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60 lines
2.1 KiB
Plaintext
60 lines
2.1 KiB
Plaintext
---
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title: LangChain (deepagents)
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description: Back deepagents and other LangGraph-style agents with a Mirage workspace via LangchainWorkspace.
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icon: /images/langchain-logo.svg
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---
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[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.
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## Install
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```bash
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uv add 'mirage-ai[deepagents]' langchain-anthropic
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```
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This pulls in `deepagents>=0.4.12`. Bring your own LangChain chat model (`langchain-anthropic`, `langchain-openai`, etc.).
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## Usage
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```python
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from deepagents import create_deep_agent
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from langchain_anthropic import ChatAnthropic
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from mirage import MountMode, Workspace
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from mirage.agents.langchain import (
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LangchainWorkspace,
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build_system_prompt,
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extract_text,
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)
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from mirage.resource.ram import RAMResource
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ws = Workspace({"/": RAMResource()}, mode=MountMode.WRITE)
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agent = create_deep_agent(
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model=ChatAnthropic(model="claude-sonnet-4-20250514"),
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system_prompt=build_system_prompt(
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mount_info={"/": "In-memory filesystem (read/write)"},
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),
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backend=LangchainWorkspace(ws),
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)
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result = agent.invoke({
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"messages": [{"role": "user", "content": "Create /report.md and summarize."}],
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})
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for text in extract_text(result["messages"]):
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print(text)
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```
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## Exports
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| Symbol | Purpose |
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| --- | --- |
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| `LangchainWorkspace` | `Backend` implementation for deepagents, wires reads, writes, edits, and shell. |
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| `extract_text` | Pulls the text content out of LangChain messages. |
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| `build_system_prompt` | Generates a system prompt that describes mounted paths to the model. |
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## Examples
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- [`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.
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- [`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.
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