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Alex Newman c648d5d8d2 feat: Knowledge Agents — queryable corpora from claude-mem (#1653 )

* feat: add knowledge agent types, store, builder, and renderer

Phase 1 of Knowledge Agents feature. Introduces corpus compilation
pipeline that filters observations from the database into portable
corpus files stored at ~/.claude-mem/corpora/.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat: add corpus CRUD HTTP endpoints and wire into worker service

Phase 2 of Knowledge Agents. Adds CorpusRoutes with 5 endpoints
(build, list, get, delete, rebuild) and registers them during
worker background initialization alongside SearchRoutes.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat: add KnowledgeAgent with V1 SDK prime/query/reprime

Phase 3 of Knowledge Agents. Uses Agent SDK V1 query() with
resume and disallowedTools for Q&A-only knowledge sessions.
Auto-reprimes on session expiry. Adds prime, query, and reprime
HTTP endpoints to CorpusRoutes.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat: add MCP tools and skill for knowledge agents

Phase 4 of Knowledge Agents. Adds build_corpus, list_corpora,
prime_corpus, and query_corpus MCP tools delegating to worker
HTTP endpoints. Includes /knowledge-agent skill with workflow docs.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: handle SDK process exit in KnowledgeAgent, add e2e test

The Agent SDK may throw after yielding all messages when the
Claude process exits with a non-zero code. Now tolerates this
if session_id/answer were already captured. Adds comprehensive
e2e test script (31 assertions) orchestrated via tmux-cli.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: use settings model ID instead of hardcoded model in KnowledgeAgent

Reads CLAUDE_MEM_MODEL from user settings via getModelId(), matching
the existing SDKAgent pattern. No more hardcoded model assumptions.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* feat: improve knowledge agents developer experience

Add public documentation page, rebuild/reprime MCP tools, and actionable
error messages. DX review scored knowledge agents 4/10 — core engineering
works (31/31 e2e) but the feature was invisible. This addresses
discoverability (docs, cross-links), API completeness (missing MCP tools),
and error quality (fix/example fields in error responses).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* docs: add quick start guide to knowledge agents page

Covers the three main use cases upfront: creating an agent, asking a
single question, and starting a fresh conversation with reprime. Includes
keeping-it-current section for rebuild + reprime workflow.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: address code review issues — path traversal, session safety, prompt injection

- Block path traversal in CorpusStore with alphanumeric name validation and resolved path check
- Harden system prompt against instruction injection from untrusted corpus content
- Validate question field as non-empty string in query endpoint
- Only persist session_id after successful prime (not null on failure)
- Persist refreshed session_id after query execution
- Only auto-reprime on session resume errors, not all query failures
- Add fenced code block language tags to SKILL.md

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: address remaining code review issues — e2e robustness, MCP validation, docs

- Harden e2e curl wrappers with connect-timeout, fallback to HTTP 000 on transport failure
- Use curl_post wrapper consistently for all long-running POST calls
- Add runtime name validation to all corpus MCP tool handlers
- Fix docs: soften hallucination guarantee to probabilistic claim
- Fix architecture diagram: add missing rebuild_corpus and reprime_corpus tools

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: enforce string[] type in safeParseJsonArray for corpus data integrity

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: add blank line before fenced code blocks in SKILL.md maintenance section

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

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Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-04-08 17:30:20 -07:00

2.4 KiB

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name, description

name	description
knowledge-agent	Build and query AI-powered knowledge bases from claude-mem observations. Use when users want to create focused "brains" from their observation history, ask questions about past work patterns, or compile expertise on specific topics.

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.

2.4 KiB Raw Blame History