openswarm-ai 1.0.3

Multi-agent orchestration framework

OpenSwarm

Cut your AI coding costs by ~70%. Define agent teams in YAML — cheap models do bulk work, expensive models make decisions. Works inside Claude Code, Cursor, Copilot, and any MCP-compatible IDE.

agents:
  - name: "senior"
    model: claude-sonnet-4-20250514
    role: senior
    rules: ["Break down tasks", "Review junior's output"]

  - name: "junior"
    model: deepseek-chat
    role: junior
    rules: ["Execute assigned tasks", "Write tests"]

openswarm run "Build user auth API" --config team.yaml

Senior breaks it down, delegates to Junior, reviews results, assembles final output. One command.

Install

pipx install "openswarm-ai[mcp]"

This installs everything: the openswarm CLI, the openswarm-mcp server, and all dependencies. The [mcp] extra adds MCP server support for IDE integration (Claude Code, Cursor, etc.).

Why pipx? It installs OpenSwarm globally in an isolated environment — available from any project, no venv conflicts. Install pipx if you don't have it. Or use brew install pipx on macOS.

Get Started

1. Add team.yaml to your project

Drop this in your project root — like a CLAUDE.md, but for your agent team:

team:
  name: "backend-team"
  goal: "Build and maintain backend services"
  workflow: hierarchical
  lead: "senior"

agents:
  - name: "senior"
    role: senior
    model: claude-sonnet-4-20250514
    host: https://api.anthropic.com
    api_key: ${ANTHROPIC_API_KEY}
    max_tokens: 4096
    rules: ["Break down tasks", "Review output before approving"]

  - name: "junior"
    role: junior
    model: deepseek-chat
    host: https://api.deepseek.com/v1
    api_key: ${DEEPSEEK_API_KEY}
    max_tokens: 2048
    rules: ["Execute assigned tasks", "Write tests for all code"]

2. Register with your IDE (one-time)

Claude Code

claude mcp add openswarm -- openswarm-mcp

Cursor

Go to Cursor Settings → MCP → Add new MCP server:

• Name: openswarm
• Type: command
• Command: openswarm-mcp

GitHub Copilot (VS Code)

Add to VS Code settings.json:

{
  "github.copilot.chat.mcp.servers": {
    "openswarm": {
      "command": "openswarm-mcp",
      "args": []
    }
  }
}

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "openswarm": {
      "command": "openswarm-mcp",
      "args": []
    }
  }
}

OpenCode

Add to your opencode.json:

{
  "mcp": {
    "openswarm": {
      "type": "local",
      "command": ["openswarm-mcp"],
      "timeout": 300000
    }
  }
}

3. Use your IDE normally

That's it. Open your project, give coding tasks — your IDE automatically delegates to the team. No special commands, no prompting needed.

Supported IDEs

Tool	Integration	Status
Claude Code	MCP server (auto-discovery via .mcp.json)	Ready
OpenCode	MCP server (auto-discovery via opencode.json)	Ready
Cursor	MCP server (via Cursor settings)	Ready
Windsurf	MCP server (via ~/.codeium/windsurf/mcp_config.json)	Ready
GitHub Copilot	MCP server (via VS Code settings.json)	Ready

Any tool that supports MCP works with OpenSwarm — the setup is the same pattern everywhere.

How It Works

User: "Build user auth API"
  ↓
Senior (Claude): decomposes task
  ├── "Write User model" → Junior (DeepSeek)
  ├── "Write endpoints"  → Junior (DeepSeek)
  └── "Design JWT strategy" → Senior handles directly
  ↓
Junior returns code → Senior reviews → requests fixes or approves
  ↓
Final result → User

How does the IDE know to use OpenSwarm? When team.yaml exists in your project, the MCP server tells your IDE to delegate all coding tasks to the team automatically. You just use your IDE normally.

What if the task doesn't match the team? For example, you have a frontend team but ask a backend question. The team's lead agent recognizes it's outside their scope and says so — your IDE then handles it directly. You never need to decide; just ask, and the system routes it to the right place.

MCP Tools

Tool	Description
openswarm_run(task, team?)	Delegate a task to an agent team (auto-selects if one team exists)
openswarm_teams()	List available teams (local + global)
openswarm_team_info(team)	Show team details — agents, models, workflow

Config discovery: team.yaml / openswarm.yaml in project root, openswarm/*.yaml subdirectory, and ~/.openswarm/teams/ globally.

CLI Usage

# Run with config file
openswarm run "Build a REST API" --config team.yaml

# Run with named team (from ~/.openswarm/teams/<name>.yaml)
openswarm run "Fix the login bug" --team backend

# Verbose — see inter-agent messages in real-time
openswarm run "Refactor auth module" --config team.yaml -v

# List all configured teams
openswarm team-list

# Show team details
openswarm team-info backend

# Run as Python module
python -m openswarm run "Do the thing" --config team.yaml

Interactive Mode (experimental)

Chat with your team in a persistent session. We're actively improving this.

openswarm interactive --team backend
openswarm interactive --team backend -v  # with real-time message display

Slash commands: /quit, /team, /history, /clear. Ctrl+C cancels current task without exiting.

