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Flanes: Feature Lanes for Agents - version control for agentic AI systems

Project description

flanes

Version Control for Agentic AI Systems

Tests Python 3.10+ License: MIT

Version control designed from the ground up for AI agents. Replaces git's line-diff model with intent-based snapshots, physically isolated workspaces, and evaluation gating.

Why Flanes?

Git assumes a single human making small, curated edits. AI agents break every part of that model.

Git Flanes
Unit of work Line diffs Full world-state snapshots
Change metadata Free-text commit message Structured intent + agent identity
Quality gate CI runs after merge Evaluation gating before accept
Parallel agents Branch conflicts Physically isolated workspaces
Cost tracking None Per-transition token/API accounting

Quick Demo

cd my-project
flanes init
# Writes files, then commits in one step:
flanes commit --prompt "Add auth module" \
  --agent-id coder-1 --agent-type feature_dev --auto-accept

# Create isolated feature lane
flanes lane create feature-auth

# Work in isolation, promote back to main
flanes promote --workspace feature-auth --target main --auto-accept

# Query history
flanes history --lane main

Or use the Python SDK:

from flanes.agent_sdk import AgentSession

session = AgentSession(
    repo_path="./my-project",
    agent_id="coder-alpha",
    agent_type="feature_developer",
)

with session.work("Add authentication module", tags=["auth"], auto_accept=True) as w:
    (w.path / "auth.py").write_text("def authenticate(): ...")
    w.record_tokens(tokens_in=2000, tokens_out=1200)
# On exit: snapshots -> proposes -> accepts (or rejects on exception)

See examples/ for runnable demos.

Installation

pip install flanes

# Optional: remote storage backends
pip install flanes[s3]    # Amazon S3 (boto3)
pip install flanes[gcs]   # Google Cloud Storage

Core Concepts

World States: Immutable snapshots of the entire project. Agents propose new world states, not diffs.

Intents: Structured metadata for every change: the instruction, who issued it, cost tracking, and semantic tags.

Transitions: Proposals to move from one state to another. Must be evaluated before acceptance.

Lanes: Isolated workstreams. Work is promoted into a target lane through evaluation, not merged.

Workspaces: Main workspace is the repo root (git-style). Feature lanes get physically isolated directories under .flanes/workspaces/.

Architecture

fla_architecture

Key Features

  • Git-style main: repo root IS the main workspace. Files stay where you expect them.
  • Physical isolation: feature workspaces are real directories. Parallel agents can't stomp on each other.
  • Smart incremental updates: workspace sync writes only changed files, not the entire tree.
  • Cross-platform locking: atomic mkdir locking works on Linux, macOS, and Windows.
  • Conflict detection: promote finds path-level collisions without content merging.
  • Evaluators: run pytest, ruff, or custom checks as gates before accepting transitions.
  • Cost tracking: per-transition token usage, wall time, and API call counts.
  • Git bridge: flanes export-git / flanes import-git for CI integration.
  • Remote storage: S3/GCS-backed sync for team collaboration.
  • MCP server: expose Flanes as tools for LLM integration via Model Context Protocol.
  • REST API: flanes serve starts a multi-threaded HTTP API.
  • Garbage collection: flanes gc removes rejected states and unreachable objects.

Documentation

Workspace Layout

my-project/
+-- .flanes/
|   +-- config.json
|   +-- store.db                        # SQLite database
|   +-- main.json                       # main workspace metadata
|   +-- workspaces/
|       +-- feature-auth/               # isolated feature workspace
|       +-- feature-auth.json
+-- app.py                              # YOUR FILES AT REPO ROOT
+-- lib/

Running Tests

pip install -e ".[dev]"
python -X utf8 -m pytest tests/ -v

License

MIT

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