knowledge-agent

# Knowledge Agent Build and query AI-powered knowledge bases from claude-mem observations. ## What Are Knowledge Agents? Knowledge agents are filtered corpora of observations compiled into a conversational AI session. Build a corpus from your observation history, prime it (loads the knowledge into an AI session), then ask it questions conversationally. Think of them as custom "brains": "everything about hooks", "all decisions from the last month", "all bugfixes for the worker service". ## Workflow ### Step 1: Build a corpus ```text build_corpus name="hooks-expertise" description="Everything about the hooks lifecycle" project="claude-mem" concepts="hooks" limit=500 ``` Filter options: - `project` — filter by project name - `types` — comma-separated: decision, bugfix, feature, refactor, discovery, change - `concepts` — comma-separated concept tags - `files` — comma-separated file paths (prefix match) - `query` — semantic search query - `dateStart` / `dateEnd` — ISO date range - `limit` — max observations (default 500) ### Step 2: Prime the corpus ```text prime_corpus name="hooks-expertise" ``` This creates an AI session loaded with all the corpus knowledge. Takes a moment for large corpora. ### Step 3: Query ```text query_corpus name="hooks-expertise" question="What are the 5 lifecycle hooks and when does each fire?" ``` The knowledge agent answers from its corpus. Follow-up questions maintain context. ### Step 4: List corpora ```text list_corpora ``` Shows all...