Agent Knowledge

agent-knowledge

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Cross-session memory and recall for AI coding assistants -- works with Claude Code, Cursor, OpenCode, Cline, Continue.dev, and Aider out of the box. Git-synced knowledge base, hybrid semantic+TF-IDF search, auto-distillation with secrets scrubbing.

Benchmark: R@5 = 97.2% (sparse) / 98.8% (hybrid) on longmemeval_s and 86.0% (sparse) / 88.4% (hybrid) on the harder longmemeval_m split — the public LongMemEval academic benchmark (Wu et al. 2024, ICLR 2025), full 500 questions per split, no LLM, no API key, runs entirely offline. +8.6pp to +13.2pp R@5 over the paper's official flat-bm25 baseline in apples-to-apples reproduction. Full per-category table, reproduction instructions, and paper-comparison details in [bench/README.md](bench/README.md).

Knowledge base with category filtering TF-IDF ranked session search

Why

AI coding sessions are ephemeral. When a session ends, everything it learned -- architecture decisions, debugging insights, project context -- is gone. The next session starts from scratch.

agent-knowledge solves this with two complementary systems:

1. Knowledge Base -- a git-synced markdown vault of structured entries (decisions, workflows, project context) that persists across sessions and machines.
2. Session Search -- TF-IDF ranked full-text search across session transcripts from all your coding tools, so agents can recall what happened before -- regardless of which tool was used.

Supported Tools

Sessions from all major AI coding assistants are auto-discovered -- if a tool is installed, its sessions appear automatically.

| Tool | Format | Auto-detected path | | ---------------- | -------------- | --------------------------------------------------------------- | | Claude Code | JSONL | ~/.claude/projects/ | | Cursor | JSONL | ~/.cursor/projects/*/agent-transcripts/ | | Codex CLI | JSONL | ~/.codex/projects/ | | Aider | Markdown/JSONL | .aider.chat.history.md / .aider.llm.history in project dirs | | Continue.dev | JSON | ~/.continue/projects/ | | Cline | JSON | VS Code globalStorage saoudrizwan.claude-dev/tasks/ | | OpenCode | SQLite | ~/.local/share/opencode/opencode.db (or $OPENCODE_DATA_DIR) |

No configuration needed. Additional session roots can be added via the AGENT_KNOWLEDGE_EXTRA_SESSION_ROOTS env var (comma-separated paths).

