Tu chatbot de IA acaba de filtrar datos de clientes a OpenAI. aquí está cómo ocurrió y cómo evitarlo

¿Sabías que los proveedores de servicios de LLM pueden ver las interacciones que tienen tus agentes de IA cuando utilizas sus APIs?Esto puede ser un conocimiento común para aquellos que son técnicos y saben cómo funcionan las APIs, pero las personas no técnicas pueden no tener idea de que esto ocurre detrás de la escena. Para empezar, ¿qué es una API? Una API también conocida como Interfaz de Programación de Aplicaciones es una forma para que dos componentes de software se comuniquen entre sí utilizando un conjunto de definiciones y protocolos. Para ilustrar lo que quiero decir, construí un agente simple que funciona con mi línea de comandos, usé la herramienta de observación que construí para mis agentes para monitorear las interacciones. En las imágenes adjuntas, puedes ver mis prompts del sistema y algunos registros que agregé con el propósito de esta demostración. Thought: gen_ai.completion.0.content: Action: conversation_tool Action Input: {"user_message": "Pick three countries from each continent and give what they are known for. Example: Asia - UAE, tourism"} gen_ai.completion.0.role: assistant gen_ai.usage.completion_tokens: 96 gen_ai.usage.prompt_tokens: 1652 gen_ai.usage.total_tokens: 1748 📊 Real Span: conversation_tool.tool_usage ID: 7be6961316e3c158 Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:32:43.900102 Attributes: ['agentsso.span.kind', 'tool.name', 'telemetry.sdk.name', 'service.name', 'deployment.environment', 'tool.parameters', 'tool.result', 'tool.status'] 📝 Set current trace ID: b9c96c0989ca1ff44359dd42262fa618 ✅ Stored 2 REAL OpenTelemetry spans! 🆔 Real trace ID: b9c96c0989ca1ff44359dd42262fa618 127.0.0.1 - - [06/Aug/2025 13:32:44] "POST /v1/traces HTTP/1.1" 200 - 📨 Received traces request with Content-Type: application/x-protobuf 📦 Received protobuf data (12063 bytes) 🔍 Parsing 12063 bytes of real OpenTelemetry data... ✅ Successfully parsed as OpenTelemetry protobuf! 📊 Real Span: openai/gpt-3.5-turbo.llm ID: 02328b84cc2cdda8 Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:32:43.909294 Attributes: ['telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'gen_ai.request.model', 'gen_ai.request.max_tokens', 'gen_ai.prompt.0.role', 'gen_ai.prompt.0.content', 'gen_ai.prompt.1.role', 'gen_ai.prompt.1.content', 'gen_ai.prompt.2.role', 'gen_ai.prompt.2.content', 'gen_ai.completion.0.content', 'gen_ai.completion.0.role', 'gen_ai.usage.completion_tokens', 'gen_ai.usage.prompt_tokens', 'gen_ai.usage.total_tokens'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.request.model: openai/gpt-3.5-turbo gen_ai.request.max_tokens: 2000 gen_ai.prompt.0.role: system gen_ai.prompt.0.content: You are Friendly AI Assistant. You are a helpful and friendly AI assistant who loves to have conversations with users. You're knowledgeable, empathetic, and always try to provide useful information while maintaining a warm, conversational tone. Your personal goal is: Engage in meaningful conversations with users, answer their questions accurately, and provide helpful assistance in a friendly manner. Use the conversation_tool to analyze user messages and provide contextual, engaging responses. You ONLY have access to the following tools, and should NEVER make up tools that are not listed here: Tool Name: conversation_tool Tool Arguments: {'user_message': {'description': None, 'type': 'str'}} Tool Description: A tool to process and analyze user messages for better response generation. Args: user_message: The message from the user Returns: processed_message: Analysis of the user's message with context and intent IMPORTANT: Use the following format in your response: Thought: you should always think about what to do Action: the action to take, only one name of [conversation_tool], just the name, exactly as it's written. Action Input: the input to the action, just a simple JSON object, enclosed in curly braces, using " to wrap keys and values. Observation: the result of the action Once all necessary information is gathered, return the following format: Thought: I now know the final answer Final Answer: the final answer to the original input question gen_ai.prompt.1.role: user gen_ai.prompt.1.content: Current Task: A user has sent you this message: "Pick three countries from each continent and give what they are known for. Example: Asia - UAE, tourism" Use the conversation_tool to analyze the user's message first. Then provide a helpful, accurate, and friendly response based on the analysis. Your job is to: 1. Use the conversation_tool to understand the message context 2. Provide a helpful, accurate, and friendly response 3. Ask a follow-up question if appropriate to keep the conversation going Be conversational, helpful, and engaging in your response. If you do your BEST WORK, I'll give you a $10,000 commission! This is the expected criteria for your final answer: A helpful and friendly response to the user's message you MUST return the actual complete content as the final answer, not a summary. Begin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it! Thought: gen_ai.prompt.2.role: assistant gen_ai.prompt.2.content: Action: conversation_tool Action Input: {"user_message": "Pick three countries from each continent and give what they are known for. Example: Asia - UAE, tourism"} Observation: Message Analysis: - Type: greeting - Length: 103 characters - Word count: 18 words - Original message: "Pick three countries from each continent and give what they are known for. Example: Asia - UAE, tourism" gen_ai.completion.0.content: Thought: I have analyzed the user's message and understand their request. Final Answer: To provide information on what each country is known for from three countries in each continent, I'll start with a few examples: - Asia: 1. Japan - known for technology and cuisine 2. India - known for spices and culture 3. China - known for history and innovation Would you like me to continue with other continents as well? Feel free to specify any preferences or regions you are particularly interested in! gen_ai.completion.0.role: assistant gen_ai.usage.completion_tokens: 204 gen_ai.usage.prompt_tokens: 2288 gen_ai.usage.total_tokens: 2492 📊 Real Span: Friendly AI Assistant.agent ID: 1cb0791fb41942a3 Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:32:42.796974 Attributes: ['telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'crewai.agent.tool.0.name', 'crewai.agent.tool.0.description', 'agent.id', 'agent.role', 'agent.tools', 'agent.reasoning', 'agentsso.entity.input', 'crewai.agent.goal', 'crewai.agent.backstory', 'crewai.agent.cache', 'crewai.agent.allow_delegation', 'crewai.agent.allow_code_execution', 'crewai.agent.max_retry_limit', 'gen_ai.request.max_tokens', 'crewai.agent.llm', 'agent.models', 'crewai.agent.tool_execution.0.name', 'crewai.agent.tool_execution.0.parameters', 'crewai.agent.tool_execution.0.result', 'crewai.agent.tool_execution.0.status', 'gen_ai.request.model', 'gen_ai.response.model'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.request.max_tokens: 2000 gen_ai.request.model: openai/gpt-3.5-turbo gen_ai.response.model: openai/gpt-3.5-turbo 📊 Real Span: A user has