> For the complete documentation index, see [llms.txt](https://whitepaper.aitech.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://whitepaper.aitech.io/agentforge/yaml/yaml-workflow-examples.md). # YAML Workflow Examples ### Multi-Agent Chain Workflow A workflow where multiple AI agents process information sequentially: ```yaml version: '1.0' blocks: start: type: starter name: Start inputs: startWorkflow: manual connections: success: agent-1-initiator agent-1-initiator: type: agent name: Agent 1 Initiator inputs: systemPrompt: You are the first agent in a chain. Your role is to analyze the input and create an initial response that will be passed to the next agent. userPrompt: |- Welcome! I'm the first agent in our chain. Input to process: Please create an initial analysis or greeting that the next agent can build upon. Be creative and set a positive tone for the chain! model: gpt-4o temperature: 0.7 apiKey: '{{OPENAI_API_KEY}}' connections: success: agent-2-enhancer agent-2-enhancer: type: agent name: Agent 2 Enhancer inputs: systemPrompt: You are the second agent in a chain. Take the output from Agent 1 and enhance it with additional insights or improvements. userPrompt: |- I'm the second agent! Here's what Agent 1 provided: Now I'll enhance this with additional details, insights, or improvements. Let me build upon their work! model: gpt-4o temperature: 0.7 apiKey: '{{OPENAI_API_KEY}}' connections: success: agent-3-refiner agent-3-refiner: type: agent name: Agent 3 Refiner inputs: systemPrompt: You are the third agent in a chain. Take the enhanced output from Agent 2 and refine it further, adding structure or organization. userPrompt: |- I'm the third agent in our chain! Here's the enhanced work from Agent 2: My job is to refine and organize this content. I'll add structure, clarity, and polish to make it even better! model: gpt-4o temperature: 0.6 apiKey: '{{OPENAI_API_KEY}}' connections: success: agent-4-finalizer agent-4-finalizer: type: agent name: Agent 4 Finalizer inputs: systemPrompt: You are the final agent in a chain of 4. Create a comprehensive summary and conclusion based on all the previous agents' work. userPrompt: |- I'm the final agent! Here's the refined work from Agent 3: As the last agent in our chain, I'll create a final, polished summary that brings together all the work from our team of 4 agents. Let me conclude this beautifully! model: gpt-4o temperature: 0.5 apiKey: '{{OPENAI_API_KEY}}' ``` ### Router-Based Conditional Workflow A workflow that uses routing logic to send data to different agents based on conditions: ```yaml version: '1.0' blocks: start: type: starter name: Start inputs: startWorkflow: manual connections: success: router-1 router-1: type: router name: Router 1 inputs: prompt: go to agent 1 if is greater than 5. else agent 2 if greater than 10. else agent 3 model: gpt-4o apiKey: '{{OPENAI_API_KEY}}' connections: success: - agent-1 - agent-2 - agent-3 agent-1: type: agent name: Agent 1 inputs: systemPrompt: say 1 model: gpt-4o apiKey: '{{OPENAI_API_KEY}}' agent-2: type: agent name: Agent 2 inputs: systemPrompt: say 2 model: gpt-4o apiKey: '{{OPENAI_API_KEY}}' agent-3: type: agent name: Agent 3 inputs: systemPrompt: say 3 model: gpt-4o apiKey: '{{OPENAI_API_KEY}}' ``` ### Web Search with Structured Output A workflow that searches the web using tools and returns structured data: ```yaml version: '1.0' blocks: 59eb07c1-1411-4b28-a274-fa78f55daf72: type: starter name: Start inputs: startWorkflow: manual connections: success: d77c2c98-56c4-432d-9338-9bac54a2d42f d77c2c98-56c4-432d-9338-9bac54a2d42f: type: agent name: Agent 1 inputs: systemPrompt: look up the user input. use structured output userPrompt: model: claude-sonnet-4-0 apiKey: '{{ANTHROPIC_API_KEY}}' tools: - type: exa title: Exa params: type: auto apiKey: '{{EXA_API_KEY}}' numResults: '' toolId: exa_search operation: exa_search isExpanded: true usageControl: auto responseFormat: |- { "name": "output_schema", "description": "Defines the structure for an output object.", "strict": true, "schema": { "type": "object", "properties": { "output": { "type": "string", "description": "The output value" } }, "additionalProperties": false, "required": ["output"] } } ``` ### Loop Processing with Collection A workflow that processes each item in a collection using a loop: ```yaml version: '1.0' blocks: start: type: starter name: Start inputs: startWorkflow: manual connections: success: food-analysis-loop food-analysis-loop: type: loop name: Food Analysis Loop inputs: count: 5 loopType: forEach collection: '["apple", "banana", "carrot"]' connections: loop: start: calorie-agent calorie-agent: type: agent name: Calorie Analyzer inputs: systemPrompt: Return the number of calories in the food userPrompt: model: claude-sonnet-4-0 apiKey: '{{ANTHROPIC_API_KEY}}' parentId: food-analysis-loop ``` ### Email Classification and Response A workflow that classifies emails and generates appropriate responses: ```yaml version: '1.0' blocks: start: type: starter name: Start inputs: startWorkflow: manual connections: success: email-classifier email-classifier: type: agent name: Email Classifier inputs: systemPrompt: Classify emails into categories and extract key information. userPrompt: | Classify this email: Categories: support, billing, sales, feedback Extract: urgency level, customer sentiment, main request model: gpt-4o apiKey: '{{OPENAI_API_KEY}}' connections: success: response-generator response-generator: type: agent name: Response Generator inputs: systemPrompt: Generate appropriate responses based on email classification. userPrompt: | Email classification: Original email: Generate a professional, helpful response addressing the customer's needs. model: gpt-4o temperature: 0.7 apiKey: '{{OPENAI_API_KEY}}' ``` --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://whitepaper.aitech.io/agentforge/yaml/yaml-workflow-examples.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.