Features

• Host-agnostic session search -- unified search across every major AI coding assistant (Claude Code, Cursor, Codex CLI, Aider, Continue.dev, Cline, OpenCode). No host name is baked into configuration — the adapter registry probes installed host roots at startup.
• Hybrid search -- semantic vector similarity blended with TF-IDF keyword ranking
• Git-synced knowledge base -- markdown vault with YAML frontmatter, auto commit and push on writes
• Automatic staleness detection -- knowledge_analyze(action: "stale_by_code_activity") cross-references file paths mentioned in each entry body against filesModified in recent session summaries. Pairs with a symbol-presence precision layer: identifiers the entry quotes (inline backticks + fenced blocks) are checked in the touched file; if they still exist, confidence downweights ×0.3. Entries with evergreen: true are exempt.
• Search-gap tracking -- knowledge_analyze(action: "search_gaps") surfaces zero-result queries over the last since_days, grouped by token-Jaccard similarity. The clearest signal for "what entries should I write next?".
• Section-priority context packer -- knowledge(action: "wakeup") assembles a multi-section bundle (identity → active_tasks → recent_decisions → known_gotchas → last_session_summary → top_weighted → semantic_fallback) within a token budget (default 800, override via token_budget or AGENT_KNOWLEDGE_WAKEUP_BUDGET). Unused section budget redistributes to later sections.
• Scored + gated promoter -- session insights promoted via a 6-signal weighted scorer with three independent gates (minScore, minRecallCount, minUniqueQueries). Runs automatically in background, on demand via knowledge_admin(action: "promote"), or benchable offline via npm run bench:promote. Emits an auditable .dreams/YYYY-MM-DD.md diary every run.
• Pluggable adapter system -- add support for new tools by implementing the SessionAdapter interface
• Embeddings -- local (Hugging Face), OpenAI, Claude/Voyage, or Gemini providers
• Fuzzy matching -- typo-tolerant search using Levenshtein distance
• 6 search scopes -- errors, plans, configs, tools, files, decisions
• 6 MCP tools -- consolidated action-based interface (knowledge, knowledge_search, knowledge_session, knowledge_graph, knowledge_analyze, knowledge_admin)
• Evergreen entries -- evergreen: true in frontmatter exempts an entry from decay in ranking AND makes it append-only under promotion. Dashboard renders a push-pin badge on these cards.
• Author attribution -- optional author: frontmatter surfaces as a muted chip on each card.
• Code graph resolution -- calls, imports, inherits edge types for code structure; directed BFS traversal (outbound/inbound/both); bulk_link for efficient ingestion; unlink_by_origin for clearing stale code edges before re-ingest; code: prefixed node IDs distinguish code from knowledge
• Temporal knowledge graph -- edges support valid_from / valid_to validity windows; as_of queries return point-in-time snapshots; invalidate action marks facts as ended without deleting them
• Hybrid scoring boosts -- proper-noun and temporal-proximity boosts on top of TF-IDF + semantic blend, capped at +66.7%, short-circuit when no signals are present
• Category as boost (not filter) -- opt into category_mode: "boost" so a wrong category guess down-ranks instead of discarding the right answer
• Verbatim session indexing -- per-message chunks (≥30 chars) embedded into the vector store so raw conversation is retrievable; toggle with AGENT_KNOWLEDGE_INDEX_VERBATIM=false
• Configurable git URL -- knowledge_admin(action: "config") for runtime setup, persisted at XDG/AppData location
• Cross-machine persistence -- knowledge syncs via git, sessions read from local storage of each tool
• Real-time dashboard -- browse, search, and manage at localhost:3423
• Secrets scrubbing -- API keys, tokens, passwords, private keys automatically redacted before git push
• Knowledge graph -- relationship edges between entries (relatedto, supersedes, dependson, contradicts, specializes, partof, alternativeto, builds_on) with BFS traversal
• Confidence/decay scoring -- entries scored by access frequency and recency; auto-promotion from candidate to established to proven
• Memory consolidation -- TF-IDF duplicate detection on write (warns of similar entries) plus knowledge_analyze(action: "consolidate") for batch dedup scanning
• Reflection cycle -- knowledge_analyze(action: "reflect") surfaces unconnected entries and generates structured prompts for the agent to identify new graph connections
• Auto-linking on write -- new entries automatically linked to top-3 similar existing entries when cosine similarity > 0.7
• Confidence metadata — entries tagged extracted (user-written) or inferred (auto-distilled, 0.85× search rank multiplier); confidence_score field carries the model's certainty 0-1
• Knowledge analysis — knowledge_analyze actions god_nodes (most-connected entries), bridges (cross-category connectors), gaps (isolated entries)
• Knowledge brief — knowledge_analyze(action: "brief") returns a cached ~200 token summary (core concepts, active projects, recent decisions, stale and gap counts) for session-start orientation
• Edge provenance — graph edges track origin (manual, auto-link, distill, reflect) so analysis can distinguish user judgment from automated heuristics
• Deterministic pre-extraction in distillation — session summaries now include git commits, error patterns, URLs accessed, and packages changed extracted via regex from bash/tool output (no LLM cost)
• Freshness metadata on every search hit — every knowledge result carries freshness: { body_age_days, last_accessed, access_count, verified_at, verification_age_days, evergreen }. Agent reads the trust signal and decides; we impose no policy demotion.
• Per-category decay windows — the "Unused" filter and bytype chart honor per-category thresholds (projects 180d, people 365d, decisions 90d, workflows 60d, notes 30d) so identity-shaped content doesn't look stale just because it isn't re-read weekly.
• Lifecycle hooks — SessionStart auto-wakeup + ingest-freshness check, UserPromptSubmit first-prompt targeted injection, PreCompact memory-flush nudge + distill, SessionEnd distill. Six hook scripts total, all fail-open, each toggleable via an AGENT_KNOWLEDGE_* env var. See [docs/HOOKS.md](docs/HOOKS.md).
• Replaces host auto-memory — on hosts with a per-session memory system (Claude Code's ~/.claude/projects/*/memory/, similar in other IDEs), route durable user facts and feedback to agent-knowledge instead. Auto-memory is machine-local and invisible to other machines; agent-knowledge is git-synced, cross-machine, searchable, and surfaces in wakeup. See the Claude Code integration note in [docs/USER-MANUAL.md](docs/USER-MANUAL.md#persistent-memory--agent-knowledge-not-host-auto-memory).