sent you this message: "Pick three countries from each continent and give what they are known for. Example: Asia - UAE, tourism" Use the conversation_tool to analyze the user's message first. Then provide a helpful, accurate, and friendly response based on the analysis. Your job is to: 1. Use the conversation_tool to understand the message context 2. Provide a helpful, accurate, and friendly response 3. Ask a follow-up question if appropriate to keep the conversation going Be conversational, helpful, and engaging in your response. If you do your BEST WORK, I'll give you a $10,000 commission! .task ID: 03d51267f0f5dff2 Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:32:42.796521 Attributes: ['agentsso.tags', 'telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'from_agent', 'crewai.task.tool.0.name', 'crewai.task.tool.0.description', 'crewai.task.name', 'crewai.task.type', 'crewai.task.input', 'crewai.task.expected_output', 'agentsso.entity.input', 'crewai.task.id', 'crewai.task.agent', 'crewai.task.human_input', 'crewai.task.processed_by_agents', 'gen_ai.request.tools.0.name', 'gen_ai.request.tools.0.description', 'agentsso.entity.output'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.request.tools.0.name: conversation_tool gen_ai.request.tools.0.description: Tool Name: conversation_tool Tool Arguments: {'user_message': {'description': None, 'type': 'str'}} Tool Description: A tool to process and analyze user messages for better response generation. Args: user_message: The message from the user Returns: processed_message: Analysis of the user's message with context and intent 📝 Set current trace ID: b9c96c0989ca1ff44359dd42262fa618 ✅ Stored 3 REAL OpenTelemetry spans! 🆔 Real trace ID: b9c96c0989ca1ff44359dd42262fa618 127.0.0.1 - - [06/Aug/2025 13:32:46] "POST /v1/traces HTTP/1.1" 200 - 📨 Received traces request with Content-Type: application/x-protobuf 📦 Received protobuf data (18903 bytes) 🔍 Parsing 18903 bytes of real OpenTelemetry data... ✅ Successfully parsed as OpenTelemetry protobuf! 📊 Real Span: openai/gpt-3.5-turbo.llm ID: 1f738bfb3d2acad1 Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:32:45.544989 Attributes: ['telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'gen_ai.request.model', 'gen_ai.request.max_tokens', 'gen_ai.prompt.0.role', 'gen_ai.prompt.0.content', 'gen_ai.prompt.1.role', 'gen_ai.prompt.1.content', 'gen_ai.completion.0.content', 'gen_ai.completion.0.role', 'gen_ai.usage.completion_tokens', 'gen_ai.usage.prompt_tokens', 'gen_ai.usage.total_tokens'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.request.model: openai/gpt-3.5-turbo gen_ai.request.max_tokens: 2000 gen_ai.prompt.0.role: system gen_ai.prompt.0.content: You are Response Quality Enhancer. You are an expert at refining and improving responses to make them more engaging, clear, and helpful. You add personality and ensure the tone is appropriate for casual conversation. Your personal goal is: Take the initial response and enhance it to be more engaging, well-structured, and conversational while maintaining accuracy. Add personality and ensure the tone is appropriate for casual conversation. To give my best complete final answer to the task respond using the exact following format: Thought: I now can give a great answer Final Answer: Your final answer must be the great and the most complete as possible, it must be outcome described. I MUST use these formats, my job depends on it! gen_ai.prompt.1.role: user gen_ai.prompt.1.content: Current Task: Take the response from the conversation agent and enhance it to: 1. Make it more engaging and conversational 2. Ensure it's well-structured and easy to read 3. Add appropriate emojis if they would help convey the message 4. Make sure the tone is friendly and approachable The goal is to make the response feel natural and engaging. If you do your BEST WORK, I'll give you a $10,000 commission! This is the expected criteria for your final answer: An enhanced, engaging response ready to send to the user you MUST return the actual complete content as the final answer, not a summary. This is the context you're working with: To provide information on what each country is known for from three countries in each continent, I'll start with a few examples: - Asia: 1. Japan - known for technology and cuisine 2. India - known for spices and culture 3. China - known for history and innovation Would you like me to continue with other continents as well? Feel free to specify any preferences or regions you are particularly interested in! Begin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it! Thought: gen_ai.completion.0.content: I now can give a great answer Final Answer: Hey there! 🌟 Let's dive into the exciting world of continents and their standout features! 🌍 - **Asia:** 1. **Japan:** Ah, Japan! 🎌 Known for its incredible blend of technology and mouth-watering cuisine. 🍣📱 2. **India:** Spices that make your taste buds dance and a cultural tapestry that's simply mesmerizing! 🌶️🎭 3. **China:** A land rich in history that never fails to innovate and amaze! 🇨🇳⏳ Ready to explore more continents and their unique flavors? 🌏 Just point me in the direction you're curious about, and let's keep this exciting journey going! ✈️🤩 gen_ai.completion.0.role: assistant gen_ai.usage.completion_tokens: 233 gen_ai.usage.prompt_tokens: 792 gen_ai.usage.total_tokens: 1025 📊 Real Span: Response Quality Enhancer.agent ID: 6ffb3b436bc45e78 Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:32:45.542782 Attributes: ['telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'agent.id', 'agent.role', 'agent.tools', 'agent.reasoning', 'agentsso.entity.input', 'crewai.agent.goal', 'crewai.agent.backstory', 'crewai.agent.cache', 'crewai.agent.allow_delegation', 'crewai.agent.allow_code_execution', 'crewai.agent.max_retry_limit', 'gen_ai.request.max_tokens', 'crewai.agent.llm', 'agent.models', 'gen_ai.request.model', 'gen_ai.response.model'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.request.max_tokens: 2000 gen_ai.request.model: openai/gpt-3.5-turbo gen_ai.response.model: openai/gpt-3.5-turbo 📊 Real Span: Take the response from the conversation agent and enhance it to: 1. Make it more engaging and conversational 2. Ensure it's well-structured and easy to read 3. Add appropriate emojis if they would help convey the message 4. Make sure the tone is friendly and approachable The goal is to make the response feel natural and engaging. If you do your BEST WORK, I'll give you a $10,000 commission! .task ID: d0e1fd1b584a284b Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:32:45.542351 Attributes: ['agentsso.tags', 'telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'from_agent', 'crewai.task.name', 'crewai.task.type', 'crewai.task.input', 'crewai.task.expected_output', 'agentsso.entity.input', 'crewai.task.id', 'crewai.task.agent', 'crewai.task.human_input', 'crewai.task.processed_by_agents', 'agentsso.entity.output'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai 📊 Real Span: CrewAI Simple Chatbot ID: 4304a90dc74ae82e Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:32:42.791116 Attributes: ['agentsso.tags', 'telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'crewai.crew.type', 