Team Config

team:
  name: "backend-team"
  goal: "Build and maintain backend services"
  workflow: hierarchical
  lead: "senior"
  max_rounds: 10

agents:
  - name: "senior"
    role: senior
    model: claude-sonnet-4-20250514
    host: https://api.anthropic.com
    api_key: ${ANTHROPIC_API_KEY}
    max_tokens: 4096
    temperature: 0.7
    rules:
      - "Break down tasks and delegate to junior"
      - "Review output before marking done"

  - name: "junior"
    role: junior
    model: deepseek-chat
    host: https://api.deepseek.com/v1
    api_key: ${DEEPSEEK_API_KEY}
    max_tokens: 2048
    temperature: 0.3
    rules:
      - "Execute assigned tasks"
      - "Write tests for all code"

Agent Config Fields

Field	Required	Default	Description
name	yes	—	Agent identifier
role	yes	—	What this agent does
model	yes	—	LLM model name
host	yes	—	API endpoint URL (OpenAI-compatible)
api_key	yes	—	API key (supports ${ENV_VAR} syntax)
max_tokens	no	4096	Max tokens per response (≥ 1)
temperature	no	0.7	Sampling temperature (0.0–2.0)
max_history	no	40	Max messages kept in agent history (≥ 1)
rules	no	[]	Agent behavior rules

What models can I use? Any model with an OpenAI-compatible API — Claude, GPT, DeepSeek, Mistral, Llama, local models via Ollama. If litellm supports it, OpenSwarm supports it.

Workflow Types

Type	How it works	Best for
hierarchical	Lead delegates, reviews, requests revisions, assembles	Dev teams, review workflows
pipeline	Sequential: A → B → C — each agent transforms output	Content pipelines, data processing
collaborative	All agents discuss → consensus	Brainstorming, code review, decision-making

Pipeline Workflow

Sequential chain where each agent receives the previous agent's output:

team:
  name: "content-pipeline"
  goal: "Write and polish articles"
  workflow: pipeline

agents:
  - name: "writer"
    role: writer
    # ...
  - name: "editor"
    role: editor
    # ...
  - name: "reviewer"
    role: reviewer
    # ...

Agents execute in config list order. No lead required.

Collaborative Workflow

All agents see the task simultaneously and discuss in rounds. First agent in the list acts as moderator. Early exit if all agents agree. Moderator synthesizes the final answer.

team:
  name: "review-panel"
  goal: "Review and decide on architecture"
  workflow: collaborative
  max_rounds: 5

agents:
  - name: "moderator"
    role: architect
    # ... (first agent = moderator)
  - name: "backend"
    role: backend-specialist
    # ...
  - name: "frontend"
    role: frontend-specialist
    # ...

Requires at least 2 agents. No lead field needed.

Error Handling

• LLM calls retry once on transient errors (rate limits, timeouts, connection issues)
• Permanent errors (bad API key, invalid model) fail immediately with a clear message
• openswarm run shows clean error output instead of tracebacks
• Config validation catches problems at load time (missing lead agent, invalid temperature/token values)

Cost Comparison

Why pay for an expensive model to write boilerplate? Let the cheap model do the heavy lifting.

Example: "Build user auth API"

Without OpenSwarm — Claude Code does everything with Claude Sonnet:

Step	Model	Input tokens	Output tokens	Cost
Decompose task	Sonnet	~2,000	~500	$0.009
Write User model	Sonnet	~3,000	~1,500	$0.019
Write endpoints	Sonnet	~4,000	~2,000	$0.024
Write tests	Sonnet	~5,000	~2,500	$0.030
Review & fix	Sonnet	~6,000	~1,500	$0.027
Total		~20,000	~8,000	~$0.109

Sonnet: $3/M input, $15/M output

With OpenSwarm — Senior (Sonnet) delegates bulk work to Junior (DeepSeek):

Step	Model	Input tokens	Output tokens	Cost
Decompose + delegate	Sonnet	~2,000	~500	$0.009
Write User model	DeepSeek	~3,000	~1,500	$0.001
Write endpoints	DeepSeek	~4,000	~2,000	$0.002
Write tests	DeepSeek	~5,000	~2,500	$0.002
Review & approve	Sonnet	~4,000	~500	$0.020
Total		~18,000	~7,000	~$0.034

DeepSeek: $0.14/M input, $0.28/M output

Result: ~70% cost reduction on the same task, with the expensive model only doing what it's good at — architecture and review.

At scale

Scenario	Without OpenSwarm	With OpenSwarm	Savings
10 features/day	~$1.09	~$0.34	69%
100 features/day	~$10.90	~$3.40	69%
With Opus as lead	~$3.00	~$0.85	72%

The more work you can route to cheap models, the more you save. Senior handles ~20% of tokens but makes the decisions that matter.

Environment Variables

Variable	Default	Purpose
OPENSWARM_CONFIG_DIR	~/.openswarm	Global config directory
OPENSWARM_LOG_LEVEL	INFO	Log level

Development

python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev,mcp]"
pytest tests/ -v
ruff check src/ tests/ && ruff format src/ tests/

License

MIT