crewyard

中文文档
0.3.0发布于 2026/11/03MIT零依赖构建来源已验证

Local-first coordination engine for independent AI coding agents.

原始使用文档
文档目录20 节
  1. Coding agents are good at writing code. Coordinating them i…
  2. One workflow. From goal to merge.
  3. Built for real engineering workflows
  4. Multi-agent, multi-provider
  5. Parallel work, without collisions
  6. Repository-aware context
  7. Review-to-merge orchestration
  8. Local by design
  9. Multiple ways to interact
  10. Installation
  11. Install with npm
  12. Install from source
  13. Quick start
  14. How it works
  15. Interfaces
  16. Safety is an architectural boundary
  17. Documentation
  18. Development
  19. Contributing
  20. Project status & licensing
Crewyard: a crew of AI coding agents planning, coding, reviewing and merging together

Ship together. Merge safely.

The local-first coordination engine for independent AI coding agents.
One workflow for planning, parallel execution, review, and integration.

Get started   ·   How it works   ·   Architecture   ·   Documentation

TypeScript Bun MCP Local first npm MIT license CI

In development toward v1.0 · MIT licensed · No hosted backend required


[!NOTE] Pre-1.0 software. Crewyard is being developed toward v1.0, and commands and configuration may still change between minor versions. Run crew <command> --help to see what your installed version supports, and check the changelog before upgrading.

Coding agents are good at writing code. Coordinating them is the hard part.

Running multiple independent agents on one repository creates a new set of engineering problems: duplicated work, conflicting edits, stale context, overflowing PR queues, repeated reviews, and merges that invalidate work already in flight.

Crewyard is the coordination layer between your agents and your repository. It gives independent agents a shared, durable workflow—without forcing them to use the same model, provider, or chat session.

Without coordinationWith Crewyard
Agents pick up overlapping tasksAtomic claims, leases, and scope reservations
Parallel work collides in one checkoutIsolated Git worktrees per task
Context drifts as branches changeVersioned repository indexing and context packs
Reviews repeat or become staleEvidence-aware incremental review
PRs accumulate and merges conflictSerialized integration and GitHub-aware queueing
Crashes lose orchestration stateDurable events, reconciliation, and recovery

One workflow. From goal to merge.

flowchart LR
    A["01 · Plan"] --> B["02 · Schedule"]
    B --> C["03 · Execute"]
    C --> D["04 · Review"]
    D --> E["05 · Integrate"]
    E --> F["06 · Reconcile"]
    F -. "Unblock dependent work" .-> B

    classDef default fill:#18181b,color:#fafafa,stroke:#52525b,stroke-width:1px;
    classDef finish fill:#134e4a,color:#f0fdfa,stroke:#2dd4bf,stroke-width:1px;
    class F finish;
  1. Plan with dependencies. Turn a goal into a validated task graph and approve the plan before execution.
  2. Schedule safely. Dispatch ready tasks subject to concurrency limits, dependencies, and reservations.
  3. Run independently. Assign work to Codex, Claude Code, OpenCode, or another compatible process in isolated worktrees.
  4. Review with evidence. Collect structured handoffs, tests, and review findings associated with the work being proposed.
  5. Integrate deliberately. Coordinate pull requests and validation without bypassing repository protections.
  6. Recover and continue. Persist events, reconcile remote state, re-index the base, and unblock downstream tasks.

Built for real engineering workflows

Multi-agent, multi-provider

Use independent coding agents and account profiles without tying the workflow to one LLM provider.

Adapters: Codex · Claude Code · OpenCode · Ollama · Generic process

Parallel work, without collisions

Atomic task claims, fenced leases, dependency-aware scheduling, and dedicated Git worktrees are designed to keep work isolated and accountable.

Repository-aware context

Build versioned indexes and retrieve code context with symbol, reference, path, and commit provenance.

Review-to-merge orchestration

Track review evidence, integration readiness, GitHub checks, queue state, and merge outcomes across the full task lifecycle.

Local by design

Keep orchestration state in a local SQLite database. No Crewyard-hosted backend is required; configured providers and GitHub may still use network services.