Codebase Ingestion

The knowledge-ingest skill populates or updates the knowledge base from a codebase directory. It uses tree-sitter for zero-token structural extraction (classes, functions, imports, call graphs, rationale comments), then clusters files into subsystems and creates knowledge entries + graph edges via existing MCP tools. Subsequent runs are incremental — only changed files are reprocessed.

/knowledge-ingest ./my-project

Uses the Agent Skills standard — works with Claude Code, OpenCode, Cursor, Codex CLI, and Gemini CLI. See [Ingestion Guide](docs/INGEST.md) for details.

Supported languages: TypeScript, JavaScript, Python, Go, Rust, Java, C, C++.

Quick Start

Install from npm

npm install -g agent-knowledge

Or clone from source

git clone https://github.com/keshrath/agent-knowledge.git
cd agent-knowledge
npm install && npm run build

Option 1: MCP server (for AI agents)

Add to your MCP client config (Claude Code, Cline, etc.):

{
  "mcpServers": {
    "agent-knowledge": {
      "command": "npx",
      "args": ["agent-knowledge"]
    }
  }
}

The dashboard auto-starts at http://localhost:3423 on the first MCP connection.

See [Setup Guide](docs/SETUP.md) for client-specific instructions (Claude Code, Cursor, Windsurf, OpenCode).

Option 2: Standalone server (for REST/WebSocket clients)

node dist/server.js --port 3423

MCP Tools (6)

Knowledge Base

| Tool | Action | Description | Parameters | | ----------- | -------- | ------------------------------------------------------------- | ------------------------------------------------ | | knowledge | list | List entries by category and/or tag | category?, tag? | | | read | Read a specific entry | path (required) | | | write | Create/update entry (auto git sync) | category, filename, content (all required) | | | delete | Delete an entry (auto git sync) | path (required) | | | sync | Manual git pull + push | -- | | | wakeup | Return L0 identity + L1 top-weighted entries (token-budgeted) | token_budget?, category? |

Search

| Tool | Description | Parameters | | ------------------ | --------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------- | | knowledge_search | General hybrid TF-IDF + semantic (no scope) | query, project?, role?, max_results?, ranked?, semantic?, category?, category_mode?, mmr?, mmr_lambda?, explain? | | | Scoped session-only recall (when scope set) | query, scope, project?, max_results? |

Response shape: {mode: "general" | "scoped", sessions, knowledge}. Scoped mode returns knowledge: [] by design.

Scopes: errors, plans, configs, tools, files, decisions, all.

Search knobs:

• mmr: true applies Maximal Marginal Relevance re-ranking (kills near-duplicate clusters in top-K). mmr_lambda 0-1, default 0.7.
• category_mode: "boost" (default) gives matching-category entries a 1.25× score multiplier instead of dropping non-matches. Pass "filter" for hard-filter behavior.
• explain: true attaches score_components: {bm25, decay, maturity, confidence, category_boost, mmr_penalty} to every knowledge hit.

Sessions

| Tool | Action | Description | Parameters | | ------------------- | --------- | -------------------------------------- | --------------------------------------------------- | | knowledge_session | list | List sessions with metadata | project? | | | get | Retrieve full session conversation | session_id, project?, include_tools?, tail? | | | summary | Session summary (topics, tools, files) | session_id, project? |

Knowledge Graph

| Tool | Action | Description | Parameters | | ----------------- | ------------------ | ----------------------------------------- | ----------------------------------------------------------------- | | knowledge_graph | link | Create/update edge between entries | source, target, rel_type, strength? | | | unlink | Remove edges between entries | source, target, rel_type? | | | invalidate | Mark edges as expired (set validto) | source, target, rel_type?, valid_to? | | | list | List edges | entry?, rel_type?, as_of? | | | traverse | Directed BFS traversal from an entry | entry, depth?, direction?, rel_type?, as_of? | | | bulk_link | Batch-create edges (code graph ingestion) | edges (array of {source, target, reltype, strength?, origin?}) | |

…

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: keshrath
• Source: keshrath/agent-knowledge
• License: MIT
• Homepage: https://www.npmjs.com/package/agent-knowledge

Install and usage instructions live in the source repository linked above.