PlateerLab/synaptic-memory
Brain-inspired knowledge graph: spreading activation, Hebbian learning, memory consolidation.
This project helps individual AI agents and multi-agent teams remember past experiences and learn from their successes and failures. It takes unstructured operational data like tool calls, decisions, and outcomes, and organizes it into a structured knowledge graph. The result is that agents can "recall" relevant historical context, similar to how a human brain associates memories, to make better future decisions. This is for anyone managing or deploying AI agents who needs them to learn and improve over time, rather than repeating mistakes.
Available on PyPI.
Use this if your AI agents struggle with memory, repeat past errors, or fail to leverage collective team knowledge from previous interactions and decisions.
Not ideal if your primary need is simple document search and retrieval without the requirement for structured learning, memory consolidation, or multi-agent knowledge sharing.
Stars
25
Forks
—
Language
Python
License
MIT
Category
Last pushed
Mar 23, 2026
Commits (30d)
0
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