feat: auto-create Gitea issues from research findings
Implement `extract_action_items()` and `create_gitea_issue()` to close the research → knowledge → actionable backlog loop. - LLM extracts 0-5 concrete action items from research reports - Each item filed as a Gitea issue with labels, source links, evidence - Labels auto-resolved/created (actionable, kimi-ready, etc.) - Child issues linked back to source research issue - `triage_research_report()` orchestrates end-to-end with dry_run support - 28 unit tests covering extraction, validation, API calls, and graceful degradation Fixes #977 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Alexander Whitestone
369
src/timmy/research_triage.py
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369
src/timmy/research_triage.py
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"""Research triage — extract action items from research reports and file Gitea issues.
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Closes the loop: research → knowledge → actionable engineering work.
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The LLM extracts action items during synthesis (not post-processed), then
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each item is filed as a Gitea issue with appropriate labels, source links,
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and evidence from the original research.
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Usage::
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from timmy.research_triage import triage_research_report
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results = await triage_research_report(
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report="## Findings\\n...",
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source_issue=946,
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)
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"""
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from __future__ import annotations
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import json
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import logging
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import re
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from dataclasses import dataclass, field
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from typing import Any
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import httpx
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from config import settings
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logger = logging.getLogger(__name__)
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# Regex to strip markdown code fences from LLM output
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_FENCE_RE = re.compile(r"^```(?:json)?\s*\n?", re.MULTILINE)
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@dataclass
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class ActionItem:
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"""A single actionable item extracted from a research report."""
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title: str
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body: str
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labels: list[str] = field(default_factory=list)
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priority: str = "medium"
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source_urls: list[str] = field(default_factory=list)
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def to_issue_body(self, source_issue: int | None = None) -> str:
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"""Format for a Gitea issue body with source attribution."""
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parts = [self.body]
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if self.source_urls:
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parts.append("\n### Source Evidence")
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for url in self.source_urls:
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parts.append(f"- {url}")
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if source_issue:
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parts.append(
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f"\n### Origin\nExtracted from research in #{source_issue}"
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)
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parts.append("\n---\n*Auto-triaged from research findings by Timmy*")
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return "\n".join(parts)
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def _build_extraction_prompt(report: str) -> str:
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"""Build the LLM prompt for extracting action items from a research report."""
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return (
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"You are triaging a research report for actionable engineering work.\n"
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"Extract 0-5 CONCRETE action items — bugs to fix, features to build,\n"
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"infrastructure to set up, or investigations to run.\n\n"
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"Rules:\n"
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"- Only include items that map to real engineering tasks\n"
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"- Skip vague recommendations or philosophical observations\n"
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"- Each item should be specific enough to become a Gitea issue\n"
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"- Include evidence/URLs from the report in source_urls\n"
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"- Priority: high (blocking or critical), medium (important), low (nice-to-have)\n"
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"- Labels: pick from [actionable, research, bug, feature, infrastructure, "
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"performance, security, kimi-ready]\n"
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" - 'kimi-ready' means a well-scoped task suitable for an AI agent\n"
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" - 'actionable' should be on every item (these are all actionable)\n\n"
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"For each item return:\n"
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'- "title": Clear, specific title with area prefix '
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'(e.g. "[MCP] Restore tool server with FastMCP")\n'
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'- "body": Detailed markdown body with:\n'
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" **What:** What needs to be done\n"
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" **Why:** Why this matters (link to research finding)\n"
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" **Suggested approach:** How to implement\n"
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" **Acceptance criteria:** How to verify\n"
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'- "labels": Array of label strings\n'
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'- "priority": One of high, medium, low\n'
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'- "source_urls": Array of URLs referenced in the research\n\n'
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"Return ONLY a JSON array of objects. Return [] if nothing is actionable.\n\n"
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f"Research report:\n{report}\n\nJSON array:"
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)
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def _parse_llm_response(raw: str) -> list[dict[str, Any]]:
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"""Parse LLM JSON response, stripping code fences if present."""
