open-swe/langgraph.json

{
  "$schema": "https://langgra.ph/schema.json",
  "python_version": "3.12",
  "graphs": {
    "agent": "agent.server:get_agent",
    "reviewer": "agent.reviewer:get_reviewer_agent",
    "analyzer": "agent.analyzer:get_analyzer"
  },
  "dependencies": ["."],
  "http": {
    "app": "agent.webapp:app"
  },
  "env": ".env" 
}
init commit 2025-05-21 14:47:56 -07:00			`{`
chore: Drop monorepo (#1029) * chore: Drop monorepo * cr 2026-03-06 16:10:34 -08:00			`"$schema": "https://langgra.ph/schema.json",`
			`"python_version": "3.12",`
init commit 2025-05-21 14:47:56 -07:00			`"graphs": {`
feat: add reviewer graph + eval target wiring (#1241) * feat: add reviewer graph + eval target wiring - New `reviewer` graph (`agent/reviewer.py`) registered in langgraph.json alongside the main `agent` graph. Reuses the same sandbox lifecycle, GH proxy auth, and middleware primitives from `agent.server`, but with a narrower tool set, a reviewer-specific system prompt, no commit/push, and the `task` (subagent) tool stripped via `_ToolExclusionMiddleware` so review stays in one context. - New `github_comment` tool: agents call it once per issue with `(file, line, body, severity)` and the eval scores those calls against golden comments. - `ensure_no_empty_msg` middleware (the no_op nudge) is intentionally not on the reviewer's stack — that middleware exists to enforce the main agent's "always finalize via Slack/Linear/PR" contract, which the reviewer doesn't have. The main agent's behavior is unchanged. - `evals/reviewer/target.py`: send PR info as a user message, extract every `github_comment` tool call (multiple expected per review) into the run output. - `evals/reviewer/judge.py`: per-example evaluator now returns a list of metrics under `{"results": [...]}` so LangSmith averages each numeric key (f1/precision/recall/tp/fp/fn) across the experiment in the UI. Dropped the broken `aggregate_pr` summary evaluator that reached for an attribute that doesn't exist on `RunTree`. - `evals/reviewer/run_eval.py`: `--limit` now slices the dataset via `client.list_examples(limit=N)` since `aevaluate` doesn't accept `max_examples`. - Makefile: `dev` and `run` targets now use `uv run` so they work without an activated venv. * resolve comments --------- Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com> 2026-05-06 10:15:58 -07:00			`"agent": "agent.server:get_agent",`
feat: tune reviewer for precision — web/wiki tools + recalibrated prompt (#1312) * feat: tune reviewer for precision — web/wiki tools + recalibrated prompt Reviewer agent now has web_search, fetch_url, and http_request alongside the finding tools, so it can verify library semantics and consult the DeepWiki auto-generated wiki for public repos (https://deepwiki.com/<owner>/<repo>) before flagging cross-file or architectural concerns. Prompt rewritten to push precision over recall: - explicit severity ladder pushing reviews toward bimodal high/low instead of defaulting to medium - ≤200-char description target (gold set averages ~186 chars; we were at ~436) - mandatory docs / wiki / code lookup before flagging concurrency, security, or perf — the three categories that dominated false positives - "do not flag" list covering compiler/linter-catchable nits, speculative claims without a concrete attacker/interleaving/scale, style preferences the codebase doesn't share, and test-quality nits on non-test diffs - smart file-selection guidance for large PRs (deprioritize generated / vendored / pure-rename hunks) Eval config switched to openai:gpt-5.5 + high reasoning effort for the next benchmark run. * trim prompt * subagent prompting * confidence ratings * added medium * enforce confidence threshold * . * reviewer: precision-tuned prompt + drop confidence gate Rewrites the reviewer system prompt around a defensibility bar (anchor + failure mode + maintainer wouldn't say "not a bug"), an explicit do-not-file list (style nits, speculation, scope-policing, same-bug fan-out), and a checklist of 10 bug archetypes drawn from a per-PR audit of the eval golden set. The audit showed 145 FPs in the last eval split ~28% speculative, ~26% style-nit, ~31% real-but-unscored (mostly same-archetype fan-out); the new prompt targets each class directly. Confidence is still recorded on every finding for post-hoc calibration but no longer gates publication — the audit showed the gate was a no-op (agent self-rated 65% of findings "high" regardless), and the prompt's defensibility bar is the actual discipline. Drops CONFIDENCE_ORDER, CONFIDENCE_THRESHOLD, the confidence_threshold kwarg on filter_findings_for_publish, the confidence_filtered score_mode, and the min_confidence kwarg on the eval target's _extract_comments — all dead once the gate is gone. Also removes the "informational" severity tier from the Severity enum, SEVERITY_ORDER, and all validators / tests / docstrings. It was reserved for FYI observations the dataset never rewards. * benchmax * adding google provider * slight steering * tuning * more tuning * fix * cleanup * reducing overfitting * Add per-repo review style profiles and inject them into the reviewer. Dashboard users can analyze historical PR review feedback per repository, edit the resulting style guide, and have it loaded from LangGraph Store at reviewer runtime (including Martian eval runs) keyed by owner/name. Co-authored-by: Cursor <cursoragent@cursor.com> * Fix review style job errors leaking exception details to clients. Return generic dashboard messages while logging full stack traces server-side. Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com> Co-authored-by: Cursor <cursoragent@cursor.com> 2026-05-20 11:35:00 -07:00			`"reviewer": "agent.reviewer:get_reviewer_agent",`
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			`"analyzer": "agent.analyzer:get_analyzer"`
feat: trigger runs from GH issues (#324) * feat: trigger runs from GH issues * cr * working * cr * done * cr * cr * cr * cr * diff label for dev vs prod * cr * cr * cr 2025-06-30 16:43:09 -06:00			`},`
chore: Drop monorepo (#1029) * chore: Drop monorepo * cr 2026-03-06 16:10:34 -08:00			`"dependencies": ["."],`
feat: trigger runs from GH issues (#324) * feat: trigger runs from GH issues * cr * working * cr * done * cr * cr * cr * cr * diff label for dev vs prod * cr * cr * cr 2025-06-30 16:43:09 -06:00			`"http": {`
chore: Drop monorepo (#1029) * chore: Drop monorepo * cr 2026-03-06 16:10:34 -08:00			`"app": "agent.webapp:app"`
			`},`
			`"env": ".env"`
init commit 2025-05-21 14:47:56 -07:00			`}`