AI-native collaborative coding inside VS Code.
Turn engineering discussions into structured decisions and executable code tasks.
Conductor combines real-time team collaboration, isolated Git workspaces, agentic code intelligence, and multi-provider AI into a single developer environment.
Modern AI coding tools are powerful — but they are mostly single-user tools.
Tools like GitHub Copilot, Cursor, and ChatGPT help individuals write code. But software development is a team activity.
Most engineering knowledge lives in meetings, chat discussions, and design reviews. By the time code is written, the reasoning behind decisions is often lost.
Conductor explores a different approach: instead of starting from code prompts, we start from engineering discussions.
Conductor transforms engineering conversations into structured inputs for AI systems.
Team Discussion
↓
AI Distillation
↓
Structured Engineering Decisions
↓
Code Intelligence Agent
↓
Implementation
This allows AI systems to understand not only the codebase, but also the context behind engineering decisions.
Teams collaborate inside shared rooms with real-time chat, file sharing, code snippets, and TODO tracking.
Each collaboration room runs inside its own Git workspace using bare repositories, Git worktrees, and a custom VS Code filesystem (conductor://). This allows AI agents to explore code safely without affecting developers' local repositories.
Conductor uses a Brain orchestrator with tool-based agent loops instead of simple RAG. The Brain (strong model) dispatches specialist sub-agents, each navigating the repository using 46 code tools (up to 40 iterations, 500K token budget). For PR review, a dedicated PR Brain v2 takes over via transfer_to_brain("pr_review") and runs a coordinator loop with two dispatch primitives: dispatch_subagent (file-range scoped, 3 falsifiable checks) and dispatch_dimension_worker (full-diff sweep through one role lens — security/correctness/concurrency/reliability/performance/test_coverage/api_contract).
| Tool | Description |
|---|---|
grep |
Regex search (ripgrep) |
read_file |
Read file content with line range |
list_files |
Directory tree |
find_symbol |
AST-based symbol definition (with role classification) |
find_references |
All usages of a symbol |
file_outline |
All definitions in a file |
get_dependencies |
Files this file imports |
get_dependents |
Files that import this file |
git_log |
Recent commits; search= param filters by commit message |
git_diff |
Diff between refs |
ast_search |
Structural AST search (ast-grep, $VAR/$$$MULTI patterns) |
get_callees |
Functions called within a function |
get_callers |
Functions that call a given function (cross-file) |
git_blame |
Per-line authorship with commit hash, author, date |
git_show |
Full commit details (message + diff); reads pre-change file at HEAD~1:path |
find_tests |
Test functions covering a given function/class |
test_outline |
Test file structure with mocks, assertions, fixtures |
trace_variable |
Data flow tracing: alias detection, arg→param mapping, sink/source patterns |
compressed_view |
File signatures + call relationships + side effects (~80% token savings) |
module_summary |
Module-level summary: services, models, functions, file list (~95% savings) |
expand_symbol |
Expand a symbol from compressed view to full source code |
run_test |
Execute a test file or function; returns pass/fail + output (optional verification) |
The Brain dispatches agents via dispatch_agent / dispatch_swarm tools, each with per-agent tool sets. A Token Budget Controller emits NORMAL → WARN_CONVERGE → FORCE_CONCLUDE signals. An Evidence Evaluator gates answers before finalising: requires file:line references, ≥2 tool calls, ≥1 file accessed.
Conductor supports AWS Bedrock (Claude, Qwen, DeepSeek, Mistral, Nova, NVIDIA, GLM), Anthropic Direct, OpenAI, Alibaba DashScope, and Moonshot. ProviderResolver health-checks all configured providers at startup and selects the best available model. All providers implement chat_with_tools().
Full Jira integration (OAuth 3LO) with 5 agent tools and a 3-phase workflow: investigate (code analysis) → mark code (TODO markers with dependencies) → update ticket. The Task Board shows Jira tickets grouped by Epic (mine=green, unassigned=orange) with dependency-aware drag-and-drop to AI Working Space.
Docker images ship with dev-default secrets. For ECS/K8s, CONDUCTOR_* environment variables override any secret in conductor.secrets.yaml. See docs/GUIDE.md §21.7 for the full variable reference.
