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{ | |
"question": "What is the weather like in London tomorrow and what is the current time in Tokyo?", | |
"chain_of_thought": "The user is asking for two distinct pieces of information: the weather in London for tomorrow and the current time in Tokyo. I will need to use the 'get_weather' tool for the first part and the 'get_time' tool for the second part. I will then combine these results to answer the user's question.", | |
"reasoning_trace": [ | |
{ | |
"step_number": 1, | |
"thought": "The first part of the question is about the weather in London tomorrow. The 'get_weather' tool seems appropriate for this. I need to specify the location as 'London' and the time as 'tomorrow'.", | |
"action": "Call get_weather(location='London', time='tomorrow')" | |
}, | |
{ |
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{ | |
"messages": [ | |
{ | |
"role": "system", | |
"content": "You are a function calling AI model. You are provided with function signatures\nwithin <tools></tools> XML tags. You may call one or more functions to assist\nwith the user query. For each function call return a json object with function\nname and arguments within <tool_call></tool_call> XML tags.\n\n\nHere are the available tools:\n<tools>\n[\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_weather\",\n \"description\": \"Get current weather conditions for a location\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"location\": {\n \"type\": \"str\",\n \"description\": \"City name or location (e.g., 'Paris', 'New York')\"\n },\n \"time\": {\n \"type\": \"str\",\n \"description\": \"Time period for weather data\"\n }\n },\n \"required\": [\n \"location\"\n ]\n }\n }\n },\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"search_web\",\n \"description\": \"Search the web for information\",\n \"par |
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{ | |
"messages": [ | |
{ | |
"role": "system", | |
"content": "You are a helpful AI assistant that demonstrates systematic problem-solving through structured reasoning steps." | |
}, | |
{ | |
"role": "user", | |
"content": "I need to find all connected components in an undirected graph. Can you explain how to do this using Depth-First Search (DFS) and provide a Python code example?" | |
}, |
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# API version for configuration evolution | |
apiVersion: v1 | |
# Agent Information | |
name: "Kubernetes SRE Agent" | |
description: "AI Agent Kubernetes SRE Agent Project." | |
version: "0.0.1" | |
url: http://localhost:8000 | |
provider_organization: AgentUp | |
provider_url: https://agentup.dev |
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> curl -s http://127.0.0.1:8000/.well-known/agent-card.json | jq | |
{ | |
"additionalInterfaces": null, | |
"capabilities": { | |
"extensions": null, | |
"pushNotifications": false, | |
"stateTransitionHistory": false, | |
"streaming": true | |
}, | |
"defaultInputModes": [ |
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{ | |
"name": "GeoSpatial Route Planner Agent", | |
"description": "Provides advanced route planning, traffic analysis, and custom map generation services. This agent can calculate optimal routes, estimate travel times considering real-time traffic, and create personalized maps with points of interest.", | |
"url": "https://georoute-agent.example.com/a2a/v1", | |
"provider": { | |
"organization": "Example Geo Services Inc.", | |
"url": "https://www.examplegeoservices.com" | |
}, | |
"iconUrl": "https://georoute-agent.example.com/icon.png", | |
"version": "1.2.0", |
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BUG_SOW_SYSTEM_PROMPT = """You are an expert software engineer helping a user create a Statement of Work (SoW) for a bug fix. | |
Your goal is to guide the user through creating a comprehensive SoW by asking relevant questions. | |
Follow these steps in a conversational, friendly manner: | |
1. Ask about the bug's symptoms and behavior | |
2. Ask about reproduction steps | |
3. Ask about the expected vs actual behavior | |
4. Ask about any error messages or logs | |
5. Ask about the environment where the bug occurs | |
6. Ask about any workarounds or temporary fixes |
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[2025-04-09T13:16:53Z INFO ragrust::query] Processing query: Add --quiet CLI flag to SQLFluff | |
Add --quiet/-q flag option to Click command interface | |
Implement flag in main CLI entry point | |
Ensure flag is recognized across all relevant commands | |
Add type hints and docstrings for new parameter | |
{ | |
"code_snippets": [ | |
{ | |
"entity_id": "361af743-97a1-4961-b906-ddf4f1113239", |
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================================================ | |
Generated Code from Agent One (OpenAI) | |
================================================ | |
FILE: src/main.py | |
```python | |
# This is a simple Python program that displays the message "Hello, World!" | |
print("Hello, World!") | |
``` | |
Change Log: |
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