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MCP server, no LLM needed.","maintainers":[{"name":"xinhua_berlin","email":"xinhua.gu@gmail.com"}],"readme":"# agent-wiki\n\n**The knowledge base that makes AI agents smarter over time.**\n\nInstead of retrieving raw fragments every query (RAG), your agent compiles, refines, and interlinks knowledge — like a team wiki that writes itself.\n\nWorks with Claude Code, Cursor, Windsurf, and any MCP client. Also installable as a native skill for Claude Code. No LLM built in — your agent IS the intelligence.\n\n[](https://www.npmjs.com/package/@agent-wiki/mcp)\n[](https://github.com/xinhuagu/agent-wiki/actions/workflows/ci.yml)\n[](https://nodejs.org)\n[](https://modelcontextprotocol.io)\n[](LICENSE)\n\n### agent-wiki's built-in 3D graph view\n\nPages as nodes, `[[wikilinks]]` as edges, edits push live — included in the main package.\n\n
\n \n
\n\n## Quick Start\n\n### Option A: MCP Server (Cursor, Windsurf, Claude Desktop, any MCP client)\n\nAdd to your MCP client config:\n\n```json\n{\n \"mcpServers\": {\n \"agent-wiki\": {\n \"command\": \"npx\",\n \"args\": [\"-y\", \"@agent-wiki/mcp\", \"serve\", \"--wiki-path\", \"/path/to/knowledge\"]\n }\n }\n}\n```\n\n### Option B: Native Skill (Claude Code)\n\n```bash\nnpm install -g @agent-wiki/mcp\n\n# Install as Claude Code plugin\nagent-wiki install claude-code\n```\n\n### Option C: CLI only\n\n```bash\nnpx @agent-wiki/mcp call wiki_search '{\"query\": \"deployment\"}'\n```\n\n### Option D: 3D Graph Viewer\n\nSee your wiki as a realtime 3D knowledge graph — edits push live via SSE. Included in the main package, no separate install needed.\n\n```bash\nnpm install -g @agent-wiki/mcp\nagent-wiki web --wiki-path ./wiki --open\n```\n\nHeavy browser libs (3d-force-graph, three.js) load from a CDN at runtime. See\n[graph-viewer/README.md](graph-viewer/README.md) for the full feature list and\ninteraction guide.\n\nThat's it. Your agent now has a persistent, structured knowledge base.\n\n## Why Not RAG?\n\n| | RAG | agent-wiki |\n|---|---|---|\n| **Approach** | Retrieve fragments at query time | Build and maintain compiled knowledge |\n| **Memory** | Stateless — forgets after each query | Persistent — knowledge accumulates |\n| **Quality** | Raw chunks, often noisy | Curated, structured, interlinked |\n| **Cost** | Embedding + retrieval every query | One-time compilation, free reads |\n| **Contradictions** | Invisible — buried in source docs | Flagged automatically by lint |\n| **Source tracking** | Lost after retrieval | Full provenance chain (raw -> wiki) |\n\n## Features\n\n| Feature | Description |\n|---------|-------------|\n| **Batch Mode** | Generic `batch` tool + semantic pipelines — collapse multi-step workflows into single requests |\n| **Knowledge Pipelines** | Unified `knowledge_ingest` modes — end-to-end ingest/digest/write-back loop without expanding the public tool surface |\n| **Structured Extraction** | PDF (per-page), DOCX, XLSX (per-sheet), PPTX (per-slide) — segments with source provenance |\n| **Immutable Sources** | SHA-256 verified `raw/` layer — write-once, tamper-proof, full provenance |\n| **Knowledge Compilation** | Agent builds structured wiki pages from raw sources — not retrieve-and-forget |\n| **BM25 Search** | Field-weighted scoring, synonym expansion, fuzzy matching, CJK tokenization — zero LLM |\n| **Hybrid Search** | Optional BM25+vector re-ranking via `@xenova/transformers` — enable with one config line, no external API |\n| **Auto-Classification** | Zero-LLM heuristic assigns entity types and tags across 10 categories |\n| **Multi-Level Indexes** | Auto-generated `index.md` at every directory level — nested topic hierarchies with sub-topic navigation |\n| **Self-Checking Lint** | Catches contradictions, broken links, orphan pages, stale content |\n| **Coverage Report** | `raw_coverage` tells the agent which raw sources have not yet been compiled into any wiki page — drives active knowledge completion |\n| **Atlassian Import** | One-command Confluence pages and Jira issues with full hierarchy. Supports both Atlassian Cloud (`*.atlassian.net`) and self-hosted Server / Data Center, with auto-routed API endpoints and Bearer / Basic auth handling. |\n| **File Versioning** | Auto-version same-name files, query latest, list all versions |\n| **Language Plugins** | Deterministic parsers + cross-file knowledge graphs for legacy code. **COBOL** shipped with field lineage in three families (shared-copybook reuse, `CALL ... USING` boundary, cross-program DB2 flow), DB2 column-level pairing, dynamic CALL resolution, and a precision / recall eval harness. **JCL** planned. See [Language Plugins](#language-plugins) below. |\n| **Skill Install** | One-command install as native skill for Claude Code and compatible clients |\n| **Git-Native** | Plain Markdown — diffable, blameable, revertable |\n| **3D Graph Viewer** | Built-in — realtime 3D graph of pages and `[[wikilinks]]`, edits push live over SSE. Run `agent-wiki web`. |\n\n## Architecture\n\nThree immutability layers, inspired by how compilers work:\n\n| Layer | Mutability | Role |\n|-------|-----------|------|\n| **raw/** | Immutable | Source documents — write-once, SHA-256 verified |\n| **wiki/** | Mutable | Compiled knowledge — structured pages that improve over time |\n| **schemas/** | Reference | Entity templates — consistent structure across knowledge types |\n\n
\n \n
\n\n## Design Principles\n\n1. **Raw is immutable** — Source documents are write-once, SHA-256 verified. Ground truth never changes.\n2. **Wiki is mutable** — Compiled knowledge improves with every interaction.\n3. **No LLM dependency** — Zero API keys, zero cost per operation. Your agent IS the intelligence.\n4. **Self-checking** — Lint catches structural issues and flags potential contradictions.\n5. **Knowledge compounds** — Every write enriches the whole wiki. Synthesis creates higher-order understanding.\n6. **Provenance matters** — Every wiki claim traces back to raw sources.\n7. **Git-native** — Plain Markdown. Every change is diffable, blameable, and revertable.\n\n## Integration\n\n| Method | Best For | Setup |\n|--------|----------|-------|\n| **MCP Server** | Cursor, Windsurf, Claude Desktop, any MCP client | Add to `.mcp.json` |\n| **Native Skill** | Claude Code (native plugin) | `agent-wiki install claude-code` |\n| **CLI** | Any agent with shell access | `agent-wiki call '{json}'` |\n| **3D Graph Viewer** | Visual exploration of the whole wiki | `agent-wiki web -w ./wiki` |\n\n## Language Plugins\n\nagent-wiki extends to source-code analysis via language plugins —\ndeterministic parsers + cross-file knowledge graphs, no LLM. Each\nplugin emits structured artifacts (`raw/parsed//`) and writes\nwiki pages with full provenance back to the source files.\n\n| Language | Status | Capabilities |\n|----------|--------|--------------|\n| **COBOL** | Shipped | AST parser (fixed-format with mainframe alphanumeric sequence areas + free-format). Programs, copybooks, sections, `CALL` (incl. dynamic-call constant propagation), `COPY` / `REPLACING` (incl. via-replacing cohorts and REPLACING-aware inferred matching), `EXEC SQL` (DB2 column-level host-var pairing), `EXEC CICS`, file access modes. Field lineage in three families: shared-copybook reuse (deterministic + inferred), `CALL ... USING` boundary, cross-program DB2 flow. Depth-bounded impact queries via `code_query`. |\n| **JCL** | Planned | Job / step / dataset / proc extraction, batch-flow wiki pages, dataset-mediated cross-program lineage. See [PRD Phase 2](docs/legacy-code-knowledge-compiler-prd.md). |\n\nTier-gate decisions (Phase C precision gates, dynamic-call resolver,\nDB2 column pairing) are evaluated against ground-truth fixtures via a\nbuilt-in [precision / recall eval harness](docs/field-lineage-eval.md) —\neach PR runs against a committed NIST CCVS slice as a corpus-level\nregression anchor.\n\n## Hybrid Search Setup\n\nUpgrade from keyword-only to semantic search with two steps:\n\n**1.** Add to `.agent-wiki.yaml`:\n\n```yaml\nsearch:\n hybrid: true\n```\n\n**2.** Run `wiki_admin` once to rebuild and embed all pages:\n\n```bash\nagent-wiki call wiki_admin '{\"action\":\"rebuild\"}'\n```\n\nThe first run downloads the `Xenova/all-MiniLM-L6-v2` model (~90 MB) from HuggingFace Hub and caches it locally. After that, every `wiki_write` automatically keeps the vector index up to date.\n\nHybrid mode blends BM25 + cosine similarity scores. If embedding fails for any reason, search falls back to pure BM25 — queries never fail.\n\nSee [Search configuration](docs/tools.md#hybrid-bm25vector-search) for weight tuning.\n\n## Documentation\n\n- [MCP Tools (15 public tools) & Entity Types](docs/tools.md)\n- [Configuration, CLI & Security](docs/configuration.md)\n- [Request Optimization — Batch Digest, Pagination, Context Limits](docs/request-optimization.md)\n\n## Acknowledgment\n\nInspired by Andrej Karpathy's [LLM Wiki](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) concept — the idea that AI agents should compile and maintain knowledge, not just retrieve raw fragments. This project is an independent, full implementation of that vision.\n\n## License\n\nMIT\n","readmeFilename":"README.md"}
![\"agent-wiki](\"docs/graph-1.gif\")
\n![\"agent-wiki](\"architecture.svg\")
\n