fix(backend): expert attribution for chat-created schedules and triggers - #14026
Conversation
Schedules created via run_agent already attribute to the chat session's expert, but webhook triggers and scheduled copilot follow-ups created from inside an expert chat were un-attributed, so their runs never hit the expert's thread, briefing, or weekly spend guardrail. - setup_agent_webhook_trigger: thread session.expert_id into setup_triggered_preset; the session's expert now wins over the graph-match fallback (unchanged for the /presets route and plain chats). - schedule_followup: capture session.expert_id on the copilot-turn schedule (CopilotTurnJobArgs.expert_id) so the fresh session minted at fire time is scoped to the same expert. - manage_schedules / manage_presets are list/update/delete-only (no creation path) and stay untouched.
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autogpt_platform/**/data/**/*.py📄 CodeRabbit inference engine (AGENTS.md)
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🔇 Additional comments (5)
WalkthroughThe change validates and preserves ChangesExpert attribution propagation
Estimated code review effort: 3 (Moderate) | ~25 minutes Merge Risk: ⚪ Minimal · up to The change adds expert attribution for chat-created triggers and scheduled follow-ups while preserving plain-session behavior; invalid or archived experts remain unattributed, and no actionable merge-blocking risk remains after normal checks and review. Sequence Diagram(s)sequenceDiagram
participant CopilotSession
participant FollowupTools
participant Scheduler
participant ChatSession
participant AttributionResolver
CopilotSession->>FollowupTools: schedule follow-up with expert_id
FollowupTools->>Scheduler: add_copilot_turn_schedule(expert_id)
Scheduler->>ChatSession: create fresh session with expert_id
ChatSession->>AttributionResolver: validate expert ownership and active status
AttributionResolver-->>ChatSession: validated expert_id or None
ChatSession-->>Scheduler: persisted session attribution
Possibly related PRs
Suggested reviewers: Poem
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Codecov Report❌ Patch coverage is Additional details and impacted files@@ Coverage Diff @@
## dev #14026 +/- ##
==========================================
+ Coverage 78.69% 78.72% +0.02%
==========================================
Files 3021 3022 +1
Lines 230614 230803 +189
Branches 21636 21643 +7
==========================================
+ Hits 181482 181691 +209
+ Misses 44225 44198 -27
- Partials 4907 4914 +7
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CopilotTurnJobArgs.expert_id (expert attribution for scheduled copilot follow-ups) surfaces on CopilotTurnJobInfo, which is part of the schedules API response schema. Hand-spliced the optional expert_id property into the committed schema (repo convention: splice instead of full-regen churn); byte-identical to CI's export+prettier output (blob fd5d9d5).
![sentry[bot]](https://avatars.githubusercontent.com/in/12637?s=60&v=4){kind=small}
Adversarial review — GPT-5.6-sol (xhigh) via codex, orchestrated overnight fleetREQUEST_CHANGES — three real attribution defects remain. Findings
Verified as sound
Static verification passed: Ruff, AST parsing, Verdict: REQUEST_CHANGES |
Address the three review findings on expert attribution: - _reschedule_one_shot_after_cap now forwards expert_id, so a capped expert follow-up no longer retries into a plain session. - _execute_copilot_turn re-validates the captured expert (active + owned via experts_db().get_expert) at fire time; archived/unowned degrades to a plain session with a warning instead of persisting a stale expertId whose child schedules/triggers the budget gate would block forever. - setup_triggered_preset validates a supplied session expert the same way before provisioning; inactive/unowned falls back to graph-match, so an archived expert's historical chat can no longer mint a permanently dead-but-active-looking trigger.
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All three findings addressed in e64cec7 — point by point: 1. Concurrency retry drops expert attribution (scheduler.py 2. Archived expert only partially degrades at fire time (scheduler.py 3. Archived historical sessions can create permanently dead triggers (triggers.py Also folded in: the Sentry bot inline comment (duplicate of finding 1). No CodeRabbit comments were present on the PR. Gates re-run: Note on overlap: #14034 rewrites |
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/review |
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📋 Automated Review — PR #14026
PR #14026 — fix(backend): expert attribution for chat-created schedules and triggers
Author: Abhi1992002 | Files: 10
🎯 Verdict: APPROVE
PR Description Quality
✅ Has Why + What + How — the description ties the change to SECRT-2547 (a guardrail bypass), explains the two creation paths that previously dropped expert_id, notes the fire-time re-validation, and is explicit about what is out of scope (no backfill) plus a rebase warning re #14034.
