Persistent memory graph for AI coding agents — semantic search, knowledge graph, and time-based decay over MCP

Synapto

Your AI agent forgets everything between sessions. Synapto fixes that.

Flat-file memory (MEMORY.md) doesn't scale — no search, no structure, no decay. Synapto gives any MCP-compatible agent a real memory: store once, recall by meaning, watch bad memories fade and good ones persist.

# remember
"Hermes uses the outbox relay pattern for Kafka"

# recall — weeks later, different session
"How does Hermes handle messaging?"
→ [stable] Hermes uses the outbox relay pattern for Kafka (score=0.94, trust=0.65)

Works with Claude Code, Cursor, Windsurf, Codex, LangGraph, Agno, or any MCP client.

Try it in 60 seconds

Docker:

git clone https://github.com/ramonlimaramos/synapto.git && cd synapto
docker compose up -d
docker compose exec synapto synapto search "hello world"

Local:

pip install synapto
createdb synapto && psql -d synapto -c "CREATE EXTENSION vector;"
synapto init
synapto search "hello world"

What it does

Search — Ask a question, get the best memory. Behind the scenes, three signals (vector similarity, full-text, and compositional algebra) are fused into one score. You just call recall.

Graph — Entities are auto-extracted and linked. Ask "what depends on Kafka?" and get an answer via graph traversal, not keyword guessing.

Decay — Core memories live forever. Ephemeral notes fade in hours. Working context lasts about a week. Memories that get used stay alive; unused ones sink.

Trust — Mark memories as helpful or not. Ba