. --- - [Captain vs Build: Why We Split the AI Agent in Two](https://capy.ai/blog/captain-vs-build): Most AI coding tools use one agent. We use two. Here's why separating planning from execution produces better code, faster. Most AI coding tools treat planning and execution as one move: you prompt, it writes. That feels fast, until it is not. Capy splits the work into two specialized agents because planning is where quality is won or lost, and a crisp spec is usually the difference between shipping once and rewriting three times. A dedicated planner forces clarity up front, cuts down wasted iterations, and gives the executor everything it needs to ship clean code in one pass. ## TL;DR - Single agent systems make humans do the orchestration work. - Captain plans, Build executes. The split forces real specs. - Better specs mean fewer loops and higher quality output. ## The bottleneck in single agent coding The loop is familiar. You prompt an agent, it writes code, you scan the diff, and you ask for changes. The agent is quick, but it can only follow what you asked for, not what you meant. Experienced engineers already know the workaround: pause and plan. They skim the codebase, map the constraints, and jot down a spec. In other words, the human does the architecture work before the agent starts typing. We asked a simple question: what if the system did that orchestration for you? ## Meet Captain and Build Capy uses two distinct agents that work in sequence. **Captain** is the technical architect. It reads codebases, researches, asks questions, and writes exhaustive specs, but it never writes production code. > "You are a technical architect who PLANS but never IMPLEMENTS." **Build** is the executor. It receives the spec, spins up an Ubuntu VM, edits files, runs commands, and ships the work. > "You are an autonomous AI agent with access to an Ubuntu virtual machine to complete the user's coding and research tasks independently and asynchronously." | | Captain | Build | | --------------- | -------------- | -------------- | | **Role** | Architect | Executor | | **Code** | Reads code | Writes code | | **Research** | Explores | Full VM access | | **Interaction** | Asks questions | Ships PRs | | **Output** | Creates specs | Runs commands | ## The planning step that changes outcomes Before Build touches a file, Captain runs the same routine a good staff engineer uses at the start of a project - explore the codebase, clarify ambiguities, write a concrete spec, and hand off a zero-guesswork brief. Think of the spec as a short PRD, not a loose prompt. A real example might read: "Add Google and GitHub OAuth. Sessions live in `src/auth/session.ts`, follow the existing token refresh pattern, rate-limit failed logins, and make sure existing sessions survive re-auth." It names the files, the constraints, and the success criteria so Build has nothing left to guess. If something is missing from the spec, it shows up later as churn. Captain's job is to make the intent explicit so Build can move straight to execution. ## The handoff in practice You describe the task in plain language - no need to polish it. Captain reads your codebase, identifies the patterns and constraints, and closes the gaps. If something is ambiguous it will ask: "Should failed logins return 401 or 403?" or "Do you want rate limiting on auth endpoints?" Once the spec is tight, Build takes over. It edits files, installs dependencies, runs tests, and opens a pull request - all autonomously. If Build drifts off track, you can step in through Captain or let Captain follow up on its own. ## Guardrails: what each agent can and cannot do Captain is intentionally read-only. It can scan any file and search the web, but it never edits code, runs terminals, or pushes commits. It plans, hands off, and gets out of the way. We keep its system prompt and toolset tightly curated for reliability. Build is the opposite - it gets a full Ubuntu VM with sudo, whatever runtimes you need, and full git access to branch, commit, push, and open PRs. It often touches many files in a single pass and will run the tests itself, see what fails, and fix it. The trade-off is that Build never asks you clarifying questions mid-task. It only sees the spec and the codebase, which is exactly why the spec needs to be good. ## Where the split pays off most The two-agent split pays off most when the work is wide or messy. Feature development like "add real-time notifications" touches the database, backend, WebSocket layer, and UI all at once. Large refactors like migrating from REST to GraphQL require mapping every endpoint and test before any code changes. Bug investigations like "users see stale data" could live anywhere from caching to queries to UI state. In each case Captain narrows the surface area first so Build can move in a straight line. For tiny edits - renaming a variable, fixing a typo, tweaking a string - you can skip Captain and go straight to Build. The planning step only pays off once tasks are bigger than a quick human edit. ## The counterintuitive latency trade Two agents sound slower on paper. In practice the back-and-forth costs more than a single planning pass. A well-specced task finishes faster than a vague prompt that needs multiple rounds of correction, and because Build runs asynchronously your wall-clock time stays low even when the task itself is long. ## Try it Start a chat, describe what you want, and let Captain explore and spec it. When the plan looks right, start Build and let it ship. You may find you enjoy the planning step more than you expected. We do. ## Integrations - [GitHub](https://capy.ai/integrations/github): Native GitHub integration for branches, PRs, and code review - [Slack](https://capy.ai/integrations/slack): Create tasks from messages, sync threads, and give agents Slack context - [Vercel](https://capy.ai/integrations/vercel): Automatic preview deployments ## Optional - [Terms of Service](https://capy.ai/terms-of-service) - [Privacy Policy](https://capy.ai/privacy-policy) - [Acceptable Use Policy](https://capy.ai/acceptable-use-policy) - [Cookie Policy](https://capy.ai/cookie-policy) - [Data Processing Addendum](https://capy.ai/data-processing-addendum) - [Subprocessors](https://capy.ai/subprocessors) - [YC Companies Program](https://capy.ai/yc): $1,000 in credits for YC companies - [Solo Founders Deal](https://capy.ai/solo): $1,000 in credits for eligible Solo Founders members