python3 scripts/swarm_entry.py

undefined

undefined

undefined

undefined

python3 scripts/swarm_entry.py --action reset

undefined

undefined

Please select a configuration method:

**A. Auto-assign** — Automatically configure the agent team based on your existing models
**B. Add new model** — I will recommend mainstream models for you to choose from
**C. Custom configuration** — You manually specify the model for each agent

Please reply with A/B/C

python3 scripts/swarm_entry.py --action init

✅ Agent Swarm configuration completed! You can now start using the agent team.

undefined

Extract all available models under `models.providers` from the returned configuration.

undefined

undefined

Please select a configuration method:

**A. Auto-assign** — Automatically configure the agent team based on your existing models
   - High-complexity tasks (coding/writing/analysis) → Use your most powerful model
   - Medium-complexity tasks (planning/design) → Use medium models
   - Lightweight tasks (search/Q&A) → Use the lowest-cost model

**B. Add new model** — I will recommend mainstream models for you to choose from
   - Claude (Anthropic)
   - GPT-4o (OpenAI)
   - Gemini (Google)
   - DeepSeek V3 (DeepSeek)
   - Qwen Max (Alibaba Cloud)
   - GLM-4 (Zhipu)

**C. Custom configuration** — You manually specify the model for each agent

Please reply with A/B/C or tell me your choice directly.

python3 scripts/setup_wizard.py

{
  "models": {
    "providers": {
      "my-deepseek": {
        "baseUrl": "https://api.deepseek.com/v1",
        "apiKey": "sk-xxx (provided by user)",
        "api": "openai-completions",
        "authHeader": "Authorization",
        "models": [{
          "id": "deepseek-chat",
          "name": "DeepSeek V3",
          "contextWindow": 64000,
          "maxTokens": 8192
        }]
      }
    }
  }
}

Receive task → Judge complexity
├── Simple task → Execute directly
└── Complex task → Enter orchestration mode

// Continuously spawn in the same round, these tasks will be executed in parallel
// After sub-tasks are completed, they will report results separately, and the main agent will collect and summarize the results

// Method 1: Direct continuous spawn
sessions_spawn({ task: "Search for LangChain materials...", agentId: "researcher", label: "research-langchain" })
sessions_spawn({ task: "Search for AutoGPT materials...", agentId: "researcher", label: "research-autogpt" })
sessions_spawn({ task: "Search for CrewAI materials...", agentId: "researcher", label: "research-crewai" })
// Three tasks are executed in parallel, reporting results separately

// Method 2: Dispatch in loop (clearer)
const frameworks = ["LangChain", "AutoGPT", "CrewAI"]
frameworks.forEach(name => {
  sessions_spawn({
    task: `Search for the features, advantages, disadvantages, and applicable scenarios of the ${name} framework, output a structured summary to /workspace/research/${name.toLowerCase()}.md`,
    agentId: "researcher",
    label: `research-${name.toLowerCase()}`
  })
})
// Sub-tasks automatically report results after completion, main agent summarizes all results

// Serial execution requires waiting for the previous task to complete, then spawn the next one after receiving the report
// Process: Research → (Wait for report) → Writing → (Wait for report) → Illustration → (Wait for report) → Review

// Step 1: First dispatch research task
sessions_spawn({ task: "Research AI Agent frameworks...", agentId: "researcher" })
// Wait for researcher to report results...

// Step 2: After receiving research results, dispatch writing task
sessions_spawn({ 
  task: "Based on the research materials in /workspace/research/, write a comparative analysis article...", 
  agentId: "writer" 
})
// Wait for writer to report...

// Step 3: After article is completed, dispatch illustration task
sessions_spawn({ task: "Generate illustrations for the article...", agentId: "designer" })

// Phase 1: Parallel research (dispatched simultaneously)
sessions_spawn({ task: "Search for LangChain...", agentId: "researcher", label: "r1" })
sessions_spawn({ task: "Search for AutoGPT...", agentId: "researcher", label: "r2" })
sessions_spawn({ task: "Search for CrewAI...", agentId: "researcher", label: "r3" })

// Wait for all 3 research tasks to complete...

// Phase 2: Serial processing (based on summarized results)
sessions_spawn({ task: "Integrate research materials and write a report...", agentId: "writer" })
// Wait for writer to complete...

sessions_spawn({ task: "Review report quality...", agentId: "reviewer" })

User: "Research mainstream AI Agent frameworks and write a comparative analysis article"

Orchestration Plan:
├── 🔍 researcher × 3 (Parallel)
│   ├── Search LangChain - Organize features, advantages/disadvantages, cases
│   ├── Search AutoGPT - Organize features, advantages/disadvantages, cases  
│   └── Search CrewAI - Organize features, advantages/disadvantages, cases
├── ✍️ writer (Serial, wait for research completion)
│   └── Integrate materials and write comparative analysis article
├── 🎨 designer (Serial)
│   └── Generate framework comparison diagram/architecture diagram
└── 🔎 reviewer (Serial)
    └── Review article quality and propose improvement suggestions

