Which integrations are available for this server?

Automates GitHub issue-to-PR workflows: creates tasks from issues, spawns parallel coding agents, runs tests, reviews pull requests, and opens/updates PRs with autonomous CI fix loops. Sends notifications about task progress, test results, and pull request activity to a Telegram chat via bot.

How do I use codeswarm?

This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.

codeswarm

Official

by aaxnet

Overview Schema Source Related Servers Discussions Admin

TypeScript

Local

IMPORTANT

Give CodeSwarm a GitHub issue. It turns it into a tested and reviewed pull request. Not AI that writes code — AI engineering team that ships production-ready PRs.

Related MCP server: genswarms-fleet-mcp

Why CodeSwarm

Before	With CodeSwarm
1 issue → hours of manual work	1 issue → 5 AI agents → 1 ready-to-merge PR
AI pair-programming	AI engineering team — GitHub-native, autonomous, self-hosted

TIP

Wow effect: 1 GitHub Issue → 5 AI agents → parallel development → tests → review → CI fixes → 1 ready-to-merge PR

How it Works

flowchart TD
    A[GitHub Issue<br/>ai:ready] --> B[Planner<br/>decomposes to JSON]
    B --> C1[Researcher]
    B --> C2[Coder]
    B --> C3[Tester]
    C1 --> D[Worktrees<br/>.codeswarm/task-xxx]
    C2 --> D
    C3 --> D
    D --> E[Tester<br/>npm test / typecheck / lint]
    E -->|fail| C2
    E -->|pass| F[Reviewer<br/>security + correctness]
    F -->|changes| C2
    F -->|approved| G[GitHub PR]
    G --> H{CI}
    H -->|pass| I[Ready to merge]
    H -->|fail| J[CI Agent<br/>parse logs → fix → push]
    J --> H

GitHub Issue (ai:ready)
  ↓ Planner → { goal, tasks: [...] }
  ├─ Researcher → finds files, git history, plan
  ├─ Coder ×N → isolated worktrees → commits
  └─ Tester → npm test / lint / typecheck
       ↓ fail → Coder
       ↓ pass → Reviewer
            ↓ changes → Coder
            ↓ approved → GitHub PR → CI → CI Agent fixes on fail → Ready

Features


Parallel worktrees	Every agent works in isolated `git worktree` under `.codeswarm/task-xxx/` — no branch collisions.
Conflict Resolver	LLM-assisted 3-way merge. Detects `CONFLICT`, resolves, re-runs tests.
Autonomous PR loop	CI failure → parse logs → generate fix → push → re-run. Zero manual intervention.
Provider-agnostic	`LLMProvider` interface. OpenAI, Anthropic, OpenRouter, Google, Ollama, LiteLLM, any OpenAI-compatible + mock.
Cost & safety guards	`max_agents`, `max_cost_usd`, `timeout_minutes`, `max_depth`. Sandboxed `filesystem: repository_only`, `network: restricted`.
Full audit trail	Every `tool.called`, `file.changed`, `test.`, `pr.` stored in `agent_events` (SQLite now, Postgres in v0.3).
Notifications	Telegram + Dashboard + GitHub comments.
MCP native	Control swarm from Claude / Cursor / OpenCode via `swarm.*` tools.

Quick Start

1. Install

npx codeswarm init

Creates codeswarm.yml + .env. Fill .env:

DATABASE_URL=file:./data/codeswarm.db
GITHUB_TOKEN=ghp_xxx
GITHUB_OWNER=your-org
GITHUB_REPO=your-repo
OPENAI_API_KEY=sk-xxx
# or ANTHROPIC_API_KEY=sk-ant-xxx
# TELEGRAM_BOT_TOKEN=xxx
# TELEGRAM_CHAT_ID=xxx

2. Run

Docker (recommended)

docker-compose up --build
# API: http://localhost:3000
# Dashboard: http://localhost:3001

Local

npm install
npm run build
npm run dev
# or
node apps/api/dist/index.js &
node apps/dashboard/dist/index.js

3. Trigger

Via label — add ai:ready to any open issue (webhook auto-creates task).

Via CLI — mock (no keys needed)

codeswarm issue run 421 --mock
# ✅ Local pipeline done: completed PR: https://github.com/example/repo/pull/438

Via CLI — real

codeswarm issue run 421
codeswarm issue plan 421   # plan only, no code

NOTE

codeswarm issue run first tries API (http://localhost:3000); if API is down, it falls back to local mock pipeline — great for demos and CI.

CLI Reference

codeswarm init                  # init codeswarm.yml + .env
codeswarm run --mock            # start API + worker hint
codeswarm issue run <num> [--mock] [--repo owner/repo]
codeswarm issue plan <num>      # planner only
codeswarm pr review <num>
codeswarm pr fix <num>          # CI fix loop
codeswarm status                # ASCII dashboard in terminal
codeswarm agents
codeswarm queue
codeswarm mcp                   # MCP server over stdio

Architecture

GitHub
  │ webhook / poller
  ▼
Orchestrator ──► Task Queue ──► Agents (×N) ──► Git Worktrees ──► Tests / CI ──► GitHub PR
  │                 │                │
  │                 │                ├─ Planner / Researcher / Coder / Tester / Reviewer / CI Agent
  │                 │                └─ WorktreeManager + ConflictResolver
  │                 └─ InMemory (MVP) / Redis + BullMQ (prod)
  └─ projects / tasks / agents / worktrees / artifacts / pull_requests / reviews / costs / agent_events
     SQLite (MVP) → PostgreSQL (v0.3)

