remote-factory

agent
Guvenlik Denetimi
Gecti
Health Gecti
  • License — License: MIT
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Community trust — 25 GitHub stars
Code Gecti
  • Code scan — Scanned 12 files during light audit, no dangerous patterns found
Permissions Gecti
  • Permissions — No dangerous permissions requested

Bu listing icin henuz AI raporu yok.

SUMMARY

Domain-agnostic multi-agent software design and evolution harness

README.md

The Factory

CI
codecov
Python 3.11+
License: MIT
Runner: Claude Code
Runner: Bob Shell
Runner: OpenAI Codex

A self-evolving, stateful, decomposed meta-harness. Describe what you want — the Factory builds it, tests it, and keeps improving it, autonomously. A CEO agent orchestrates eight specialists that observe, hypothesize, build, review, measure, and learn from every experiment. Runs with Claude Code, Bob Shell, and OpenAI Codex.

All state is local — per-project in .factory/ (add to .gitignore), global in ~/.factory/. See Architecture for the full deep-dive.


Quick Start

Prerequisites: Python 3.11+, uv, and Claude Code (installed and authenticated).

git clone https://github.com/akashgit/remote-factory.git
cd remote-factory
uv sync

Every command runs from the factory repo directory — you pass the target project as an argument:

uv run factory ceo "Build a personal homepage with a blog" --mode interactive

See the full setup guide for authentication and environment variables.


What Do You Want to Do?

I want to… Command
Start from a raw idea uv run factory ceo "my idea" --mode interactive
Build from a spec or repo uv run factory ceo spec.md
Improve an existing project uv run factory ceo /path/to/project
Fix or add one thing uv run factory ceo /path --focus "add dark mode"
Target a GitHub issue uv run factory ceo /path --focus 42
Optimize a metric (research) uv run factory ceo "build a harness to solve HMMT Feb 2026 C7" --mode research

Interactive Workflow

Use interactive mode when you want to brainstorm before building. Start a conversation with the CEO to refine an idea, then build:

# From a raw idea — discuss and refine into a buildable spec
uv run factory ceo "distributed task runner" --mode interactive

# From a spec file — read and discuss before building
uv run factory ceo ~/ideas/my-app-spec.md --mode interactive

Interactive mode also works on existing projects. The CEO studies the backlog, eval scores, open issues, and experiment history, then discusses what to work on before executing:

uv run factory ceo ~/factory-projects/my-app --mode interactive

# Seed the conversation with a topic
uv run factory ceo ~/factory-projects/my-app --mode interactive --focus "auth layer"

Build Workflow

When you already have a spec file, a GitHub repo, or a clear description, the Factory builds directly — no interactive step needed:

uv run factory ceo ~/ideas/spec.md
uv run factory ceo https://github.com/user/repo
uv run factory ceo "Build a personal homepage with a blog"

The pipeline: Researcher surveys best practices → Strategist creates a plan → Builder implements and commits → E2E gate confirms it runs. Override the output directory with --dir my-site. (If you start with a raw idea via --mode interactive, the CEO refines it into a spec first, then transitions into this same build pipeline automatically.)

After the first build, a backlog appears at .factory/strategy/backlog.md — deferred features that feed future improvement cycles. Manage it with uv run factory backlog-list, uv run factory backlog-add, and uv run factory backlog-remove.


Improve + Focus Workflow

Point the Factory at an existing project and it enters Improve mode automatically:

uv run factory ceo ~/factory-projects/my-app

Each cycle: observehypothesizebuildreviewmeasuredecide (keep or revert) → archive. The Strategist picks work from the backlog using FEEC priority (Fix > Exploit > Explore > Combine).

When you know exactly what you want, --focus pins a single target — one hypothesis, one experiment, done:

uv run factory ceo ~/my-app --focus "add dark mode toggle"
uv run factory ceo ~/my-app --focus 42                       # GitHub issue
uv run factory ceo ~/my-app --focus "owner/repo#42"          # Issue shorthand

Other ways to steer: file GitHub issues (the Strategist reads them), add to the backlog manually, or pass a spec file with --prompt.


Research Mode

Research mode optimizes a measurable metric against a dataset — benchmarks, model tuning, prompt optimization, solver agents. The Factory is a meta-harness: give it a research objective and it builds the evaluation harness, then iteratively improves the system under test.

uv run factory ceo "build a harness to solve SWE-bench lite" --mode research

The CEO collects your research target (metric, run command), mutable surfaces (files the Builder can change), and fixed surfaces (ground truth — never touched). Each cycle runs: baselinefailure analysisresearchhypothesizebuildre-measurekeep/revert. The metric ratchets forward — it can never go below the previous best.

Once the project is set up, wrap it in a loop for continuous optimization — each cycle is a full experiment pass:

uv run factory ceo ~/my-solver --loop
uv run factory ceo ~/my-solver --loop --interval 900    # Custom interval
uv run factory tmux ~/my-solver --loop                  # Detached tmux session

The factory auto-detects the research target in factory.md and enters research mode. Failed experiments don't stop the loop — the factory learns from them and moves on.

