knowledgevault 0.8.0

CLI-first personal knowledge base for AI agents — structured, navigable, zero extra cost

kvault

Tell your AI agent to build you a knowledge base. That's it.

pip install knowledgevault

kvault gives your coding agent persistent, structured memory. It runs as a CLI tool that any agent can call via shell — Claude Code, OpenAI Codex, Cursor, or any tool that can execute commands. kvault itself requires no extra API keys, hosted service, or database.

Your agent creates entities (people, projects, notes), navigates the hierarchy via parent summaries, and keeps everything in sync — all through simple CLI commands.

Who is this for?

Developers using Claude Code, OpenAI Codex, Cursor, VS Code + Copilot, or any AI coding tool who want their agent to remember things between sessions — contacts, projects, meeting notes, research — in a structured, navigable format.

What makes it different?

	kvault	Anthropic memory server	Notion AI / Mem.ai	obsidian-claude-pkm
Structure	Hierarchical entities with navigable tree	Flat JSON	Rich docs, flat search	Obsidian vault
Agent-native	CLI commands, works in any subprocess	MCP server	Chat/sidebar workflow	Template, not runtime
Service model	Plain files + local CLI	Local server	Hosted workspace	Local vault
Navigation	Parent summaries at every level	None	AI-generated	Manual
Search	Agent uses its own search tools (grep, find, etc.)	Built-in	Built-in	Manual

Quickstart (30 seconds)

1. Install

pip install knowledgevault

2. Initialize a knowledge base

kvault init ./my_kb --name "Your Name"

3. Tell your agent

"Use kvault CLI commands to manage my knowledge base at ./my_kb"

Your agent reads the generated AGENTS.md for workflow instructions and starts working. Use --kb-root ./my_kb from the directory containing my_kb, or pass an absolute path.

Tool-specific tips:

Tool	Setup
Claude Code	Works automatically — reads AGENTS.md as project instructions
OpenAI Codex CLI	Tell it: "Read AGENTS.md for the kvault workflow, then use shell commands to manage ./my_kb"
Gemini CLI	Symlink AGENTS.md → GEMINI.md, or paste the workflow rules into your system prompt
Cursor / Copilot	Add AGENTS.md contents to your .cursorrules or workspace instructions

Try it: import your ChatGPT history

The best way to see kvault in action is to point it at data you already have. ChatGPT lets you export your entire conversation history — years of questions, people mentioned, projects discussed, decisions made — and your agent can turn it into a structured, navigable knowledge base in minutes.

Chat exports can contain sensitive information. kvault stores the files locally in your KB, but your agent may read excerpts while processing them and send those excerpts to its model provider. Review or redact the export first if that matters for your use case.

1. Export your ChatGPT data

Download a ChatGPT data export from ChatGPT's export controls. The archive includes conversations.json.

2. Unzip it into your KB

unzip chatgpt-export.zip -d my_kb/sources/chatgpt

3. Tell your agent to process it

Read through my ChatGPT export in sources/chatgpt/conversations.json.
Extract the people, projects, and ideas I've discussed most frequently.
Create entities for each one in the knowledge base.

Your agent will use kvault CLI commands to create structured entries with frontmatter and propagate summaries.

The 2-call write workflow

# Call 1: Write entity (stdin = frontmatter + markdown body)
kvault write people/contacts/acme --create --reasoning "New customer" --json --kb-root ./my_kb <<'EOF'
---
source: meeting_2026-02-25
aliases: [ACME Corp]
---
# ACME Corp
Key customer acquired at trade show...
EOF
# → {"success": true, "ancestors": [{path, current_content, has_meta}, ...]}

# Call 2: Agent reads ancestors, composes updated summaries
kvault update-summaries --json --kb-root ./my_kb <<'EOF'
[
  {"path": "people/contacts", "content": "# Contacts\n...updated..."},
  {"path": "people", "content": "# People\n...updated..."}
]
EOF
# → {"success": true, "updated": ["people/contacts", "people"], "count": 2}

What an entity looks like

Each entity is a directory with a single _summary.md file containing YAML frontmatter:

---
created: 2026-02-06
updated: 2026-02-06
source: manual
aliases: [Morgan Lee, [e-mail]]
email: [e-mail]
---
# Morgan Lee

Research collaborator tracking evaluation notes and follow-up questions.

Required frontmatter: source, aliases (kvault sets created/updated automatically)

What a knowledge base looks like

my_kb/
├── _summary.md                          # Root: executive overview
├── AGENTS.md                            # Agent workflow instructions
├── people/
│   ├── _summary.md                      # "12 contacts across 3 categories"
│   ├── family/
│   │   └── _summary.md
│   ├── friends/
│   │   ├── _summary.md
│   │   └── alex_rivera/
│   │       └── _summary.md
│   └── contacts/
│       ├── _summary.md
│       └── sarah_chen/
│           └── _summary.md
├── projects/
│   ├── _summary.md
│   └── launch_plan/
│       └── _summary.md
├── journal/
│   └── 2026-02/
│       └── log.md
└── .kvault/
    └── logs.db                          # Observability

