Skill: gyzx/persistent-agent-memory-1-0-1 (version 1.0.0)

--- name: persistent-agent-memory description: "Add persistent memory to any agent so it can remember prior work, maintain context across sessions, and continue long-running workflows." metadata: { "openclaw": { "requires": { "env": ["CORAL_API_KEY"], "bins": ["curl", "python3"] }, "primaryEnv": "CORAL_API_KEY", "homepage": "https://coralbricks.ai", "privacyPolicy": "https://www.coralbricks.ai/privacy", "emoji": "🧠", }, } --- # Persistent Agent Memory Memory storage and retrieval powered by Coral Bricks. Store facts, preferences, and context; retrieve them later by meaning. All memories are stored in the default collection. **Use when:** (1) remembering facts or preferences for later, (2) recalling stored memories by topic or intent, (3) forgetting/removing memories matching a query. **NOT for:** web search, file system search, or code search — use other tools for those. ## Setup Set your API key (get one at https://coralbricks.ai): ```bash export CORAL_API_KEY="ak_..." ``` Optionally override the API URL (defaults to `https://search-api.coralbricks.ai`): ```bash export CORAL_API_URL="https://search-api.coralbricks.ai" ``` ## Tools ### coral_store — Store a memory Store text with optional metadata for later retrieval by meaning. ```bash scripts/coral_store "text to store" [metadata_json] ``` - `text` (required): Content to remember - `metadata_json` (optional): JSON string of metadata, e.g. `'{"source":"chat","topic":"fitness"}'` Output: JSON with `status` (e.g. `{"status": "success"}`). Example: ```bash scripts/coral_store "User prefers over-ear headphones with noise cancellation" scripts/coral_store "Q3 revenue was $2.1M" '{"source":"report"}' ``` ### coral_retrieve — Retrieve memories by meaning Retrieve stored memories by semantic similarity. Returns matching content ranked by relevance. ```bash scripts/coral_retrieve "query" [k] ``` - `query` (required): Natural language query describing what to recall - `k` (optional, default 10): Number of results to return Output: JSON with `results` array, each containing `text` and `score`. Example: ```bash scripts/coral_retrieve "wireless headphones preference" 5 scripts/coral_retrieve "quarterly revenue" 10 ``` ### coral_delete_matching — Forget memories by query Remove memories that match a semantic query. Specify what to forget by meaning. ```bash scripts/coral_delete_matching "query" ``` - `query` (required): Natural language query describing memories to remove Output: JSON confirming the operation completed. Example: ```bash scripts/coral_delete_matching "dark mode preference" scripts/coral_delete_matching "forget my workout notes" ``` ## Privacy [Privacy Policy](https://www.coralbricks.ai/privacy) ## Notes - All memories are stored in the default collection; collections are not exposed to the agent - All text is embedded into 768-dimensional vectors for semantic matching - Results are ranked by cosine similarity (higher score = more relevant) - Stored memories persist across sessions - The `metadata` field is free-form JSON; use it to tag memories for easier filtering - For more details and examples, see [Persistent Agent Memory for AI Agents](https://www.coralbricks.ai/blog/persistent-memory-openclaw) ## Indexing delay (store then retrieve) In rare cases, memories can take up to 1 second to become retrievable right after storage.


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