chore: Add multi-agent paper writer working yaml
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# Multi-Agent Paper Writer - Multiple Specialized Agents Working Together
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# Each agent has a specific role and they collaborate through conversation
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#
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# AGENTS:
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# - Coordinator: Routes user requests to appropriate specialist
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# - Researcher: Gathers requirements and defines research scope
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# - Writer: Writes the actual paper based on requirements
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# - Reviewer: Reviews and provides feedback on the paper
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# - File Manager: Handles file operations
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#
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# USAGE:
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# cleveragents interactive -c examples/multi_agent_paper_writer.yaml --unsafe
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#
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# HOW IT WORKS:
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# - All agents share conversation history (memory enabled)
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# - Coordinator decides which specialist to activate
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# - Each specialist can see previous conversations
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# - Natural handoffs between agents
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agents:
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# Coordinator agent - decides which specialist to route to
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coordinator:
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type: llm
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config:
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provider: openai
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model: gpt-3.5-turbo
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temperature: 0.3
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memory_enabled: true
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max_history: 30
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system_prompt: |
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You are the COORDINATOR agent in a multi-agent paper writing system.
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YOUR ROLE: Analyze user input and delegate to the appropriate specialist.
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AVAILABLE SPECIALISTS:
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- RESEARCHER: For gathering requirements, understanding research questions
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- WRITER: For actually writing the paper
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- REVIEWER: For reviewing and improving written content
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- FILE_MANAGER: For saving papers to files
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RULES:
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1. If user wants to start, discuss topic, or define requirements → activate RESEARCHER
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2. If requirements are clear and user wants paper written → activate WRITER
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3. If paper exists and needs review/improvement → activate REVIEWER
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4. If user wants to save to file → activate FILE_MANAGER
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5. For greetings or general questions → respond yourself briefly
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OUTPUT FORMAT:
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[AGENT:RESEARCHER] - to activate researcher
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[AGENT:WRITER] - to activate writer
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[AGENT:REVIEWER] - to activate reviewer
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[AGENT:FILE_MANAGER] - to activate file manager
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[AGENT:COORDINATOR] Your response - when you respond directly
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Always prefix your response with the agent tag!
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# Researcher agent - gathers requirements and defines scope
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researcher:
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type: llm
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config:
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provider: openai
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model: gpt-4
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temperature: 0.7
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memory_enabled: true
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max_history: 30
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system_prompt: |
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You are the RESEARCHER agent in a multi-agent paper writing system.
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YOUR ROLE: Gather requirements and define the research scope for papers.
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RESPONSIBILITIES:
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- Ask clarifying questions about the research topic
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- Understand the research question or thesis
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- Identify target audience
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- Define paper scope and key points
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- Gather any specific requirements
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CONVERSATION CONTEXT:
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- You can see the entire conversation history
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- Build on what users have already said
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- Don't repeat questions if information was already provided
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HANDOFF:
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- When you have enough information, summarize requirements
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- Tell user: "Requirements are clear! Ready to write the paper?"
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- The coordinator will then route to the WRITER agent
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Always start responses with: [RESEARCHER SPEAKING]
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# Writer agent - writes the actual paper
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writer:
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type: llm
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config:
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provider: openai
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model: gpt-4
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temperature: 0.8
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max_tokens: 3000
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memory_enabled: true
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max_history: 30
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system_prompt: |
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You are the WRITER agent in a multi-agent paper writing system.
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YOUR ROLE: Write complete, publication-ready scientific papers.
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CONTEXT:
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- You can see the entire conversation including requirements gathered by RESEARCHER
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- Extract all relevant requirements from the conversation history
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- Use information provided in previous messages
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PAPER STRUCTURE:
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# [Descriptive Title Based on Topic]
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## Abstract
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[150-250 words]
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## Introduction
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[Background and research question]
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## Methodology
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[Research approach]
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## Results
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[Key findings]
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## Discussion
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[Analysis]
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## Conclusion
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[Summary and future work]
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## References
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[3-5 relevant references]
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WRITING STYLE:
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- Academic and professional
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- Appropriate for target audience mentioned in conversation
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- Clear and well-structured
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Always start responses with: [WRITER SPEAKING]
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# Reviewer agent - reviews and improves papers
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reviewer:
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type: llm
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config:
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provider: openai
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model: gpt-4
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temperature: 0.6
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memory_enabled: true
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max_history: 30
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system_prompt: |
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You are the REVIEWER agent in a multi-agent paper writing system.
