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> [!CAUTION] > Merging this PR will automatically publish to **PyPI** and create a **GitHub release**. For the full release process, see [`.github/RELEASING.md`](https://github.com/langchain-ai/deepagents/blob/main/.github/RELEASING.md). --- _Release notes preview: keep this section in sync with the package `CHANGELOG.md`. Publish reads the merged CHANGELOG via `release.yml`, not this PR description — keep them aligned anyway so the PR stays an accurate historical record for reviewers and anyone returning later._ --- ## [0.7.9](https://github.com/langchain-ai/deepagents/compare/deepagents==0.7.8...deepagents==0.7.9) (2026-08-25) ### Features - Disabled tracing inputs on middleware. ([#5377](https://github.com/langchain-ai/deepagents/issues/5377)) ### Bug fixes - Exclude tools from execution when `excluded_tools` is set in harness profiles. ([#5809](https://github.com/langchain-ai/deepagents/issues/5809)) - Enforce full criterion coverage in `RubricMiddleware`. ([#5234](https://github.com/langchain-ai/deepagents/issues/5234)) - Clarified zero execute-timeout semantics. ([#5752](https://github.com/langchain-ai/deepagents/issues/5752)) _End release notes preview._ --- > [!NOTE] > A **community contributors** list and a **Special thanks** section (crediting the users who filed the issues this release's PRs closed) are appended to the GitHub release notes automatically at publish time (see [Release Pipeline](https://github.com/langchain-ai/deepagents/blob/main/.github/RELEASING.md#release-pipeline), step 3). --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: langchain-oss-automated-triage[bot] <248757908+langchain-oss-automated-triage[bot]@users.noreply.github.com>
Examples
Real agents and patterns built on Deep Agents.
Featured
Deep Agents CodeA pre-built coding Deep Agent in your terminal — similar to Claude Code or Codex — powered by any LLM. Includes an interactive TUI, web search, remote sandboxes, persistent memory, custom skills, and human-in-the-loop approval. |
Open SWEAn open-source, async coding agent for your org's internal workflows. Runs each task in an isolated cloud sandbox, integrates with Slack, Linear, and GitHub, and ships PRs end-to-end. |
In the wild
Production agents powered by the LangChain stack:
| Project | Description |
|---|---|
| LangSmith Fleet | No-code platform for building AI agents from templates; connect your accounts and let the agent handle routine work |
| Chat LangChain | Documentation assistant that answers questions about LangChain, LangGraph, and LangSmith (source) |
All examples
Research
| Example | Description |
|---|---|
| Deep Research | Multi-step web research with Tavily, parallel sub-agents, and strategic reflection |
| MCP Docs Agent | Docs research agent using MCP tools over LangChain documentation |
Coding
| Example | Description |
|---|---|
| Coding Agent | Autonomous coding agent in a LangSmith sandbox |
| Nemotron Research Agent | NVIDIA Nemotron Super for research + GPU-accelerated execution via RAPIDS |
Content
| Example | Description |
|---|---|
| Content Builder | Blog posts, LinkedIn posts, and tweets with memory (AGENTS.md), skills, and subagents |
| Text-to-SQL | Natural language to SQL with planning and skill-based workflows on the Chinook demo database |
| LLM Wiki | Script-first LLM wiki synced via langsmith hub init/pull/push |
Deployable services
| Example | Description |
|---|---|
| Content Writer | Content writer with per-user memory and Supabase auth |
| GTM Strategist | GTM strategy agent coordinating sync and async subagents |
| Async Subagent Server | Self-hosted Agent Protocol server exposing a researcher as an async subagent |
Advanced patterns
| Example | Description |
|---|---|
| Ralph Loop | Autonomous looping with fresh context each iteration, using the filesystem for persistence |
| Agents as Folders | Download a zip, unzip, and run |
| Better Harness | Eval-driven outer-loop optimization of a Deep Agents harness |
| Rubric Middleware | Grader-model rubric feedback loop that revises output until all criteria pass |
Each example has its own README with setup instructions.
Contributing an example
See the Contributing Guide for general contribution guidelines.
When adding a new example:
- Use uv for dependency management with a
pyproject.tomlanduv.lock(commit the lock file) - Pin to deepagents version — use a version range (e.g.,
>=0.3.5,<0.4.0) in dependencies - Include a
READMEwith clear setup and usage instructions - Add tests for reusable utilities or non-trivial helper logic
- Keep it focused — each example should demonstrate one use-case or workflow
- Follow the structure of existing examples (see
deep_research/ortext-to-sql-agent/as references)
Resources
- LangChain Academy — Comprehensive, free courses on LangChain libraries and products, made by the LangChain team.
- Code of Conduct — community guidelines and standards