Repo layout

codeswarm/
├── apps/
│   ├── api/          # HTTP API, webhook, orchestrator
│   ├── worker/       # queue worker (scale horizontally)
│   └── dashboard/    # real-time Web UI
├── packages/
│   ├── core/         # types, state-machine, cost, DB schema
│   ├── llm/          # OpenAI/Anthropic/Google/Ollama/LiteLLM/Mock
│   ├── agents/       # 6 agents + orchestrator
│   ├── github/       # Issues/PRs/Comments/Checks
│   ├── git/          # worktree manager
│   ├── queue/        # InMemory / Redis
│   ├── sandbox/      # security
│   ├── mcp/          # MCP server
│   └── telegram/     # notifier
├── tests/            # vitest (15 tests)
├── examples/         # wallet race condition
└── docker/

MCP

Control the swarm from any MCP client:

{
  "mcpServers": {
    "codeswarm": { "command": "npx", "args": ["codeswarm", "mcp"] }
  }
}

Tool	Description
`swarm.list_tasks()`	List tasks with status & progress
`swarm.run_task(issueNumber)`	Start swarm for an issue
`swarm.cancel_task(taskId)`	Cancel running task
`swarm.retry_task(taskId)`	Retry failed task
`swarm.spawn_agent(taskId, role)`	Spawn specific agent
`swarm.status()`	`activeAgents`, `queueLength`, `prsToday`, `successRate`
`swarm.review_pr(prNumber)`	Trigger reviewer

Dashboard

http://localhost:3001 — live polling of http://localhost:3000/api/status

┌─────────────────────────────────────────┐
│ CODESWARM                               │
├─────────────────────────────────────────┤
│ ACTIVE AGENTS: 7                        │
│ QUEUE: 12                               │
│ PRs TODAY: 8                            │
│ SUCCESS RATE: 81%                       │
├─────────────────────────────────────────┤
│ #421 Wallet race condition   ██████ 80% │
│ #422 API timeout             ████░░ 55% │
└─────────────────────────────────────────┘

Terminal dashboard: codeswarm status

Configuration

codeswarm.yml (see codeswarm.yml.example):

task:
  max_agents: 4
  max_retries: 3
  max_cost_usd: 2.00
  timeout_minutes: 30
swarm:
  max_total_agents: 10
  max_depth: 2
llm:
  provider: openai # openai | anthropic | openrouter | google | ollama | litellm
  model: gpt-4o-mini
  temperature: 0.2
queue:
  type: memory # or redis
  redisUrl: redis://localhost:6379
sandbox:
  enabled: true
  network: restricted
  filesystem: repository_only

Cost example: gpt-4o-mini ≈ $0.15/$0.6 per 1M tokens. A full pipeline ~$0.01–0.05 with mock, ~$0.2–0.6 with real models.

Comparison

Tool	Positioning
OpenHands	AI developer
SWE-agent	Issue → code fix
Aider	AI pair programming
CodeSwarm	Orchestration of multiple AI developers — GitHub-native, multi-agent, parallel, autonomous, self-hosted, provider-agnostic

Roadmap

Full roadmap with timeline, Gantt, metrics and exit criteria → ROADMAP.md

gantt
    title CodeSwarm — Timeline
    dateFormat  YYYY-MM-DD
    axisFormat %b %Y
    section MVP
    v0.1 shipped    :done, v01, 2026-09-01, 2026-09-01
    section Scale
    v0.2 Parallel   :done, v02, 2026-09-01, 2026-09-02
    section Depth
    v0.3 Plugins    :done, v03, 2026-09-02, 2026-09-03
    section GA
    v1.0 Platform   :done, v10, 2026-09-03, 2026-09-04

Version	Status	Target	Highlights
v0.1	✅ Shipped	Sep 2026	Issues → Planner/Coder/Tester/Reviewer → PR, worktrees, OpenAI/Anthropic, Docker, SQLite, CLI
v0.2	✅ Shipped	Sep 2026	Parallel coders ×5, Redis/BullMQ, CI fix loop, Telegram, MCP, Dashboard SSE, cost guards
v0.3	✅ Shipped	Sep 2026	Conflict auto-merge, Postgres, multi-repo, vector memory, custom agents, plugin system
v1.0	✅ Shipped	Sep 2026	GitHub App, org RBAC, self-hosted models, marketplace, gVisor sandbox, analytics

3 coders in parallel without file stomp
80% red CI auto-fixed, cost never exceeds $2.00, p95 < 8 min visible in Dashboard

90% conflicts auto-merged, SQLite → Postgres via flag, 2 community agents contributed

TIP

Want to shape the roadmap? React with 👍 on issues labeledroadmap or open a roadmap/proposal.

Contributing

See CONTRIBUTING.md. PRs welcome!

npm install
npm run build   # tsc -b
npm test        # vitest — 15 tests

License

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityMaintained

ResponsivenessNo issues

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Table of Contents

Why CodeSwarm

How it Works

Features

Quick Start

1. Install

2. Run

3. Trigger

CLI Reference

Architecture

MCP

Dashboard

Configuration

Comparison

Roadmap

Contributing

License

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Swarm MCP

genswarms-fleet-mcp

agent-mesh MCP Server

taskswarm