forked from Rockachopa/Timmy-time-dashboard
Kimi Agent
3590c1689e
Co-authored-by: Kimi Agent <kimi@timmy.local> Co-committed-by: Kimi Agent <kimi@timmy.local>
335 lines
12 KiB
Python
335 lines
12 KiB
Python
"""Agentic loop — multi-step task execution with progress tracking.
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Provides `run_agentic_loop()`, the engine behind the `plan_and_execute` tool.
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When the model recognises a task needs 3+ sequential steps, it calls
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`plan_and_execute(task)` which spawns this loop in the background.
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Flow:
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1. Planning — ask the model to break the task into numbered steps
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2. Execution — run each step sequentially, feeding results forward
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3. Adaptation — on failure, ask the model to adapt the plan
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4. Summary — ask the model to summarise what was accomplished
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Progress is broadcast via WebSocket so the dashboard can show live updates.
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"""
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from __future__ import annotations
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import asyncio
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import logging
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import re
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import threading
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import time
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import uuid
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from collections.abc import Callable
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from dataclasses import dataclass, field
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Data structures
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# ---------------------------------------------------------------------------
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@dataclass
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class AgenticStep:
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"""Result of a single step in the agentic loop."""
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step_num: int
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description: str
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result: str
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status: str # "completed" | "failed" | "adapted"
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duration_ms: int
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@dataclass
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class AgenticResult:
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"""Final result of the entire agentic loop."""
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task_id: str
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task: str
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summary: str
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steps: list[AgenticStep] = field(default_factory=list)
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status: str = "completed" # "completed" | "partial" | "failed"
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total_duration_ms: int = 0
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# ---------------------------------------------------------------------------
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# Agent factory
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# ---------------------------------------------------------------------------
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_loop_agent = None
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_loop_agent_lock = threading.Lock()
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def _get_loop_agent():
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"""Create a fresh agent for the agentic loop.
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Returns the same type of agent as `create_timmy()` but with a
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dedicated session so it doesn't pollute the main chat history.
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"""
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global _loop_agent
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if _loop_agent is None:
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with _loop_agent_lock:
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if _loop_agent is None:
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from timmy.agent import create_timmy
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_loop_agent = create_timmy()
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return _loop_agent
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# ---------------------------------------------------------------------------
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# Plan parser
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# ---------------------------------------------------------------------------
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_STEP_RE = re.compile(r"^\s*(\d+)[.)]\s*(.+)$", re.MULTILINE)
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def _parse_steps(plan_text: str) -> list[str]:
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"""Extract numbered steps from the model's planning output."""
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matches = _STEP_RE.findall(plan_text)
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if matches:
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return [desc.strip() for _, desc in matches]
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# Fallback: split on newlines, ignore blanks
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return [line.strip() for line in plan_text.strip().splitlines() if line.strip()]
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# ---------------------------------------------------------------------------
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# Core loop
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# ---------------------------------------------------------------------------
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async def run_agentic_loop(
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task: str,
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*,
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session_id: str = "agentic",
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max_steps: int = 0,
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on_progress: Callable | None = None,
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) -> AgenticResult:
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"""Execute a multi-step task with planning, execution, and adaptation.
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Args:
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task: Full description of the task to execute.
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session_id: Agno session_id for conversation continuity.
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max_steps: Max steps to execute (0 = use config default).
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on_progress: Optional async callback(description, step_num, total_steps).
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Returns:
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AgenticResult with steps, summary, and status.
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"""
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from config import settings
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if max_steps <= 0:
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max_steps = getattr(settings, "max_agent_steps", 10)
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task_id = str(uuid.uuid4())[:8]
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start_time = time.monotonic()
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agent = _get_loop_agent()
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result = AgenticResult(task_id=task_id, task=task, summary="")
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# ── Phase 1: Planning ──────────────────────────────────────────────────
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plan_prompt = (
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f"Break this task into numbered steps (max {max_steps}). "
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f"Return ONLY a numbered list, nothing else.\n\n"
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f"Task: {task}"
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)
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try:
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plan_run = await asyncio.to_thread(
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agent.run, plan_prompt, stream=False, session_id=f"{session_id}_plan"
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)
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plan_text = plan_run.content if hasattr(plan_run, "content") else str(plan_run)
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except Exception as exc: # broad catch intentional: agent.run can raise any error
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logger.error("Agentic loop: planning failed: %s", exc)
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result.status = "failed"
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result.summary = f"Planning failed: {exc}"
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result.total_duration_ms = int((time.monotonic() - start_time) * 1000)
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return result
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steps = _parse_steps(plan_text)
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if not steps:
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result.status = "failed"
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result.summary = "Planning produced no steps."
