Which integrations are available for this server?

Provides integration with Atlassian products, enabling import of Confluence pages and Jira issues with full hierarchy, supporting both Atlassian Cloud and self-hosted Server/Data Center. Allows importing Confluence pages with full hierarchy, supporting both Cloud and self-hosted instances, via a one-command import. Allows importing Jira issues with full hierarchy, supporting both Cloud and self-hosted instances, via a one-command import.

How do I use agent-wiki?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@agent-wiki search the wiki for deployment instructions" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

agent-wiki

by xinhuagu

Overview Schema Related Servers Score Discussions

TypeScript

Local

agent-wiki

The knowledge base that makes AI agents smarter over time.

Instead of retrieving raw fragments every query (RAG), your agent compiles, refines, and interlinks knowledge — like a team wiki that writes itself.

Works 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.

npm Node MCP License: MIT

agent-wiki's built-in 3D graph view

Pages as nodes, [[wikilinks]] as edges, edits push live — included in the main package.

Quick Start

Option A: MCP Server (Cursor, Windsurf, Claude Desktop, any MCP client)

Add to your MCP client config:

{
  "mcpServers": {
    "agent-wiki": {
      "command": "npx",
      "args": ["-y", "@agent-wiki/mcp", "serve", "--wiki-path", "/path/to/knowledge"]
    }
  }
}

Option B: Native Skill (Claude Code)

npm install -g @agent-wiki/mcp

# Install as Claude Code plugin
agent-wiki install claude-code

Option C: CLI only

npx @agent-wiki/mcp call wiki_search '{"query": "deployment"}'

Option D: 3D Graph Viewer

See your wiki as a realtime 3D knowledge graph — edits push live via SSE. Included in the main package, no separate install needed.

npm install -g @agent-wiki/mcp
agent-wiki web --wiki-path ./wiki --open

Heavy browser libs (3d-force-graph, three.js) load from a CDN at runtime. See graph-viewer/README.md for the full feature list and interaction guide.

That's it. Your agent now has a persistent, structured knowledge base.

Related MCP server: kontexta

Why Not RAG?

	RAG	agent-wiki
Approach	Retrieve fragments at query time	Build and maintain compiled knowledge
Memory	Stateless — forgets after each query	Persistent — knowledge accumulates
Quality	Raw chunks, often noisy	Curated, structured, interlinked
Cost	Embedding + retrieval every query	One-time compilation, free reads
Contradictions	Invisible — buried in source docs	Flagged automatically by lint
Source tracking	Lost after retrieval	Full provenance chain (raw -> wiki)

Features

Feature	Description
Batch Mode	Generic `batch` tool + semantic pipelines — collapse multi-step workflows into single requests
Knowledge Pipelines	Unified `knowledge_ingest` modes — end-to-end ingest/digest/write-back loop without expanding the public tool surface
Structured Extraction	PDF (per-page), DOCX, XLSX (per-sheet), PPTX (per-slide) — segments with source provenance
Immutable Sources	SHA-256 verified `raw/` layer — write-once, tamper-proof, full provenance
Knowledge Compilation	Agent builds structured wiki pages from raw sources — not retrieve-and-forget
BM25 Search	Field-weighted scoring, synonym expansion, fuzzy matching, CJK tokenization — zero LLM
Hybrid Search	Optional BM25+vector re-ranking via `@xenova/transformers` — enable with one config line, no external API
Auto-Classification	Zero-LLM heuristic assigns entity types and tags across 10 categories
Multi-Level Indexes	Auto-generated `index.md` at every directory level — nested topic hierarchies with sub-topic navigation
Self-Checking Lint	Catches contradictions, broken links, orphan pages, stale content
Coverage Report	`raw_coverage` tells the agent which raw sources have not yet been compiled into any wiki page — drives active knowledge completion
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.
File Versioning	Auto-version same-name files, query latest, list all versions
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 below.
Skill Install	One-command install as native skill for Claude Code and compatible clients
Git-Native	Plain Markdown — diffable, blameable, revertable
3D Graph Viewer	Built-in — realtime 3D graph of pages and `[[wikilinks]]`, edits push live over SSE. Run `agent-wiki web`.

Architecture

Three immutability layers, inspired by how compilers work:

Layer	Mutability	Role
raw/	Immutable	Source documents — write-once, SHA-256 verified
wiki/	Mutable	Compiled knowledge — structured pages that improve over time
schemas/	Reference	Entity templates — consistent structure across knowledge types

Design Principles

Raw is immutable — Source documents are write-once, SHA-256 verified. Ground truth never changes.
Wiki is mutable — Compiled knowledge improves with every interaction.
No LLM dependency — Zero API keys, zero cost per operation. Your agent IS the intelligence.
Self-checking — Lint catches structural issues and flags potential contradictions.
Knowledge compounds — Every write enriches the whole wiki. Synthesis creates higher-order understanding.
Provenance matters — Every wiki claim traces back to raw sources.
Git-native — Plain Markdown. Every change is diffable, blameable, and revertable.

Integration

Method	Best For	Setup
MCP Server	Cursor, Windsurf, Claude Desktop, any MCP client	Add to `.mcp.json`
Native Skill	Claude Code (native plugin)	`agent-wiki install claude-code`
CLI	Any agent with shell access	`agent-wiki call <tool> '{json}'`
3D Graph Viewer	Visual exploration of the whole wiki	`agent-wiki web -w ./wiki`

Language Plugins

agent-wiki extends to source-code analysis via language plugins — deterministic parsers + cross-file knowledge graphs, no LLM. Each plugin emits structured artifacts (raw/parsed/<lang>/) and writes wiki pages with full provenance back to the source files.

Language	Status	Capabilities
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`.
JCL	Planned	Job / step / dataset / proc extraction, batch-flow wiki pages, dataset-mediated cross-program lineage. See PRD Phase 2.

Tier-gate decisions (Phase C precision gates, dynamic-call resolver, DB2 column pairing) are evaluated against ground-truth fixtures via a built-in precision / recall eval harness — each PR runs against a committed NIST CCVS slice as a corpus-level regression anchor.

Hybrid Search Setup

Upgrade from keyword-only to semantic search with two steps:

1. Add to .agent-wiki.yaml:

search:
  hybrid: true

2. Run wiki_admin once to rebuild and embed all pages:

agent-wiki call wiki_admin '{"action":"rebuild"}'

The 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.

Hybrid mode blends BM25 + cosine similarity scores. If embedding fails for any reason, search falls back to pure BM25 — queries never fail.

See Search configuration for weight tuning.

Documentation

Acknowledgment

Inspired by Andrej Karpathy's LLM Wiki 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.

License

MIT

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

5hResponse time

1dRelease cycle

30Releases (12mo)

Resources

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