---
title: LangChain (deepagents)
description: Back deepagents and other LangGraph-style agents with a Mirage workspace via LangchainWorkspace.
icon: /images/langchain-logo.svg
---

[deepagents](https://github.com/langchain-ai/deepagents) is LangChain's framework for long-horizon coding agents. It accepts a pluggable `backend` for filesystem and shell operations, Mirage ships one.

## Install

```bash
uv add 'mirage-ai[deepagents]' langchain-anthropic
```

This pulls in `deepagents>=0.4.12`. Bring your own LangChain chat model (`langchain-anthropic`, `langchain-openai`, etc.).

## Usage

```python
from deepagents import create_deep_agent
from langchain_anthropic import ChatAnthropic

from mirage import MountMode, Workspace
from mirage.agents.langchain import (
    LangchainWorkspace,
    build_system_prompt,
    extract_text,
)
from mirage.resource.ram import RAMResource

ws = Workspace({"/": RAMResource()}, mode=MountMode.WRITE)

agent = create_deep_agent(
    model=ChatAnthropic(model="claude-sonnet-4-20250514"),
    system_prompt=build_system_prompt(
        mount_info={"/": "In-memory filesystem (read/write)"},
    ),
    backend=LangchainWorkspace(ws),
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "Create /report.md and summarize."}],
})
for text in extract_text(result["messages"]):
    print(text)
```

## Exports

| Symbol | Purpose |
| --- | --- |
| `LangchainWorkspace` | `Backend` implementation for deepagents, wires reads, writes, edits, and shell. |
| `extract_text` | Pulls the text content out of LangChain messages. |
| `build_system_prompt` | Generates a system prompt that describes mounted paths to the model. |

## Examples

- [`examples/python/agents/langchain/s3_deepagent.py`](https://github.com/strukto-ai/mirage/blob/main/examples/python/agents/langchain/s3_deepagent.py), read-only S3 exploration.
- [`examples/python/agents/langchain/databricks_volume_deepagent.py`](https://github.com/strukto-ai/mirage/blob/main/examples/python/agents/langchain/databricks_volume_deepagent.py), Databricks volume exploration inside Databricks Apps or local SDK-auth setups.
