Mem-LLM

Mem-LLM is a privacy-first Python framework for building memory-enabled AI assistants that run locally.

What's New in v2.5.1

• Declared the missing pydantic dependency, so graph memory works on a clean install instead of silently disabling itself.
• Declared the missing python-multipart dependency for the api extra, so mem_llm.api_server imports.

What's New in v2.5.0

• Added OpenAI-compatible and llama.cpp backend support.
• Added MemoryRouter for core memory, archival memory, recall, graph context, and KB retrieval.
• Added temporal graph memory with current and historical graph search.
• Simplified repeated backend and chat context-building code.

Quick Start

Install

pip install mem-llm

Ollama

from mem_llm import MemAgent

agent = MemAgent(backend="ollama", model="granite4:3b")
agent.set_user("alice")
print(agent.chat("My name is Alice."))
print(agent.chat("What is my name?"))

LM Studio

from mem_llm import MemAgent

agent = MemAgent(backend="lmstudio", model="qwen3.5-2b")
agent.set_user("alice")
print(agent.chat("Summarize Python in one sentence."))

llama.cpp

Start llama.cpp server with an OpenAI-compatible endpoint:

llama-server.exe -m C:\path\to\model.gguf --alias local-model --host 127.0.0.1 --port 8080

Then connect Mem-LLM:

from mem_llm import MemAgent

agent = MemAgent(
    backend="llamacpp",
    model="local-model",
    base_url="http://localhost:8080",
)
agent.set_user("alice")
print(agent.chat("Remember that I prefer concise answers."))

Core Features

• Persistent memory per user (JSON or SQLite)
• Multi-backend support (Ollama, LM Studio, OpenAI-compatible APIs, llama.cpp)
• MemoryRouter with core memory, archival memory, recall, and graph context
• Temporal graph memory with current and historical lookup
• Tool calling system (@tool, built-in tools, validation)
• Streaming responses
• Knowledge base integration
• Conversation analytics
• REST API and Web UI

Repository Layout

• Memory LLM/ - main package source and release files
• quickstart/ - step-by-step usage examples & tutorials

Links

• PyPI: https://pypi.org/project/mem-llm/
• Documentation: Memory LLM/README.md
• Changelog: Memory LLM/CHANGELOG.md
• Issues: https://github.com/emredeveloper/Mem-LLM/issues

License

Mem-LLM is released under the MIT License.