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cleaned = raw.strip()
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# Strip markdown code fences
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if cleaned.startswith("```"):
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cleaned = cleaned.split("\n", 1)[-1].rsplit("```", 1)[0].strip()
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items = json.loads(cleaned)
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if not isinstance(items, list):
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return []
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return items
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def _validate_action_item(raw_item: dict[str, Any]) -> ActionItem | None:
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"""Validate and convert a raw dict to an ActionItem, or None if invalid."""
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if not isinstance(raw_item, dict):
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return None
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title = raw_item.get("title", "").strip()
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body = raw_item.get("body", "").strip()
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if not title or len(title) < 10:
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return None
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if not body or len(body) < 20:
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return None
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labels = raw_item.get("labels", [])
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if isinstance(labels, str):
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labels = [l.strip() for l in labels.split(",") if l.strip()]
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if not isinstance(labels, list):
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labels = []
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# Ensure 'actionable' label is always present
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if "actionable" not in labels:
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labels.insert(0, "actionable")
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priority = raw_item.get("priority", "medium").strip().lower()
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if priority not in ("high", "medium", "low"):
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priority = "medium"
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source_urls = raw_item.get("source_urls", [])
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if not isinstance(source_urls, list):
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source_urls = []
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return ActionItem(
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title=title,
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body=body,
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labels=labels,
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priority=priority,
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source_urls=source_urls,
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)
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async def extract_action_items(
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report: str,
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llm_caller: Any | None = None,
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) -> list[ActionItem]:
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"""Extract actionable engineering items from a research report.
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Uses the LLM to identify concrete tasks, bugs, features, and
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infrastructure work from structured research output.
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Args:
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report: The research report text (markdown).
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llm_caller: Optional async callable(prompt) -> str for LLM.
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Falls back to the cascade router.
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Returns:
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List of validated ActionItem objects (0-5 items).
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"""
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if not report or not report.strip():
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return []
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prompt = _build_extraction_prompt(report)
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try:
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if llm_caller is not None:
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raw = await llm_caller(prompt)
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else:
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raw = await _call_llm(prompt)
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except Exception as exc:
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logger.warning("LLM extraction failed: %s", exc)
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return []
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if not raw or not raw.strip():
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return []
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try:
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raw_items = _parse_llm_response(raw)
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except (json.JSONDecodeError, ValueError) as exc:
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logger.warning("Failed to parse LLM action items: %s", exc)
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return []
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items = []
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for raw_item in raw_items[:5]: # Safety cap
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item = _validate_action_item(raw_item)
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if item is not None:
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items.append(item)
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logger.info("Extracted %d action items from research report", len(items))
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return items
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async def _call_llm(prompt: str) -> str:
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"""Call the cascade router for LLM completion.
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Falls back gracefully if the router is unavailable.
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"""
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from infrastructure.router import get_router
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router = get_router()
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messages = [{"role": "user", "content": prompt}]
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result = await router.complete(messages=messages, temperature=0.1)
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return result.get("content", "") if isinstance(result, dict) else str(result)
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async def create_gitea_issue(
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item: ActionItem,
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source_issue: int | None = None,
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) -> dict[str, Any] | None:
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"""Create a Gitea issue from an ActionItem via the REST API.
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Args:
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item: The action item to file.
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source_issue: Parent research issue number to link back to.
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Returns:
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The created issue dict from Gitea API, or None on failure.