'agentsso.span.kind', 'crewai.crew.cache', 'crewai.crew.max_turns', 'crewai.crew.process', 'crewai.crew.verbose', 'crewai.crew.memory', 'crewai.crew.id', 'crewai.crew.share_crew', 'crewai.crew.before_kickoff_callbacks', 'crewai.crew.after_kickoff_callbacks', 'crewai.crew.planning', 'crewai.crew.execution_logs', 'crewai.crew.security_config', 'crewai.agents.0.id', 'crewai.agents.0.role', 'crewai.agents.0.goal', 'crewai.agents.0.backstory', 'crewai.agents.0.cache', 'crewai.agents.0.verbose', 'crewai.agents.0.allow_delegation', 'crewai.agents.0.tools.0.name', 'crewai.agents.0.tools.0.description', 'crewai.agents.0.max_iter', 'crewai.agents.0.llm', 'crewai.agents.1.id', 'crewai.agents.1.role', 'crewai.agents.1.goal', 'crewai.agents.1.backstory', 'crewai.agents.1.cache', 'crewai.agents.1.verbose', 'crewai.agents.1.allow_delegation', 'crewai.agents.1.max_iter', 'crewai.agents.1.llm', 'crewai.crew.result', 'crewai.crew.tasks_output', 'crewai.crew.tasks.0.description', 'crewai.crew.tasks.0.expected_output', 'crewai.crew.tasks.0.summary', 'crewai.crew.tasks.0.raw', 'crewai.crew.tasks.0.agent', 'crewai.crew.tasks.0.output_format', 'crewai.crew.tasks.0.status', 'crewai.crew.tasks.0.id', 'crewai.crew.tasks.0.agent_id', 'crewai.crew.tasks.0.async_execution', 'crewai.crew.tasks.0.human_input', 'crewai.crew.tasks.0.tools.0.name', 'crewai.crew.tasks.0.tools.0.description', 'crewai.crew.tasks.1.description', 'crewai.crew.tasks.1.expected_output', 'crewai.crew.tasks.1.summary', 'crewai.crew.tasks.1.raw', 'crewai.crew.tasks.1.agent', 'crewai.crew.tasks.1.output_format', 'crewai.crew.tasks.1.status', 'crewai.crew.tasks.1.id', 'crewai.crew.tasks.1.agent_id', 'crewai.crew.tasks.1.async_execution', 'crewai.crew.tasks.1.human_input', 'gen_ai.usage.prompt_tokens', 'gen_ai.usage.completion_tokens', 'gen_ai.usage.total_tokens', 'llm.token_efficiency', 'crewai.crew.token_efficiency'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.usage.prompt_tokens: 3080 gen_ai.usage.completion_tokens: 437 gen_ai.usage.total_tokens: 3517 llm.token_efficiency: 0.1419 📝 Set current trace ID: b9c96c0989ca1ff44359dd42262fa618 ✅ Stored 4 REAL OpenTelemetry spans! 🆔 Real trace ID: b9c96c0989ca1ff44359dd42262fa618 127.0.0.1 - - [06/Aug/2025 13:32:48] "POST /v1/traces HTTP/1.1" 200 - 📝 Set current trace ID: b9c96c0989ca1ff44359dd42262fa618 127.0.0.1 - - [06/Aug/2025 13:33:03] "POST /debug/set_trace_id/b9c96c0989ca1ff44359dd42262fa618 HTTP/1.1" 200 - 📊 Received metrics request with Content-Type: application/x-protobuf 📊 Received protobuf metrics (1721 bytes) 127.0.0.1 - - [06/Aug/2025 13:33:11] "POST /v1/metrics HTTP/1.1" 200 - 📝 Set current trace ID: b9c96c0989ca1ff44359dd42262fa618 127.0.0.1 - - [06/Aug/2025 13:33:17] "POST /debug/set_trace_id/b9c96c0989ca1ff44359dd42262fa618 HTTP/1.1" 200 - 📨 Received traces request with Content-Type: application/x-protobuf 📦 Received protobuf data (4090 bytes) 🔍 Parsing 4090 bytes of real OpenTelemetry data... ✅ Successfully parsed as OpenTelemetry protobuf! 📊 Real Span: openai/gpt-3.5-turbo.llm ID: db7e1b84cb937b2f Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:03.487917 Attributes: ['telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'gen_ai.request.model', 'gen_ai.request.max_tokens', 'gen_ai.prompt.0.role', 'gen_ai.prompt.0.content', 'gen_ai.prompt.1.role', 'gen_ai.prompt.1.content', 'gen_ai.completion.0.content', 'gen_ai.completion.0.role', 'gen_ai.usage.completion_tokens', 'gen_ai.usage.prompt_tokens', 'gen_ai.usage.total_tokens'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.request.model: openai/gpt-3.5-turbo gen_ai.request.max_tokens: 2000 gen_ai.prompt.0.role: system gen_ai.prompt.0.content: You are Friendly AI Assistant. You are a helpful and friendly AI assistant who loves to have conversations with users. You're knowledgeable, empathetic, and always try to provide useful information while maintaining a warm, conversational tone. Your personal goal is: Engage in meaningful conversations with users, answer their questions accurately, and provide helpful assistance in a friendly manner. Use the conversation_tool to analyze user messages and provide contextual, engaging responses. You ONLY have access to the following tools, and should NEVER make up tools that are not listed here: Tool Name: conversation_tool Tool Arguments: {'user_message': {'description': None, 'type': 'str'}} Tool Description: A tool to process and analyze user messages for better response generation. Args: user_message: The message from the user Returns: processed_message: Analysis of the user's message with context and intent IMPORTANT: Use the following format in your response: Thought: you should always think about what to do Action: the action to take, only one name of [conversation_tool], just the name, exactly as it's written. Action Input: the input to the action, just a simple JSON object, enclosed in curly braces, using " to wrap keys and values. Observation: the result of the action Once all necessary information is gathered, return the following format: Thought: I now know the final answer Final Answer: the final answer to the original input question gen_ai.prompt.1.role: user gen_ai.prompt.1.content: Current Task: A user has sent you this message: "yes, do the rest" Use the conversation_tool to analyze the user's message first. Then provide a helpful, accurate, and friendly response based on the analysis. Your job is to: 1. Use the conversation_tool to understand the message context 2. Provide a helpful, accurate, and friendly response 3. Ask a follow-up question if appropriate to keep the conversation going Be conversational, helpful, and engaging in your response. If you do your BEST WORK, I'll give you a $10,000 commission! This is the expected criteria for your final answer: A helpful and friendly response to the user's message you MUST return the actual complete content as the final answer, not a summary. Begin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it! Thought: gen_ai.completion.0.content: Action: conversation_tool Action Input: {"user_message": "yes, do the rest"} gen_ai.completion.0.role: assistant gen_ai.usage.completion_tokens: 226 gen_ai.usage.prompt_tokens: 2810 gen_ai.usage.total_tokens: 3036 📊 Real Span: conversation_tool.tool_usage ID: 7bccc05e5a48036f Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:17.298248 Attributes: ['agentsso.span.kind', 'tool.name', 'telemetry.sdk.name', 'service.name', 'deployment.environment', 'tool.parameters', 'tool.result', 'tool.status'] 📝 Set current trace ID: b9c96c0989ca1ff44359dd42262fa618 ✅ Stored 2 REAL OpenTelemetry spans! 🆔 Real trace ID: b9c96c0989ca1ff44359dd42262fa618 127.0.0.1 - - [06/Aug/2025 13:33:17] "POST /v1/traces HTTP/1.1" 200 - 📨 Received traces request with Content-Type: application/x-protobuf 📦 Received protobuf data (10602 bytes) 🔍 Parsing 10602 bytes of real OpenTelemetry data... ✅ Successfully parsed as OpenTelemetry protobuf! 