# Start the backend
cd backend
pip install -r requirements.txt
uvicorn app.main:app --reloadOpen the VS Code extension and start a session. Then ask questions like:
- "Where is the loan approval logic implemented?"
- "Trace how the payment service is called."
- "Explain the dependency graph of this module."
┌──────────────────────────────────┐ ┌──────────────────────────────────────────┐
│ VS Code Extension │ │ FastAPI Backend │
│ │ │ │
│ ┌────────────────────────────┐ │ WS │ ┌───────────────────────────────────┐ │
│ │ SessionFSM │ │◄────┼──│ WebSocket Manager (rooms/broadcast)│ │
│ │ WebSocketService │ │ │ └───────────────────────────────────┘ │
│ │ CollabPanel + @AI commands │ │ │ │
│ │ /ask, /pr slash menu │ │ │ ┌───────────────────────────────────┐ │
│ └────────────────────────────┘ │ │ │ Brain Orchestrator (strong) │ │
│ │ │ │ dispatch_agent / dispatch_swarm │ │
│ ┌────────────────────────────┐ │HTTP │ │ transfer_to_brain (PR Brain) │ │
│ │ WorkspaceClient │◄─┼─────┼──│ ask_user (mid-loop clarify) │ │
│ │ WorkspacePanel (wizard) │ │ │ │ TaskTelemetry per-task usage │ │
│ │ FileSystemProvider │ │ │ └───────────────────────────────────┘ │
│ └────────────────────────────┘ │ │ │
│ │ │ ┌───────────────────────────────────┐ │
│ │ │ │ AgentLoopService (sub-agents) │ │
│ │ │ │ 4-layer system prompt │ │
│ │ │ │ LLM ←→ 42 Code Tools │ │
│ │ │ │ → BudgetController │ │
│ │ │ │ → EvidenceEvaluator → SSE stream │ │
└──────────────────────────────────┘ │ └───────────────────────────────────┘ │
│ │
│ ┌───────────────────────────────────┐ │
│ │ AI Provider Layer │ │
│ │ ProviderResolver → health check │ │
│ │ ├─ ClaudeBedrockProvider │ │
│ │ ├─ ClaudeDirectProvider │ │
│ │ └─ OpenAIProvider │ │
│ └───────────────────────────────────┘ │
│ │
│ ┌───────────────────────────────────┐ │
│ │ Git Workspace Service │ │
│ │ bare clone → worktree per room │ │
│ └───────────────────────────────────┘ │
│ │
│ ┌───────────────────────────────────┐ │
│ │ PostgreSQL (Liquibase-managed) │ │
│ │ tables + task telemetry │ │
│ └───────────────────────────────────┘ │
└──────────────────────────────────────────┘
Current prototype includes:
- VS Code collaboration extension with slash-command
@AIchat and workflow visualization - FastAPI backend with Brain orchestrator (dispatches specialist agents)
- Agentic code intelligence (46 tools, 4-layer prompt architecture)
- PR Brain v2 — coordinator-worker (agent-as-tool) PR review: a Sonnet coordinator surveys the diff, dispatches scope-bounded workers (scoped
dispatch_subagent+ dimension-sliceddispatch_dimension_worker) from 7 role templates, classifies severity itself, and runs deterministic post-passes (P8 reflection against Phase 2 facts, P11 per-finding verifier, P13 phantom-symbol scanners for Python/Go/Java, P14 stub-caller detector, diff-scope filter). Mandatory-dispatch detector (Tier 1 path + Tier 2+-line content) forces security/reliability coverage on auth/crypto/migration PRs regardless of survey. - Fact Vault (short-term memory per PR review — task-scoped SQLite cache shared across sub-agents; Phase 9.15)
- Hardened tree-sitter scan — subprocess-isolated parsing with SIGKILL-on-timeout + JSX-depth heuristic; tree-sitter upgraded to 0.25 / language-pack (Phase 9.18)
- Atlassian readonly enrichment — PR Brain pre-fetches linked Jira tickets + Confluence pages via a service-account API token (one classic token covers both products), splices the flattened body into the coordinator's context so severity calibrates against acceptance criteria and intent drift is caught (Phase 7.8.6); see
docs/JIRA_TICKET_STANDARD.mdfor the ticket shape this expects - Isolated Git workspaces per room
- Task Board: TODO dependency markers (
{jira:TICKET#N|after:M|blocked:OTHER}), Epic-grouped Jira tickets, drag-and-drop AI Working Space - Chat persistence: write-through micro-batch Postgres + Redis hot cache
- Browser tools: Playwright Chromium automation for web browsing from agents
- Multi-provider AI support (Bedrock, Anthropic, OpenAI, DashScope, Moonshot)
- Task-hierarchy telemetry (per-task token usage + cost tracking via the
tasktable) - Jira integration (OAuth 3LO, 5 agent tools, 3-phase investigate→mark→update workflow)
- Cloud-ready:
CONDUCTOR_*env vars override secrets for ECS/K8s deployment - 2045+ automated tests (533 tool-related + parity)
Upcoming features:
- AI decision distillation from discussions
- Code change proposals with diff preview and review
- Model B delegate authentication (no PAT required)
- Enterprise access control and audit export
- Persistent codebase memory (background file-summary indexer)
- Teams and Slack integrations
See ROADMAP.md for full details.
