open-swe
An Open-Source Asynchronous Coding Agent
- 11.1k stars
- 1.3k forks
- 201 open issues
- Language: Python
- License: MIT
- updated
Install
git clone https://github.com/langchain-ai/open-swe && cp open-swe/*.md ~/.claude/agents/- Clone the repository and copy the agent .md definitions into ~/.claude/agents (or .claude/agents inside a project).
- Start a new Claude Code session to load the agents.
- Delegate work to them with the Task/Agent tool or by name.
Editor's note
Open SWE is an open-source framework for building internal coding agents, built on LangGraph and the Deep Agents library. It provides a complete architecture for running autonomous software engineering tasks triggered via Slack threads or Linear tickets, executing work inside isolated cloud sandboxes from providers including Modal, Daytona, Runloop, and LangSmith. The agent uses a small, curated toolset covering shell execution, file operations, web fetching, API calls, and subagent spawning via a `task` tool that fans out parallel work to child agents. Context is assembled from Linear issue descriptions, Slack thread history, and an optional `AGENTS.md` file in the target repository that encodes project conventions. Completed work is submitted as GitHub pull requests automatically. The framework is compatible with both Anthropic Claude models and OpenAI models through the Claude Code ecosystem and standard API access. Engineering teams at organizations that want a self-hosted, customizable alternative to commercial coding agents are the primary audience.
4 items in this repository
Monitor a GitHub pull request until CI is green, diagnose failures, and rerun only evidence-backed flaky GitHub Actions jobs.
First-time analysis of a repository with no prior reviewer outcomes. Crawl historical merged-PR review feedback with the gh CLI (plus any preloaded samples), extract the team's review norms, and synthesize the initial per-repo review-style prompt. Use this for a cold-start repo; use continual-learning instead once the reviewer has accumulated finding outcomes.
Nightly refinement of an existing per-repo review-style prompt using this reviewer's own finding outcomes. Read confirmed (resolved-by-commit / thumbs-up) and dismissed (thumbs-down) findings, promote the bug patterns the team actually fixes, demote the false-positive patterns, reconcile against the current prompt, and save the refined version. Use this once outcomes exist; use bootstrap-repo-analysis for a cold-start repo.
Author the HTML for a plan artifact, dashboard iframe, or Slack attachment — structure, design plan, available runtime, theming, and craft. Read this before writing HTML for save_plan, output_iframe, or slack_attach_html.
Subagents overview
An open-source software factory built on Deep Agents by LangChain.
Open SWE turns engineering work into a repeatable system: investigate a codebase, implement changes, validate them, and deliver a pull request. It also reviews pull requests, learns repository-specific review preferences, monitors CI, and responds to feedback. Built by LangChain, it is open source and deployable in your infrastructure.
Note
Under active development. Expect breaking changes and rough edges. We’re not accepting issues or external contributions at this time. You’re welcome to explore and fork the code, but correctness, stability, and compatibility are not guaranteed.
If you really want to deploy it yourself, follow the installation guide. Deploying it as is requires Managed Deep Agents, which runs its agent; without it, we recommend forking and adapting the code to your own runtime.
What Open SWE does
flowchart LR
A[Issues, conversations, PRs, schedules] --> B[Plan and investigate]
B --> C[Implement in an isolated sandbox]
C --> D[Validate and deliver a PR]
D --> E[Review, CI, and feedback]
E -->|Follow-up work| BBuild: Investigate repositories, edit code, run focused validation, and open or update pull requests.
Parallelize: Use subagents for research and independent work.
Review: Run on-demand or opt-in automatic reviews, publish findings to GitHub, and learn from historical feedback.
Investigate: Use read-only PR chat to understand a change without implementing changes.
Operate: Schedule recurring tasks and monitor opted-in PRs with
/baby-sit, diagnosing failures and rerunning only evidence-backed flaky jobs.Customize: Choose models, reasoning effort, instructions, skills, integrations, and sandbox providers.
Start and continue work from the dashboard, GitHub issues and PR conversations, or Slack. Linear supports issue-comment triggers and replies through a configured Linear MCP connection. Cloud coding follow-ups reuse the thread’s context and sandbox; independent threads can run in parallel.
How it works
Deep Agents supplies planning, filesystem, shell, skills, and subagent primitives. LangGraph provides durable execution and thread state. Open SWE adds engineering tools, integrations, authorization, and user interfaces. The graph entrypoints are declared in langgraph.json, with an architecture inventory.
Cloud coding runs in persistent, per-thread Linux sandboxes with tooling supplied by workspace scripts or snapshots. An unreachable coding sandbox is not silently replaced. LangSmith is the default sandbox and tracing provider; other providers and local execution are configurable. PR chat does not need a sandbox.
Control and safety
GitHub access: Coding sandboxes normally receive installation-wide GitHub App access; selected workflows use narrower repository scopes. Workspace repository bindings control routing and preloaded checkouts, not a separate credential boundary. See GitHub access.
Integrations: MCP connections layer instance-wide, workspace-specific, and personal tools. Configure their scope and credentials in the customization guide.
Approvals: Workflow-file approval prompts guard detected Git pushes, not every possible shell or API write. Reviewers are instructed not to commit or push; PR chat excludes mutation tools.
Sandboxes have powerful tools and may have network access. Use least-privilege credentials, restrict repositories and integrations, and tailor approval policies to your deployment. Local execution does not provide cloud sandbox isolation.
Documentation
Customization guide — Models, sandboxes, tools, skills, prompts, triggers, and middleware
Workspaces reference — Routing, settings, images, and access
Human review in Slack — Review requests in a repository's Slack channel, merged once reviewers approve
Expedited Slack review — Human approval for small pull requests
Backend API documentation — Live API docs and the generated OpenAPI schema
Original announcement — Background on the internal coding-agent framework
Security policy — Report security concerns privately
License
Open SWE is licensed under the MIT License.
That license covers Open SWE's own code only. It does not cover anything under vendor/, which is not licensed by this repository at all.
What people ask about open-swe
What is langchain-ai/open-swe?
langchain-ai/open-swe is subagents for the Claude AI ecosystem. An Open-Source Asynchronous Coding Agent It has 11.1k GitHub stars and its last recorded update is dated 2026-10-23.
How do I install open-swe?
You can install open-swe by cloning the repository (https://github.com/langchain-ai/open-swe) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is langchain-ai/open-swe safe to use?
Our security agent has analyzed langchain-ai/open-swe and assigned a Trust Score of 100/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.
Who maintains langchain-ai/open-swe?
langchain-ai/open-swe is maintained by langchain-ai. The last recorded GitHub activity is dated 2026-10-23, with 201 open issues.
Are there alternatives to open-swe?
Yes. On ClaudeWave you can browse similar subagents at /categories/agents, sorted by popularity or recent activity.
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