Mason Daugherty
55b60ca08d
`deepagents-code` adds a `memory.auto_save` setting (env `DEEPAGENTS_CODE_MEMORY_AUTO_SAVE` / `[memory].auto_save`, default on). Disable it to keep loading memory into context while stopping the agent from automatically writing learnings back to your `AGENTS.md` files. --- Automatic memory saving in `deepagents-code` is prompt-driven: `MemoryMiddleware` injects guidance telling the agent to proactively persist learnings to the `AGENTS.md` sources. Until now the only switch was `enable_memory`, which is all-or-nothing — turning it off also stops memory from being loaded into context. This adds a way to keep loading memory while turning the automatic saving off. A new `memory.auto_save` option (env `DEEPAGENTS_CODE_MEMORY_AUTO_SAVE`, `[memory].auto_save` in `config.toml`; defaults on) flips `MemoryMiddleware` to a read-only prompt when disabled. The read-only prompt keeps the trust/verification framing but drops the "proactively persist learnings" guidance, so memory still informs the agent and explicit saves (e.g. the `remember` skill) keep working — only unprompted auto-saving stops. To support this, the SDK gains a canonical `MEMORY_READONLY_SYSTEM_PROMPT`, exported from `deepagents.middleware` alongside `MEMORY_SYSTEM_PROMPT`. Made by [Open SWE](https://openswe.vercel.app/agents/825df1d1-b8e0-91c3-f7f6-dac1fcdd5c5e) --------- Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com>
🧠🤖 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.