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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.
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:
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.
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/), and [LangSmith](https://smith.langchain.com/) — and you can plug in your own. See the [Customization Guide](CUSTOMIZATION.md#1-sandbox) for details.
GitHub operations are performed with `GH_TOKEN=dummy gh` inside the sandbox, backed by the LangSmith proxy. Plus the built-in Deep Agents tools: `read_file`, `write_file`, `edit_file`, `ls`, `glob`, `grep`, `write_todos`, and `task` (subagent spawning).
- **`AGENTS.md`** — If the repo contains an `AGENTS.md` file at the root, it's read from the sandbox and injected into the system prompt. This is your repo-level equivalent of Stripe's rule files: encoding conventions, testing requirements, and architectural decisions that every agent run should follow.
- **Source context** — The full Linear issue (title, description, comments) or Slack thread history is assembled and passed to the agent, so it starts with rich context rather than discovering everything through tool calls.
**Subagents:** The Deep Agents framework natively supports spawning child agents via the `task` tool. The main agent can fan out independent subtasks to isolated subagents — each with its own middleware stack, todo list, and file operations. This is similar to Ramp's child sessions for parallel work.
- **`check_message_queue_before_model`** — Injects follow-up messages (Linear comments or Slack messages that arrive mid-run) before the next model call. You can message the agent while it's working and it'll pick up your input at its next step.
- **`notify_step_limit_reached`** — After-agent hook that posts a Slack reply when the agent hits the model-call limit, so users get a clear signal instead of silence.
- **Slack** — Mention the bot in any thread. Supports `repo:owner/name` syntax to specify which repo to work on. The agent replies in-thread with status updates and PR links.
- **Linear** — Comment `@openswe` on any issue. The agent reads the full issue context, reacts with 👀 to acknowledge, and posts results back as comments.
- **GitHub** — Tag `@openswe` in PR comments on agent-created PRs to have it address review feedback and push fixes to the same branch.
The agent is instructed to run linters, formatters, and tests before committing, and is responsible end-to-end for committing, pushing, opening/updating the draft PR, and replying in the source channel.
This is an area where you can extend Open SWE for your org: add deterministic CI checks, visual verification, or review gates as additional middleware. See the [Customization Guide](CUSTOMIZATION.md#6-middleware) for how.
