Skill: osvfelices/memorine (version 0.1.0)

--- name: memorine description: Human-like memory for AI agents. Facts, events, procedures, contradiction detection, forgetting curve, and cross-agent sharing. Pure Python + SQLite. version: 0.2.0 metadata: openclaw: requires: bins: - python3 install: - kind: uv package: memorine bins: [memorine] entry: uv run python -m memorine.mcp_server homepage: https://github.com/osvfelices/memorine --- # Memorine Persistent memory for OpenClaw agents. No APIs, no Docker, no external services. Just Python and SQLite. ## What it does - **Facts** -- store, recall, and search with full-text search (FTS5). Near-duplicates are reinforced, contradictions are flagged automatically. - **Forgetting curve** -- memories decay over time if not accessed, just like human memory. Old garbage cleans itself up. - **Events** -- log what happened with causal chains (this caused that, which caused this other thing). - **Procedures** -- track multi-step workflows, learn which steps tend to fail, anticipate what you will need before starting a task. - **Cross-agent sharing** -- agents share facts with each other or the whole team through the same database. - **Semantic search** (optional) -- install `memorine[embeddings]` to add meaning-based search on top of keyword search. ## 14 MCP tools Once installed, your agents get access to: | Tool | What it does | |------|-------------| | `memorine_learn` | Store a fact. Detects contradictions. | | `memorine_learn_batch` | Batch-learn multiple facts at once. | | `memorine_recall` | Search memory by query. Ranked by importance and recency. | | `memorine_log_event` | Record an event. Supports causal chains. | | `memorine_events` | Search past events by text or tags. | | `memorine_share` | Share a fact with another agent or the team. | | `memorine_team_knowledge` | Get collective knowledge across agents. | | `memorine_profile` | Full cognitive profile of what an agent knows. | | `memorine_anticipate` | Predict what you need for a task before starting. | | `memorine_procedure_start` | Start tracking a procedure. | | `memorine_procedure_step` | Log a step result in a running procedure. | | `memorine_procedure_complete` | Mark a procedure as done. | | `memorine_correct` | Fix a fact that turned out to be wrong. | | `memorine_stats` | Database stats: facts, events, procedures, db size. | ## Setup Install Memorine: ```bash pip install memorine ``` Add the MCP server to your OpenClaw config (`openclaw.json`): ```json { "mcpServers": { "memorine": { "command": "python3", "args": ["-m", "memorine.mcp_server"] } } } ``` Allow the tools for your agents: ```json { "tools": { "allow": [ "memorine_learn", "memorine_recall", "memorine_log_event", "memorine_events", "memorine_share", "memorine_team_knowledge", "memorine_profile", "memorine_anticipate", "memorine_procedure_start", "memorine_procedure_step", "memorine_procedure_complete", "memorine_correct", "memorine_stats", "memorine_learn_batch" ] } } ``` Optional semantic search (adds meaning-based matching on top of keyword search): ```bash pip install memorine[embeddings] ``` ## Security Each agent can only modify its own data. Forget, correct, link, and procedure operations all validate ownership. No agent can touch another agent's memories. ## How it works Everything lives in a single SQLite file at `~/.memorine/memorine.db`. FTS5 handles keyword search, triggers keep indexes in sync, and WAL mode allows concurrent reads. The forgetting curve uses `retention = e^(-days / stability)` where stability grows each time a memory is accessed. No LLM needed for any of this.


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