Show HN: Mnemo – local-first AI memory layer for any LLM (Rust, SQLite,petgraph)

Local-first AI memory layer for any LLM. Persistent knowledge graph, entity extraction, semantic retrieval — no cloud required. Most LLMs forget everything the moment a conversation ends. mnemo fixes that. mnemo is a sidecar service that watches every conversation you feed it, extracts named entities and relationships using an LLM, builds a persistent knowledge graph in SQLite, and injects relevant context back into future prompts — automatically, in under 50ms. It works with Ollama (fully local, free), OpenAI, Anthropic, or any OpenAI-compatible API. It ships as a single static binary with zero cloud dependency. your app │ ▼ POST /ingest ──► entity extraction (LLM) ──► knowledge graph (SQLite + petgraph) │ POST /retrieve ◄── scoring + ranking ◄── graph traversal + full-text search │ ▼ context_prompt ──► inject into your LLM prompt You POST raw text to /ingest (a conversation turn, a document, a note). mnemo sends it to your configured LLM and extracts entities (people, tools, places, concepts) and the relationships between them. Entities are deduplicated by name+type, aliases are merged, and everything is written to SQLite. The in-memory petgraph is updated atomically. On POST /retrieve, mnemo runs a 6-stage pipeline: full-text chunk search → entity name search → graph expansion (BFS over the knowledge graph) → relation filter → score+rank → assemble a context_prompt string. You inject context_prompt into your LLM's system prompt. Done. Quickstart Path A — Docker + Ollama (fully free, recommended) git clone https://github.com/zaydmulani09/mnemo cd mnemo docker compose up -d # Pull the llama3 model the first time (~4 GB) docker exec mnemo-ollama ollama pull llama3 # Verify everything is healthy curl http://localhost:8080/health Path B — Binary (Ollama or OpenAI running separately) cargo install --path crates/mnemo-api # With Ollama export MNEMO_LLM_BASE_URL=http://localhost:11434/v1 mnemo-api # With OpenAI export MNEMO_LLM_BASE_URL=https://api.openai.com/v1 export MNEMO_LLM_API_KEY=sk-... export MNEMO_LLM_MODEL=gpt-4o-mini export MNEMO_LLM_PROVIDER=openai mnemo-api Path C — Python SDK pip install mnemo-sdk from mnemo import MnemoClient client = MnemoClient() # server at http://localhost:8080 # Store a memory client.ingest("I'm building a Rust vector database called vecdb") # Get context for injection into your next LLM prompt print(client.get_context("what am I working on?")) API Reference All endpoints accept and return application/json. Base URL: http://localhost:8080. // IngestRequest { "content": "string", // required — text to store "source": "string", // required — e.g. "chat", "email", "cli" "session_id": "string|null", // optional — group related chunks "metadata": {} // optional — arbitrary JSON } // RetrievalQuery { "text": "string", // required — query text "session_id": "string|null", // optional — filter by session "max_chunks": 10, // default 10 "max_entities": 20, // default 20 "min_confidence": 0.5, // default 0.5 "include_graph": true, // default true — expand via knowledge graph "graph_depth": 2 // default 2 — BFS depth for graph expansion } Full endpoint documentation with curl examples: docs/api.md Environment variables Variable Default Description MNEMO_DB_PATH mnemo.db SQLite database file path MNEMO_PORT 8080 API server port MNEMO_LLM_BASE_URL http://localhost:11434/v1 OpenAI-compatible LLM base URL MNEMO_LLM_MODEL llama3 Model name for entity extraction MNEMO_LLM_API_KEY ollama API key (any value works for Ollama) MNEMO_LLM_PROVIDER ollama Provider type: ollama, openai, anthropic, custom TOML config file Pass --config path/to/config.toml to mnemo-api. See mnemo.example.toml: db_path = "mnemo.db" port = 8080 [llm] provider = "ollama" base_url = "http://localhost:11434/v1" model = "llama3" api_key = "ollama" timeout_secs = 30 max_retries = 3 max_tokens = 2048 temperature = 0.1 Environment variables take precedence over TOML values. The active config source is reported in GET /health → config_source. cargo install --path crates/mnemo-cli Usage: # Store a memory mnemo ingest "I use Neovim and prefer dark mode" # Retrieve relevant context mnemo search "what editor do I use?" # List all extracted entities mnemo entities # Show entity detail + graph neighbors mnemo entity --neighbors # List memory chunks mnemo chunks # Server health mnemo health # Memory statistics mnemo stats # Delete everything (prompts for confirmation) mnemo wipe # Skip confirmation prompt mnemo wipe --yes # Point at a non-default server mnemo --server http://192.168.1.10:8080 stats Python SDK Install: pip install mnemo-sdk See sdk/python/README.md for the full API reference. import asyncio from mnemo import AsyncMnemoClient async def main(): async with AsyncMnemoClient() as client: await client.ingest( "Alice is a principal engineer at Stripe working on payment infrastructure.", session_id="session-001", ) context = await client.get_context( "what does Alice work on?", session_id="session-001", ) print

Show HN: Mnemo – local-first AI memory layer for any LLM (Rust, SQLite,petgraph)

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