agent-wiki
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.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
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
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.
agent-wiki is also evolving into the reference implementation of Open Knowledge Format (OKF): a Git-native package format for agent-maintained knowledge with immutable source evidence, mutable compiled pages, schemas, indexes, evidence metadata, and machine-checkable governance.
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-codeOption 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 --openHeavy 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: BENZEMA's Personal Knowledge Card
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 |
Knowledge Pipelines | Unified |
Structured Extraction | PDF (per-page), DOCX, XLSX (per-sheet), PPTX (per-slide) — segments with source provenance |
Immutable Sources | SHA-256 verified |
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 |
Auto-Classification | Zero-LLM heuristic assigns entity types and tags across 10 categories |
Multi-Level Indexes | Auto-generated |
Self-Checking Lint | Catches contradictions, broken links, orphan pages, stale content |
Coverage Report |
|
Atlassian Import | One-command Confluence pages and Jira issues with full hierarchy. Supports both Atlassian Cloud ( |
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, |
Skill Install | One-command install as native skill for Claude Code and compatible clients |
Git-Native | Plain Markdown — diffable, blameable, revertable |
Open Knowledge Format (OKF) | Directional package contract: |
3D Graph Viewer | Built-in — realtime 3D graph of pages and |
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 |
Open Knowledge Format (OKF)
The same layers are being formalized as OKF: a portable knowledge package that another agent or tool can inspect without a hosted service.
agent-wiki-package/
agent-wiki.yaml # portable package manifest
raw/ # immutable source evidence
wiki/ # mutable compiled knowledge
schemas/ # page/entity contracts
indexes/ # rebuildable search/graph artifacts
evidence/ # provenance, confidence, coverage, page classes
logs/ # optional operational telemetryImportant distinction: .agent-wiki.yaml remains runtime/operator config; agent-wiki.yaml is the portable OKF manifest. OKF v0.1 now has an executable path: schemas/agent-wiki-okf.schema.json, wiki_admin action: "format-check", and optional persisted package reports at evidence/okf-report.json via wiki_admin action: "rebuild" with okf_report: true.
See Open Knowledge Format for the thesis and OKF Adoption Plan for the implementation plan and feasibility assessment.
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 |
Native Skill | Claude Code (native plugin) |
|
CLI | Any agent with shell access |
|
3D Graph Viewer | Visual exploration of the whole 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, |
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: true2. 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
Request Optimization — Batch Digest, Pagination, Context Limits
Open Knowledge Format — Git-native knowledge packages for agents
OKF Adoption Plan — feasibility, current-state audit, and implementation plan
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
Available Tools
15 toolsbatchA
Execute multiple tool calls in a single request. Reduces tool-call count for LLM subscriptions that bill per-request (e.g. GitHub Copilot). Supports ANY combination of tools — e.g. read 5 wiki pages, write 3 pages, add 2 raw files, search, all in one call. Wiki index rebuild is automatically deduplicated (runs once at the end, not per-write). Each operation is independent — one failure does not abort the batch. Nested batch calls are not allowed.
Inputs: operations (required).
code_parseA
Parse a source file from raw/ into structured code knowledge (AST, normalized model, summary). Currently supports COBOL (.cbl, .cob, .cpy). Persists artifacts under raw/parsed/cobol/. Optionally traces all references to a variable.
Inputs: path (required), trace_variable.
code_queryA
Query parsed code knowledge. Select query_type to control behavior:
trace_variable: Trace all references to a variable in a parsed source file — shows where it is read, written, or passed, grouped by section/paragraph.impact: Query the compiled knowledge graph for downstream impact — returns affected programs, copybooks, or datasets grouped by dependency depth, with evidence and uncertainty markers.procedure_flow: Query parsed procedure/section PERFORM flow for one source file — returns section-level and paragraph-level flow, optionally focused on one procedure.field_lineage: Query compiled field-lineage artifacts — returns deterministic and inferred matches for one field, optionally narrowed to a copybook or qualified name.dataflow_edges: Query field-level dataflow edges — MOVE/COMPUTE/ADD assignment, EXEC SQL host-variable, and CALL USING parameter edges. Filter byfrom/to, or setfield+transitive: trueto follow chains across the full graph.
