Alex Newman
c648d5d8d2
* 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> --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
81 lines
2.4 KiB
Markdown
81 lines
2.4 KiB
Markdown
---
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name: knowledge-agent
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description: 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.
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---
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# Knowledge Agent
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Build and query AI-powered knowledge bases from claude-mem observations.
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## What Are Knowledge Agents?
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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.
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Think of them as custom "brains": "everything about hooks", "all decisions from the last month", "all bugfixes for the worker service".
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## Workflow
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### Step 1: Build a corpus
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```text
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build_corpus name="hooks-expertise" description="Everything about the hooks lifecycle" project="claude-mem" concepts="hooks" limit=500
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```
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Filter options:
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- `project` — filter by project name
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- `types` — comma-separated: decision, bugfix, feature, refactor, discovery, change
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- `concepts` — comma-separated concept tags
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- `files` — comma-separated file paths (prefix match)
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- `query` — semantic search query
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- `dateStart` / `dateEnd` — ISO date range
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- `limit` — max observations (default 500)
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### Step 2: Prime the corpus
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```text
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prime_corpus name="hooks-expertise"
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```
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This creates an AI session loaded with all the corpus knowledge. Takes a moment for large corpora.
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### Step 3: Query
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```text
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query_corpus name="hooks-expertise" question="What are the 5 lifecycle hooks and when does each fire?"
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```
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The knowledge agent answers from its corpus. Follow-up questions maintain context.
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### Step 4: List corpora
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```text
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list_corpora
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```
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Shows all corpora with stats and priming status.
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## Tips
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- **Focused corpora work best** — "hooks architecture" beats "everything ever"
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- **Prime once, query many times** — the session persists across queries
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- **Reprime for fresh context** — if the conversation drifts, reprime to reset
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- **Rebuild to update** — when new observations are added, rebuild then reprime
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## Maintenance
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### Rebuild a corpus (refresh with new observations)
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```text
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rebuild_corpus name="hooks-expertise"
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```
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After rebuilding, reprime to load the updated knowledge:
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### Reprime (fresh session)
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```text
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reprime_corpus name="hooks-expertise"
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```
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Clears prior Q&A context and reloads the corpus into a new session.
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