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README.md
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<h1>Open SWE - An Open-Source Asynchronous Coding Agent</h1>
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</div>
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> [!WARNING]
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> **⚠️ DEPRECATION NOTICE**
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>
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> This repository is no longer actively maintained and will not receive further updates. The project has been deprecated and users are advised to seek alternative solutions for their coding agent needs.
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Open SWE is an open-source cloud-based asynchronous coding agent built with [LangGraph](https://docs.langchain.com/oss/javascript/langgraph/overview). It autonomously understands codebases, plans solutions, and executes code changes across entire repositories—from initial planning to opening pull requests.
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> [!TIP]
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> Try out Open SWE yourself using our [public demo](https://swe.langchain.com)!
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>
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> **Note: you're required to set your own LLM API keys to use the demo.**
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> [!NOTE]
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> 📚 See the **Open SWE documentation files [here](https://github.com/langchain-ai/open-swe/tree/main/apps/docs)**
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>
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> 💬 Read the **announcement blog post [here](https://blog.langchain.com/introducing-open-swe-an-open-source-asynchronous-coding-agent/)**
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>
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> 📺 Watch the **announcement video [here](https://youtu.be/TaYVvXbOs8c)**
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# Features
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- 📝 **Planning**: Open SWE has a dedicated planning step which allows it to deeply understand complex codebases and nuanced tasks. You're also given the ability to accept, edit, or reject the proposed plan before it's executed.
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- 🤝 **Human in the loop**: With Open SWE, you can send it messages while it's running (both during the planning and execution steps). This allows for giving real time feedback and instructions without having to interrupt the process.
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- 🏃 **Parallel Execution**: You can run as many Open SWE tasks as you want in parallel! Since it runs in a sandbox environment in the cloud, you're not limited by the number of tasks you can run at once.
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- 🧑💻 **End to end task management**: Open SWE will automatically create GitHub issues for tasks, and create pull requests which will close the issue when implementation is complete.
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- 🔗 **Trigger from Linear, Slack, or GitHub** — mention `@openswe` in a Linear comment, Slack thread, or GitHub PR comment to kick off a task
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- 👀 **Instant acknowledgement** — reacts with 👀 the moment it picks up your message so you know it's on it
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- 💬 **Message it while it's running** — send follow-up messages mid-task and it'll pick them up before its next step
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- 🔀 **Run multiple tasks in parallel** — each task runs in its own isolated cloud sandbox, no queuing
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- 🔐 **GitHub OAuth built-in** — authenticates with your GitHub account automatically, no token setup needed
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- 🚀 **Opens PRs automatically** — commits changes and opens a draft PR when done, linked back to your Linear ticket
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## Installation
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### Prerequisites
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- Python 3.11+
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- [uv](https://docs.astral.sh/uv/) package manager
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- [LangGraph CLI](https://langchain-ai.github.io/langgraph/cloud/reference/cli/)
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- [ngrok](https://ngrok.com/) (for exposing local webhooks)
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### 1. Clone the repo
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```bash
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git clone https://github.com/langchain-ai/open-swe.git
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cd open-swe/apps/agent
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```
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### 2. Install dependencies
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```bash
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uv sync
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```
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### 3. Set environment variables
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Create a `.env` file in `apps/agent/` with the following:
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```bash
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# LangSmith
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LANGSMITH_API_KEY_PROD="" # Your LangSmith API key
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LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
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LANGSMITH_HOST_API_URL="https://api.host.langchain.com"
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LANGCHAIN_TRACING_V2="true"
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LANGCHAIN_PROJECT=""
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# LLM
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ANTHROPIC_API_KEY="" # Anthropic API key (recommended default provider)
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# GitHub OAuth (via LangSmith agent auth)
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GITHUB_OAUTH_PROVIDER_ID="" # GitHub OAuth provider ID from LangSmith
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X_SERVICE_AUTH_JWT_SECRET="" # Secret for service JWT tokens
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# GitHub App (Bot)
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GITHUB_APP_ID="" # GitHub App ID
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GITHUB_APP_PRIVATE_KEY="-----BEGIN RSA PRIVATE KEY-----
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...
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-----END RSA PRIVATE KEY-----
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"
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GITHUB_APP_INSTALLATION_ID="" # GitHub App installation ID
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# GitHub Webhook
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GITHUB_WEBHOOK_SECRET="" # Secret for verifying GitHub webhooks
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# Linear
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LINEAR_API_KEY="" # Linear API key
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LINEAR_WEBHOOK_SECRET="" # Secret for verifying Linear webhooks
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# Slack (optional)
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SLACK_BOT_TOKEN=""
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SLACK_BOT_USER_ID=""
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SLACK_BOT_USERNAME=""
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SLACK_SIGNING_SECRET=""
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# Sandbox
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DEFAULT_SANDBOX_TEMPLATE_NAME="" # LangSmith sandbox template name (uses default if not set)
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# Token encryption
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TOKEN_ENCRYPTION_KEY="" # 32-byte url-safe base64 key for encrypting GitHub tokens
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```
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### 4. Run the agent
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In one terminal, start the LangGraph dev server:
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```bash
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uv run langgraph dev
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```
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In a second terminal, start the webhook server:
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```bash
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make run
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```
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### 5. Expose webhooks with ngrok
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In a third terminal, expose the webhook server so Linear/GitHub/Slack can reach it:
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```bash
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ngrok http 8000
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```
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Use the ngrok HTTPS URL as your webhook endpoint when configuring Linear, GitHub, and Slack integrations (e.g. `https://xxxx.ngrok.io/webhooks/linear`).
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The LangGraph server runs on `http://localhost:2024` and the webhook server on `http://localhost:8000`.
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---
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## Usage
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Open SWE can be used in multiple ways:
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- 🖥️ **From the UI**. You can create, manage and execute Open SWE tasks from the [web application](https://swe.langchain.com).
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- 📝 **From GitHub**. You can start Open SWE tasks directly from GitHub issues simply by adding a label `open-swe`, or `open-swe-auto` (adding `-auto` will cause Open SWE to automatically accept the plan, requiring no intervention from you). The default `open-swe` labels now use Claude Opus 4.5 for optimal performance. Note: `open-swe-max` and `open-swe-max-auto` labels are deprecated and should no longer be used.
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- 📋 **From Linear**. Mention `@openswe` in a comment on any Linear issue and describe the task you want it to perform (e.g. `@openswe fix the login bug described above`). The agent will pick up the issue context along with your instructions and start working on it.
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- 🐙 **GitHub (for Open SWE-generated PRs)**. Once Open SWE completes implementation, it pushes changes to a branch `open-swe/<thread-id>` and opens a **draft pull request** linking back to the originating Linear issue. From there, you can review the code, request changes, and merge when ready.
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