Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/langchain-ai/open-swe/agents-mdgit clone --depth 1 https://github.com/langchain-ai/open-sweWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.03814 | $0.03814 |
| Opus 5 | $0.01907 | $0.01907 |
| Sonnet 5 | $0.00763 | $0.00763 |
| Haiku 4.5 | $0.00381 | $0.00381 |
Grade A, and why
open-swe AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
This file provides guidance to Coding Agents when working with code in this repository.
Project
Open SWE is an open-source coding-agent framework built on LangGraph + Deep Agents (deepagents.create_deep_agent). It runs as a LangGraph app: each thread spawns its own isolated cloud sandbox, and the agent is invoked from Slack, Linear, or GitHub (PR comments, plus auto-review on opened / ready-for-review).
A separate reviewer graph runs read-only code reviews on PRs, and a review-style analyzer graph learns per-repo review style from historical PRs.
Commands
Dependencies are managed with uv. Tests use pytest (asyncio_mode = "auto"). Lint/format is ruff (line-length 100, target py311). Type checking is basedpyright (typeCheckingMode = "standard"). requires-python = ">=3.11"; langgraph.json pins the runtime to 3.12.
make install # uv sync --extra dev (pytest, ruff, …)
make dev # uv run langgraph dev — serves all three graphs + the FastAPI app from langgraph.json
make run # uvicorn agent.webapp:app --reload --port 8000 (FastAPI only, no LangGraph runtime)
make test # uv run pytest -vvv tests/
make test TEST_FILE=tests/github/test_open_pull_request.py # single test file
uv run pytest -vvv tests/github/test_open_pull_request.py::test_name # single test
make lint # ruff check + ruff format --diff
make format # ruff format + ruff check --fix
make typecheck # basedpyright agent tests
langgraph.json declares three graph entrypoints and the FastAPI app, all served together by langgraph dev:
| Graph | Entrypoint | Purpose |
|---|---|---|
agent |
agent.server:get_agent |
Main coding agent (Slack/Linear/GitHub-triggered). |
reviewer |
agent.reviewer:get_reviewer_agent |
Read-only PR reviewer. Findings model + publish_review. |
analyzer |
agent.analyzer:get_analyzer |
Learns per-repo reviewer style from historical PRs and this reviewer's own finding outcomes. |
scheduler |
agent.scheduler:get_scheduler |
Fans deterministic cron tasks into scheduled agent runs, reconciliation, and /baby-sit PR checks. |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 148 lines · 3,814 tokens per session scan A a449f22e3660
open-swe AGENTS.md is an instructions file published in the GitHub repository langchain-ai/open-swe (10,633 stars, last pushed 2d ago), licensed MIT. It adds 3,814 tokens to every session, about $0.0191 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
LangBot AGENTS.md
Instructions for langbot-app/LangBot, covering agents.md, quick facts, essential commands, where to look and cross-repo sdk work.
awesome-ChatGPT-repositories CLAUDE.md
Instructions for taishi-i/awesome-ChatGPT-repositories, covering awesome-chatgpt-repositories — claude code guide, repository structure, plugin skill (when installed via /plugin), local standalone command (when repo is cloned) and compact data format (plugins/awesome-chatgpt-search/data/).
dify AGENTS.md
AGENTS.md instructions for langgenius/dify, covering agents.md and repository gotchas.
dify CLAUDE.md
Claude Code instructions for langgenius/dify, a project described as: Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or self-hosted, so teams move from prototype to production without rebuilding the stack.
flock AGENTS.md
Instructions for Onelevenvy/flock, covering agents.md, project overview, build & development commands, rust backend and build the cli.
cyrus copilot-instructions.md
Copilot instructions for cyrusagents/cyrus: Note, there is a need to maintain the use of '--print' when running the claude exec commands because that is what makes it non-interactive.