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"""
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if not settings.gitea_enabled or not settings.gitea_token:
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logger.debug("Gitea not configured — skipping issue creation")
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return None
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owner, repo = settings.gitea_repo.split("/", 1)
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api_url = f"{settings.gitea_url}/api/v1/repos/{owner}/{repo}/issues"
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body = item.to_issue_body(source_issue=source_issue)
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payload: dict[str, Any] = {
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"title": item.title,
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"body": body,
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}
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# Resolve label names to IDs
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label_ids = await _resolve_label_ids(item.labels, owner, repo)
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if label_ids:
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payload["labels"] = label_ids
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try:
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async with httpx.AsyncClient(timeout=15) as client:
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resp = await client.post(
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api_url,
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headers={
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"Authorization": f"token {settings.gitea_token}",
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"Content-Type": "application/json",
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},
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json=payload,
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)
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if resp.status_code in (200, 201):
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issue_data = resp.json()
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logger.info(
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"Created Gitea issue #%s: %s",
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issue_data.get("number", "?"),
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item.title[:60],
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)
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return issue_data
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logger.warning(
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"Gitea issue creation failed (HTTP %s): %s",
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resp.status_code,
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resp.text[:200],
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)
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return None
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except (httpx.ConnectError, httpx.ReadError, ConnectionError) as exc:
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logger.warning("Gitea connection failed: %s", exc)
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return None
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except Exception as exc:
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logger.error("Unexpected error creating Gitea issue: %s", exc)
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return None
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async def _resolve_label_ids(
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label_names: list[str],
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owner: str,
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repo: str,
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) -> list[int]:
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"""Resolve label names to Gitea label IDs, creating missing labels.
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Returns a list of integer label IDs for the issue payload.
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"""
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if not label_names:
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return []
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labels_url = f"{settings.gitea_url}/api/v1/repos/{owner}/{repo}/labels"
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headers = {
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"Authorization": f"token {settings.gitea_token}",
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"Content-Type": "application/json",
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}
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try:
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async with httpx.AsyncClient(timeout=10) as client:
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# Fetch existing labels
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resp = await client.get(labels_url, headers=headers)
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if resp.status_code != 200:
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return []
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existing = {l["name"]: l["id"] for l in resp.json()}
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label_ids = []
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for name in label_names:
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if name in existing:
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label_ids.append(existing[name])
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else:
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# Auto-create missing labels with a default color
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create_resp = await client.post(
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labels_url,
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headers=headers,
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json={"name": name, "color": "#0075ca"},
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)
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if create_resp.status_code in (200, 201):
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label_ids.append(create_resp.json()["id"])
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return label_ids
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except Exception as exc:
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logger.debug("Label resolution failed: %s", exc)
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return []
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async def triage_research_report(
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report: str,
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source_issue: int | None = None,
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llm_caller: Any | None = None,
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dry_run: bool = False,
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) -> list[dict[str, Any]]:
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"""End-to-end: extract action items from research and file Gitea issues.
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This is the main entry point that closes the research → backlog loop.
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Args:
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report: Research report text (markdown).
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source_issue: The Gitea issue number that produced this research.
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llm_caller: Optional async callable(prompt) -> str for LLM calls.
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dry_run: If True, extract items but don't create issues.
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Returns:
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List of dicts with 'action_item' and 'gitea_issue' (or None) keys.
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"""
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items = await extract_action_items(report, llm_caller=llm_caller)
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if not items:
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logger.info("No action items extracted from research report")
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return []
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results = []
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for item in items:
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if dry_run:
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results.append({"action_item": item, "gitea_issue": None})
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continue
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issue_data = await create_gitea_issue(item, source_issue=source_issue)
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results.append({"action_item": item, "gitea_issue": issue_data})
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created_count = sum(1 for r in results if r["gitea_issue"] is not None)
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logger.info(
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"Research triage complete: %d items extracted, %d issues created",
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len(results),
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created_count,
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)
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return results
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348
tests/timmy/test_research_triage.py
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348
tests/timmy/test_research_triage.py
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"""Tests for research triage — action item extraction and Gitea issue filing."""
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import json
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from unittest.mock import AsyncMock, MagicMock, patch
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import httpx
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import pytest
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from timmy.research_triage import (
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ActionItem,
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_parse_llm_response,
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_resolve_label_ids,
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_validate_action_item,
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create_gitea_issue,
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extract_action_items,
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triage_research_report,
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)
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# ---------------------------------------------------------------------------
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# ActionItem
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# ---------------------------------------------------------------------------
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SAMPLE_REPORT = """
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## Research: MCP Abstraction Layer
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### Finding 1: FastMCP overhead is negligible
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FastMCP averages 26.45ms per tool call. Total overhead <3% of budget.
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### Finding 2: Agno tool calling is broken
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Agno issues #2231, #2625 document persistent breakage with Ollama.
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Fix: Use Ollama's `format` parameter with Pydantic JSON schemas.
