🧠🤖 Deep Agents Code
Quick Install
curl -LsSf https://langch.in/dcode | bash
# With model provider extras
# OpenAI, Anthropic, and Gemini are included by default
DEEPAGENTS_CODE_EXTRAS="nvidia,ollama" curl -LsSf https://langch.in/dcode | bash
Run:
dcode
🤔 What is this?
The fastest way to start using Deep Agents. deepagents-code is a pre-built coding agent in your terminal — similar to Claude Code or Cursor — powered by any LLM that supports tool calling. One install command and you're up and running, no code required.
What deepagents-code adds on top of the SDK:
- Interactive TUI — rich terminal interface with streaming responses
- Conversation resume — pick up where you left off across sessions
- Web search — ground responses in live information
- Remote sandboxes — run code in isolated environments (LangSmith, AgentCore, Daytona, Modal, Runloop, & more)
- Persistent memory — agent remembers context across conversations
- Custom skills — extend the agent with your own slash commands
- Headless mode — run non-interactively for scripting and CI
- Human-in-the-loop — approve or reject tool calls before execution
🔒 Security model
By default, dcode trusts the directory you run it in. Human-in-the-loop approval gates model-requested tool calls, but project artifacts are read before any approval prompt.
Do not run dcode in a directory you do not trust without a sandbox backend. For untrusted repositories, use a remote sandbox so execution is isolated from your machine. Running dcode in a directory lets that directory's files shape execution. See THREAT_MODEL.md for details.
📖 Resources
- Documentation
- Changelog
- Source code
- Deep Agents SDK — underlying agent harness
- LangChain Academy — Comprehensive, free courses on LangChain libraries and products, made by the LangChain team.
- Code of Conduct — community guidelines and standards
📕 Releases & Versioning
See our Releases and Versioning policies.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see the Contributing Guide.
🤝 Acknowledgements
This project was primarily inspired by Claude Code, and initially was largely an attempt to see what made Claude Code general purpose, and make it even more so.
1 maintainer
Release files for deepagents-code 0.1.77
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| deepagents_code-0.1.77.tar.gz | 4.3 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| deepagents_code-0.1.77-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 6.6 MB
Release files / deepagents_code-0.1.77.tar.gz
| Download URL | deepagents_code-0.1.77.tar.gz |
|---|---|
| Size | 4.3 MB |
| Tags | Source |
|
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Release files / deepagents_code-0.1.77-py3-none-any.whl
| Download URL | deepagents_code-0.1.77-py3-none-any.whl |
|---|---|
| Size | 2.3 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