cd backend
pytest # all tests (1655+)
pytest tests/test_code_tools.py -v # code tools (139 tests)
pytest tests/test_agent_loop.py -v # agent loop + 4-layer prompt (55 tests)
pytest tests/test_brain.py -v # Brain orchestrator (64 tests)
pytest tests/test_jira_tools.py -v # Jira agent tools (21 tests)
pytest tests/test_ai_provider.py -v # AI providers (131 tests)
pytest tests/test_compressed_tools.py -v # compressed view tools (24 tests)
pytest tests/test_code_review.py -v # code review pipeline (67 tests)
pytest --cov=. --cov-report=html # coverage report
# Tool parity (Python ↔ TypeScript)
make test-parity # contract + shape + subprocess validationWe welcome contributors interested in:
- AI developer tools
- Collaborative coding environments
- Agentic code intelligence
- Backend Guide — code walkthrough (EN + 中文)
- Roadmap — project phases and ADRs
- Claude — guide for AI coding assistants
现代 AI 编程工具很强大——但大多数都是单人工具。
GitHub Copilot、Cursor、ChatGPT 帮助个人写代码。但软件开发本质上是团队活动。
大多数工程知识存在于会议、聊天讨论和设计评审中。等到代码写出来，决策背后的原因往往已经消失了。
Conductor 探索一种不同的方式：不从代码提示出发，而从工程讨论出发。
Conductor 将工程对话转化为 AI 系统的结构化输入。
团队讨论
↓
AI 提炼
↓
结构化工程决策
↓
代码智能 Agent
↓
代码实现
这让 AI 系统不仅理解代码库，还能理解工程决策背后的上下文。
团队在共享房间内协作，支持实时聊天、文件共享、代码片段和 TODO 追踪。
每个协作房间运行在独立的 Git 工作区中，使用裸仓库、Git worktree 和自定义 VS Code 文件系统（conductor://）。AI Agent 可以安全探索代码，不影响开发者本地仓库。
Conductor 使用 Brain 编排器和基于工具的 Agent 循环，而非简单的 RAG。Brain（强模型）分发专业子 Agent，每个 Agent 通过 46 个代码工具迭代探索代码库（最多 40 轮迭代，50 万 token 预算）。PR 评审由专门的 PR Brain v2 接管（通过 transfer_to_brain("pr_review")），运行协调循环，配两个分发原语：dispatch_subagent（按文件范围 + 3 个可证伪 check）和 dispatch_dimension_worker（按 bug 类别从一个 role lens 扫描整个 diff —— security/correctness/concurrency/reliability/performance/test_coverage/api_contract）。
工具详情见上方英文部分。
Brain 通过 dispatch_agent / dispatch_swarm 分发 Agent，每个 Agent 配有专属工具集。Token 预算控制器发出 NORMAL → WARN_CONVERGE → FORCE_CONCLUDE 信号。证据评估器在最终确认答案前把关：要求文件:行号引用、≥2 次工具调用、≥1 个已访问文件。
支持 AWS Bedrock（Claude、Qwen、DeepSeek、Mistral、Nova 等）、Anthropic Direct、OpenAI、阿里 DashScope 和 Moonshot。ProviderResolver 在启动时对所有已配置的提供商做健康检查，自动选择最优模型。所有提供商均实现 chat_with_tools()。
# 启动后端
cd backend
