Zep: A long-term memory store for LLM applications

Zep stores, summarizes, embeds, indexes, and enriches LLM app / chatbot histories, and exposes them via simple, low-latency APIs. Zep allows developers to focus on developing their AI apps, rather than on building memory persistence, search, and enrichment infrastructure.

Zep's Extractor model is easily extensible, with a simple, clean interface available to build new enrichment functionality, such as summarizers, entity extractors, embedders, and more.

Key Features:

• Fast! Zep's async extractors operate independently of the your chat loop, ensuring a snappy user experience.
• Memory operations like addMemory(), getMemory(), searchMemory(), deleteMemory() with soft & hard deletes
• Long-term memory persistence, with access to historical messages irrespective of your summarization strategy.
• Auto-summarization of memory messages based on a configurable message window. A series of summaries are stored, providing flexibility for future summarization strategies.
• Hybrid Vector search over memories and metadata, with messages automatically embedded on creation.
• Entity Extractor that extracts named entities and intents from messages and stores them in the message metadata.
• Auto-token counting of memories and summaries, allowing finer-grained control over prompt assembly.
• OpenAI & Azure OpenAI Service models for your applications
• Python and JavaScript SDKs.
• Langchain memory and retriever support.

Quick Start

Read the docs: https://getzep.github.io

1. Clone this repo

git clone https://github.com/getzep/zep.git

2. Add your OpenAI API key to a .env file in the root of the repo:

ZEP_OPENAI_API_KEY=<your key here>

3. Start the Zep server:

docker-compose up

This will start a Zep server on port 8000, and a Postgres database on port 5432.

4. Access Zep via the Python or Javascript SDKs:

Python

async with ZepClient(base_url) as client:
    role = "user"
    content = "who was the first man to go to space?"
    message = Message(role=role, content=content)
    memory = Memory()
    memory.messages = [message]
    # Add a memory
    result = await client.aadd_memory(session_id, memory)

See zep-python for installation and use docs.

Javascript

 // Add memory
 const role = "user";
 const content = "I'm looking to plan a trip to Iceland. Can you help me?"
 const message = new Message({ role, content });
 const memory = new Memory();
 memory.messages = [message];
 const result = await client.addMemoryAsync(session_id, memory);
...

Zep Documentation

Server installation and SDK usage documentation is available here: https://getzep.github.io

Acknowledgements

h/t to the Motorhead and Langchain projects for inspiration.