> ## Documentation Index
> Fetch the complete documentation index at: https://reagent-ai.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# LangChain

> Auto-capture tool calls and results using the LangChain callback handler.

## Installation

```bash theme={null}
uv add reagent-flow-langchain
```

## Setup

Create a `ReagentCallbackHandler` and pass it to your chain:

```python theme={null}
from reagent_flow_langchain import ReagentCallbackHandler
import reagent_flow

handler = ReagentCallbackHandler()

with reagent_flow.session("chain") as s:
    chain.invoke({"input": "..."}, config={"callbacks": [handler]})

s.assert_called("my_tool")
```

## How it works

The callback handler hooks into LangChain's event system:

| LangChain event | What gets logged                                  |
| --------------- | ------------------------------------------------- |
| `on_llm_end`    | Tool calls from the LLM response (`log_llm_call`) |
| `on_tool_end`   | Tool execution results (`log_tool_result`)        |

Tool results are automatically unwrapped from `ToolMessage.content` and JSON-parsed when possible, so `assert_tool_output_matches` can validate dict/list shapes.

## Full example

```python theme={null}
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from reagent_flow_langchain import ReagentCallbackHandler
import reagent_flow

@tool
def search(query: str) -> dict:
    """Search for information."""
    return {"results": [{"title": "Result 1"}], "count": 1}

llm = ChatOpenAI(model="gpt-4o").bind_tools([search])
handler = ReagentCallbackHandler()

with reagent_flow.session("search-agent") as s:
    result = llm.invoke(
        "Find information about Q3 earnings",
        config={"callbacks": [handler]},
    )

s.assert_called("search")
s.assert_tool_output_matches("search", schema={
    "results": [{"title": str}],
    "count": int,
})
```

## Parallel tool calls

The handler correctly tracks parallel tool calls using `call_id` from LangChain's response. When the LLM requests multiple tools in a single turn, each result is matched to its corresponding call.