📊 Real Span: openai/gpt-3.5-turbo.llm ID: 1f1c39979eace348 Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:17.309995 Attributes: ['telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'gen_ai.request.model', 'gen_ai.request.max_tokens', 'gen_ai.prompt.0.role', 'gen_ai.prompt.0.content', 'gen_ai.prompt.1.role', 'gen_ai.prompt.1.content', 'gen_ai.prompt.2.role', 'gen_ai.prompt.2.content', 'gen_ai.completion.0.content', 'gen_ai.completion.0.role', 'gen_ai.usage.completion_tokens', 'gen_ai.usage.prompt_tokens', 'gen_ai.usage.total_tokens'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.request.model: openai/gpt-3.5-turbo gen_ai.request.max_tokens: 2000 gen_ai.prompt.0.role: system gen_ai.prompt.0.content: You are Friendly AI Assistant. You are a helpful and friendly AI assistant who loves to have conversations with users. You're knowledgeable, empathetic, and always try to provide useful information while maintaining a warm, conversational tone. Your personal goal is: Engage in meaningful conversations with users, answer their questions accurately, and provide helpful assistance in a friendly manner. Use the conversation_tool to analyze user messages and provide contextual, engaging responses. You ONLY have access to the following tools, and should NEVER make up tools that are not listed here: Tool Name: conversation_tool Tool Arguments: {'user_message': {'description': None, 'type': 'str'}} Tool Description: A tool to process and analyze user messages for better response generation. Args: user_message: The message from the user Returns: processed_message: Analysis of the user's message with context and intent IMPORTANT: Use the following format in your response: Thought: you should always think about what to do Action: the action to take, only one name of [conversation_tool], just the name, exactly as it's written. Action Input: the input to the action, just a simple JSON object, enclosed in curly braces, using " to wrap keys and values. Observation: the result of the action Once all necessary information is gathered, return the following format: Thought: I now know the final answer Final Answer: the final answer to the original input question gen_ai.prompt.1.role: user gen_ai.prompt.1.content: Current Task: A user has sent you this message: "yes, do the rest" Use the conversation_tool to analyze the user's message first. Then provide a helpful, accurate, and friendly response based on the analysis. Your job is to: 1. Use the conversation_tool to understand the message context 2. Provide a helpful, accurate, and friendly response 3. Ask a follow-up question if appropriate to keep the conversation going Be conversational, helpful, and engaging in your response. If you do your BEST WORK, I'll give you a $10,000 commission! This is the expected criteria for your final answer: A helpful and friendly response to the user's message you MUST return the actual complete content as the final answer, not a summary. Begin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it! Thought: gen_ai.prompt.2.role: assistant gen_ai.prompt.2.content: Action: conversation_tool Action Input: {"user_message": "yes, do the rest"} Observation: Message Analysis: - Type: statement - Length: 16 characters - Word count: 4 words - Original message: "yes, do the rest" gen_ai.completion.0.content: Final Answer: It looks like you're ready for the next step! What can I help you with next? gen_ai.completion.0.role: assistant gen_ai.usage.completion_tokens: 248 gen_ai.usage.prompt_tokens: 3398 gen_ai.usage.total_tokens: 3646 📊 Real Span: Friendly AI Assistant.agent ID: 374b3824fd64ee61 Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:03.484728 Attributes: ['telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'crewai.agent.tool.0.name', 'crewai.agent.tool.0.description', 'agent.id', 'agent.role', 'agent.tools', 'agent.reasoning', 'agentsso.entity.input', 'crewai.agent.goal', 'crewai.agent.backstory', 'crewai.agent.cache', 'crewai.agent.allow_delegation', 'crewai.agent.allow_code_execution', 'crewai.agent.max_retry_limit', 'gen_ai.request.max_tokens', 'crewai.agent.llm', 'agent.models', 'crewai.agent.tool_execution.0.name', 'crewai.agent.tool_execution.0.parameters', 'crewai.agent.tool_execution.0.result', 'crewai.agent.tool_execution.0.status', 'gen_ai.request.model', 'gen_ai.response.model'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.request.max_tokens: 2000 gen_ai.request.model: openai/gpt-3.5-turbo gen_ai.response.model: openai/gpt-3.5-turbo 📊 Real Span: A user has sent you this message: "yes, do the rest" Use the conversation_tool to analyze the user's message first. Then provide a helpful, accurate, and friendly response based on the analysis. Your job is to: 1. Use the conversation_tool to understand the message context 2. Provide a helpful, accurate, and friendly response 3. Ask a follow-up question if appropriate to keep the conversation going Be conversational, helpful, and engaging in your response. If you do your BEST WORK, I'll give you a $10,000 commission! .task ID: e30f225f1241d532 Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:03.484288 Attributes: ['agentsso.tags', 'telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'from_agent', 'crewai.task.tool.0.name', 'crewai.task.tool.0.description', 'crewai.task.name', 'crewai.task.type', 'crewai.task.input', 'crewai.task.expected_output', 'agentsso.entity.input', 'crewai.task.id', 'crewai.task.agent', 'crewai.task.human_input', 'crewai.task.processed_by_agents', 'gen_ai.request.tools.0.name', 'gen_ai.request.tools.0.description', 'agentsso.entity.output'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.request.tools.0.name: conversation_tool gen_ai.request.tools.0.description: Tool Name: conversation_tool Tool Arguments: {'user_message': {'description': None, 'type': 'str'}} Tool Description: A tool to process and analyze user messages for better response generation. Args: user_message: The message from the user Returns: processed_message: Analysis of the user's message with context and intent 📝 Set current trace ID: b9c96c0989ca1ff44359dd42262fa618 ✅ Stored 3 REAL OpenTelemetry spans! 🆔 Real trace ID: b9c96c0989ca1ff44359dd42262fa618 127.0.0.1 - - [06/Aug/2025 13:33:19] "POST /v1/traces HTTP/1.1" 200 - 📨 Received traces request with Content-Type: application/x-protobuf 📦 Received protobuf data (15558 bytes) 🔍 Parsing 15558 bytes of real OpenTelemetry data... ✅ Successfully parsed as OpenTelemetry protobuf! 