Metadata-Version: 2.4
Name: vault-for-llm
Version: 0.6.44
Summary: Local-first Markdown + SQLite memory for LLM agents, with keyword search, optional embeddings, MCP, and bounded citation tools.
Author: Vault-for-LLM Contributors
License-Expression: Apache-2.0
Keywords: knowledge-base,vector-search,sqlite,onnx,embeddings,local-first
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Database
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pyyaml>=6.0
Requires-Dist: sqlite-vec<1.0,>=0.1.6
Provides-Extra: semantic
Requires-Dist: onnxruntime<2.0,>=1.16; extra == "semantic"
Requires-Dist: optimum[onnxruntime]<3.0,>=2.0; extra == "semantic"
Requires-Dist: transformers<5.0,>=4.35; extra == "semantic"
Requires-Dist: sentencepiece>=0.1; extra == "semantic"
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: ruff>=0.1; extra == "dev"
Provides-Extra: mcp
Requires-Dist: mcp>=1.0; extra == "mcp"
Requires-Dist: anthropic<1.0,>=0.30; extra == "mcp"
Provides-Extra: supabase
Requires-Dist: supabase<3.0,>=2.0; extra == "supabase"
Dynamic: license-file

# Vault-for-LLM

Local-first project memory for AI agents.

Vault-for-LLM turns project notes, decisions, bugs, SOPs, Obsidian notes, and
agent-written memory candidates into a portable SQLite vault that agents can
search, read in bounded ranges, cite, test, back up, and sync when needed.

It is not trying to replace your model, your wiki, or hosted memory systems.
It sits between them: a small governed memory layer that helps agents reuse
project knowledge without losing source, scope, or reviewability.

## Why It Exists

Most agent failures are practical, not mysterious:

- a new session forgets why a decision was made
- an agent reads the wrong outdated note
- useful fixes stay buried in chat history
- private observations get mixed with shared project knowledge
- a team cannot tell whether retrieval is actually working

Vault-for-LLM is built for that practical gap. It gives agents a place to ask:

> What has this project already learned, where did it come from, and am I
> allowed to use it?

## What It Is Good At

- **Local-first memory** - Markdown and SQLite by default. No cloud service is
  required for core use.
- **Agent-friendly retrieval** - CLI and MCP tools for search, bounded reads,
  candidate memory, Document Map inspection, and optional remote reads.
- **Candidate-first writes** - agents can propose memory before it becomes
  active knowledge.
- **Governance metadata** - scope, sensitivity, owner agent, allowed agents,
  memory type, and expiry travel with each memory.
- **Obsidian bridge** - import existing Obsidian notes into Vault, or export
  compiled Vault knowledge back into Obsidian-readable Markdown.
- **Optional remote sharing** - Supabase sync and read-only RPC templates let
  agents on different machines share reviewed memory.
- **Measurable recall** - Search QA and onboarding benchmarks measure whether
  agents can find the right source, not just sound confident.

## When To Use It

Use Vault-for-LLM when:

- you work with Claude Code, Codex, Hermes Agent, OpenClaw, OpenCode, n8n, or
  another agent that needs project context across sessions
- you want a shared project memory without giving every agent raw private notes
- you already have Markdown or Obsidian notes and want agents to search them
  with citations
- you need local-first storage but optional Supabase sharing for other hosts
- you care about retrieval quality enough to test it

Do not start here if you only need a hosted vector database, a personal notes
app, or an automatic conversation memory product.

## Install

### Agent-Driven Install

For most users, the easiest path is to ask an agent to install it:

```text
Install Vault-for-LLM for this project. Use vault-for-llm[mcp]==0.6.44.
Ask whether the vault should be shared, private, domain-specific, or temporary.
Ask for a stable project directory and generate a stable venv script for
long-lived agent jobs. Ask separately about MCP, semantic search, Supabase,
Obsidian import, Headroom compression, and memory-agent guidance. Install only
the optional dependencies I approve. Finish with a search/read/propose smoke test.
```

The agent should use the guided installer:

```bash
python3 -m venv .venv
source .venv/bin/activate
pip install "vault-for-llm[mcp]==0.6.44"

vault setup-agent
```

For non-interactive agent installs:

```bash
vault setup-agent \
  --non-interactive \
  --agent codex \
  --scope shared \
  --agent-project-dir ~/Vaults/my-project \
  --features core,mcp,supabase,headroom \
  --write-stable-venv-script \
  --supabase-setup simple \
  --remote-reader shell \
  --json
```

This can generate `agent-install/setup-stable-venv.sh`, so scheduled jobs and
MCP commands do not depend on a disposable `/tmp` virtualenv.

### Manual Quickstart

```bash
pip install "vault-for-llm[mcp]==0.6.44"

vault init ~/Vaults/demo
vault add "First lesson" \
  --content "The bug was caused by a missing cache key. The fix was adding provider metadata." \
  --project-dir ~/Vaults/demo
vault compile --project-dir ~/Vaults/demo --no-embed
vault search "cache key" --project-dir ~/Vaults/demo
```

## Daily Agent Flow

The intended loop is simple:

1. **Search first** - find likely source notes.
2. **Read bounded ranges** - avoid dumping whole documents into context.
3. **Answer with sources** - keep citations tied to original Vault content.
4. **Propose memory** - let agents suggest new lessons as candidates.
5. **Review before promotion** - keep active memory clean and auditable.

