Skill: projectsnowwork/memu (version 1.0.0)

--- name: memu description: > Persistent memory infrastructure for 24/7 agents. Replaces flat-file memory with a three-layer architecture (Resource → Memory Item → Memory Category) that reduces token costs by 70-90% and enables proactive context retrieval. Built on NevaMind AI's open-source memU framework (v1.4.0). 92.09% accuracy on LoCoMo benchmark. version: 1.0.0 author: ProjectSnowWork homepage: https://github.com/NevaMind-AI/memU license: AGPL-3.0 tags: - agent-memory - long-term-context - proactive-agents - token-optimization - memory-infrastructure - pgvector metadata: openclaw: requires: env: - OPENAI_API_KEY bins: - python3 primaryEnv: OPENAI_API_KEY --- # memU: Persistent Memory for 24/7 Agents You are integrating memU, an open-source memory framework by NevaMind AI, into an agent that needs to remember, learn, and act proactively across long-running sessions. ## When to Use This Skill Use memU when the agent needs to: - Retain and retrieve information across sessions spanning days, weeks, or months - Reduce token costs from injecting raw conversation history into context (70-90% reduction) - Act proactively — surface relevant context before the user explicitly asks - Process multi-modal inputs (conversations, documents, images, logs) into structured memory - Distinguish between current and outdated information with temporal awareness Do NOT use memU for: - Single-session chatbots that don't need persistence - Simple key-value storage (use a database directly) - Real-time streaming memory (not yet supported) ## Core Concepts memU organizes memory in three layers: 1. **Resources** — Raw, immutable inputs (conversations, documents, images). The ground truth. 2. **Memory Items** — Extracted atomic facts with timestamps, provenance, and confidence scores. Queryable and versioned. 3. **Memory Categories** — Emergent clusters that self-organize as items accumulate. Enable broad context retrieval. Two retrieval strategies are available: - **Embedding (RAG)** — Vector similarity search. Fast (50-150ms). Best for factual recall. - **LLM** — Deep semantic reasoning over memory files. Slower (500-2000ms). Best for nuanced, cross-category queries. ## Installation ```bash pip install memu-py ``` Optional persistent storage: ```bash docker run -d --name memu-postgres \ -e POSTGRES_USER=postgres -e POSTGRES_PASSWORD=postgres \ -e POSTGRES_DB=memu -p 5432:5432 pgvector/pgvector:pg16 ``` ## Minimal Integration ```python import asyncio from memu import MemoryService service = MemoryService( llm_profiles={ "default": { "provider": "openai", "base_url": "https://api.openai.com/v1", "api_key": "sk-your-key", "chat_model": "gpt-4o-mini", "embed_model": "text-embedding-3-small", } } ) # Store await service.memorize( resource_payload=[{"role": "user", "content": "I deploy on Tuesdays."}], modality="conversation", ) # Retrieve results = await service.retrieve( query=[{"role": "user", "content": "When should we deploy?"}], method="embedding", ) ``` ## Included Files - **README.md** — Full architecture explanation, 3 real-world scenarios, cost/performance tables, integration guides, troubleshooting - **FAQ.md** — 10 high-frequency questions with detailed answers - **RELEASE.md** — Release announcement - **METADATA.yaml** — ClawHub form metadata - **examples/** — 4 complete, runnable Python scripts: - `example_1_minimal.py` — In-memory mode, no database - `example_2_openclaw_integration.py` — Replace OpenClaw default memory - `example_3_production.py` — Logging, retries, metrics - `example_4_scenarios.py` — Research assistant, email triage, system monitoring ## Attribution This is a community Skill packaging the official [memU](https://github.com/NevaMind-AI/memU) project by [NevaMind AI](https://github.com/NevaMind-AI). It does not modify or extend memU's code.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)