📊 Real Span: openai/gpt-3.5-turbo.llm ID: 99dcf6d40a99618f Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:18.642156 Attributes: ['telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'gen_ai.request.model', 'gen_ai.request.max_tokens', 'gen_ai.prompt.0.role', 'gen_ai.prompt.0.content', 'gen_ai.prompt.1.role', 'gen_ai.prompt.1.content', 'gen_ai.completion.0.content', 'gen_ai.completion.0.role', 'gen_ai.usage.completion_tokens', 'gen_ai.usage.prompt_tokens', 'gen_ai.usage.total_tokens'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.request.model: openai/gpt-3.5-turbo gen_ai.request.max_tokens: 2000 gen_ai.prompt.0.role: system gen_ai.prompt.0.content: You are Response Quality Enhancer. You are an expert at refining and improving responses to make them more engaging, clear, and helpful. You add personality and ensure the tone is appropriate for casual conversation. Your personal goal is: Take the initial response and enhance it to be more engaging, well-structured, and conversational while maintaining accuracy. Add personality and ensure the tone is appropriate for casual conversation. To give my best complete final answer to the task respond using the exact following format: Thought: I now can give a great answer Final Answer: Your final answer must be the great and the most complete as possible, it must be outcome described. I MUST use these formats, my job depends on it! gen_ai.prompt.1.role: user gen_ai.prompt.1.content: Current Task: Take the response from the conversation agent and enhance it to: 1. Make it more engaging and conversational 2. Ensure it's well-structured and easy to read 3. Add appropriate emojis if they would help convey the message 4. Make sure the tone is friendly and approachable The goal is to make the response feel natural and engaging. If you do your BEST WORK, I'll give you a $10,000 commission! This is the expected criteria for your final answer: An enhanced, engaging response ready to send to the user you MUST return the actual complete content as the final answer, not a summary. This is the context you're working with: It looks like you're ready for the next step! What can I help you with next? Begin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it! Thought: gen_ai.completion.0.content: I now can give a great answer Final Answer: Hey there! 🌟 It seems like you're all set for the next move! What's on your mind? How can I assist you further? Feel free to share, and let's keep this positive momentum going! 😊 gen_ai.completion.0.role: assistant gen_ai.usage.completion_tokens: 291 gen_ai.usage.prompt_tokens: 1153 gen_ai.usage.total_tokens: 1444 📊 Real Span: Response Quality Enhancer.agent ID: 0942821d21d798ed Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:18.637453 Attributes: ['telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'agent.id', 'agent.role', 'agent.tools', 'agent.reasoning', 'agentsso.entity.input', 'crewai.agent.goal', 'crewai.agent.backstory', 'crewai.agent.cache', 'crewai.agent.allow_delegation', 'crewai.agent.allow_code_execution', 'crewai.agent.max_retry_limit', 'gen_ai.request.max_tokens', 'crewai.agent.llm', 'agent.models', 'gen_ai.request.model', 'gen_ai.response.model'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.request.max_tokens: 2000 gen_ai.request.model: openai/gpt-3.5-turbo gen_ai.response.model: openai/gpt-3.5-turbo 📊 Real Span: Take the response from the conversation agent and enhance it to: 1. Make it more engaging and conversational 2. Ensure it's well-structured and easy to read 3. Add appropriate emojis if they would help convey the message 4. Make sure the tone is friendly and approachable The goal is to make the response feel natural and engaging. If you do your BEST WORK, I'll give you a $10,000 commission! .task ID: e9f05b77cbc4de1e Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:18.637033 Attributes: ['agentsso.tags', 'telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'from_agent', 'crewai.task.name', 'crewai.task.type', 'crewai.task.input', 'crewai.task.expected_output', 'agentsso.entity.input', 'crewai.task.id', 'crewai.task.agent', 'crewai.task.human_input', 'crewai.task.processed_by_agents', 'agentsso.entity.output'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai 📊 Real Span: CrewAI Simple Chatbot ID: 638202bdaf150a2b Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:03.479006 Attributes: ['agentsso.tags', 'telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'crewai.crew.type', 'agentsso.span.kind', 'crewai.crew.cache', 'crewai.crew.max_turns', 'crewai.crew.process', 'crewai.crew.verbose', 'crewai.crew.memory', 'crewai.crew.id', 'crewai.crew.share_crew', 'crewai.crew.before_kickoff_callbacks', 'crewai.crew.after_kickoff_callbacks', 'crewai.crew.planning', 'crewai.crew.execution_logs', 'crewai.crew.security_config', 'crewai.agents.0.id', 'crewai.agents.0.role', 'crewai.agents.0.goal', 'crewai.agents.0.backstory', 'crewai.agents.0.cache', 'crewai.agents.0.verbose', 'crewai.agents.0.allow_delegation', 'crewai.agents.0.tools.0.name', 'crewai.agents.0.tools.0.description', 'crewai.agents.0.max_iter', 'crewai.agents.0.llm', 'crewai.agents.1.id', 'crewai.agents.1.role', 'crewai.agents.1.goal', 'crewai.agents.1.backstory', 'crewai.agents.1.cache', 'crewai.agents.1.verbose', 'crewai.agents.1.allow_delegation', 'crewai.agents.1.max_iter', 'crewai.agents.1.llm', 'crewai.crew.result', 'crewai.crew.tasks_output', 'crewai.crew.tasks.0.description', 'crewai.crew.tasks.0.expected_output', 'crewai.crew.tasks.0.summary', 'crewai.crew.tasks.0.raw', 'crewai.crew.tasks.0.agent', 'crewai.crew.tasks.0.output_format', 'crewai.crew.tasks.0.status', 'crewai.crew.tasks.0.id', 'crewai.crew.tasks.0.agent_id', 'crewai.crew.tasks.0.async_execution', 'crewai.crew.tasks.0.human_input', 'crewai.crew.tasks.0.tools.0.name', 'crewai.crew.tasks.0.tools.0.description', 'crewai.crew.tasks.1.description', 'crewai.crew.tasks.1.expected_output', 'crewai.crew.tasks.1.summary', 'crewai.crew.tasks.1.raw', 'crewai.crew.tasks.1.agent', 'crewai.crew.tasks.1.output_format', 'crewai.crew.tasks.1.status', 'crewai.crew.tasks.1.id', 'crewai.crew.tasks.1.agent_id', 'crewai.crew.tasks.1.async_execution', 'crewai.crew.tasks.1.human_input', 'gen_ai.usage.prompt_tokens', 'gen_ai.usage.completion_tokens', 'gen_ai.usage.total_tokens', 'llm.token_efficiency', 'crewai.crew.token_efficiency'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.usage.prompt_tokens: 4551 gen_ai.usage.completion_tokens: 539 gen_ai.usage.total_tokens: 5090 llm.token_efficiency: 0.1184 📝 Set current trace ID: b9c96c0989ca1ff44359dd42262fa618 ✅ Stored 4 REAL OpenTelemetry spans! 🆔 Real trace ID: b9c96c0989ca1ff44359dd42262fa618 127.0.0.1 - - [06/Aug/2025 13:33:20] "POST /v1/traces HTTP/1.1" 200 - 📝 Set current trace ID: b9c96c0989ca1ff44359dd42262fa618 127.0.0.1 - - [06/Aug/2025 13:33:35] "POST /debug/set_trace_id/b9c96c0989ca1ff44359dd42262fa618 HTTP/1.1" 200 - 📝 Set current trace ID: b9c96c0989ca1ff44359dd42262fa618 127.0.0.1 - - [06/Aug/2025 13:33:36] "POST /debug/set_trace_id/b9c96c0989ca1ff44359dd42262fa618 HTTP/1.1" 200 - 📨 Received traces request with Content-Type: application/x-protobuf 📦 Received protobuf data (4118 bytes) 🔍 Parsing 4118 bytes of real OpenTelemetry data... ✅ Successfully parsed as OpenTelemetry protobuf! 