open-swe/agent/utils/analyzer_skills.py

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feat: outcomes dataset + bootstrap/continual split via skills (#1365) * fix: reset stale sandbox creation sentinel Co-authored-by: Johannes du Plessis <51395795+johannes117@users.noreply.github.com> * fix: treat SANDBOX_CREATING as a timestamped cross-process lock Only reset the sentinel when proven stale (older than the creation timeout); otherwise wait for the worker that holds the lock so a concurrent run does not create a duplicate sandbox. * feat(analyzer): outcomes dataset + bootstrap/continual split via skills Rename the review_style_analyzer graph to `analyzer` and split it into two modes, plus capture reviewer finding outcomes for continual learning. - Outcomes dataset: upsert resolved-by-commit (positive), dismissed (false positive), and GitHub/Slack thumbs findings into a single LangSmith dataset (openswe-reviewer-outcomes), keyed deterministically per finding+source. Emit points wired into update_finding, resolve_finding_thread, and the GitHub/Slack reaction handlers. - Two playbooks delivered as deepagents skills (bootstrap-repo-analysis, continual-learning), served as virtual files via a CompositeBackend /skills/ route + StateBackend (seeded into the run files channel at invoke time, never written to the sandbox). Mode is set by the launcher; continual runs fall back to the GitHub App installation token. - Split launcher into start_bootstrap_analysis + start_continual_run; register a per-repo nightly continual-learning cron when bootstrap completes. - New read_finding_outcomes tool feeds confirmed/dismissed findings back to the continual playbook. Tests for outcome label mapping, skills helper, and cron idempotency. * fix(analyzer): anchor continual cron runs to a real thread_id The nightly continual-learning cron is threadless, and get_analyzer early-returns an empty agent when configurable.thread_id is missing — so every cron-launched run no-op'd before reading outcomes or saving a refined prompt. Include the repo's deterministic analyzer thread_id in the continual run configurable so the run executes; the threadless run carries no message history, so nightly runs don't accumulate context. * refactor(analyzer): move cron lifecycle calls out of the review-styles store Drop the inline `analyzer_cron` imports from review_styles.py (added only to dodge a circular import) by relocating the cron-trigger calls to the layer above the store: registration to the save_review_style tool (after a prompt is saved) and removal to the dashboard delete route. review_styles.py is now a pure store again with top-level imports only. * refactor: hoist reviewer_outcomes imports to module level Move the two inline emit_finding_status_outcome imports introduced in this PR (update_finding, resolve_finding_thread) to top-level imports. reviewer_outcomes only depends on langsmith, so there is no circular import to avoid. --------- Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com>
2026-06-01 13:25:12 -07:00
"""Repo-bundled analyzer skills, served to the agent as virtual files.
The two analyzer playbooks live as ``SKILL.md`` files under ``agent/skills/``.
They are surfaced to the deepagents ``SkillsMiddleware`` via a ``StateBackend``
mounted at ``/skills/`` in a ``CompositeBackend`` — so the agent reads them with
``read_file`` without anything ever being written to the execution sandbox.
The ``files`` channel is seeded at invoke time (see the launchers). Because
``CompositeBackend`` strips the ``/skills/`` route prefix before delegating to the
``StateBackend``, the seeded keys are the *stripped* paths (e.g.
``/bootstrap-repo-analysis/SKILL.md``), while the agent and ``SkillsMiddleware``
address them under ``/skills/...``.
"""
from __future__ import annotations
from pathlib import Path
from typing import Any
SKILLS_DIR = Path(__file__).resolve().parent.parent / "skills"
SKILLS_ROUTE = "/skills/"
BOOTSTRAP_SKILL = "bootstrap-repo-analysis"
CONTINUAL_SKILL = "continual-learning"
ANALYZER_MODES = {"bootstrap": BOOTSTRAP_SKILL, "continual": CONTINUAL_SKILL}
def skill_path_for_mode(mode: str) -> str:
"""Return the agent-facing ``/skills/<name>/SKILL.md`` path for a run mode."""
skill = ANALYZER_MODES.get(mode, BOOTSTRAP_SKILL)
return f"{SKILLS_ROUTE}{skill}/SKILL.md"
def build_skill_files() -> dict[str, Any]:
"""Return ``{stripped_path: FileData}`` for every bundled analyzer skill.
Seed this into the run input's ``files`` so the ``/skills/`` StateBackend route
can serve them. Keys omit the ``/skills`` prefix (stripped by the composite
route); values are ``FileData`` v2 entries.
