ormequ/claude-mem/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".

Query in English, always

Both `build_corpus` and `query_corpus` use an English-only embedding model. A non-English query lands in a degenerate region where texts are similar by script rather than by meaning — it returns confident, plausible-looking results that have nothing to do with the question, and misses English records even when they are exactly on target.

• build_corpus(query=…) — the semantic filter must be in English. Keep

identifiers (service names, file paths, ticket ids, error strings) verbatim; translate only the prose around them.

• query_corpus(question=…) — same rule for the Q&A question.
• Other languages are fine for specific literal matches (a Russian error message,

a file path) — just don't use them for the semantic part of the query.

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.