I built a local-first AI memory layer for LLMs in Rust (no cloud, no API keys)

#rust #ai #opensource #llm

Every LLM app has the same problem — the model forgets everything between
conversations. Cloud solutions like Mem0 exist but they send your data
to their servers. I built mnemo to solve this locally.

What it does

mnemo runs as a sidecar process next to your app. You POST text to it,
it extracts named entities and relationships using a local LLM (Ollama),
builds a persistent knowledge graph, and injects relevant context back
into your prompts automatically.

The stack

Rust — core engine, 4 crates (mnemo-core, mnemo-api, mnemo-cli, mnemo-bench)
SQLite + WAL mode — persistent storage, survives restarts
petgraph — in-memory knowledge graph with BFS traversal
Axum — REST API sidecar any app can call
Ollama — fully local LLM, zero API costs

Fully free by default

docker compose up -d
docker exec mnemo-ollama ollama pull llama3
curl http://localhost:8080/health

Works with OpenAI or Anthropic too if you bring your own key.

Python SDK

from mnemo import MnemoClient

client = MnemoClient()
client.ingest("I'm building a Rust vector database called vecdb")
print(client.get_context("what am I working on?"))