openswarm-ai 1.0.1

Multi-agent orchestration framework

OpenSwarm

Design AI teams in YAML. Cheap models do bulk work, expensive models make decisions — you control who does what.

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

pip install openswarm-ai

# With MCP server support
pip install openswarm-ai[mcp]

Integration

The easiest way to use OpenSwarm — add a team.yaml to your project and let your AI IDE delegate to your agent team automatically.

Supported

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.

Setup (one-time)

pip install openswarm-ai[mcp]

Then register the MCP server with your tool:

Claude Code

claude mcp add openswarm -- openswarm-mcp

OpenCode

Add to your opencode.json:

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

Cursor

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

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

Windsurf

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

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

GitHub Copilot (VS Code)

Add to VS Code settings.json:

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

Usage (per project)

Add a team.yaml to your project root — like CLAUDE.md, but for your agent team:

# team.yaml
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"]

  - 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"]

That's it. The MCP server auto-discovers team.yaml from the working directory. Claude Code and OpenCode see the tools and delegate when the task matches — no special prompting needed.

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

The server also discovers teams from openswarm/*.yaml in the project and ~/.openswarm/teams/ globally.

CLI Usage

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

# Run with named team (looks up ~/.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 module
python -m openswarm run "Do the thing" --config team.yaml

Interactive Mode (experimental)

Interactive REPL for chatting with your team. We're actively improving this as a separate effort.

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	Default	Description
name	required	Agent identifier
role	required	Agent role description
model	required	LLM model name
host	required	API endpoint URL
api_key	required	API key (supports ${ENV_VAR} syntax)
max_tokens	4096	Max tokens per response (≥ 1)
temperature	0.7	Sampling temperature (0.0–2.0)
max_history	40	Max messages kept in agent history (≥ 1)
rules	[]	Agent behavior rules

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

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)

Environment Variables

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

Development

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

Why OpenSwarm? Cost Comparison

Running everything through one expensive model wastes money. Most coding tasks — writing boilerplate, implementing endpoints, writing tests — don't need the most capable model. OpenSwarm lets you route work to the right model.

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.

License

MIT