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agent-knowledge

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Cross-session memory and recall for AI agents — git-synced knowledge base, hybrid semantic+TF-IDF search, auto-distillation with secrets scrubbing

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  • ✓Documented (README)
Last scanned: 10/2/2026
Install in Claude Code / Claude Desktop
Method: Manual
Claude Code CLI
git clone https://github.com/keshrath/agent-knowledge
claude_desktop_config.json (Claude Desktop)
{
  "mcpServers": {
    "agent-knowledge": {
      "command": "node",
      "args": ["/path/to/agent-knowledge/dist/index.js"]
    }
  }
}
1. Run the command above in your terminal (Claude Code), or paste the JSON config into claude_desktop_config.json (Claude Desktop).
2. Replace any <placeholder> values with your API keys or paths.
3. Restart Claude. The MCP server and its tools appear automatically.
💡 Clone https://github.com/keshrath/agent-knowledge and follow its README for install instructions.
Casos de uso

Resumen de MCP Servers

# agent-knowledge

[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
[![Node >= 20](https://img.shields.io/badge/Node-%3E%3D%2020-brightgreen.svg)](https://nodejs.org)
[![Tests: 563 passing](https://img.shields.io/badge/Tests-563%20passing-brightgreen.svg)]()
[![MCP Tools: 6](https://img.shields.io/badge/MCP%20Tools-6-blueviolet.svg)]()
[![LongMemEval R@5: 98.8%](https://img.shields.io/badge/LongMemEval%20R%405-98.8%25-brightgreen.svg)]()

**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).

<table>
<tr>
<td><img src="docs/assets/knowledge-light.png" alt="Knowledge Base (light)" width="480"></td>
<td><img src="docs/assets/search-light.png" alt="Session Search" width="480"></td>
</tr>
<tr>
<td align="center"><em>Knowledge base with category filtering</em></td>
<td align="center"><em>TF-IDF ranked session search</em></td>
</tr>
</table>

## 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: <string>` 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 (related_to, supersedes, depends_on, contradicts, specializes, part_of, alternative_to, 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, 
ai-agentsclaude-codeembeddingsknowledge-basemcpmemorymodel-context-protocolsemantic-searchtfidf

Lo que la gente pregunta sobre agent-knowledge

¿Qué es keshrath/agent-knowledge?

+

keshrath/agent-knowledge es mcp servers para el ecosistema de Claude AI. Cross-session memory and recall for AI agents — git-synced knowledge base, hybrid semantic+TF-IDF search, auto-distillation with secrets scrubbing Tiene 18 estrellas en GitHub y su última actualización registrada es del 2026-10-01.

¿Cómo se instala agent-knowledge?

+

Puedes instalar agent-knowledge clonando el repositorio (https://github.com/keshrath/agent-knowledge) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.

¿Es seguro usar keshrath/agent-knowledge?

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¿Quién mantiene keshrath/agent-knowledge?

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keshrath/agent-knowledge es mantenido por keshrath. La última actividad registrada en GitHub es del 2026-10-01, con 0 issues abiertos.

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