memvault

A local, OKF-compatible knowledge engine for AI agents. Capture your Codex / Claude / Gemini sessions, retrieve them with hybrid semantic + keyword search, serve them to every agent harness over MCP, visualize them as an interactive graph, and export to a portable Open Knowledge Format bundle.

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What is this?

Google's Open Knowledge Format (OKF) standardized how to store agent knowledge — markdown files with YAML frontmatter. It deliberately leaves out the hard parts: retrieval, capture, serving, and enforcement.

memvault is that missing engine. Point it at a directory of markdown notes (an OKF bundle) and it becomes a living, queryable, agent-served knowledge base.

	OKF (the format)	memvault (the engine)
Storage format	✅ markdown + frontmatter	uses OKF
Retrieval	— (out of scope)	✅ hybrid semantic + keyword (RRF)
Capture	— (BigQuery agent only)	✅ Codex / Claude / Gemini sessions
Serving to agents	—	✅ one MCP server, every harness
Visualize	static viewer	✅ interactive graph
Privacy	unspecified	✅ secret scrubbing + sensitivity gate

memvault produces and consumes OKF v0.1 bundles — it rides the standard, it doesn't replace it.

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See it

Every page is a node; every cross-link is an edge. Search, filter by type, switch layouts, and read any concept with its backlinks — all in one self-contained HTML file (no server):

Generated from the public demo bundle in examples/demo with memvault viz. Your own graph stays local.

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Quickstart

# install (from a clone)
pip install -e .              # add ".[neural]" for real multilingual embeddings
                              # add ".[yaml]"  for robust YAML frontmatter

# point at your knowledge bundle (default: ~/llm-wiki)
export MEMVAULT_WIKI=~/llm-wiki

# 1. capture your agent conversations (Codex / Claude Code / Gemini)
memvault ingest

# 2. build the semantic index
memvault index

# 3. search (hybrid semantic + keyword)
memvault search "what did I decide about the auth refactor"

# 4. visualize -> writes viz.html you can open in any browser
memvault viz

# 5. export a portable OKF bundle
memvault export --out ./okf-bundle

# 6. serve to your agents over MCP (stdio)
memvault serve

Try it on the bundled demo with no setup:

memvault viz --wiki examples/demo --out demo.html && open demo.html

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Wire it into your agents (one command)

memvault registers itself into every harness it detects — registering the MCP server and a wiki-first routing block, so your agents actually consult the wiki:

memvault install            # detect + wire (backs up every file it touches)
memvault install --check    # show wiring status
memvault install --dry-run  # preview, change nothing
memvault install --uninstall

Harness	Capability	Enforcement
Claude Code	MCP server + .mcp	SessionStart / UserPromptSubmit hooks inject wiki context
Codex CLI	[mcp_servers.memvault] in config.toml	AGENTS.md routing (+ opt-in user_prompt_submit hook)
OpenCode	drop-in plugin/llm-wiki.js (coexists with omo)	AGENTS.md routing
anything MCP	memvault serve (stdio)	AGENTS.md routing

Or register the stdio server manually anywhere MCP is supported:

{ "command": "memvault", "args": ["serve", "--wiki", "/path/to/bundle"] }

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How it works

  ~/.codex  ~/.claude  ~/.gemini        markdown bundle (OKF)
        \       |        /                      |
         ▼      ▼       ▼                        ▼
   ingest (sessions) ───────────────►  raw/manifests/*.jsonl
                                               │
                              index (hashing or neural embeddings)
                                               │
        ┌──────────────┬───────────────┬───────┴────────┐
        ▼              ▼               ▼                ▼
     search        serve (MCP)       viz            export (OKF)
   hybrid RRF    every harness   interactive graph   portable bundle

• Capture — reads only visible chat turns; tool output, attachments, and credential-looking strings are skipped or scrubbed; sensitive sessions are reduced to counts. Incremental: unchanged files are not re-read.
• Retrieve — dense cosine over an embedding index fused with a lexical scorer via Reciprocal Rank Fusion. Default embedder is a dependency-free numpy hashing encoder (Korean + English, offline, deterministic); pip install ".[neural]" upgrades to a multilingual transformer automatically.
• Serve — a pure-stdlib MCP stdio server exposing wiki_answer_context, wiki_search, wiki_semantic_search, and wiki pages as memvault:// resources.
• Visualize / Export — vendored OKF viewer renders the graph; export emits a conformant OKF v0.1 bundle (frontmatter mapped, wikilinks normalized, index.md generated).

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Configuration

Setting	Env	CLI	Default
Knowledge bundle root	MEMVAULT_WIKI	--wiki	~/llm-wiki
Home root (session scan)	MEMVAULT_HOME	--home	~

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Relationship to OKF

memvault is an independent project. It targets the Open Knowledge Format v0.1 specification published by Google Cloud, and bundles OKF's reference viewer (Apache-2.0). It is not affiliated with or endorsed by Google. See NOTICE.

License

Apache-2.0. See LICENSE.