langchain-recallio 1.1.0
Drop-in RecallIO Memory for LangChain
Requires Python: >=3.8

# Recallio Memory for LangChain

This repository provides an implementation of `RecallioMemory`, a drop-in replacement for `BaseMemory` from [LangChain](https://python.langchain.com/). It stores conversation history in the [Recallio](https://app.recallio.ai) cloud service using the official `recallio` client.

## Features

- **Scoped writes** – memories can be associated with a specific `session_id` or `user_id`.
- **TTL support** – pass a time-to+live in seconds and it will be converted to an expiration timestamp stored alongside each memory.
- **Vector recall** – previous messages are retrieved from Recallio using semantic search. Memories can also be filtered by custom tags.

## Installation

```bash
pip install recallio langchain langchain-recallio openai
```

## Usage

```python
#Setup: API Keys & Imports
from langchain_recallio.memory import RecallioMemory
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
import os

# Set your keys here or use environment variables
RECALLIO_API_KEY = os.getenv("RECALLIO_API_KEY", "YOUR_RECALLIO_API_KEY")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_OPENAI_API_KEY")

#Initialize RecallioMemory
memory = RecallioMemory(
    project_id="project_abc",
    api_key=RECALLIO_API_KEY,
    session_id="demo-session-001",
    user_id="demo-user-42",
    default_tags=["test", "langchain"],
    return_messages=True,
)

#Build a LangChain ConversationChain with RecallioMemory
# You can swap in any supported LLM here
llm = ChatOpenAI(api_key=OPENAI_API_KEY, temperature=0)
prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "The following is a friendly conversation between a human and an AI. "
            "The AI is talkative and provides lots of specific details from its context. "
            "If the AI does not know the answer to a question, it truthfully says it does not know.",
        ),
        ("placeholder", "{history}"),  # RecallioMemory will fill this slot
        ("human", "{input}"),
    ]
)

# LCEL chain that returns an AIMessage
base_chain = prompt | llm


# Create a stateful chain using RecallioMemory
def chat_with_memory(user_input: str):
    # Load conversation history from memory
    memory_vars = memory.load_memory_variables({"input": user_input})

    # Run the chain with history and user input
    response = base_chain.invoke(
        {"input": user_input, "history": memory_vars.get("history", "")}
    )

    # Save the conversation to memory
    memory.save_context({"input": user_input}, {"output": response.content})

    return response
```

## Example: Chat with Memory

```python
# First user message – note the AI remembers the name
resp1 = chat_with_memory("Hi! My name is Guillaume. Remember that.")
print("Bot:", resp1.content)

# Second user message – AI should recall the name from memory
resp2 = chat_with_memory("What is my name?")
print("Bot:", resp2.content)
```

The tests use mocks and do not require a valid Recallio API key.

## License

This project is distributed under the MIT license.