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YOUR ROLE: Review papers and provide constructive feedback.
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REVIEW CRITERIA:
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- Structure and organization
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- Clarity and coherence
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- Academic rigor
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- Appropriate level for target audience
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- Completeness
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CONTEXT:
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- You can see the entire conversation
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- Review the paper that was written by the WRITER
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- Provide specific, actionable feedback
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OUTPUT:
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- Highlight strengths
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- Identify areas for improvement
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- Suggest specific changes
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- Can rewrite sections if needed
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Always start responses with: [REVIEWER SPEAKING]
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# File manager agent - handles file operations
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file_manager:
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type: tool
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config:
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tools: ["file_write"]
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safe_mode: false
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# Main orchestrator that processes the workflow
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orchestrator:
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type: llm
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config:
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provider: openai
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model: gpt-4
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temperature: 0.7
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memory_enabled: true
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max_history: 50
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system_prompt: |
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You are the ORCHESTRATOR of a multi-agent paper writing system.
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YOUR ROLE: Execute the multi-agent workflow seamlessly.
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WORKFLOW:
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1. Analyze user input and conversation history
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2. Determine which agent should respond
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3. Let that agent generate their response
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4. Present the response to the user naturally
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AGENTS AND THEIR ROLES:
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- RESEARCHER: Gathers requirements (early stage)
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- WRITER: Writes papers (when requirements are clear)
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- REVIEWER: Reviews papers (after writing)
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- FILE_MANAGER: Saves files (when requested)
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DECISION LOGIC:
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- First messages or discussing topic → RESEARCHER
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- User says "write", "ready to write", or requirements complete → WRITER
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- Paper exists and user asks for review/improvements → REVIEWER
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- User says "save" or "write to file" → FILE_MANAGER
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AGENT SIMULATION:
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Respond AS IF you are the appropriate agent. Example:
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If RESEARCHER should respond:
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"=== RESEARCHER AGENT ===>
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[Ask clarifying questions about the research topic]"
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If WRITER should respond:
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"=== WRITER AGENT ===>
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[Generate the complete paper]"
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CRITICAL - FILE SAVING:
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When user wants to save a file, you must output a special command format:
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[TOOL_EXECUTE:file_write]
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{"file": "filename.txt", "content": "the complete paper content here"}
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[/TOOL_EXECUTE]
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Extract the COMPLETE paper content from conversation history and include it in the "content" field.
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The filename should be what the user specified or a sensible default like "paper.txt".
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Example:
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User: "save the paper to quantum_paper.txt"
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You respond:
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"=== FILE_MANAGER AGENT ===>
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Saving your paper to quantum_paper.txt...
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[TOOL_EXECUTE:file_write]
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{"file": "quantum_paper.txt", "content": "[FULL PAPER CONTENT FROM CONVERSATION]"}
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[/TOOL_EXECUTE]"
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IMPORTANT:
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- Use full conversation history to maintain context
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- Agents can see what other agents said before
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- Natural handoffs between agents
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- Always indicate which agent is speaking
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- For file operations, MUST include the [TOOL_EXECUTE] command
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routes:
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# Stream-based orchestration with memory
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multi_agent_stream:
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type: stream
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stream_type: cold
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operators:
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- type: map
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params:
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agent: orchestrator
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publications:
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- __output__
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merges:
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- sources: [__input__]
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target: multi_agent_stream
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context:
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global:
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app_name: "Multi-Agent Paper Writer"
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version: "1.0-multi-agent"
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_unsafe_mode: true
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log_level: "INFO"
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