Multiple ways to interact

Operate through a CLI, MCP tools, portable Agent Skills, a background service, or a terminal dashboard—all backed by the same application services.

Installation

Crewyard targets Linux (x64, arm64), macOS (Apple Silicon, Intel), and Windows (x64). You need Git 2.38+, at least one compatible coding-agent CLI, and the authenticated GitHub CLI for GitHub integration.

Install with npm

npm install --global crewyard
crew version

npm installs a prebuilt binary for your platform; Node.js 22+ is only needed for the small launcher. Standalone binaries that need neither Node.js nor Bun are attached to every GitHub release.

Install from source

Requires Bun 1.4.4+.

git clone https://github.com/orafaelfragoso/crewyard.git
cd crewyard
bun install --frozen-lockfile
bun run crew doctor

In a source checkout, run bun run crew from the repository root wherever this README uses crew.

See installation and upgrades for supported platforms, direct binary installs, checksums, backups, and removal.

Quick start

New to Crewyard? The getting-started guide walks through every step and what each one does.

# 1. Register your repository
cd ~/src/my-repo
crew doctor
crew init --repo .

# 2. Add an account: reuse the Claude Code login you already have
crew account add --provider claude --name personal --use-default-config
crew account test claude/personal

# 3. Create agents that use it, and turn on AI review
crew agent create --name architect --role architect --account claude/personal
crew agent create --name engineer  --account claude/personal
crew agent create --name reviewer  --role reviewer  --account claude/personal
crew config set review.reviewer_agent reviewer --yes

# 4. Give it a goal, review the plan, approve it
crew goal create --title "Add JWT authentication" --description-file goal.md
crew goal plan GOAL-1 --agent architect
crew goal status GOAL-1
crew goal approve GOAL-1 --yes

# 5. Let it work, and watch
crew service start
crew dashboard

GOAL-1 is the ID that goal create prints. Codex and OpenCode work the same way with --provider codex or --provider opencode. For separate logins, several accounts or API keys, see Accounts; to change or remove agents, see Agents and policies.

To let Claude Code, Codex and OpenCode sessions you start yourself use Crewyard's tasks and tools, run crew install (add --dry-run first to see the changes). The guide lists where it installs the MCP server and Skills.

The background service keeps dispatching ready tasks; the MCP server alone does not keep work running after its host exits.

Getting started →  ·  Accounts →  ·  Agents →  ·  CLI reference →

How it works

Crewyard is a local orchestration system, not an LLM gateway. It coordinates tools you already use while maintaining its own authoritative task and integration state.

flowchart TB
    DEV["Developer / automation"] --> CLI["CLI & terminal dashboard"]
    AG["Codex · Claude Code · OpenCode · Other"] --> MCP["MCP server & Agent Skills"]

    CLI --> APP["Shared application services"]
    MCP --> APP

    APP --> PLAN["Task graph & scheduler"]
    APP --> CTX["Versioned index & context"]
    APP --> REV["Review & integration"]
    APP --> EVT["Events & observability"]

    PLAN --> WORK["Isolated Git worktrees"]
    REV <--> GH["GitHub & CI"]
    PLAN --> DB[("Local SQLite")]
    CTX --> DB
    REV --> DB
    EVT --> DB

    classDef default fill:#18181b,color:#fafafa,stroke:#52525b,stroke-width:1px;
    classDef store fill:#134e4a,color:#f0fdfa,stroke:#2dd4bf;
    class DB store;

The architecture is deliberately provider-agnostic. The CLI and MCP server are thin interfaces over shared application services. SQLite WAL provides durable coordination; external GitHub and process side effects are reconciled rather than treated as a single database transaction.

For an engineering-level explanation, see the architecture overview, indexing model, and task/lease design.

Interfaces

InterfaceDesigned forReference
CLI (crew)Humans, scripts, and command-capable agentsCLI reference
MCP (crew mcp)Agent hosts that support Model Context ProtocolMCP reference
Agent SkillsPortable instructions for planning, execution, review, integration, recovery, and contextSkills guide
Local service (crew service)Continuous dispatch, reconciliation, and integrationArchitecture
Terminal dashboard (crew dashboard)Operational visibility into active work and eventsCLI reference

Safety is an architectural boundary

Autonomous agents should not be able to bypass coordination simply by asking for it.