For MCP-capable runtimes:

```bash
vault-mcp --project-dir ~/Vaults/my-project --tool-profile core
```

Recommended core tools:

- `vault_search`
- `vault_read_range`
- `vault_memory_propose`
- `vault_memory_candidates`
- `vault_map_show`

Full MCP and token-budget guidance: [docs/mcp_memory_workflow.md](docs/mcp_memory_workflow.md).

## Memory Model

Vault uses depth layers for how memory is used:

| Layer | Purpose |
|---|---|
| `L0` | identity and project framing |
| `L1` | stable facts, rules, and preferences |
| `L2` | active context, summaries, current work |
| `L3` | detailed knowledge, SOPs, bugs, decisions, source notes |

Access is not controlled by layer alone. Use governance metadata for policy:

- `scope`: private, project, shared, public
- `sensitivity`: low, medium, high, restricted
- `owner_agent`
- `allowed_agents`
- `memory_type`
- `expires_at`

Design notes: [docs/memory_governance.md](docs/memory_governance.md).

## Memory Maintenance Agents

Vault can generate guidance for Profile, Dream, and Forgetting agents. These
agents are conservative by default: Dream runs produce reports first, cleanup
checks look for stale entries, duplicates, and weak metadata, and `apply_safe`
paths should create backups so rollback remains possible. Promotion, deletion,
archive, or expiry actions should stay candidate-only until a user-approved
policy allows stronger automation.

Setup guide: [docs/agent_install.md](docs/agent_install.md).
Governance details: [docs/memory_governance.md](docs/memory_governance.md).

## Integrations

| System | Path |
|---|---|
| Claude Code / Codex / OpenCode | CLI or local stdio MCP |
| Hermes Agent / OpenClaw | CLI, MCP, generated agent install files |
| n8n | generated Supabase sync and remote-reader workflow templates |
| Coze or hosted agents | Supabase read-only RPC and OpenAPI template |
| Obsidian | import existing notes, export compiled Vault knowledge |
| Headroom | optional compression after Vault has narrowed context |

Start here: [docs/agent_integrations.md](docs/agent_integrations.md).

## Optional Supabase Sharing

SQLite remains the source of truth. Supabase is optional.

Use it when agents on different machines or hosted platforms need to read a
shared, filtered copy of reviewed project memory.

```bash
pip install "vault-for-llm[supabase]==0.6.44"
python -m scripts.sync_to_supabase --db ~/Vaults/my-project/vault.db --document-map --health
```

Setup guide: [docs/supabase_setup.md](docs/supabase_setup.md).
Read policy template: [docs/supabase_read_policy.sql](docs/supabase_read_policy.sql).

## Obsidian

Import an existing Obsidian vault:

```bash
vault import obsidian --vault ~/Documents/ObsidianVault --project-dir ~/Vaults/my-project --dry-run
vault import obsidian --vault ~/Documents/ObsidianVault --project-dir ~/Vaults/my-project --compile
```

Export compiled Vault knowledge back into Obsidian-readable notes:

```bash
vault export obsidian --project-dir ~/Vaults/my-project --vault ~/Documents/ObsidianVault
```

The importer skips `.obsidian`, `.trash`, `.git`, and generated Vault export
folders by default.

## Retrieval Quality

Vault includes lightweight QA tools so retrieval can be tested instead of
trusted by intuition alone.

```bash
vault search-qa run \
  --qa-file benchmarks/search_qa/basic.en.json \
  --mode keyword \
  --output /tmp/vault-searchqa.json
```

Current evidence is intentionally described as retrieval evidence, not final
answer quality:

- project onboarding benchmark: Vault found source-backed project memory across
  28/28 tasks in local proof runs
- LoCoMo retrieval probes: hierarchical session + local evidence-window
  retrieval reached high evidence recall in official-scored categories
- official answerer/judge scores are separate and require model-provider runs

More detail: [docs/agent_onboarding_benchmark.md](docs/agent_onboarding_benchmark.md) and
[docs/search_qa_benchmarking.md](docs/search_qa_benchmarking.md).

## Maturity

| Area | Status |
|---|---|
| local SQLite, Markdown compile, keyword search | stable |
| CLI setup, candidate memory, bounded reads | usable |
| MCP tools | usable, profile selection recommended |
| Obsidian import/export | usable |
| Supabase sync and remote read templates | advanced optional |
| semantic search, rerank, benchmark adapters | evolving |
| Profile / Dream / Forgetting agents | guidance-first, not autonomous deletion |

Vault-for-LLM is still pre-1.0. The core local path is intentionally conservative;
advanced integrations are powerful, but should be enabled deliberately.

## Documentation Map

- Agent install runbook: [docs/agent_install.md](docs/agent_install.md)
- CLI reference: [docs/cli_reference.md](docs/cli_reference.md)
- Agent integrations: [docs/agent_integrations.md](docs/agent_integrations.md)
- Memory governance: [docs/memory_governance.md](docs/memory_governance.md)
- Supabase setup: [docs/supabase_setup.md](docs/supabase_setup.md)
- MCP workflow: [docs/mcp_memory_workflow.md](docs/mcp_memory_workflow.md)
- PageIndex / Headroom comparison: [docs/comparisons/pageindex_headroom.md](docs/comparisons/pageindex_headroom.md)
- Vision notes: [docs/vision.md](docs/vision.md)

## Development

```bash
git clone https://github.com/zycaskevin/Vault-for-LLM.git
cd Vault-for-LLM
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev,mcp]"
pytest -q
```

## License

Apache-2.0. See [LICENSE](LICENSE).
