Phillip Tarrant
91187a0728
Implement the core autonomy layer — AgentLoop streams LLM responses, parses tool calls, executes them with permission checks, feeds results back, and repeats until the task completes or finish is called. - Add FinishTool for explicit loop termination - Add tools parameter to LLMClient.stream_chat() for function calling - Add compact tool result display (status line, not full output) - Refactor REPL to delegate to AgentLoop.run_turn() - Fix Ollama null content rejection (always send content as string) - Add finish to auto_approve permissions - 9 unit tests for agent loop (34 total, zero regressions) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
"""FinishTool — signals agent loop termination."""
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from typing import Any, ClassVar
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from pydantic import BaseModel, Field
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from app.models.config import AppConfig
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from app.models.tool_call import ToolResult, ToolResultStatus
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from app.tools.base import BaseTool
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class FinishParams(BaseModel):
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"""Parameters for the finish tool."""
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message: str = Field(default="Task complete.", description="Final message to the user")
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class FinishTool(BaseTool):
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"""Signal that the agent has completed its task."""
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name: ClassVar[str] = "finish"
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description: ClassVar[str] = (
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"Call this tool when you have completed the user's task. "
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"Provide a brief summary message."
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)
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params_model: ClassVar[type[BaseModel]] = FinishParams
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def execute(self, *, tool_call_id: str, **kwargs: Any) -> ToolResult:
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message = kwargs.get("message", "Task complete.")
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return ToolResult(
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tool_call_id=tool_call_id,
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tool_name=self.name,
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status=ToolResultStatus.SUCCESS,
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output=message,
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)
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