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result.total_duration_ms = int((time.monotonic() - start_time) * 1000)
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return result
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# Enforce max_steps — track if we truncated
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planned_steps = len(steps)
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steps = steps[:max_steps]
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total_steps = len(steps)
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was_truncated = planned_steps > total_steps
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# Broadcast plan
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await _broadcast_progress(
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"agentic.plan_ready",
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{
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"task_id": task_id,
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"task": task,
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"steps": steps,
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"total": total_steps,
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},
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)
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# ── Phase 2: Execution ─────────────────────────────────────────────────
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completed_results: list[str] = []
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for i, step_desc in enumerate(steps, 1):
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step_start = time.monotonic()
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recent = completed_results[-2:] if completed_results else []
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context = (
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f"Task: {task}\n"
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f"Step {i}/{total_steps}: {step_desc}\n"
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f"Recent progress: {recent}\n\n"
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f"Execute this step and report what you did."
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)
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try:
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step_run = await asyncio.to_thread(
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agent.run, context, stream=False, session_id=f"{session_id}_step{i}"
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)
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step_result = step_run.content if hasattr(step_run, "content") else str(step_run)
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# Clean the response
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from timmy.session import _clean_response
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step_result = _clean_response(step_result)
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step = AgenticStep(
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step_num=i,
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description=step_desc,
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result=step_result,
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status="completed",
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duration_ms=int((time.monotonic() - step_start) * 1000),
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)
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result.steps.append(step)
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completed_results.append(f"Step {i}: {step_result[:200]}")
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# Broadcast progress
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await _broadcast_progress(
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"agentic.step_complete",
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{
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"task_id": task_id,
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"step": i,
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"total": total_steps,
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"description": step_desc,
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"result": step_result[:200],
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},
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)
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if on_progress:
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await on_progress(step_desc, i, total_steps)
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except Exception as exc: # broad catch intentional: agent.run can raise any error
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logger.warning("Agentic loop step %d failed: %s", i, exc)
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# ── Adaptation: ask model to adapt ─────────────────────────────
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adapt_prompt = (
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f"Step {i} failed with error: {exc}\n"
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f"Original step was: {step_desc}\n"
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f"Adapt the plan and try an alternative approach for this step."
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)
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try:
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adapt_run = await asyncio.to_thread(
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agent.run,
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adapt_prompt,
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stream=False,
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session_id=f"{session_id}_adapt{i}",
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)
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adapt_result = (
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adapt_run.content if hasattr(adapt_run, "content") else str(adapt_run)
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)
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from timmy.session import _clean_response
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adapt_result = _clean_response(adapt_result)
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step = AgenticStep(
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step_num=i,
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description=f"[Adapted] {step_desc}",
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result=adapt_result,
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status="adapted",
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duration_ms=int((time.monotonic() - step_start) * 1000),
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)
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result.steps.append(step)
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completed_results.append(f"Step {i} (adapted): {adapt_result[:200]}")
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await _broadcast_progress(
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"agentic.step_adapted",
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{
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"task_id": task_id,
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"step": i,
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"total": total_steps,
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"description": step_desc,
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"error": str(exc),
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"adaptation": adapt_result[:200],
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},
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)
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if on_progress:
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await on_progress(f"[Adapted] {step_desc}", i, total_steps)
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except Exception as adapt_exc: # broad catch intentional: agent.run can raise any error
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logger.error("Agentic loop adaptation also failed: %s", adapt_exc)
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step = AgenticStep(
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step_num=i,
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description=step_desc,
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result=f"Failed: {exc}; Adaptation also failed: {adapt_exc}",
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status="failed",
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duration_ms=int((time.monotonic() - step_start) * 1000),
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)
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result.steps.append(step)
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completed_results.append(f"Step {i}: FAILED")
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# ── Phase 3: Summary ───────────────────────────────────────────────────
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completed_count = sum(1 for s in result.steps if s.status == "completed")
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adapted_count = sum(1 for s in result.steps if s.status == "adapted")
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failed_count = sum(1 for s in result.steps if s.status == "failed")
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parts = [f"Completed {completed_count}/{total_steps} steps"]
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if adapted_count:
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parts.append(f"{adapted_count} adapted")
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if failed_count:
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parts.append(f"{failed_count} failed")
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result.summary = f"{task}: {', '.join(parts)}."
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# Determine final status
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if was_truncated:
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result.status = "partial"
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elif len(result.steps) < total_steps:
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result.status = "partial"
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elif any(s.status == "failed" for s in result.steps):
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result.status = "partial"
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else:
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result.status = "completed"
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result.total_duration_ms = int((time.monotonic() - start_time) * 1000)
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await _broadcast_progress(
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"agentic.task_complete",
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{
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"task_id": task_id,
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"status": result.status,
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"steps_completed": len(result.steps),
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"summary": result.summary[:300],
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"duration_ms": result.total_duration_ms,
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},
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)
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return result
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# ---------------------------------------------------------------------------
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# WebSocket broadcast helper
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# ---------------------------------------------------------------------------
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async def _broadcast_progress(event: str, data: dict) -> None:
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"""Broadcast agentic loop progress via WebSocket (best-effort)."""
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try:
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from infrastructure.ws_manager.handler import ws_manager
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await ws_manager.broadcast(event, data)
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except (ImportError, AttributeError, ConnectionError, RuntimeError) as exc:
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logger.warning("Agentic loop broadcast failed: %s", exc)
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logger.debug("Agentic loop: WS broadcast failed for %s", event)
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