MemoChain

MemoChain is a lightweight Python library for adding memory to stateless LLM APIs. It allows developers to easily store and retrieve conversation history across sessions with minimal setup.

Key features:

• Supports any LLM API (OpenAI, DeepSeek, Claude, Mistral, etc.)
• Built-in Groq integration
• Local JSON-based storage (no external DB or backend)
• Session-based memory tracking
• Clean, modular design for easy use in any project

Installation

pip install memochain

Quickstart

Use with your own LLM (e.g. OpenAI)

from memochain.memory_store import load_context, append_to_history
import openai

openai.api_key = "your-openai-api-key"
session_id = "demo-session"

append_to_history(session_id, "user", "Tell me a joke.")
history = load_context(session_id)

response = openai.ChatCompletion.create(
    model="gpt-3.5-turbo",
    messages=history
)

assistant_msg = response["choices"][0]["message"]["content"]
append_to_history(session_id, "assistant", assistant_msg)

print("Assistant:", assistant_msg)

Use with built-in Groq support

from memochain.groq_client import GroqLLMClient
from memochain.memory_store import append_to_history, get_session_history

session_id = "groq-session"
client = GroqLLMClient()

append_to_history(session_id, "user", "Who won the World Cup in 2022?")
history = get_session_history(session_id)
response = client.chat(history)

append_to_history(session_id, "assistant", response)
print("Assistant:", response)

How It Works

MemoChain stores all message history locally in:

• .memochain_history.json — stores conversation history per session
• .memochain_sessions.json — tracks active session IDs

This allows memory persistence across runs without a backend or database.

Example Scripts

See the examples/ folder:

• custom_llm_openai_example.py — OpenAI API usage
• groq_chat_example.py — Chat example using Groq integration

To test locally:

python examples/groq_chat_example.py

License

MIT

Author

Yash Thapliyal – GitHub