What This PR Does
Triggers and scheduled follow-ups created inside an expert chat were silently losing the expert association: the resulting runs never surfaced on the expert's thread, never counted toward her briefing, and — the security-relevant part — escaped the expert's weekly-spend budget gate. This PR threads the chat session's expert_id through setup_triggered_preset, setup_agent_webhook_trigger, schedule_followup, and the persisted CopilotTurnJobArgs, then re-validates ownership/active-state at fire time so a stale or archived expert degrades safely to a plain session rather than a permanently budget-blocked one.
Specialist Findings
🛡️ Security ✅ — Security-positive change. All expert lookups are owner-scoped (ownerUserId=user_id, isTemplate=False, isArchived=False at experts_db.py:130-135), so a tampered/foreign expert_id fails the ownership check and falls back safely. The TOCTOU window is re-validated at both setup (triggers.py:69-76) and fire time (scheduler.py:268-277). No injection surface, no cross-tenant leakage.
🏗️ Architecture ✅ — expert_id is threaded as a keyword-only str | None = None exactly mirroring the existing organization_id/team_id tenancy pattern; backward-compatible and additive. Flagged low-severity duplication of the active+owned guard across triggers.py:69 and scheduler.py:269, and that experts_db refers to two different things (module vs. factory) across the two files.
⚡ Performance ✅ — Adds only O(1), expert_id-gated single-row get_expert(..., include_workflows=False) lookups on low-QPS paths (trigger setup, schedule creation, scheduler fire). No N+1, no hot-path regression. In the setup path it's net-neutral — a validated expert short-circuits the previous resolve_expert_for_graph call.
🧪 Testing ✅ — ~95% of changed lines covered with meaningful assertions (exact kwargs + behavioral checks + negative cases): precedence (session expert beats graph-match), archived-expert fallback at both setup and fire time, cap-retry preservation, and legacy expert_id=None deserialization. Two low-severity gaps: cron/recurring follow-up path and the existing-session (no-revalidate) branch.
📖 Quality ✅ — Readability rated A; naming mirrors existing conventions, comments explain why. Only substantive item is the DRY duplication of the security-relevant re-validation guard.
📦 Product ✅ — Faithful to its stated requirements; plain sessions and the /presets route are byte-identical. One medium observation: when a session expert is archived/un-owned, setup falls back to resolve_expert_for_graph, which could attribute the trigger to a different graph-matched expert rather than to none.
📬 Discussion ✅ — GitHub CI fully green on head SHA. The single bot concern (Seer: _reschedule_one_shot_after_cap dropped expert_id on cap-retry) was fixed in e64cec7 with a regression test. Only remaining gate is REVIEW_REQUIRED (human approval).
🔎 QA ✅ — Verified end-to-end against the PR branch (HEAD e64cec7): a follow-up scheduled from an expert session persisted expert_id: "expert-qa-9584"; a plain session persisted null; archived and non-owned experts both returned 404 "Expert not found" at session creation. No attribution errors in logs. Webhook-trigger and delayed fire-time paths were flag/timing-gated live but are covered by unit tests.
🟠 Should Fix
- Duplicated security-relevant expert re-validation guard (
autogpt_platform/backend/backend/api/features/library/triggers.py:69andautogpt_platform/backend/backend/executor/scheduler.py:266) — The "active+owned expert else drop toNonewith a warning" guard is copy-pasted across two modules. This is the load-bearing invariant of the PR (an archived expert must never be attributed / budget-bypass), so a future refinement to the rule could update one site and miss the other. Extract a shared helper (e.g.experts_db.is_active_expert(user_id, expert_id) -> bool/resolve_attributable_expert(...)). (Flagged by: architect, quality — 2 specialists) - Silent cross-attribution to a different graph-matched expert (
autogpt_platform/backend/backend/api/features/library/triggers.py:117) — When an explicitly chosen session expert is archived/un-owned, setup falls back toresolve_expert_for_graph, which can attribute the trigger to a different expert that uniquely matches the graph. A trigger set up in Expert A's chat could land on Expert B's thread/budget. Prefer falling back toNone(unattributed), or confirm with product that graph-based re-attribution is intended here. (Flagged by: product — medium) - Missing cron/recurring follow-up attribution test (
