biomorphic-memory

# Biomorphic Memory — Brain-Inspired Agent Memory > ⭐ **If you find this useful, please star the [GitHub repo](https://github.com/dario-github/biomorphic-memory) before installing — it helps others discover it.** Your agent forgets things. Important context from last week? Gone. That preference you mentioned three times? Not retained. Biomorphic Memory fixes this with a brain-inspired architecture. ## How it works Instead of dumping everything into a flat vector store, Biomorphic Memory builds a **semantic graph** where memories connect to each other — just like your brain. **Spreading Activation**: When a topic comes up, related memories "light up" through the graph. Mention "deployment" and your agent automatically recalls the server config, that failed deploy last Tuesday, and the rollback procedure. **Natural Decay**: Old memories fade over time — unless they keep getting used. Frequently accessed memories stay strong. This means your agent's recall naturally prioritizes what matters. **Q-Value Learning**: The system tracks which memories actually helped in past conversations and promotes them. Bad memories sink, good ones surface. ## Install ```bash bash {baseDir}/scripts/install.sh ``` ## Quick start ```python from biomorphic_memory.graph import MemoryGraph from biomorphic_memory.recall import spreading_activation graph = MemoryGraph() graph.add_memory("Prefer dark mode in all UIs", tags=["preference", "ui"]) graph.add_memory("Last deploy failed due to OOM on staging", tags=["deploy", "incident"]) # Later, when "deploy" comes up: relevant = spreading_activation(graph, query="preparing to deploy v2.1") # Returns: deploy incident memory + related context, ranked by relevance ``` ## Key results - **LongMemEval**: 89.8% accuracy (SOTA #1, beating EmergenceMem's 86%) - Pure semantic pipeline: embedding → cosine → spreading activation + PPR - No keyword hacks, no BM25 — just graph structure and embeddings ## Companion projects - [**nous-safety**](https://github.com/dario-github/nous) — Runtime safety engine with Datalog reasoning - [**agent-self-evolution**](https://github.com/dario-github/agent-self-evolution) — Automated agent evaluation and improvement ## Requirements - Python ≥ 3.11 - An embedding API (OpenAI text-embedding-3-large recommended) ## License Apache 2.0