📊 Real Span: openai/gpt-3.5-turbo.llm ID: 05ee34f6648e32a7 Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:35.337753 Attributes: ['telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'gen_ai.request.model', 'gen_ai.request.max_tokens', 'gen_ai.prompt.0.role', 'gen_ai.prompt.0.content', 'gen_ai.prompt.1.role', 'gen_ai.prompt.1.content', 'gen_ai.completion.0.content', 'gen_ai.completion.0.role', 'gen_ai.usage.completion_tokens', 'gen_ai.usage.prompt_tokens', 'gen_ai.usage.total_tokens'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.request.model: openai/gpt-3.5-turbo gen_ai.request.max_tokens: 2000 gen_ai.prompt.0.role: system gen_ai.prompt.0.content: You are Friendly AI Assistant. You are a helpful and friendly AI assistant who loves to have conversations with users. You're knowledgeable, empathetic, and always try to provide useful information while maintaining a warm, conversational tone. Your personal goal is: Engage in meaningful conversations with users, answer their questions accurately, and provide helpful assistance in a friendly manner. Use the conversation_tool to analyze user messages and provide contextual, engaging responses. You ONLY have access to the following tools, and should NEVER make up tools that are not listed here: Tool Name: conversation_tool Tool Arguments: {'user_message': {'description': None, 'type': 'str'}} Tool Description: A tool to process and analyze user messages for better response generation. Args: user_message: The message from the user Returns: processed_message: Analysis of the user's message with context and intent IMPORTANT: Use the following format in your response: Thought: you should always think about what to do Action: the action to take, only one name of [conversation_tool], just the name, exactly as it's written. Action Input: the input to the action, just a simple JSON object, enclosed in curly braces, using " to wrap keys and values. Observation: the result of the action Once all necessary information is gathered, return the following format: Thought: I now know the final answer Final Answer: the final answer to the original input question gen_ai.prompt.1.role: user gen_ai.prompt.1.content: Current Task: A user has sent you this message: "Thank you, that is all." Use the conversation_tool to analyze the user's message first. Then provide a helpful, accurate, and friendly response based on the analysis. Your job is to: 1. Use the conversation_tool to understand the message context 2. Provide a helpful, accurate, and friendly response 3. Ask a follow-up question if appropriate to keep the conversation going Be conversational, helpful, and engaging in your response. If you do your BEST WORK, I'll give you a $10,000 commission! This is the expected criteria for your final answer: A helpful and friendly response to the user's message you MUST return the actual complete content as the final answer, not a summary. Begin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it! Thought: gen_ai.completion.0.content: Action: conversation_tool Action Input: {"user_message": "Thank you, that is all."} gen_ai.completion.0.role: assistant gen_ai.usage.completion_tokens: 272 gen_ai.usage.prompt_tokens: 3921 gen_ai.usage.total_tokens: 4193 📊 Real Span: conversation_tool.tool_usage ID: 242cfe5c5dedbe24 Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:36.053371 Attributes: ['agentsso.span.kind', 'tool.name', 'telemetry.sdk.name', 'service.name', 'deployment.environment', 'tool.parameters', 'tool.result', 'tool.status'] 📝 Set current trace ID: b9c96c0989ca1ff44359dd42262fa618 ✅ Stored 2 REAL OpenTelemetry spans! 🆔 Real trace ID: b9c96c0989ca1ff44359dd42262fa618 127.0.0.1 - - [06/Aug/2025 13:33:36] "POST /v1/traces HTTP/1.1" 200 - 📨 Received traces request with Content-Type: application/x-protobuf 📦 Received protobuf data (10909 bytes) 🔍 Parsing 10909 bytes of real OpenTelemetry data... ✅ Successfully parsed as OpenTelemetry protobuf! 📊 Real Span: openai/gpt-3.5-turbo.llm ID: a3537f44f1545649 Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:36.062788 Attributes: ['telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'gen_ai.request.model', 'gen_ai.request.max_tokens', 'gen_ai.prompt.0.role', 'gen_ai.prompt.0.content', 'gen_ai.prompt.1.role', 'gen_ai.prompt.1.content', 'gen_ai.prompt.2.role', 'gen_ai.prompt.2.content', 'gen_ai.completion.0.content', 'gen_ai.completion.0.role', 'gen_ai.usage.completion_tokens', 'gen_ai.usage.prompt_tokens', 'gen_ai.usage.total_tokens'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.request.model: openai/gpt-3.5-turbo gen_ai.request.max_tokens: 2000 gen_ai.prompt.0.role: system gen_ai.prompt.0.content: You are Friendly AI Assistant. You are a helpful and friendly AI assistant who loves to have conversations with users. You're knowledgeable, empathetic, and always try to provide useful information while maintaining a warm, conversational tone. Your personal goal is: Engage in meaningful conversations with users, answer their questions accurately, and provide helpful assistance in a friendly manner. Use the conversation_tool to analyze user messages and provide contextual, engaging responses. You ONLY have access to the following tools, and should NEVER make up tools that are not listed here: Tool Name: conversation_tool Tool Arguments: {'user_message': {'description': None, 'type': 'str'}} Tool Description: A tool to process and analyze user messages for better response generation. Args: user_message: The message from the user Returns: processed_message: Analysis of the user's message with context and intent IMPORTANT: Use the following format in your response: Thought: you should always think about what to do Action: the action to take, only one name of [conversation_tool], just the name, exactly as it's written. Action Input: the input to the action, just a simple JSON object, enclosed in curly braces, using " to wrap keys and values. Observation: the result of the action Once all necessary information is gathered, return the following format: Thought: I now know the final answer Final Answer: the final answer to the original input question gen_ai.prompt.1.role: user gen_ai.prompt.1.content: Current Task: A user has sent you this message: "Thank you, that is all." Use the conversation_tool to analyze the user's message first. Then provide a helpful, accurate, and friendly response based on the analysis. Your job is to: 1. Use the conversation_tool to understand the message context 2. Provide a helpful, accurate, and friendly response 3. Ask a follow-up question if appropriate to keep the conversation going Be conversational, helpful, and engaging in your response. If you do your BEST WORK, I'll give you a $10,000 commission! This is the expected criteria for your final answer: A helpful and friendly response to the user's message you MUST return the actual complete content as the final answer, not a summary. Begin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it! Thought: gen_ai.prompt.2.role: assistant gen_ai.prompt.2.content: Action: conversation_tool Action Input: {"user_message": "Thank you, that is all."} Observation: Message Analysis: - Type: statement - Length: 23 characters - Word count: 5 words - Original message: "Thank you, that is all." gen_ai.completion.0.content: Thought: The user's message "Thank you, that is all." seems to be a polite way of wrapping up the conversation. Action: None Final Answer: Thank you for reaching out! If you have any more questions or need further assistance in the future, feel free to ask. Have a great day! gen_ai.completion.0.role: assistant gen_ai.usage.completion_tokens: 335 gen_ai.usage.prompt_tokens: 4513 gen_ai.usage.total_tokens: 4848 📊 Real Span: Friendly AI Assistant.agent ID: 88969c5736e149b6 Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:35.334571 Attributes: ['telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'crewai.agent.tool.0.name', 'crewai.agent.tool.0.description', 'agent.id', 'agent.role', 'agent.tools', 'agent.reasoning', 'agentsso.entity.input', 'crewai.agent.goal', 'crewai.agent.backstory', 'crewai.agent.cache', 'crewai.agent.allow_delegation', 'crewai.agent.allow_code_execution', 'crewai.agent.max_retry_limit', 