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### Recommendation
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Implement three-tier router for structured output.
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"""
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SAMPLE_LLM_RESPONSE = json.dumps(
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[
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{
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"title": "[Router] Implement three-tier structured output router",
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"body": (
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"**What:** Build a three-tier router that uses Ollama's "
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"`format` parameter for structured output.\n"
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"**Why:** Agno's native tool calling is broken (#2231, #2625). "
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"Pydantic JSON schemas with `format` bypass the issue.\n"
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"**Suggested approach:** Add format parameter support to "
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"CascadeRouter.\n"
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"**Acceptance criteria:** Tool calls return valid JSON matching "
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"the Pydantic schema."
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),
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"labels": ["actionable", "feature", "kimi-ready"],
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"priority": "high",
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"source_urls": ["https://github.com/agno-agi/agno/issues/2231"],
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},
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]
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)
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class TestActionItem:
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def test_to_issue_body_basic(self):
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item = ActionItem(title="Test", body="Test body")
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body = item.to_issue_body()
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assert "Test body" in body
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assert "Auto-triaged" in body
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def test_to_issue_body_with_source_issue(self):
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item = ActionItem(title="Test", body="Test body")
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body = item.to_issue_body(source_issue=946)
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assert "#946" in body
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assert "Origin" in body
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def test_to_issue_body_with_source_urls(self):
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item = ActionItem(
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title="Test",
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body="Body",
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source_urls=["https://example.com/finding"],
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)
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body = item.to_issue_body()
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assert "https://example.com/finding" in body
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assert "Source Evidence" in body
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# ---------------------------------------------------------------------------
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# _parse_llm_response
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# ---------------------------------------------------------------------------
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class TestParseLlmResponse:
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def test_plain_json(self):
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items = _parse_llm_response('[{"title": "foo"}]')
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assert len(items) == 1
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assert items[0]["title"] == "foo"
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def test_fenced_json(self):
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raw = '```json\n[{"title": "bar"}]\n```'
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items = _parse_llm_response(raw)
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assert len(items) == 1
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assert items[0]["title"] == "bar"
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def test_empty_array(self):
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assert _parse_llm_response("[]") == []
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def test_non_array_returns_empty(self):
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assert _parse_llm_response('{"title": "not an array"}') == []
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def test_invalid_json_raises(self):
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with pytest.raises(json.JSONDecodeError):
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_parse_llm_response("not json at all")
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# ---------------------------------------------------------------------------
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# _validate_action_item
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# ---------------------------------------------------------------------------
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class TestValidateActionItem:
