Metadata-Version: 2.4
Name: openswarm-ai
Version: 1.0.3
Summary: Multi-agent orchestration framework
Author-email: Erfan Momeni <erfamm5@gmail.com>
License: MIT
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: litellm>=1.40.0
Requires-Dist: typer>=0.12.0
Requires-Dist: pydantic>=2.0
Requires-Dist: pyyaml>=6.0
Requires-Dist: rich>=13.0
Requires-Dist: prompt-toolkit>=3.0
Provides-Extra: mcp
Requires-Dist: mcp[cli]>=1.0; extra == "mcp"
Provides-Extra: dev
Requires-Dist: pytest>=8.0; extra == "dev"
Requires-Dist: pytest-asyncio>=0.23; extra == "dev"
Requires-Dist: ruff>=0.4; extra == "dev"
Dynamic: license-file

# OpenSwarm

[![PyPI version](https://img.shields.io/pypi/v/openswarm-ai?label=pypi)](https://pypi.org/project/openswarm-ai/)
[![Python 3.12+](https://img.shields.io/badge/python-3.12%2B-blue)](https://pypi.org/project/openswarm-ai/)
[![License: MIT](https://img.shields.io/badge/license-MIT-green)](https://github.com/erfamm/openswarm/blob/master/LICENSE)

**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.

```yaml
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"]
```

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

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

## Install

```bash
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](https://pipx.pypa.io/stable/how-to/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:

```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 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)

<details open>
<summary><strong>Claude Code</strong></summary>

```bash
claude mcp add openswarm -- openswarm-mcp
```
</details>

<details>
<summary><strong>Cursor</strong></summary>

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

- Name: `openswarm`
- Type: `command`
- Command: `openswarm-mcp`
</details>

<details>
<summary><strong>GitHub Copilot (VS Code)</strong></summary>

Add to VS Code `settings.json`:

```json
{
  "github.copilot.chat.mcp.servers": {
    "openswarm": {
      "command": "openswarm-mcp",
      "args": []
    }
  }
}
```
</details>

<details>
<summary><strong>Windsurf</strong></summary>

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

```json
{
  "mcpServers": {
    "openswarm": {
      "command": "openswarm-mcp",
      "args": []
    }
  }
}
```
</details>

<details>
<summary><strong>OpenCode</strong></summary>

Add to your `opencode.json`:

```json
{
  "mcp": {
    "openswarm": {
      "type": "local",
      "command": ["openswarm-mcp"],
      "timeout": 300000
    }
  }
}
```
</details>

### 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

```bash
# 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.

```bash
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

```yaml
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](https://docs.litellm.ai/docs/providers) 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:

```yaml
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.

```yaml
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

```bash
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
