fava-trails 0.4.6

FAVA Trails 🫛👣 — Federated Agents Versioned Audit Trail. VCS-backed memory for AI agents via MCP.

FAVA Trails

Federated Agents Versioned Audit Trail — VCS-backed memory for AI agents via MCP.

Every thought, decision, and observation is stored as a markdown file with YAML frontmatter, tracked in a Jujutsu (JJ) colocated git monorepo. Agents interact through MCP tools — they never see VCS commands.

Why

• Supersession tracking — when an agent corrects a belief, the old version is hidden from default recall. No contradictory memories.
• Draft isolation — working thoughts stay in drafts/. Other agents only see promoted thoughts.
• Trust Gate — an LLM-based reviewer validates thoughts before they enter shared truth. Hallucinations stay contained in draft.
• Full lineage — every thought carries who wrote it, when, and why it changed.
• Crash-proof — JJ auto-snapshots. No unsaved work.
• Engine/Fuel split — this repo is the engine (stateless MCP server). Your data lives in a separate repo you control.

Install

Prerequisites

Install Jujutsu (JJ) — FAVA Trails uses JJ as its VCS engine:

fava-trails install-jj

Or install manually from jj-vcs.github.io/jj.

From PyPI (recommended)

pip install fava-trails

From source (for development)

git clone https://github.com/MachineWisdomAI/fava-trails.git
cd fava-trails
uv sync

Quick Start

Set up your data repo

# Create an empty repo on GitHub (or any git remote), then clone it
git clone https://github.com/YOUR-ORG/fava-trails-data.git

# Bootstrap it (creates config, .gitignore, initializes JJ)
fava-trails bootstrap fava-trails-data

Register the MCP server

Add to ~/.claude.json (Claude Code) or claude_desktop_config.json (Claude Desktop):

If installed from PyPI:

{
  "mcpServers": {
    "fava-trails": {
      "command": "fava-trails-server",
      "env": {
        "FAVA_TRAILS_DATA_REPO": "/path/to/fava-trails-data"
      }
    }
  }
}

If installed from source:

{
  "mcpServers": {
    "fava-trails": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "--directory", "/path/to/fava-trails", "fava-trails-server"],
      "env": {
        "FAVA_TRAILS_DATA_REPO": "/path/to/fava-trails-data"
      }
    }
  }
}

For Claude Desktop on Windows (accessing WSL):

{
  "mcpServers": {
    "fava-trails": {
      "command": "wsl.exe",
      "args": [
        "-e", "bash", "-lc",
        "FAVA_TRAILS_DATA_REPO=/path/to/fava-trails-data fava-trails-server"
      ]
    }
  }
}

Use it

Agents call MCP tools. Core workflow:

save_thought(trail_name="myorg/eng/my-project", content="My finding about X", source_type="observation")
  → creates a draft in drafts/

propose_truth(trail_name="myorg/eng/my-project", thought_id=thought_id)
  → promotes to observations/ (visible to all agents)

recall(trail_name="myorg/eng/my-project", query="X")
  → finds the promoted thought

Agents interact through MCP tools — they never see VCS commands. JJ expertise is not required.

Cross-Machine Sync

FAVA Trails uses git remotes for cross-machine sync. The fava-trails bootstrap command sets push_strategy: immediate which auto-pushes after every write.

Setting up a second machine

# 1. Install FAVA Trails
pip install fava-trails

# 2. Install JJ
fava-trails install-jj

# 3. Clone the SAME data repo
git clone https://github.com/YOUR-ORG/fava-trails-data.git

# 4. Initialize JJ colocated mode + track remote
cd fava-trails-data
jj git init --colocate
jj bookmark track main@origin

# 5. Register MCP (same config as above, with local paths)

Both machines push/pull through the same git remote. Use the sync MCP tool to pull latest thoughts from other machines.

Manual push (if auto-push is off)

cd /path/to/fava-trails-data
jj bookmark set main -r @-
jj git push --bookmark main

NEVER use git push origin main after JJ colocates — it misses thought commits. See AGENTS_SETUP_INSTRUCTIONS.md for the correct protocol.

Architecture

fava-trails (this repo)        fava-trails-data (your repo)
├── src/fava_trails/           ├── config.yaml
│   ├── server.py  ←── MCP ──→├── .gitignore
│   ├── cli.py                 └── trails/
│   ├── trail.py                   └── myorg/eng/project/
│   ├── config.py                      └── thoughts/
│   ├── trust_gate.py                      ├── drafts/
│   └── vcs/                               ├── decisions/
│       └── jj_backend.py                  ├── observations/
└── tests/                                 └── preferences/

• Engine (fava-trails) — stateless MCP server, Apache-2.0. Install via pip install fava-trails.
• Fuel (fava-trails-data) — your organization's trail data, private.

Configuration

Environment variables:

Variable	Read by	Purpose	Default
FAVA_TRAILS_DATA_REPO	Server	Root directory for trail data (monorepo root)	~/.fava-trails
FAVA_TRAILS_DIR	Server	Override trails directory location (absolute path)	$FAVA_TRAILS_DATA_REPO/trails
FAVA_TRAILS_SCOPE_HINT	Server	Broad scope hint baked into tool descriptions	(none)
FAVA_TRAILS_SCOPE	Agent	Project-specific scope from .env file	(none)

The server reads $FAVA_TRAILS_DATA_REPO/config.yaml for global settings. Minimal config.yaml:

trails_dir: trails          # relative to FAVA_TRAILS_DATA_REPO
remote_url: null            # git remote URL (optional)
push_strategy: manual       # manual | immediate

When push_strategy: immediate, the server auto-pushes after every successful write. Push failures are non-fatal.

See AGENTS_SETUP_INSTRUCTIONS.md for full config reference including trust gate and per-trail overrides.

Development

uv run pytest -v          # run tests
uv run pytest --cov       # with coverage

Docs

• AGENTS.md — Agent-facing: MCP tools reference, scope discovery, thought lifecycle, agent conventions
• AGENTS_USAGE_INSTRUCTIONS.md — Canonical usage: scope discovery, session protocol, agent identity
• AGENTS_SETUP_INSTRUCTIONS.md — Data repo setup, config reference, trust gate prompts
• docs/fava_trails_faq.md — Detailed FAQ for framework authors and ML engineers

Contributing

See CONTRIBUTING.md for setup instructions, how to run tests, and PR expectations.

See CHANGELOG.md for release history.