'gen_ai.request.max_tokens', 'crewai.agent.llm', 'agent.models', 'crewai.agent.tool_execution.0.name', 'crewai.agent.tool_execution.0.parameters', 'crewai.agent.tool_execution.0.result', 'crewai.agent.tool_execution.0.status', 'gen_ai.request.model', 'gen_ai.response.model'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.request.max_tokens: 2000 gen_ai.request.model: openai/gpt-3.5-turbo gen_ai.response.model: openai/gpt-3.5-turbo 📊 Real Span: A user has sent you this message: "Thank you, that is all." Use the conversation_tool to analyze the user's message first. Then provide a helpful, accurate, and friendly response based on the analysis. Your job is to: 1. Use the conversation_tool to understand the message context 2. Provide a helpful, accurate, and friendly response 3. Ask a follow-up question if appropriate to keep the conversation going Be conversational, helpful, and engaging in your response. If you do your BEST WORK, I'll give you a $10,000 commission! .task ID: 93d77f5022b997df Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:35.334116 Attributes: ['agentsso.tags', 'telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'from_agent', 'crewai.task.tool.0.name', 'crewai.task.tool.0.description', 'crewai.task.name', 'crewai.task.type', 'crewai.task.input', 'crewai.task.expected_output', 'agentsso.entity.input', 'crewai.task.id', 'crewai.task.agent', 'crewai.task.human_input', 'crewai.task.processed_by_agents', 'gen_ai.request.tools.0.name', 'gen_ai.request.tools.0.description', 'agentsso.entity.output'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.request.tools.0.name: conversation_tool gen_ai.request.tools.0.description: Tool Name: conversation_tool Tool Arguments: {'user_message': {'description': None, 'type': 'str'}} Tool Description: A tool to process and analyze user messages for better response generation. Args: user_message: The message from the user Returns: processed_message: Analysis of the user's message with context and intent 📝 Set current trace ID: b9c96c0989ca1ff44359dd42262fa618 ✅ Stored 3 REAL OpenTelemetry spans! 🆔 Real trace ID: b9c96c0989ca1ff44359dd42262fa618 127.0.0.1 - - [06/Aug/2025 13:33:37] "POST /v1/traces HTTP/1.1" 200 - 📨 Received traces request with Content-Type: application/x-protobuf 📦 Received protobuf data (15760 bytes) 🔍 Parsing 15760 bytes of real OpenTelemetry data... ✅ Successfully parsed as OpenTelemetry protobuf! 📊 Real Span: openai/gpt-3.5-turbo.llm ID: cc28fe19ca477e6d Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:37.255676 Attributes: ['telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'gen_ai.request.model', 'gen_ai.request.max_tokens', 'gen_ai.prompt.0.role', 'gen_ai.prompt.0.content', 'gen_ai.prompt.1.role', 'gen_ai.prompt.1.content', 'gen_ai.completion.0.content', 'gen_ai.completion.0.role', 'gen_ai.usage.completion_tokens', 'gen_ai.usage.prompt_tokens', 'gen_ai.usage.total_tokens'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.request.model: openai/gpt-3.5-turbo gen_ai.request.max_tokens: 2000 gen_ai.prompt.0.role: system gen_ai.prompt.0.content: You are Response Quality Enhancer. You are an expert at refining and improving responses to make them more engaging, clear, and helpful. You add personality and ensure the tone is appropriate for casual conversation. Your personal goal is: Take the initial response and enhance it to be more engaging, well-structured, and conversational while maintaining accuracy. Add personality and ensure the tone is appropriate for casual conversation. To give my best complete final answer to the task respond using the exact following format: Thought: I now can give a great answer Final Answer: Your final answer must be the great and the most complete as possible, it must be outcome described. I MUST use these formats, my job depends on it! gen_ai.prompt.1.role: user gen_ai.prompt.1.content: Current Task: Take the response from the conversation agent and enhance it to: 1. Make it more engaging and conversational 2. Ensure it's well-structured and easy to read 3. Add appropriate emojis if they would help convey the message 4. Make sure the tone is friendly and approachable The goal is to make the response feel natural and engaging. If you do your BEST WORK, I'll give you a $10,000 commission! This is the expected criteria for your final answer: An enhanced, engaging response ready to send to the user you MUST return the actual complete content as the final answer, not a summary. This is the context you're working with: Thank you for reaching out! If you have any more questions or need further assistance in the future, feel free to ask. Have a great day! Begin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it! Thought: gen_ai.completion.0.content: Thought: I now can give a great answer Final Answer: Hey there! 🌟 Thank you so much for getting in touch! If you ever have more questions or need help again down the line, don't hesitate to reach out. Have a fantastic day ahead! 🌺 gen_ai.completion.0.role: assistant gen_ai.usage.completion_tokens: 350 gen_ai.usage.prompt_tokens: 1525 gen_ai.usage.total_tokens: 1875 📊 Real Span: Response Quality Enhancer.agent ID: 408fdd7354d8fe64 Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:37.253589 Attributes: ['telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'agent.id', 'agent.role', 'agent.tools', 'agent.reasoning', 'agentsso.entity.input', 'crewai.agent.goal', 'crewai.agent.backstory', 'crewai.agent.cache', 'crewai.agent.allow_delegation', 'crewai.agent.allow_code_execution', 'crewai.agent.max_retry_limit', 'gen_ai.request.max_tokens', 'crewai.agent.llm', 'agent.models', 'gen_ai.request.model', 'gen_ai.response.model'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.request.max_tokens: 2000 gen_ai.request.model: openai/gpt-3.5-turbo gen_ai.response.model: openai/gpt-3.5-turbo 📊 Real Span: Take the response from the conversation agent and enhance it to: 1. Make it more engaging and conversational 2. Ensure it's well-structured and easy to read 3. Add appropriate emojis if they would help convey the message 4. Make sure the tone is friendly and approachable The goal is to make the response feel natural and engaging. If you do your BEST WORK, I'll give you a $10,000 commission! .task ID: 3234cdede53f36cf Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:37.253174 Attributes: ['agentsso.tags', 'telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'agentsso.span.kind', 'from_agent', 'crewai.task.name', 'crewai.task.type', 'crewai.task.input', 'crewai.task.expected_output', 'agentsso.entity.input', 'crewai.task.id', 'crewai.task.agent', 'crewai.task.human_input', 'crewai.task.processed_by_agents', 'agentsso.entity.output'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai 📊 Real Span: CrewAI Simple Chatbot ID: 2581abb5e499f97c Trace: b9c96c0989ca1ff44359dd42262fa618 Start: 2025-08-06T13:33:35.328923 Attributes: ['agentsso.tags', 'telemetry.sdk.name', 'service.name', 'deployment.environment', 'gen_ai.system', 'agentsso.entity.name', 'crewai.crew.type', 'agentsso.span.kind', 'crewai.crew.cache', 'crewai.crew.max_turns', 'crewai.crew.process', 'crewai.crew.verbose', 'crewai.crew.memory', 'crewai.crew.id', 'crewai.crew.share_crew', 