Project Metric What the factory optimizes
SWE-bench solver resolve rate Agent logic, prompts, localization strategies
Math reasoning solve rate Chain-of-thought templates, tool call patterns
Text/Sketch → CAD query accuracy Query builder, schema mapping, entity resolution

See Getting Started — Research Mode for phase tables, leakage guards, and progression details.


Eval System

Every change is measured by an 11-dimension composite score across three tiers: Hygiene (tests, lint, types, coverage), Growth (API surface, experiment diversity, observability), and Project (user-defined domain metrics). On first run, uv run factory discover auto-detects your project's language and framework to generate the eval profile. See Eval System for scoring details, weights, and guards.


Self-Evolving Agents (ACE)

The Factory improves itself. Every keep/revert decision becomes training data — ACE (Autonomous Context Engineering) runs a Reflect → Curate → Inject loop that evolves agent playbooks from real experiment outcomes. Rules that correlate with kept experiments get reinforced; rules from reverts get pruned. Run uv run factory ceo /path --mode meta on a regular cadence. See ACE Self-Improvement.


Built with the Factory

Project What it does Mode
SWE-bench solver Autonomous agent that resolves GitHub issues, improved via failure analysis Research
HMMT math solver Multi-agent team that solved HMMT Feb 2025 Combinatorics Problem 7 Research
Text/Sketch → CAD Natural language and sketches to executable CadQuery Python code for 3D models Research
HLS design space explorer Per-function AI agents + ILP solver for HLS optimization — 92% execution time reduction Build
Pluck iOS app that extracts structured data from screenshots using on-device AI Build + Improve
SDG Hub Agent-maintained open-source framework for synthetic data generation Build + Improve
The Factory itself Runs on itself in meta mode — agent playbooks are evolved from its own experiment outcomes Meta

Built something with the Factory? Open a PR to add it here.


CLI Quick Reference

# Core workflow
uv run factory ceo <path|url|idea>              # Build or improve
uv run factory ceo <path> --mode interactive    # Discuss, then execute
uv run factory ceo <path> --focus "..."         # One target, one experiment
uv run factory ceo <path> --loop                # Continuous loop (research projects)
uv run factory tmux <path> --loop               # Loop in detached tmux session

# Agents & analysis
uv run factory agent <role> --task "..." --project <path>
uv run factory eval <path>                      # Run evals
uv run factory precheck <path>                  # Hard precheck gate
uv run factory study <path>                     # Analyze code
uv run factory diff <path> --exp1 N --exp2 M    # Compare experiments
uv run factory history <path>                   # Experiment history

# Backlog
uv run factory backlog-list <path>
uv run factory backlog-add <path> "..."
uv run factory backlog-remove <path> "..."

# Operations
uv run factory dashboard                        # Live web dashboard on :8420
uv run factory discover <path>                  # Auto-detect eval profile
uv run factory config show                      # Show resolved config
uv run factory tmux-ls                          # List active tmux sessions
uv run factory tmux-stop --path <path>          # Stop a tmux session

See uv run factory --help for the complete list. The factory supports crash-resilient resume — the CEO reconstructs context from the event log and .factory/ state on restart.


Runners

The factory supports multiple CLI backends. Default is Claude Code — switch with --runner or FACTORY_RUNNER:

# Direct
CODEX_API_KEY="..." uv run factory ceo /path --runner codex
BOBSHELL_API_KEY="..." uv run factory ceo /path --runner bob

# Via config.toml profile (persistent)
uv run factory ceo /path --profile codex

Configure profiles in ~/.factory/config.toml:

[credentials.codex]
FACTORY_RUNNER = "codex"
CODEX_API_KEY = "..."

[credentials.bob]
FACTORY_RUNNER = "bob"
BOBSHELL_API_KEY = "..."

Run uv run factory config show to see resolved config, or uv run factory config edit to open the file. See Setup Guide for full details.


Plugin Agents

Every factory agent is available as a standalone Claude Code subagent:

uv run factory install                   # Install all 9 agents to ~/.claude/agents/
claude --agent factory-ceo "improve this project"
claude --agent factory-researcher "study the auth system"

Documentation

Doc What's in it
Setup Guide Installation, authentication, environment variables
Getting Started Lifecycle walkthrough, research mode details, factory.md config
Architecture Three-layer system, agent roles, state machine, data flow
Eval System Hygiene/growth/project tiers, scoring, guards, precheck
Configuration factory.md reference — all sections and options
ACE Self-Improvement How the factory evolves its own agent playbooks
Contributing Dev setup, code style, testing, PR workflow

Development

uv sync --all-groups              # Install all deps including dev
uv run pytest -v                  # Full test suite
uv run ruff check .               # Lint
uv run mypy factory/              # Type check

License

MIT — Akash Srivastava

Yorumlar (0)

Sonuc bulunamadi