---
title: "open-swe"
type: "tool"
slug: "langchain-ai-open-swe"
canonical_url: "https://www.graphcanon.com/tools/langchain-ai-open-swe"
github_url: "https://github.com/langchain-ai/open-swe"
homepage_url: "https://www.langchain.com/blog/open-swe-an-open-source-framework-for-internal-coding-agents"
stars: 10124
forks: 1163
primary_language: "Python"
license: "MIT"
categories: ["ai-agents"]
tags: ["claudecode", "llms", "agents", "llm", "ai", "anthropic", "agent"]
updated_at: "2026-07-07T18:34:24.943926+00:00"
---

# open-swe

> Open-source framework for building internal coding agents.

An open-source asynchronous coding agent that enables engineering teams to create their own bespoke internal AI bots connected to internal systems with contextual permissions.

## Facts

- Repository: https://github.com/langchain-ai/open-swe
- Homepage: https://www.langchain.com/blog/open-swe-an-open-source-framework-for-internal-coding-agents
- Stars: 10,124 · Forks: 1,163 · Open issues: 21 · Watchers: 53
- Primary language: Python
- License: MIT
- Last pushed: 2026-07-07T17:39:15+00:00

## Categories

- [AI Agents](/categories/ai-agents.md)

## Tags

claudecode, llms, agents, llm, ai, anthropic, agent

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## README (excerpt)

```text
<div align="center">
  <a href="https://github.com/langchain-ai/open-swe">
    <picture>
      <source media="(prefers-color-scheme: dark)" srcset="static/dark.svg">
      <source media="(prefers-color-scheme: light)" srcset="static/light.svg">
      <img alt="Open SWE Logo" src="static/dark.svg" width="35%">
    </picture>
  </a>
</div>

<div align="center">
  <h3>Open-source framework for building your org's internal coding agent.</h3>
</div>

<div align="center">
  <a href="https://opensource.org/licenses/MIT" target="_blank"><img src="https://img.shields.io/github/license/langchain-ai/open-swe" alt="License"></a>
  <a href="https://github.com/langchain-ai/open-swe/stargazers" target="_blank"><img src="https://img.shields.io/github/stars/langchain-ai/open-swe" alt="GitHub Stars"></a>
  <a href="https://github.com/langchain-ai/langgraph" target="_blank"><img src="https://img.shields.io/badge/Built%20on-LangGraph-blue" alt="Built on LangGraph"></a>
  <a href="https://github.com/langchain-ai/deepagents" target="_blank"><img src="https://img.shields.io/badge/Built%20on-Deep%20Agents-blue" alt="Built on Deep Agents"></a>
  <a href="https://x.com/langchain" target="_blank"><img src="https://img.shields.io/twitter/url/https/twitter.com/langchain.svg?style=social&label=Follow%20%40LangChain" alt="Twitter / X"></a>
</div>

<br>

Elite engineering orgs like Stripe, Ramp, and Coinbase are building their own internal coding agents — Slackbots, CLIs, and web apps that meet engineers where they already work. These agents are connected to internal systems with the right context, permissioning, and safety boundaries to operate with minimal human oversight.

Open SWE is the open-source version of this pattern. Built on [LangGraph](https://langchain-ai.github.io/langgraph/) and [Deep Agents](https://github.com/langchain-ai/deepagents), it gives you the same architecture those companies built internally: cloud sandboxes, Slack and Linear invocation, subagent orchestration, and automatic PR creation — ready to customize for your own codebase and workflows.

> [!NOTE]
> 💬 Read the **announcement blog post [here](https://blog.langchain.com/open-swe-an-open-source-framework-for-internal-coding-agents/)**

---

## Architecture

Open SWE makes the same core architectural decisions as the best internal coding agents. Here's how it maps to the patterns described in [this overview](https://x.com/kishan_dahya/status/2028971339974099317) of Stripe's Minions, Ramp's Inspect, and Coinbase's Cloudbot:

### 1. Agent Harness — Composed on Deep Agents

Rather than forking an existing agent or building from scratch, Open SWE **composes** on the [Deep Agents](https://github.com/langchain-ai/deepagents) framework — similar to how Ramp built on top of OpenCode. This gives you an upgrade path (pull in upstream improvements) while letting you customize the orchestration, tools, and middleware for your org.

```python
create_deep_agent(
    model="openai:gpt-5.5",
    system_prompt=construct_system_prompt(...),
    tools=[http_request, fetch_url, linear_comment, slack_thread_reply],
    backend=sandbox_backend,
    middleware=[ToolErrorMiddleware(), check_message_queue_before_model, ...],
)
```

### 2. Sandbox — Isolated Cloud Environments

Every task runs in its own **isolated cloud sandbox** — a remote Linux environment with full shell access. The repo is cloned in, the agent gets full permissions, and the blast radius of any mistake is fully contained. No production access, no confirmation prompts.

Open SWE supports multiple sandbox providers out of the box — [Modal](https://modal.com/), [Daytona](https://www.daytona.io/), [Runloop](https://www.runloop.ai/), [E2B](https://e2b.dev/), and [LangSmith](https://smith.langchain.com/) — and you can plug in your own. See the [Customization Guide](CUSTOMIZATION.md#1-sandbox) for details.

This follows the principle all three companies converge on: **isolate first, then give full permissions inside the boundary.**

- Eac
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/langchain-ai-open-swe`](/api/graphcanon/tools/langchain-ai-open-swe)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_