Inputs: query_type (required), path, variable, node_id, +13 more.
knowledge_ingestA
Knowledge ingestion pipeline. Select mode to control behavior:
batch: Batch import, extract, chunk, and pack source documents into digest packs. Scans a directory (or single file), imports to raw/, extracts text with structural provenance (per-page PDF, per-sheet XLSX, per-slide PPTX), chunks into fixed-line segments, then packs into markdown digest packs under raw/digest-packs/{topic}/. Files already in raw/ are skipped.digest_write: Write LLM-generated digest summaries to wiki with structured provenance. Creates or updates one or more wiki pages from digested content, linking back to source raw files and digest packs. Index is rebuilt once at the end. Each page gets auto-classified, auto-routed, and timestamped.
Inputs: mode (required), source_path, pattern, maxFiles, +5 more.
raw_coverageA
Report which raw/ files are not yet referenced by any wiki page. Answers 'what should I compile next?' — returns uncovered files sorted by recency/size, plus overall coverage ratio. Matches frontmatter 'sources' and inline 'raw/...' body references. Parsed artifacts (raw/parsed/) are excluded.
Inputs: limit, sort, tag.
raw_ingestA
Ingest raw source documents into the knowledge base. Select mode to control the ingestion method:
add: Add a local file or content string (immutable, SHA-256 verified). Supports directory imports. Single images (<10MB) returned inline — you MUST immediately call wiki_write to describe them.fetch: Download a file from a URL into raw/ (arXiv abstract URLs auto-converted to PDF). Single images returned inline — you MUST immediately call wiki_write to describe them.import_confluence: Recursively import Confluence pages with attachments and hierarchy. Supports both Cloud (*.atlassian.net/wiki/...) and Server / Data Center ({host}/spaces/...). Defaults to reading the CONFLUENCE_API_TOKEN env var; passauth_envto point at any other variable. Token format accepted:email:api-token(Cloud Basic),Bearer <pat>(explicit), or a bare PAT (Bearer prefix added automatically).import_jira: Import a Jira issue with comments, attachments, and linked issues. Supports both Cloud and Server / Data Center; auto-falls-back to REST API v2 on older Server / DC. Defaults to reading the JIRA_API_TOKEN env var; passauth_envto point at any other variable. Token format accepted:email:api-token(Cloud Basic),Bearer <pat>(explicit), or a bare PAT (Bearer prefix added automatically).
Inputs: mode (required), filename, content, source_path, +13 more.
raw_listA
List all raw source documents with metadata (path, source URL, download time, hash, size).
Inputs: no named parameters.
raw_readA
Read a raw source document's content and metadata. Raw files are immutable — this is read-only. Text/SVG files return content as string; document files (PDF, DOCX, XLSX, PPTX) have text extracted automatically; other binary files (images, etc.) return metadata only.
Pagination by format:
PDF: use 'pages' for page ranges (e.g. '1-5')
PPTX: use 'pages' for slide ranges (e.g. '1-10')
XLSX: use 'sheet' to read a specific sheet; response always includes 'sheet_names'
DOCX / text: use 'offset' + 'limit' for line-based pagination (default limit: 200)
For large documents, paginate rather than reading all at once.
Inputs: filename (required), pages, sheet, offset, +1 more.
raw_versionsA
List all versions of a raw file, sorted by version number, with the latest version marked. Given a base filename (e.g. 'report.xlsx'), returns all matching versions (report.xlsx as v1, report_v2.xlsx, report_v3.xlsx, etc.) with metadata and a 'latest' field pointing to the newest file.