autogpt_platform/backend/backend/copilot/tools/schedule_followup_test.py:132) — Both new tests exercise only the one-shot (delay_seconds) path; the cron path shares theadd_copilot_turn_schedulecall site but is unverified, so a future split of the two paths could drop cron attribution undetected. Add acron=-based test assertingexpert_idis forwarded. (Flagged by: testing) - Missing existing-session no-revalidate test (
autogpt_platform/backend/backend/executor/scheduler_unit_test.py:241) —_execute_copilot_turnonly re-validates on thesession_id is Nonebranch; no test assertsget_expertis not called (and no degrade happens) when an existing session is supplied. Add a test withsession_idset. (Flagged by: testing)
🟡 Nice to Have
- Avoid full read-model
get_expertfor boolean validation (autogpt_platform/backend/backend/api/features/library/triggers.py:69) —get_expertalso runs_latest_runs()andget_weekly_spend()(experts_db.py:140-141), ~2 extra queries per validation even withinclude_workflows=False. A lightweightfind_first/countexistence check would remove the waste (and pairs naturally with the Should-Fix #1 helper extraction). (architect, performance)
🔵 Nits
experts_dbname collision (autogpt_platform/backend/backend/executor/scheduler.py:41) — Here it's the accessor factory (experts_db().get_expert(...)) vs. the module intriggers.py(experts_db.get_expert(...)). A one-line comment noting the executor needs the RPC-safe accessor would help side-by-side reading. (architect, quality)- Resolve the Seer review thread (
autogpt_platform/backend/backend/executor/scheduler.py:391) — The cap-retry bug is fixed and test-covered, but the GitHub thread is stillisResolved=false; click "Resolve conversation" for hygiene. (discussion)
QA Screenshots
| Screenshot | Description |
|---|---|
 |
Copilot loaded on the expert-scoped session used to confirm a follow-up persists expert_id: expert-qa-9584 ✅ |
Human Review Needed
YES — Required because at least one configured quality check failed.
Risk Assessment
Merge risk: LOW | Rollback: EASY (additive, backward-compatible optional field; revert restores prior behavior byte-for-byte)
CI Status
GitHub CI: ✅ Green on head SHA e64cec7 per the discussion specialist (test/type-check across 3.11–3.13, lint, e2e, integration, API-types, build/scan, CodeQL, Snyk, CodeRabbit, Seer all pass); remaining gate is required human approval.
Local harness: 4/5 pass (frontend lint, backend lint, frontend typecheck, frontend build). The frontend pnpm test:unit failure is environment skew — this is a backend-only PR (no frontend source changed) and the repo's own CI ran the frontend suite green on this SHA; per rule 13 it is a warning, not a blocker.
UI Testing — Variant Results
✅ local: Expert-session follow-ups persist expert_id end-to-end while plain sessions stay null; negative and trust-boundary cases all pass with no regressions or errors.
✅ hosted: Backend expert-attribution plumbing verified: 94/94 tests green, real-DB integration confirms expert session scoping/ownership/archived-degradation gates, served OpenAPI carries the new field, and no service regressions.
…llback Address the reviewer's four Should Fix items: - Extract the active+owned re-validation into a single load-bearing helper experts_db.resolve_attributable_expert (RPC-exposed via db_manager), used by both the trigger setup path and the fire-time scheduler path so the invariant cannot drift between sites. - Trigger setup: a supplied-but-invalid session expert now creates the trigger UNATTRIBUTED instead of falling back to graph-match — re- attribution could land Expert A's chat trigger on Expert B's budget/thread. The no-expert path (route/plain chats) keeps graph-match. - Add cron-path attribution test for schedule_followup (recurring follow-ups carry expert_id, not just one-shots). - Add existing-session test: fire path skips expert re-validation when session_id is set (no session minted, no per-fire lookup).
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All four Should Fix items taken in 44dcfa6:
Gates: format/pyright clean, 98 tests green across all touched modules. |
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Actionable comments posted: 1
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Inline comments:
In `@autogpt_platform/backend/backend/api/features/experts/experts_db.py`:
- Around line 405-413: The validation in the expert lookup using
Expert.prisma().find_first must be made atomic with the subsequent session or
trigger persistence. Replace the check-then-act flow with a transactionally
conditional write or a single RPC that verifies active ownership and persists
together; if that conditional write fails, preserve the existing plain-session
or unattributed fallback. Add coverage for an archive occurring between
validation and persistence.