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def test_valid_item(self):
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raw = {
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"title": "[Area] A specific clear title",
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"body": "Detailed body with enough content to be useful.",
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"labels": ["actionable", "bug"],
|
||||
"priority": "high",
|
||||
}
|
||||
item = _validate_action_item(raw)
|
||||
assert item is not None
|
||||
assert item.title == "[Area] A specific clear title"
|
||||
assert item.priority == "high"
|
||||
assert "actionable" in item.labels
|
||||
|
||||
def test_short_title_rejected(self):
|
||||
raw = {"title": "Short", "body": "Detailed body with enough content here."}
|
||||
assert _validate_action_item(raw) is None
|
||||
|
||||
def test_short_body_rejected(self):
|
||||
raw = {"title": "A perfectly fine title here", "body": "Too short"}
|
||||
assert _validate_action_item(raw) is None
|
||||
|
||||
def test_missing_title_rejected(self):
|
||||
raw = {"body": "Detailed body with enough content to be useful."}
|
||||
assert _validate_action_item(raw) is None
|
||||
|
||||
def test_non_dict_rejected(self):
|
||||
assert _validate_action_item("not a dict") is None
|
||||
|
||||
def test_actionable_label_auto_added(self):
|
||||
raw = {
|
||||
"title": "A perfectly fine title here",
|
||||
"body": "Detailed body with enough content to be useful.",
|
||||
"labels": ["bug"],
|
||||
}
|
||||
item = _validate_action_item(raw)
|
||||
assert item is not None
|
||||
assert "actionable" in item.labels
|
||||
|
||||
def test_labels_as_csv_string(self):
|
||||
raw = {
|
||||
"title": "A perfectly fine title here",
|
||||
"body": "Detailed body with enough content to be useful.",
|
||||
"labels": "bug, feature",
|
||||
}
|
||||
item = _validate_action_item(raw)
|
||||
assert item is not None
|
||||
assert "bug" in item.labels
|
||||
assert "feature" in item.labels
|
||||
|
||||
def test_invalid_priority_defaults_medium(self):
|
||||
raw = {
|
||||
"title": "A perfectly fine title here",
|
||||
"body": "Detailed body with enough content to be useful.",
|
||||
"priority": "urgent",
|
||||
}
|
||||
item = _validate_action_item(raw)
|
||||
assert item is not None
|
||||
assert item.priority == "medium"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# extract_action_items
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestExtractActionItems:
|
||||
@pytest.mark.asyncio
|
||||
async def test_extracts_items_from_report(self):
|
||||
mock_llm = AsyncMock(return_value=SAMPLE_LLM_RESPONSE)
|
||||
items = await extract_action_items(SAMPLE_REPORT, llm_caller=mock_llm)
|
||||
assert len(items) == 1
|
||||
assert "three-tier" in items[0].title.lower()
|
||||
assert items[0].priority == "high"
|
||||
mock_llm.assert_called_once()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_empty_report_returns_empty(self):
|
||||
items = await extract_action_items("")
|
||||
assert items == []
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_llm_failure_returns_empty(self):
|
||||
mock_llm = AsyncMock(side_effect=RuntimeError("LLM down"))
|
||||
items = await extract_action_items(SAMPLE_REPORT, llm_caller=mock_llm)
|
||||
assert items == []
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_llm_returns_empty_string(self):
|
||||
mock_llm = AsyncMock(return_value="")
|
||||
items = await extract_action_items(SAMPLE_REPORT, llm_caller=mock_llm)
|
||||
assert items == []
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_llm_returns_invalid_json(self):
|
||||
mock_llm = AsyncMock(return_value="not valid json")
|
||||
items = await extract_action_items(SAMPLE_REPORT, llm_caller=mock_llm)
|
||||
assert items == []
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_caps_at_five_items(self):
|
||||
many_items = [
|
||||
{
|
||||
"title": f"[Area] Action item number {i} is specific",
|
||||
"body": f"Detailed body for action item {i} with enough words.",
|
||||
"labels": ["actionable"],
|
||||
"priority": "medium",
|
||||
}
|
||||
for i in range(10)
|
||||
]
|
||||
mock_llm = AsyncMock(return_value=json.dumps(many_items))
|
||||
items = await extract_action_items(SAMPLE_REPORT, llm_caller=mock_llm)
|
||||
assert len(items) <= 5
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# create_gitea_issue
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestCreateGiteaIssue:
|
||||
@pytest.mark.asyncio
|
||||
async def test_creates_issue_via_api(self):
|
||||
item = ActionItem(
|
||||
title="[Test] Create a test issue",
|
||||
body="This is a test issue body with details.",
|
||||
labels=["actionable"],
|
||||
)
|
||||
issue_resp = MagicMock()
|
||||
issue_resp.status_code = 201
|
||||
issue_resp.json.return_value = {"number": 42, "title": item.title}
|
||||
|
||||
mock_client = AsyncMock()
|
||||
mock_client.post.return_value = issue_resp
|
||||
|
||||
with (
|
||||
patch("timmy.research_triage.settings") as mock_settings,
|
||||
patch("timmy.research_triage._resolve_label_ids", new_callable=AsyncMock, return_value=[1]),
|
||||
patch("timmy.research_triage.httpx.AsyncClient") as mock_cls,
|
||||
):
|
||||
mock_settings.gitea_enabled = True
|
||||
mock_settings.gitea_token = "test-token"
|
||||
mock_settings.gitea_repo = "owner/repo"
|
||||
mock_settings.gitea_url = "http://localhost:3000"
|
||||
mock_cls.return_value.__aenter__ = AsyncMock(return_value=mock_client)
|
||||
mock_cls.return_value.__aexit__ = AsyncMock(return_value=False)
|
||||
result = await create_gitea_issue(item, source_issue=946)
|
||||
|
||||
assert result is not None
|
||||
assert result["number"] == 42
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_returns_none_when_disabled(self):
|
||||
item = ActionItem(title="[Test] Disabled test", body="Body content here.")
