* 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
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Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com>
* feat: move github workflows to gh cli
Use LangSmith proxy auth to support gh-driven GitHub workflows while removing custom GitHub wrapper tools.
* docker ignore + snapshot and docker image updates
* updated image and instructions
* removing open_pr if needed after agent call
- Root cause: LLM occasionally generates strings like '1, 80' or '170, "limit": 60'
for integer fields, causing a Pydantic ValidationError and wasting an LLM turn
- Change: add SanitizeToolInputsMiddleware in agent/middleware/sanitize_tool_inputs.py
that extracts the leading integer from any string value in offset/limit before
the call reaches Pydantic validation; registered before ToolErrorMiddleware in server.py
- Verified: 14 unit tests covering all three production trace patterns pass
Co-authored-by: LangSmith Forge <forge-agent@langsmith.ai>
Co-authored-by: Johannes du Plessis <johannes@langchain.dev>
* fix: notify users via Slack when agent hits model call step limit
- Root cause: GraphRecursionError at 1000 steps bypassed all @after_agent
middleware including open_pr_if_needed, leaving users with no notification
- Change: Added ModelCallLimitMiddleware(run_limit=60) to intercept gracefully
before the hard recursion limit, and added notify_step_limit_reached
@after_agent middleware to post a Slack thread reply when the limit fires
- Verified: 107 existing tests pass, no regressions
* fix: harden step-limit Slack notification
Ensure the step-limit notification runs after the PR safety net and cover the new middleware behavior with focused unit tests.
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Co-authored-by: LangSmith Forge <forge-agent@langsmith.ai>
Co-authored-by: Johannes du Plessis <johannes@langchain.dev>
