VyasaGraph — Persistent Agent Memory

No more agentic amnesia. Gives your agent short-term + long-term memory: hot survivable context + knowledge graph for permanent recall. Semantic search, grap...

This skill appears coherent for adding local persistent memory, but before installing: 1) Review the vyasagraph npm package and the GitHub source to confirm what it writes and whether it makes any network calls or telemetry beyond OpenAI embeddings. 2) Do not store secrets, API keys, payment data, or personal health data in the memory files; follow the SKILL.md guidance. 3) Consider running the package in a sandbox/container and pin the package version. 4) Inspect file permissions for SESSION-STATE.md and memory.db, and encrypt or restrict access if needed. 5) Only set OPENAI_API_KEY if you accept that embeddings (derived from stored entity text) will be sent to OpenAI; verify the code path to ensure conversation transcripts are not leaked. 6) If you lack time to audit, treat this as untrusted third-party code and limit its data and execution privileges.

Type: OpenClaw Skill Name: vyasagraph Version: 1.2.0 The vyasagraph skill implements a dual-layer memory system (short-term and long-term) for AI agents using local file storage (memory.db and SESSION-STATE.md). The instructions in SKILL.md explicitly guide the agent to maintain state and record new information, which is consistent with the stated purpose of providing persistent memory. While it optionally uses the OpenAI API for text embeddings, the documentation clearly outlines this behavior and provides privacy warnings, with no evidence of data exfiltration, malicious execution, or deceptive prompt injection.