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 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 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 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 list_corpora Shows all corpora with stats and priming status. Tips Focused corpora work best — "hooks architecture" beats "everything ever" Prime once, query many times — the session persists across queries Reprime for fresh context — if the conversation drifts, reprime to reset Rebuild to update — when new observations are added, rebuild then reprime Maintenance Rebuild a corpus (refresh with new observations) rebuild_corpus name="hooks-expertise" After rebuilding, reprime to load the updated knowledge: Reprime (fresh session) reprime_corpus name="hooks-expertise" Clears prior Q&A context and reloads the corpus into a new session.