User: "Help me refactor the authentication module of this project"

Orchestration Plan:
├── 📋 pm (Optional)
│   └── Analyze requirements and break down refactoring steps
├── 👨‍💻 coder
│   └── Analyze existing code and implement refactoring
└── 🔎 reviewer (Serial)
    └── Code review to ensure quality

User: "Analyze this sales data and generate a monthly report"

Orchestration Plan:
├── 📊 analyst
│   └── Data cleaning, statistical analysis, insight discovery
├── ✍️ writer (Serial)
│   └── Write analysis report
└── 🎨 designer (Serial)
    └── Generate data visualization charts

User: "Help me set up automatic daily checks of GitHub trending every morning"

Orchestration Plan:
├── 🤖 automator
│   └── Write script + set cron scheduled task

Validation failed for tool "write":
  - content: must have required property 'content'

// Phase 1: Write each chapter in parallel
sessions_spawn({ task: "Write Chapter 1-2: Abstract and Background...", agentId: "writer", label: "ch01" })
sessions_spawn({ task: "Write Chapter 3-4: Core Content...", agentId: "writer", label: "ch02" })
sessions_spawn({ task: "Write Chapter 5-6: Conclusion...", agentId: "writer", label: "ch03" })

// Phase 2: After all chapters are completed, splice with exec
exec(`
  cat sections/ch01.md > FINAL-REPORT.md
  cat sections/ch02.md >> FINAL-REPORT.md
  cat sections/ch03.md >> FINAL-REPORT.md
`)

undefined

undefined

Sub-agent task fails
    ↓
Main agent receives failure notification
    ↓
Analyze error cause
    ├── Output too long → Split into multiple sub-tasks, dispatch more writers in parallel
    ├── Tool unavailable → Switch to exec or other solutions
    ├── Model limitation → Adjust thinking/model configuration
    └── Timeout → Split task or extend timeout
    ↓
Execute solution
    ↓
Summarize results

// Original task failed (output too long and truncated)
// After receiving notification, main agent switches to segmented strategy

// Solution: Split into 3 sub-tasks
sessions_spawn({
  task: "Write Chapter 1-2 of the report (Abstract, Background), limit to 1500 words...",
  agentId: "writer",
  label: "report-part1"
})

sessions_spawn({
  task: "Write Chapter 3-4 of the report (Core Content), limit to 1500 words...",
  agentId: "writer",
  label: "report-part2"
})

sessions_spawn({
  task: "Write Chapter 5-6 of the report (Conclusion, References), limit to 1000 words...",
  agentId: "writer",
  label: "report-part3"
})

// Merge with exec after all are completed

undefined

---

sessions_spawn({
  task: "Specific task description, including necessary context and expected output format",
  agentId: "researcher",   // Specify Agent ID
  model: "glm",            // Optional, override Agent's default model
  thinking: "off",         // Optional, control thinking mode (off/minimal/low/medium/high)
  label: "task-name",      // Optional, for tracking
  runTimeoutSeconds: 300   // Optional, timeout in seconds
})

sessions_spawn({
  task: "Generate illustrations for the article...",
  agentId: "designer",
  thinking: "off"    // Mandatory! Gemini Image does not support thinking
})

A good task description should include:
1. Clear goal - What to do
2. Necessary context - Background information
3. Output requirements - Format, save location
4. Constraints - Limits and precautions

Example:
"Search for the latest information on the LangChain framework and organize the following content:
1. Core features and architecture
2. Advantages and disadvantages
3. Typical use cases
4. Comparison with other frameworks

Output format: Markdown
Save to: /workspace/research/langchain.md
Language: Chinese"

undefined

Detailed template see [references/statistics-template.md](references/statistics-template.md)

/workspace/agents/
├── pm/           # 📋 Product Manager
│   ├── SOUL.md   # Personality definition
│   └── AGENTS.md # Work specifications
├── researcher/   # 🔍 Researcher
├── coder/        # 👨‍💻 Programmer
├── writer/       # ✍️ Writer
├── designer/     # 🎨 Designer
├── analyst/      # 📊 Analyst
├── reviewer/     # 🔎 Reviewer
├── assistant/    # 💬 Assistant
└── automator/    # 🤖 Automator

undefined

undefined

/workspace/agents/<agent_id>/
└── memory/
    ├── experience.md    # Human-readable experience records
    └── experience.json  # Structured experience data

undefined

undefined

undefined

**Method 2: Agent actively reads experience**

Add guidelines to the agent's AGENTS.md:
```markdown

undefined

undefined

This way, the agent will automatically summarize experience after completing the task without manual intervention.

python3 scripts/init_agents.py --base-path /workspace/agents