Inputs: filename (required).
wiki_adminA
Wiki administration and maintenance. Select action to control behavior:
init: Initialize a new knowledge base — creates wiki/, raw/, schemas/ directories and default templates.config: Show current workspace configuration: directories, lint settings, search settings, entity templates.format-check: Validate the portable OKF package manifest (agent-wiki.yaml), package inventory, and conformance findings against OKF v0.1.rebuild: Rebuild index.md, timeline.md, code knowledge graphs, and optionally the vector index (when search.hybrid is enabled). Set evidence_report=true to also regenerate the evidence report and persist it to wiki/evidence-report.md as part of the rebuild. Set okf_report=true to persist evidence/okf-report.json.lint: Run comprehensive health checks: contradictions, orphan pages, broken links, raw file integrity (SHA-256), synthesis page integrity. Set apply_fixes=true to auto-repair fixable issues.evidence-report: Aggregate evidence-first telemetry into a corpus-level Markdown report (source coverage, lineage diagnostics, 4-week write trend, Phase 2b readiness gates). Set write=true to also persist the report to wiki/evidence-report.md.
Inputs: action (required), path, workspace, apply_fixes, +3 more.
wiki_deleteB
Delete a wiki page. Cannot delete system pages (index.md, log.md, timeline.md).
Inputs: page (required).
wiki_listA
List all wiki pages, optionally filtered by entity type or tag.
Inputs: type, tag.
wiki_readA
Read one or more wiki pages. Single page: pass page. Multiple pages: pass pages array — reads all in one request, saving N-1 round trips. RECOMMENDED WORKFLOW: (1) call without 'section' to get the TOC for large pages, (2) use wiki_search 'section' field to jump directly to the relevant heading, (3) call with 'section' to read only that part. Large pages without 'section' are truncated at 200 lines — check 'truncated' and 'toc' in the response.
Inputs: page, pages, section, offset, +1 more.
wiki_searchA
Full-text keyword search across all wiki pages. Returns paths, scores, and snippets sorted by relevance. Uses BM25 by default; switches to hybrid BM25+vector re-ranking when search.hybrid: true is set in .agent-wiki.yaml (requires wiki_admin action:rebuild to build the vector index first). Set include_content=true for simple inline content (with optional inline_budget cap). Set read_top_n to additionally read the top N unique matching pages (deduplicated) — enables combined search+read in one call; returns a pages array with full content and nextReads for unread matches. Use type or tags to narrow results without a separate wiki_list call. When no results are found, returns a knowledge_gap field with a suggested page slug, title, type, and tags — use it to decide what to create with wiki_write.
Inputs: query (required), limit, type, tags, +6 more.
wiki_writeA
Create or update a wiki page. Content should include YAML frontmatter (title, type, tags, sources) and Markdown body. Timestamps (created/updated) are auto-managed. Auto-routes root-level pages to matching topic subdirectories (via frontmatter topic field or tag matching). Auto-links: scans body for mentions of existing page titles and injects [[slug|text]] links automatically (skips code blocks, existing links, URLs). Wiki pages are MUTABLE — they represent compiled knowledge that improves over time. Set return_content: true to include the final written content in the response — eliminates the follow-up wiki_read call in write-then-reference workflows.
Inputs: page (required), content (required), source, return_content.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 15 tools
Each tool has a clearly distinct purpose, with unique names and detailed descriptions that prevent confusion. Overlaps are minimal and well-explained, e.g., raw_ingest vs knowledge_ingest handle different stages of ingestion.
All tool names follow a consistent verb_noun pattern using snake_case, such as wiki_read, raw_ingest, code_query. This makes the API predictable and easy to navigate.
With 15 tools, the set is well-scoped for a comprehensive knowledge base server, covering ingestion, parsing, search, and administration without being excessive.
The tool surface covers the full lifecycle of a wiki/knowledge base: init, config, add/read/update/delete content, search, raw file management, code analysis, and maintenance tasks like linting and rebuilds. No obvious gaps.
Maintenance
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