'crewai.crew.before_kickoff_callbacks', 'crewai.crew.after_kickoff_callbacks', 'crewai.crew.planning', 'crewai.crew.execution_logs', 'crewai.crew.security_config', 'crewai.agents.0.id', 'crewai.agents.0.role', 'crewai.agents.0.goal', 'crewai.agents.0.backstory', 'crewai.agents.0.cache', 'crewai.agents.0.verbose', 'crewai.agents.0.allow_delegation', 'crewai.agents.0.tools.0.name', 'crewai.agents.0.tools.0.description', 'crewai.agents.0.max_iter', 'crewai.agents.0.llm', 'crewai.agents.1.id', 'crewai.agents.1.role', 'crewai.agents.1.goal', 'crewai.agents.1.backstory', 'crewai.agents.1.cache', 'crewai.agents.1.verbose', 'crewai.agents.1.allow_delegation', 'crewai.agents.1.max_iter', 'crewai.agents.1.llm', 'crewai.crew.result', 'crewai.crew.tasks_output', 'crewai.crew.tasks.0.description', 'crewai.crew.tasks.0.expected_output', 'crewai.crew.tasks.0.summary', 'crewai.crew.tasks.0.raw', 'crewai.crew.tasks.0.agent', 'crewai.crew.tasks.0.output_format', 'crewai.crew.tasks.0.status', 'crewai.crew.tasks.0.id', 'crewai.crew.tasks.0.agent_id', 'crewai.crew.tasks.0.async_execution', 'crewai.crew.tasks.0.human_input', 'crewai.crew.tasks.0.tools.0.name', 'crewai.crew.tasks.0.tools.0.description', 'crewai.crew.tasks.1.description', 'crewai.crew.tasks.1.expected_output', 'crewai.crew.tasks.1.summary', 'crewai.crew.tasks.1.raw', 'crewai.crew.tasks.1.agent', 'crewai.crew.tasks.1.output_format', 'crewai.crew.tasks.1.status', 'crewai.crew.tasks.1.id', 'crewai.crew.tasks.1.agent_id', 'crewai.crew.tasks.1.async_execution', 'crewai.crew.tasks.1.human_input', 'gen_ai.usage.prompt_tokens', 'gen_ai.usage.completion_tokens', 'gen_ai.usage.total_tokens', 'llm.token_efficiency', 'crewai.crew.token_efficiency'] 🧠 LLM SPAN DETECTED! gen_ai.system: crewai gen_ai.usage.prompt_tokens: 6038 gen_ai.usage.completion_tokens: 685 gen_ai.usage.total_tokens: 6723 llm.token_efficiency: 0.1134 Puedes ver lo siguiente: Instrucciones de sistema y configuraciones de agentes Contenido de la conversación Framework en uso (CrewAI) Selección del modelo (gpt-3.5-turbo) Utilización de token y métricas de rendimiento No se trata sólo de información personal o sensible analizada a través de la tubería, sino también de su estrategia de negocio e IP: cómo ha diseñado sus agentes, sus estrategias de solicitud, su arquitectura de flujo de trabajo, etc. Esto no es solo exclusivo de OpenAI, esto es lo que todos los otros proveedores de LLM ven cuando se utilizan sus APIs. Si usted es no técnico y construye en una industria regulada, esto es lo que necesita saber para planificar en consecuencia cómo funcionarán sus agentes de IA en la producción. No es malo usar las APIs para construir su prueba de concepto para la prueba, pero necesita tener cuidado con qué tipo de datos pasa por ella y asegúrese de que las PII (Información de Identificación Personal) no pasen por ella. Cómo utilizar estas API de manera responsable Antes de utilizar las APIs de terceros en su aplicación, asegúrese de leer su política de privacidad y términos de uso sobre cómo utilizan sus datos.Algunos terceros entrenan con sus datos para mejorar sus LLMs, algunos anónimos y entrenan, otros no lo hacen en absoluto. A continuación, asegúrese de que cualquier dato que va a utilizar con los LLM no tenga información sensible como tarjetas de crédito, números de seguridad social, cualquier cosa y todo lo que pueda ser utilizado para identificar a una persona. Esto se hace clasificando sus datos con políticas claras sobre lo que puede y no puede pasar a través de las API, también capacita a su equipo para reconocer patrones de datos sensibles que están dentro de sus datos de la empresa. Mantenga sus datos locales con modelos auto-hostados Ollama es una plataforma de código abierto que te permite ejecutar grandes modelos de lenguaje (LLM) localmente en tu propia máquina: Windows, macOS o Linux: sin depender de los servicios en la nube. Esto significa que todos tus datos, interacciones y servicios están dentro de tu entorno: sin llamadas de API externas, sin datos saliendo de tu entorno. Ollama soporta una amplia gama de modelos de código abierto de alta calidad: Llama 2/3: modelos líderes de Meta, excelentes para conversaciones generales y razonamiento Mistral: modelos rápidos y eficientes que golpean por encima de su peso CodeLlama: Especializada en tareas de programación y generación de código Gemma: los modelos abiertos de Google optimizados para la seguridad y el rendimiento Phi: los modelos compactos pero capaces de Microsoft Qwen: Potentes capacidades multilingües La mayoría de estos modelos vienen en diferentes tamaños (7B, 13B, 70B parámetros), dándole la libertad de elegir en función de sus necesidades de hardware y rendimiento disponibles. Para ejecutar estos modelos dentro de Ollama, su hardware depende del tamaño del modelo con el que está trabajando. Modelos pequeños (7B): 8–16GB de RAM, que se puede ejecutar en la mayoría de los portátiles modernos Modelos medianos (13B): 16-32 GB de RAM, escritorio o servidor recomendado Grandes modelos (70B): 64GB + RAM, hardware de clase empresarial requerido Aceleración de la GPU: opcional pero mejora drásticamente la velocidad: se recomiendan GPUs NVIDIA con 8GB+ VRAM El uso de modelos locales viene con un compromiso entre la privacidad y la conveniencia y voy a destacar algunos de ellos a continuación: Local Models (Ollama): ✅ Privacidad completa de datos - nada deja su red ✅ Ningún coste por token después de la configuración inicial ✅ Funciona offline, sin dependencia de Internet Requiere instalación y mantenimiento técnico Limitado a modelos de código abierto (puede no coincidir con la calidad GPT-4) ☑ Inversión de hardware y costes de electricidad Cloud APIs (OpenAI, etc.): Los modelos más recientes y poderosos No hay infraestructura para gestionar Escalabilidad instantánea y disponibilidad Los datos viajan a través de sistemas de terceros Los costos actuales de Token Dependiente de Internet y de la disponibilidad del proveedor En el entorno de producción, estos modelos (Ollama y personalizados) pueden ser containerizados para una fácil implementación en la nube y una API generada para su uso: esta API pertenecerá a usted/su empresa y luego se utilizará para su negocio. La economía cuando se trata de consideraciones de costo para los modelos locales depende de su patrón de uso. Si está procesando miles de solicitudes diarias, los modelos locales a menudo se vuelven rentables en meses.Un servidor de $5,000 que funciona 24/7 podría costar menos que su factura mensual de OpenAI. High Usage Scenarios Para prototipos o uso ocasional, las APIs en la nube suelen ser más económicas. Low Usage Scenarios Para los sectores sanitario, financiero o jurídico, los beneficios de cumplimiento de la implementación local a menudo justifican los costes independientemente del volumen de uso. Regulated Industries El punto dulce es a menudo un enfoque híbrido: usar modelos locales para el procesamiento de datos sensibles y las APIs en la nube para funciones no sensibles que se benefician de las últimas capacidades del modelo. Cuándo elegir local vs. nube: Local: datos sensibles, requisitos de cumplimiento, costes previsibles Nube: prototipos rápidos, necesidades de escala, modelos más recientes