CLI commands

Category	Commands
Entity	kvault read, kvault write, kvault list, kvault delete, kvault move
Summary	kvault read-summary, kvault write-summary, kvault update-summaries, kvault ancestors
Journal	kvault journal
Status	kvault status, kvault tree
Validation	kvault validate, kvault check
Artifacts	kvault artifact daily
Logs	kvault log summary
UI	kvault ui
Init	kvault init

Agent-facing commands support --json for machine-readable output. --kb-root overrides auto-detection on KB-bound commands, and it works before or after the subcommand:

kvault read people/friends/alice --json --kb-root ./my_kb
kvault artifact daily --json --kb-root ./my_kb

Optional local UI

Install the UI extra to browse a KB in a local read-only web app:

pip install "knowledgevault[ui]"
kvault ui --kb-root ./my_kb

Optional MCP compatibility

CLI remains the primary interface for shell-capable agents. For MCP-native clients, kvault also ships a stdio MCP compatibility server:

pip install "knowledgevault[mcp]"
kvault-mcp --kb-root /absolute/path/to/my_kb

knowledgevault[mcp] requires Python 3.10+. The server is bound to one KB root per process; use a separate kvault-mcp process for each KB. You can pass the root with --kb-root or set KVAULT_KB_ROOT.

Generic MCP client config:

{
  "mcpServers": {
    "kvault": {
      "command": "kvault-mcp",
      "args": ["--kb-root", "/absolute/path/to/my_kb"]
    }
  }
}

Root-pinned config for shared runtimes:

{
  "mcpServers": {
    "kvault": {
      "command": "kvault-mcp",
      "args": ["--kb-root", "/absolute/path/to/my_kb"],
      "env": {
        "KVAULT_ALLOWED_ROOTS": "/absolute/path/to/my_kb"
      }
    }
  }
}

The compatibility server exposes root-bound tools for status, entity CRUD, listing, summary read/write/update, ancestor lookup, journaling, daily artifact generation, validation, and phase logging:

Category	Tools
Lifecycle	kvault_init, kvault_status
Entities	kvault_read_entity, kvault_write_entity, kvault_list_entities, kvault_delete_entity, kvault_move_entity
Summaries	kvault_read_summary, kvault_write_summary, kvault_update_summaries, kvault_get_parent_summaries, kvault_get_ancestors, kvault_propagate_all
Journal / artifacts	kvault_write_journal, kvault_generate_daily_artifact
Validation / logging	kvault_validate_kb, kvault_log_phase

Compatibility tools accept an optional kg_root argument for older clients, but it must match the server-bound root. kvault_init reports bound-root status and rejects mismatched roots; it does not create or reinitialize a KB.

MCP clients should use the same propagation workflow as the CLI:

1. Call kvault_status or kvault_list_entities to orient.
2. Call kvault_read_entity / kvault_read_summary before editing.
3. Call kvault_write_entity with durable frontmatter and body content.
4. Use the returned ancestors or kvault_get_parent_summaries to update parent summaries.
5. Call kvault_update_summaries so every parent remains a useful rollup.
6. Call kvault_validate_kb after larger edits.

Optional root pinning (multi-tenant hardening)

For shared runtimes, pin allowed roots:

export KVAULT_ALLOWED_ROOTS="/absolute/path/to/my_kb"

Python API

from pathlib import Path
from kvault.core import operations as ops

kg_root = Path("my_kb")

# Read/write entities
entity = ops.read_entity(kg_root, "people/contacts/sarah_chen")
result = ops.write_entity(kg_root, "people/contacts/new_person", "# Content", create=True)

# Scan and search
from kvault import scan_entities, EntityResearcher
entities = scan_entities(kg_root)
researcher = EntityResearcher(kg_root)
action, target, confidence = researcher.suggest_action("Morgan Lee")

Integrity check

Run kvault check to catch stale summaries and weak parent rollups:

kvault check --kb-root /absolute/path/to/my_kb

[KB] warnings keep the existing nonzero exit behavior. SUMMARY: warnings are warn-only by default, capped at 5 lines, and call out parent summaries that are too short, omit immediate children, or contain placeholder/redirect language. Use --no-summary-quality to skip that audit.

If your tool supports pre-prompt hooks, you can automate this. For example, in Claude Code's .claude/settings.json:

{
  "hooks": {
    "UserPromptSubmit": [
      {
        "type": "command",
        "command": "kvault check --kb-root /absolute/path/to/my_kb"
      }
    ]
  }
}

It's just files

kvault produces Markdown with YAML frontmatter in a plain directory. No proprietary format, no database to export from. Your existing tools work out of the box:

Want to...	Use
Semantic search	Embed the .md files with any vector tool (OpenAI, Chroma, txtai, etc.)
Visual browsing	Open the KB directory in Obsidian or Logseq
Publish as a site	Point Hugo, Jekyll, or Astro at the directory
CI validation	Run kvault validate or kvault check in a GitHub Action
Bulk export	find . -name _summary.md + yq over the frontmatter

Development

pip install -e ".[dev,ui,mcp]"
pytest -q
ruff check .
black --check kvault/ tests/
mypy kvault/ --ignore-missing-imports

License

MIT