langgraph-docs

Deep Agents is an agent harness. An opinionated, ready-to-run agent out of the box. Instead of wiring up prompts, tools, and context management yourself, you get a working agent immediately and customize what you need.

What's included:

• Planning — write_todos / read_todos for task breakdown and progress tracking
• Filesystem — read_file, write_file, edit_file, ls, glob, grep for reading and writing context
• Shell access — execute for running commands (with sandboxing)
• Sub-agents — task for delegating work with isolated context windows
• Smart defaults — Prompts that teach the model how to use these tools effectively
• Context management — Auto-summarization when conversations get long, large outputs saved to files

```bash

pip install deepagents

# or

uv add deepagents

```

```python

from deepagents import create_deep_agent

agent = create_deep_agent()

result = agent.invoke({"messages": [{"role": "user", "content": "Research LangGraph and write a summary"}]})

```

The agent can plan, read/write files, and manage its own context. Add tools, customize prompts, or swap models as needed.

Add your own tools, swap models, customize prompts, configure sub-agents, and more. See the [documentation](https://docs.langchain.com/oss/python/deepagents/overview) for full details.

```python

from langchain.chat_models import init_chat_model

agent = create_deep_agent(

model=init_chat_model("openai:gpt-4o"),

tools=[my_custom_tool],

system_prompt="You are a research assistant.",

)

```

MCP is supported via [langchain-mcp-adapters](https://github.com/langchain-ai/langchain-mcp-adapters).

Try Deep Agents instantly from the terminal:

```bash

uv tool install deepagents-cli

deepagents

```

The CLI adds conversation resume, web search, remote sandboxes (Modal, Runloop, Daytona), persistent memory, custom skills, and human-in-the-loop approval. See the [CLI documentation](https://docs.langchain.com/oss/python/deepagents/cli) for more. Using the Deep Agents requires setting an API Key before running (ex: ANTHROPIC_API_KEY).

create_deep_agent returns a compiled [LangGraph](https://docs.langchain.com/oss/python/langgraph/overview) graph. Use it with streaming, Studio, checkpointers, or any LangGraph feature.

Why should I use this?

• 100% open source — MIT licensed, fully extensible
• Provider agnostic — Works with Claude, OpenAI, Google, or any LangChain-compatible model
• Built on LangGraph — Production-ready runtime with streaming, persistence, and checkpointing
• Batteries included — Planning, file access, sub-agents, and context management work out of the box
• Get started in seconds — pip install deepagents or uv add deepagents and you have a working agent
• Customize in minutes — Add tools, swap models, tune prompts when you need to

• [Documentation](https://docs.langchain.com/oss/python/deepagents/overview) — Full API reference and guides
• [Examples](examples/) — Working agents and patterns
• [CLI](https://docs.langchain.com/oss/python/deepagents/cli) — Interactive terminal interface

Deep Agents follows a "trust the LLM" model. The agent can do anything its tools allow. Enforce boundaries at the tool/sandbox level, not by expecting the model to self-police.