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from __future__ import annotations
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import sys
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from pathlib import Path
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from huggingface_hub import ChatCompletionInputTool
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FILENAME_CONFIG = "agent.json"
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PROMPT_FILENAMES = ("PROMPT.md", "AGENTS.md")
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DEFAULT_AGENT = {
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"model": "Qwen/Qwen2.5-72B-Instruct",
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"provider": "nebius",
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"servers": [
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{
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"type": "stdio",
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"command": "npx",
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"args": [
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"-y",
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"@modelcontextprotocol/server-filesystem",
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str(Path.home() / ("Desktop" if sys.platform == "darwin" else "")),
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],
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},
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{
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"type": "stdio",
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"command": "npx",
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"args": ["@playwright/mcp@latest"],
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},
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],
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}
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DEFAULT_SYSTEM_PROMPT = """
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You are an agent - please keep going until the user’s query is completely
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resolved, before ending your turn and yielding back to the user. Only terminate
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your turn when you are sure that the problem is solved, or if you need more
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info from the user to solve the problem.
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If you are not sure about anything pertaining to the user’s request, use your
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tools to read files and gather the relevant information: do NOT guess or make
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up an answer.
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You MUST plan extensively before each function call, and reflect extensively
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on the outcomes of the previous function calls. DO NOT do this entire process
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by making function calls only, as this can impair your ability to solve the
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problem and think insightfully.
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""".strip()
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MAX_NUM_TURNS = 10
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TASK_COMPLETE_TOOL: ChatCompletionInputTool = ChatCompletionInputTool.parse_obj( # type: ignore[assignment]
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{
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"type": "function",
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"function": {
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"name": "task_complete",
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"description": "Call this tool when the task given by the user is complete",
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"parameters": {
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"type": "object",
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"properties": {},
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},
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},
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}
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)
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ASK_QUESTION_TOOL: ChatCompletionInputTool = ChatCompletionInputTool.parse_obj( # type: ignore[assignment]
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{
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"type": "function",
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"function": {
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"name": "ask_question",
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"description": "Ask the user for more info required to solve or clarify their problem.",
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"parameters": {
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"type": "object",
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"properties": {},
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},
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},
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}
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)
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EXIT_LOOP_TOOLS: list[ChatCompletionInputTool] = [TASK_COMPLETE_TOOL, ASK_QUESTION_TOOL]
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DEFAULT_REPO_ID = "tiny-agents/tiny-agents"
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