Skip to main content

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

flanes-0.4.1.tar.gz (157.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

flanes-0.4.1-py3-none-any.whl (102.1 kB view details)

Uploaded Python 3

File details

Details for the file flanes-0.4.1.tar.gz.

File metadata

  • Download URL: flanes-0.4.1.tar.gz
  • Upload date:
  • Size: 157.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.6

File hashes

Hashes for flanes-0.4.1.tar.gz
Algorithm Hash digest
SHA256 c1538fa4cee2a344ea0e43fe1f93f6f2bec29bfc950321e923b73865d57c1b48
MD5 7957576a822f3a225870bebbc6a56143
BLAKE2b-256 d7178c6e42ee8c42fd12a90d751c832a7b811a6dbafc20eb8449923dacd9b431

See more details on using hashes here.

File details

Details for the file flanes-0.4.1-py3-none-any.whl.

File metadata

  • Download URL: flanes-0.4.1-py3-none-any.whl
  • Upload date:
  • Size: 102.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.6

File hashes

Hashes for flanes-0.4.1-py3-none-any.whl
Algorithm Hash digest
SHA256 9f3a743f669e921cdf4f00a66e9bb080e27acfea08ff96674823fd8eaf8149c5
MD5 16f0377d25641cac19932f234ac9ea94
BLAKE2b-256 1cba2cebd0a70ccfaa9730c76ed0ab441463e6747af1f024bf5424ab1c80f83d

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page