conductor-ai 0.4.0

A framework for humans who orchestrate multiple AI coding agents.

🎵 Conductor

A framework for humans who orchestrate multiple AI coding agents.

Structured handoff · Trust calibration · Continuous improvement

English · 中文

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Supported AI Coding Agents

⌨️ CLI	Claude Code Anthropic	Codex CLI OpenAI	Gemini CLI Google	Kimi Code Moonshot AI
🖥️ IDE	Cursor AI IDE	Windsurf Codeium	Antigravity Google DeepMind	GitHub Copilot Chat
	Aider Open Source	OpenCode Open Source	Continue Open Source	+ any tool that reads/writes files

Conductor is file-protocol based — any AI coding tool that can read and write files is compatible.

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The Problem

You're running Claude Code in one terminal, Cursor in another, maybe Codex for review — all on different projects. By end of day:

• 🤯 You can't remember what each agent decided
• 🔁 New sessions repeat mistakes from yesterday
• 📂 HANDOFF notes, devlogs, and error logs are scattered everywhere
• 🤔 You don't know which agent to trust for which task

Conductor brings order to this chaos.

What's Inside

📖 Methodology — The 12 Dimensions of Human-Agent Interaction

We identified 12 dimensions that matter when a human works with multiple AI agents:

#	Dimension	What It Covers
1	Handoff Management	Passing context between sessions without loss
2	Knowledge Capture	Recording decisions, errors, and QA pairs
3	Trust Calibration	Knowing when to verify vs. trust the agent
4	Cognitive Load	Managing your mental bandwidth across agents
5	Prompt Quality	Improving how you communicate with agents
6	Agent Profiling	Tracking each agent's strengths and weaknesses
7	Tool Selection	Picking the right agent for the right task
8	Feedback Loops	Turning errors into prevention rules
9	Attention Allocation	Deciding which project needs you right now
10	Disagreement Resolution	Handling conflicting agent advice
11	Cross-Agent Consistency	Keeping agents aligned on decisions
12	Energy Modeling	Adjusting oversight based on your fatigue

Read more: docs/

📁 Templates — Copy, paste, done

Template	Purpose
HANDOFF.md	Session-end context handoff
CLAUDE.md	Agent rules with handoff protocol
ERROR_BOOK.md	AI mistake tracker for trust calibration
TRUST_PROFILE.md	Agent reliability scorecard
DESIGN.md	UI design system for consistent agent output

🧰 CLI — See all your projects at a glance

$ conductor status

🎵 Conductor · Project Status
┌─────────────┬────────────┬────────┬──────────────────────┐
│ Project     │ Last Active│ Status │ Next Step            │
├─────────────┼────────────┼────────┼──────────────────────┤
│ wenyuan     │ 6h ago     │ ✅     │ Refactor README      │
│ network-opt │ 2h ago     │ ✅     │ VPS setup            │
│ conductor   │ 30m ago    │ ✅     │ Write tests          │
│ old-project │ 3d ago     │ 🔴     │ Archive or continue? │
└─────────────┴────────────┴────────┴──────────────────────┘
 📅 2026-04-09 │ 4 projects │ 5 decisions │ 12 files Δ

$ conductor digest ./my-project

🎵 Conductor · Project Digest
  📁 my-project  │  📅 2026-04-01 → 2026-04-09  │  🔄 5 sessions

  📋 Decisions Made
  ┌────────────┬──────────────────────────────┐
  │ 2026-04-09 │ Chose JWT over sessions      │
  │ 2026-04-08 │ Python 3.9+ compatibility    │
  └────────────┴──────────────────────────────┘

  ⚠️ Errors & Pitfalls
  ┌────────────┬──────────────────────────────┐
  │ 2026-04-09 │ bcrypt 5.x broke hashes      │
  └────────────┴──────────────────────────────┘

$ conductor memory add "Uses FastAPI + PostgreSQL" -t fact -g backend
✅ Memory #1 added (fact)

$ conductor memory search "FastAPI"
🔍 matches: #1 [fact] Uses FastAPI + PostgreSQL

Quick Start

Option A: Just use the templates (no install needed)

1. Copy templates/HANDOFF.md.template to your project as HANDOFF.md
2. Copy the handoff protocol from templates/CLAUDE.md.template into your project's CLAUDE.md
3. Tell your AI: "Read HANDOFF.md before starting. Update it before ending."

Option B: Install the CLI

pip install conductor-ai
conductor init ./my-project
conductor status

Core Concepts

The Handoff Protocol

Every session ends with a structured handoff:

## 2026-04-09
- done: Implemented user auth module
- decisions: Chose JWT over sessions (stateless, scales better)
- pitfall: bcrypt 5.x changed default rounds — broke existing hashes
- next: Add password reset flow

500 token max. If you can't summarize it, you don't understand it.

→ Full protocol

Trust Calibration

Don't blindly trust or distrust your AI. Calibrate per domain:

Layer	Method
L1	Verify outcomes — does the code run?
L2	Cross-verify — have another agent review
L3	Progressive trust — try on one file first
L4	Demand explanation — ask WHY, not just WHAT

→ Full framework

Task Sizing (S/M/L)

Not every task needs a full planning cycle:

Size	Time	Process
S	< 30min	Just do it → test → commit → HANDOFF
M	1-3h	Brief plan → execute → HANDOFF
L	> 3h	Brainstorm → plan → TDD → review → HANDOFF

→ Full guide

Why Conductor?

vs.	Difference
CrewAI / LangGraph	They orchestrate agent-to-agent. We orchestrate human-to-agents.
OpenSpec	OpenSpec manages specs within one session. We manage across sessions and agents.
CLAUDE.md alone	CLAUDE.md is one file. We're a complete methodology + tools.
Nothing	You're losing decisions, repeating mistakes, and burning context window tokens.

Roadmap

• v0.1 — Methodology docs + templates + conductor status
• v0.2 — conductor digest — extract decisions/errors from project history
• v0.3 — conductor retro — interactive post-session agent review
• v0.4 — conductor memory — persistent cross-session knowledge store

Philosophy

"If you just drive the AI to work and walk away, you'll never know what you don't know. The original sin of AI-assisted development is not reviewing, not reflecting, not improving."

Conductor is built on three principles:

1. Structure over ceremony — Lightweight protocols that actually get followed, not heavy processes that get skipped.
2. Observe, then trust — Build trust through data (error books, trust profiles), not assumptions.
3. The human improves too — It's not just about making AI better. It's about making you better at working with AI.

Contributing

Contributions welcome! Please read the methodology docs first to understand the philosophy.

License

MIT