> ## Documentation Index
> Fetch the complete documentation index at: https://docs.hystersis.com/llms.txt
> Use this file to discover all available pages before exploring further.

# LangChain Integration

> Use Hystersis with LangChain

# LangChain Integration

Use Hystersis memory components in LangChain applications.

## Installation

```bash theme={null}
pip install hystersis langchain
```

## Memory Component

```python theme={null}
from langchain.memory import ConversationBufferMemory
from hystersis.integrations.langchain import AgentMemoryMemory

# Use Hystersis as LangChain memory
memory = AgentMemoryMemory(
    session_id="chatbot-001",
    memory_type="conversation",
    api_key="your-key"
)
```

## Retriever

```python theme={null}
from hystersis.integrations.langchain import AgentMemoryRetriever

retriever = AgentMemoryRetriever(
    memory_type="user",
    top_k=5,
    api_key="your-key"
)

# Use in chain
from langchain.chains import RetrievalQA

qa = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever
)
```

## Example: Conversational Agent

```python theme={null}
from langchain.agents import AgentExecutor, ConversationalAgent
from langchain.memory import ConversationBufferMemory
from hystersis.integrations.langchain import AgentMemoryMemory

memory = AgentMemoryMemory(
    session_id="agent-001",
    api_key="your-key"
)

agent = ConversationalAgent.from_llm_and_tools(
    llm=llm,
    tools=tools,
    memory=memory
)

executor = AgentExecutor.from_agent_and_tools(
    agent=agent,
    tools=tools,
    memory=memory
)
```