"""
feat: managed LangGraph Cloud + Vercel migration (PR2 — code fixes + docs) (#65) * fix(dashboard): managed-cloud OAuth hardening + admin user-mapping endpoint Prepare the dashboard backend for the managed LangGraph Cloud + Vercel runtime, where the API is HTTPS and cross-site from the UI. - OAuth redirect_uri (#2): coerce a schemeless DASHBOARD_API_BASE_URL to https:// in _api_base_url() so GitHub stops rejecting login with "redirect_uri not associated with this application". _cookie_security() now treats a schemeless (managed) value as Secure; SameSite=None too, consistent with the coerced scheme. - OAuth state cookie (#3): document that osw_oauth_state is host-only by design (a Domain cookie is unsafe across *.vercel.app, a public suffix), so login must always start on the stable alias to avoid "oauth state mismatch". Operational contract; no behavioral change. - Admin user mappings (#4): add POST /admin/user-mappings so an admin can set the github_login -> work_email link from the dashboard instead of a raw Store write. New "admin" MappingSource provenance value. * fix(webapp): refresh user-mapping cache on GitHub webhook paths On managed LangGraph Cloud the backend runs multiple replicas, so the per-process GitHub<->work-email mapping cache can be stale on the replica handling a webhook (a mapping created on another replica is invisible until refresh). process_github_pr_comment and process_github_issue now refresh the cache from the durable Store before resolving the author's email, matching the existing Slack mention path (process_slack_mention). * perf(webapp): defer deepagents import to speed custom-app cold start The custom FastAPI app (agent.webapp:app, the langgraph.json http.app) pulled deepagents -> langchain_anthropic -> anthropic into its import graph via dashboard.routes, only to build skill/chat seed files. Defer those create_file_data imports into the functions that use them. Removes deepagents/langchain_anthropic/anthropic from app import entirely and roughly halves module-import wall time (~0.6-0.8s -> ~0.35s warm; larger cold-start saving since native anthropic init is skipped). Behavior identical. (reviewer_diff already imports deepagents under TYPE_CHECKING.) * feat(ui): set work_email user mappings from the admin dashboard Add an "Add / update" form to the admin User mappings section and the adminUpsertUserMapping API client method, wiring the new POST /admin/user-mappings endpoint. Admins can now create or update a github_login -> work_email mapping directly instead of waiting for the user to self-connect Slack. * docs: document managed LangGraph Cloud + Vercel deployment - INSTALLATION §10: add the managed production env triad (LANGGRAPH_URL, DASHBOARD_BASE_URL + DASHBOARD_API_BASE_URL with https://, empty VITE_DASHBOARD_API_BASE_URL for same-origin), the stable-alias login and vercel.json stable-deployment-URL requirements, multi-replica cache note, plus redirect_uri-scheme and oauth-state-mismatch troubleshooting. Refresh the langgraph.json snippet to all six graphs. - README: reframe deployment around the managed migration; link the plan. - deploy/MIGRATION.md: import the self-hosted -> managed migration plan.
2026-06-29 19:58:38 -04:00
# Deferred import: deepagents (and its langchain_anthropic / anthropic
# transitive deps) is heavy (~0.7s) and is otherwise pulled into the custom
# FastAPI app's import chain via dashboard.routes, slowing cold start. It's
# only needed when a skill bundle is actually built (analyzer launch), so
# import it lazily here.
from deepagents.backends.utils import create_file_data
feat: outcomes dataset + bootstrap/continual split via skills (#1365) * fix: reset stale sandbox creation sentinel Co-authored-by: Johannes du Plessis <51395795+johannes117@users.noreply.github.com> * fix: treat SANDBOX_CREATING as a timestamped cross-process lock Only reset the sentinel when proven stale (older than the creation timeout); otherwise wait for the worker that holds the lock so a concurrent run does not create a duplicate sandbox. * feat(analyzer): outcomes dataset + bootstrap/continual split via skills Rename the review_style_analyzer graph to `analyzer` and split it into two modes, plus capture reviewer finding outcomes for continual learning. - Outcomes dataset: upsert resolved-by-commit (positive), dismissed (false positive), and GitHub/Slack thumbs findings into a single LangSmith dataset (openswe-reviewer-outcomes), keyed deterministically per finding+source. Emit points wired into update_finding, resolve_finding_thread, and the GitHub/Slack reaction handlers. - Two playbooks delivered as deepagents skills (bootstrap-repo-analysis, continual-learning), served as virtual files via a CompositeBackend /skills/ route + StateBackend (seeded into the run files channel at invoke time, never written to the sandbox). Mode is set by the launcher; continual runs fall back to the GitHub App installation token. - Split launcher into start_bootstrap_analysis + start_continual_run; register a per-repo nightly continual-learning cron when bootstrap completes. - New read_finding_outcomes tool feeds confirmed/dismissed findings back to the continual playbook. Tests for outcome label mapping, skills helper, and cron idempotency. * fix(analyzer): anchor continual cron runs to a real thread_id The nightly continual-learning cron is threadless, and get_analyzer early-returns an empty agent when configurable.thread_id is missing — so every cron-launched run no-op'd before reading outcomes or saving a refined prompt. Include the repo's deterministic analyzer thread_id in the continual run configurable so the run executes; the threadless run carries no message history, so nightly runs don't accumulate context. * refactor(analyzer): move cron lifecycle calls out of the review-styles store Drop the inline `analyzer_cron` imports from review_styles.py (added only to dodge a circular import) by relocating the cron-trigger calls to the layer above the store: registration to the save_review_style tool (after a prompt is saved) and removal to the dashboard delete route. review_styles.py is now a pure store again with top-level imports only. * refactor: hoist reviewer_outcomes imports to module level Move the two inline emit_finding_status_outcome imports introduced in this PR (update_finding, resolve_finding_thread) to top-level imports. reviewer_outcomes only depends on langsmith, so there is no circular import to avoid. --------- Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com>
2026-06-01 13:25:12 -07:00
files: dict[str, Any] = {}
for skill in ANALYZER_MODES.values():
skill_md = SKILLS_DIR / skill / "SKILL.md"
text = skill_md.read_text(encoding="utf-8")
files[f"/{skill}/SKILL.md"] = create_file_data(text)
return files