|
||||
with patch("timmy.research_triage.settings") as mock_settings:
|
||||
mock_settings.gitea_enabled = False
|
||||
mock_settings.gitea_token = ""
|
||||
result = await create_gitea_issue(item)
|
||||
assert result is None
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_handles_connection_error(self):
|
||||
item = ActionItem(
|
||||
title="[Test] Connection fail",
|
||||
body="Body content for connection test.",
|
||||
)
|
||||
mock_client = AsyncMock()
|
||||
mock_client.post.side_effect = httpx.ConnectError("refused")
|
||||
|
||||
with (
|
||||
patch("timmy.research_triage.settings") as mock_settings,
|
||||
patch("timmy.research_triage._resolve_label_ids", new_callable=AsyncMock, return_value=[]),
|
||||
patch("timmy.research_triage.httpx.AsyncClient") as mock_cls,
|
||||
):
|
||||
mock_settings.gitea_enabled = True
|
||||
mock_settings.gitea_token = "test-token"
|
||||
mock_settings.gitea_repo = "owner/repo"
|
||||
mock_settings.gitea_url = "http://localhost:3000"
|
||||
mock_cls.return_value.__aenter__ = AsyncMock(return_value=mock_client)
|
||||
mock_cls.return_value.__aexit__ = AsyncMock(return_value=False)
|
||||
result = await create_gitea_issue(item)
|
||||
assert result is None
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# triage_research_report (integration)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestTriageResearchReport:
|
||||
@pytest.mark.asyncio
|
||||
async def test_dry_run_extracts_without_filing(self):
|
||||
mock_llm = AsyncMock(return_value=SAMPLE_LLM_RESPONSE)
|
||||
results = await triage_research_report(
|
||||
SAMPLE_REPORT, source_issue=946, llm_caller=mock_llm, dry_run=True
|
||||
)
|
||||
assert len(results) == 1
|
||||
assert results[0]["action_item"] is not None
|
||||
assert results[0]["gitea_issue"] is None
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_empty_report_returns_empty(self):
|
||||
results = await triage_research_report("", llm_caller=AsyncMock(return_value="[]"))
|
||||
assert results == []
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_end_to_end_with_mock_gitea(self):
|
||||
mock_llm = AsyncMock(return_value=SAMPLE_LLM_RESPONSE)
|
||||
|
||||
issue_resp = MagicMock()
|
||||
issue_resp.status_code = 201
|
||||
issue_resp.json.return_value = {"number": 99, "title": "test"}
|
||||
|
||||
mock_client = AsyncMock()
|
||||
mock_client.post.return_value = issue_resp
|
||||
|
||||
with (
|
||||
patch("timmy.research_triage.settings") as mock_settings,
|
||||
patch("timmy.research_triage._resolve_label_ids", new_callable=AsyncMock, return_value=[]),
|
||||
patch("timmy.research_triage.httpx.AsyncClient") as mock_cls,
|
||||
):
|
||||
mock_settings.gitea_enabled = True
|
||||
mock_settings.gitea_token = "test-token"
|
||||
mock_settings.gitea_repo = "owner/repo"
|
||||
mock_settings.gitea_url = "http://localhost:3000"
|
||||
mock_cls.return_value.__aenter__ = AsyncMock(return_value=mock_client)
|
||||
mock_cls.return_value.__aexit__ = AsyncMock(return_value=False)
|
||||
results = await triage_research_report(
|
||||
SAMPLE_REPORT, source_issue=946, llm_caller=mock_llm
|
||||
)
|
||||
|
||||
assert len(results) == 1
|
||||
assert results[0]["gitea_issue"]["number"] == 99
|
||||
Reference in New Issue
Block a user