mirror of
https://github.com/furyhawk/agent_alpha.git
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114 lines
4.1 KiB
Python
114 lines
4.1 KiB
Python
"""PydanticDeep assistant — deep agentic coding assistant.
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PydanticDeep is built on PydanticAI and provides:
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- Filesystem tools: ls, read_file, write_file, edit_file, glob, grep
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- Task management: write_todos, planning subagent
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- Subagent delegation
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- Skills system (SKILL.md files)
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- Memory persistence (MEMORY.md across sessions)
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- Context file discovery (AGENTS.md, SOUL.md from workspace root)
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- Built-in web search and web fetch
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Backend types (set via PYDANTIC_DEEP_BACKEND_TYPE):
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"state" — In-memory (default, no persistence)
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"daytona" — Daytona cloud workspace (isolated, cloud-native)
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Configuration via settings:
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PYDANTIC_DEEP_BACKEND_TYPE : "state" | "daytona" (default: "state")
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PYDANTIC_DEEP_INCLUDE_SUBAGENTS : enable subagent delegation (default: True)
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PYDANTIC_DEEP_INCLUDE_SKILLS : enable skills system (default: True)
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PYDANTIC_DEEP_INCLUDE_PLAN : enable planner subagent (default: True)
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PYDANTIC_DEEP_INCLUDE_MEMORY : enable persistent MEMORY.md (default: True)
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PYDANTIC_DEEP_INCLUDE_EXECUTE : enable shell execution (default: False)
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PYDANTIC_DEEP_WEB_SEARCH : enable built-in web search (default: True)
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"""
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from __future__ import annotations
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import time
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from dataclasses import dataclass
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from typing import Any
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from pydantic_ai import Agent
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from backend.repositories.memory_repository import MemoryRepository
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from pydantic_deep.deps import DeepAgentDeps
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class AgentService:
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"""Thin orchestrator for agent execution.
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Receives a pre-built ``Agent`` and its ``MemoryRepository`` via
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constructor injection so the lifespan (or tests) control wiring.
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No lazy initialisation — the agent is ready to run immediately.
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"""
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def __init__(self, agent: Agent[Any, str], memory_repo: MemoryRepository) -> None:
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self._agent = agent
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self._memory_repo = memory_repo
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# ── Lifecycle ──────────────────────────────────────────────────────────
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async def shutdown(self) -> None:
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"""Release agent resources."""
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self._agent = None
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# ── Execution ──────────────────────────────────────────────────────────
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async def ask(self, prompt: str, session_id: str | None = None) -> AskResult:
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"""Send a prompt to the agent and return the text output with usage stats."""
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di = DeepAgentDeps(backend=self._memory_repo.backend)
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t0 = time.monotonic()
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result = await self._agent.run(prompt, deps=di)
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elapsed = time.monotonic() - t0
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usage = result.usage
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return AskResult(
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output=result.output,
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input_tokens=usage.input_tokens,
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output_tokens=usage.output_tokens,
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total_tokens=usage.input_tokens + usage.output_tokens,
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elapsed_seconds=round(elapsed, 2),
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)
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@dataclass
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class AskResult:
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"""Result of a single agent ``ask()`` call with token usage information."""
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output: str
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"""The agent's text reply."""
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input_tokens: int = 0
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"""Number of prompt tokens sent to the model."""
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output_tokens: int = 0
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"""Number of completion tokens generated by the model."""
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total_tokens: int = 0
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"""Sum of input + output tokens."""
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elapsed_seconds: float = 0.0
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"""Wall-clock time in seconds the agent took to respond."""
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# ---------------------------------------------------------------------------
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# Singleton — wired once at startup by the lifespan hook in ``app.py``.
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# Routes access it through ``get_service()``.
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# ---------------------------------------------------------------------------
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_service: AgentService | None = None
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def get_service() -> AgentService:
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"""Return the singleton AgentService instance."""
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assert _service is not None, "AgentService has not been initialised — did the lifespan run?"
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return _service
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def set_service(service: AgentService) -> None:
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"""Set the module-level singleton (called by the lifespan hook)."""
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global _service
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_service = service
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