Skill: dario-github/biomorphic-memory (version 0.1.0)

--- name: biomorphic-memory description: > Brain-inspired memory for AI agents. Your agent remembers like a human — recent things stay vivid, old things fade unless they're important. Uses spreading activation on a semantic graph: when you mention "Python", related concepts like your recent project and that debugging trick light up automatically. No more "I already told you this." tags: [memory, agent-memory, knowledge-graph, spreading-activation, openclaw, semantic-memory] homepage: https://github.com/dario-github/biomorphic-memory version: 0.1.0 metadata: {"openclaw":{"homepage":"https://github.com/dario-github/biomorphic-memory","requires":{"bins":["python3"]}}} --- # 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

path: scripts/install.sh
content: #!/usr/bin/env bash
set -euo pipefail

REPO="https://github.com/dario-github/biomorphic-memory.git"
INSTALL_DIR="${BIOMORPHIC_INSTALL_DIR:-$HOME/.biomorphic-memory}"

echo "🧠 Installing Biomorphic Memory — Brain-Inspired Agent Memory"
echo "============================================================="

python3 -c "import sys; assert sys.version_info >= (3, 11), f'Python 3.11+ required, got {sys.version}'" 2>/dev/null || {
    echo "❌ Python 3.11+ is required"
    exit 1
}

if [ -d "$INSTALL_DIR" ]; then
    echo "📦 Updating existing installation..."
    cd "$INSTALL_DIR" && git pull --ff-only 2>/dev/null || {
        rm -rf "$INSTALL_DIR"
        git clone "$REPO" "$INSTALL_DIR"
        cd "$INSTALL_DIR"
    }
else
    git clone "$REPO" "$INSTALL_DIR"
    cd "$INSTALL_DIR"
fi

pip install -e . 2>/dev/null || { echo "❌ Install failed"; exit 1; }

echo "✅ Biomorphic Memory installed at: $INSTALL_DIR"


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