pip install -r requirements.txt
uvicorn app.main:app --reload
# 启动扩展
cd extension
npm install
npm run compile
# 在 VS Code 中按 F5 启动扩展开发主机打开 VS Code 扩展并开始会话，然后提问例如：
- "贷款审批逻辑在哪里实现的？"
- "追踪支付服务是如何被调用的。"
- "解释这个模块的依赖图。"
架构图见上方英文部分。
当前原型包括：
- VS Code 协作扩展（斜杠命令
@AI聊天与工作流可视化面板） - FastAPI 后端（Brain 编排器分发专业 Agent）
- Agentic 代码智能（46 个工具，4 层 prompt 架构）
- PR Brain v2 —— 协调-worker（agent-as-tool）PR 评审：Sonnet coordinator 扫描 diff，从 7 个 role 模板分发 scope-bounded worker（按文件范围的
dispatch_subagent+ 按 bug 类别的dispatch_dimension_worker），自己分类 severity，并运行确定性后置检查（P8 现存事实反思、P11 逐条 finding 验证、P13 Python/Go/Java 幻觉符号扫描器、P14 stub 调用检测、diff-scope 过滤）。强制分发检测器（Tier 1 路径 + Tier 2+行内容）在 auth/crypto/migration PR 上强制 security/reliability 覆盖。 - Fact Vault（PR review 会话级短期记忆 —— 任务作用域 SQLite 缓存，跨 sub-agent 共享；Phase 9.15）
- 硬化的 tree-sitter 扫描 —— 子进程隔离解析 + SIGKILL 超时 + JSX 嵌套深度启发式；tree-sitter 升级到 0.25 + language-pack（Phase 9.18）
- Atlassian 只读富化 —— PR Brain 用服务账号 API token（一把 classic token 通吃 Jira + Confluence）预拉取 PR 关联的 Jira 工单和 Confluence 设计文档，把铺平后的正文塞进 coordinator 上下文，让 severity 按 acceptance criteria 校准、并能抓到 intent drift（Phase 7.8.6）；工单形态规范见
docs/JIRA_TICKET_STANDARD.md - 每个房间独立的 Git 工作区
- 任务面板：TODO 依赖标记（
{jira:TICKET#N|after:M|blocked:OTHER}）、Epic 分组 Jira 票、拖拽 AI 工作区 - 聊天持久化：写穿透 micro-batch Postgres + Redis 热缓存
- 浏览器工具：Playwright Chromium 自动化
- 多提供商 AI 支持（Bedrock、Anthropic、OpenAI、DashScope、Moonshot）
- 任务层级遥测（
task表记录每个任务的 token 用量与成本） - Jira 集成（OAuth 3LO，5 个 Agent 工具，3 阶段 investigate→mark→update 流程）
- 云部署就绪：
CONDUCTOR_*环境变量覆盖 ECS/K8s 部署的 secrets - 2045+ 自动化测试（533 工具相关 + parity）
即将推出的功能：
- 从讨论中 AI 提炼工程决策
- 代码变更提案与 diff 预览审查
- Model B 委托认证（无需 PAT）
- 企业级访问控制与审计导出
- 持久化代码库记忆（后台文件摘要索引）
- Teams 和 Slack 集成
详见 ROADMAP.md。
cd backend
pytest # 所有测试 (1655+)
pytest --cov=. --cov-report=html # 覆盖率报告
# 工具一致性验证（Python ↔ TypeScript）
make test-parity在 config/conductor.secrets.yaml 中配置 AI 提供商凭证（参考 config/conductor.secrets.yaml.example）。
非敏感配置在 config/conductor.settings.yaml 中。
云部署时，通过 CONDUCTOR_* 环境变量覆盖 secrets.yaml 中的值。详见 docs/GUIDE.md §21.7。
欢迎对以下方向感兴趣的贡献者：
- AI 开发者工具
- 协作编码环境
- Agentic 代码智能