  • Fenced task leases prevent stale workers from submitting work as the current owner.
  • Scoped worktrees separate parallel tasks and protect developer checkouts.
  • Approval and policy gates control high-impact transitions.
  • GitHub branch protections remain authoritative during integration.
  • Idempotent outbox operations support retries and recovery after interruption.
  • Local credential handling keeps secrets out of repository configuration and orchestration event logs.

Security properties depend on configuration and implementation maturity. Read the security model and default policies before enabling unattended workflows.

Documentation

Start hereLearn more
Getting startedFrom install to your first agent-run task, step by step
AccountsLogging agents in: existing logins, separate logins, API keys
Agents and policiesRoles, using several accounts, changing and removing agents
InstallationSupported setups, local data, upgrades, and backups
CLI referenceCommands, options, and machine-readable output
MCP host setupConnecting existing coding-agent tools
Agent SkillsReusable workflows for compatible agents
ArchitectureServices, storage, scheduling, and system boundaries
Security modelTrust boundaries, permissions, and operational safety
Recovery runbooksFailures, restarts, and reconciliation

Development

The codebase is a Bun/TypeScript monorepo with packages for scheduling, providers, indexing, reviews, integration, storage, and telemetry.

bun install --frozen-lockfile
bun run typecheck
bun run lint
bun test
bun run build:binaries  # platform binaries, manifest and checksums
bun run build:npm       # npm tarballs, after binaries have been built

See AGENTS.md for code conventions, the architecture documentation for package boundaries, and the release process for distribution.

Contributing

Contributions are welcome. Read the contributing guide and the code of conduct, and use GitHub Issues for bug reports and feature requests. Report vulnerabilities privately as described in the security policy.

Project status & licensing

Crewyard is under active development toward v1.0. Releases are cut automatically from main with release-please; every release is listed in the changelog and on the releases page. See the release process for how npm packages and platform binaries are built and published.

Licensed under the MIT License. Copyright © 2026 Rafael Fragoso.


More agents. Less coordination overhead.

Built for developers who want autonomous work to reach a safe, reviewable merge.

Get started · Explore the architecture · Issues

版本与发布记录

crewyard 共发布 5 个版本,首次发布于 2026/11/03;过去 12 个月发布了 5 个版本。最新版本附带 npm provenance 构建来源证明,可追溯到公开的 CI 构建记录。

版本发布日期距今
0.3.0latest2026/11/03今天
0.2.02026/11/03今天
0.1.12026/11/03今天
0.1.02026/11/03今天
0.0.0-stage2026/11/03今天

关于 crewyard 的常见问题

crewyard 是什么?

独立AI编码代理的局部优先协调引擎。

如何安装 crewyard?

在项目目录运行 npm install crewyard,或使用 pnpm add / yarn add / bun add。它还提供命令行工具 crew,可以用 npx crew 直接运行。

crewyard 支持 TypeScript 吗?

该包的 package.json 没有声明类型入口。需要类型提示时,可以在 npm 上查找社区维护的 @types/crewyard 是否存在。

crewyard 需要什么版本的 Node.js?

package.json 的 engines 字段要求 Node.js >=22。版本不符时 npm 默认只给出警告,开启 engine-strict 后会拒绝安装。

crewyard 可以商用吗?

crewyard 使用 MIT 许可。宽松许可:允许商业使用、修改和再分发,通常只需在分发时保留原版权与许可声明。以上为概括,不构成法律意见,请以许可原文为准。

crewyard 还在维护吗?

最新版本 0.3.0 发布于 2026/11/03(今天);至今共发布 5 个版本,其中过去 12 个月发布了 5 个;首次发布于 2026/11/03。发布频率只是参考信号:功能稳定的工具可能很少需要更新,建议结合源码仓库的 issue 和提交记录判断。