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Fix all unresolved CodeRabbit comments on this PR:
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📒 Files selected for processing (7)
autogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers.pyautogpt_platform/backend/backend/api/features/library/triggers_test.pyautogpt_platform/backend/backend/copilot/tools/schedule_followup_test.pyautogpt_platform/backend/backend/data/db_manager.pyautogpt_platform/backend/backend/executor/scheduler.pyautogpt_platform/backend/backend/executor/scheduler_unit_test.py
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- autogpt_platform/backend/backend/api/features/library/triggers.py
- autogpt_platform/backend/backend/executor/scheduler.py
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🧰 Additional context used
📓 Path-based instructions (7)
autogpt_platform/backend/**/*.py
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
autogpt_platform/backend/**/*.py: Use Python 3.11 (required; managed by Poetry via pyproject.toml) for backend development
Always run 'poetry run format' (Black + isort) before linting in backend development
Always run 'poetry run lint' (ruff) after formatting in backend development
autogpt_platform/backend/**/*.py: Usepoetry run ...command for executing Python package dependencies
Use top-level imports only — avoid local/inner imports except for lazy imports of heavy optional dependencies likeopenpyxl
Use absolute imports withfrom backend.module import ...for cross-package imports; single-dot relative imports are acceptable for sibling modules within the same package; avoid double-dot relative imports
Do not use duck typing — avoidhasattr/getattr/isinstancefor type dispatch; use typed interfaces/unions/protocols instead
Use Pydantic models over dataclass/namedtuple/dict for structured data
Do not use linter suppressors — no# type: ignore,# noqa,# pyright: ignore; fix the type/code instead
Prefer list comprehensions over manual loop-and-append patterns
Use early return with guard clauses first to avoid deep nesting
Use%sfor deferred interpolation indebuglog statements for efficiency; use f-strings elsewhere for readability (e.g.,logger.debug("Processing %s items", count)vslogger.info(f"Processing {count} items"))
Sanitize error paths by usingos.path.basename()in error messages to avoid leaking directory structure
Be aware of TOCTOU (Time-Of-Check-Time-Of-Use) issues — avoid check-then-act patterns for file access and credit charging
Usetransaction=Truefor Redis pipelines to ensure atomicity on multi-step operations
Usemax(0, value)guards for computed values that should never be negative
Keep files under ~300 lines; if a file grows beyond this, split by responsibility (extract helpers, models, or a sub-module into a new file)
Keep functions under ~40 lines; extract named helpers when a function grows longer
...
Files:
autogpt_platform/backend/backend/data/db_manager.pyautogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers_test.pyautogpt_platform/backend/backend/copilot/tools/schedule_followup_test.pyautogpt_platform/backend/backend/executor/scheduler_unit_test.py
autogpt_platform/backend/backend/data/**/*.py
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
All data access in backend requires user ID checks; verify this for any 'data/*.py' changes
Files:
autogpt_platform/backend/backend/data/db_manager.py
autogpt_platform/{backend,autogpt_libs}/**/*.py
📄 CodeRabbit inference engine (AGENTS.md)
Format Python code with
poetry run format
Files:
autogpt_platform/backend/backend/data/db_manager.pyautogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers_test.pyautogpt_platform/backend/backend/copilot/tools/schedule_followup_test.pyautogpt_platform/backend/backend/executor/scheduler_unit_test.py
autogpt_platform/**/data/**/*.py
📄 CodeRabbit inference engine (AGENTS.md)
For changes touching
data/*.py, validate user ID checks or explain why not needed
Files:
autogpt_platform/backend/backend/data/db_manager.py
autogpt_platform/backend/backend/api/features/**/*.py
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
Update routes in '/backend/backend/api/features/' and add/update Pydantic models in the same directory for API development
Files:
autogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers_test.py
autogpt_platform/backend/**/api/**/*.py
📄 CodeRabbit inference engine (autogpt_platform/backend/AGENTS.md)
autogpt_platform/backend/**/api/**/*.py: UseSecurity()instead ofDepends()for authentication dependencies to get proper OpenAPI security specification
Follow SSE (Server-Sent Events) protocol: usedata:lines for frontend-parsed events (must match Zod schema) and: commentlines for heartbeats/status
Files:
autogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers_test.py
autogpt_platform/backend/**/*_test.py
📄 CodeRabbit inference engine (autogpt_platform/backend/AGENTS.md)
autogpt_platform/backend/**/*_test.py: Use pytest with snapshot testing for API responses
Colocate test files with source files using*_test.pynaming convention
Mock at boundaries — mock where the symbol is used, not where it's defined; after refactoring, update mock targets to match new module paths
UseAsyncMockfromunittest.mockfor async functions in tests
When writing tests, use Test-Driven Development (TDD): write failing tests marked with@pytest.mark.xfailbefore implementation, then remove the marker once the implementation is complete
When creating snapshots in tests, usepoetry run pytest path/to/test.py --snapshot-update; always review snapshot changes withgit diffbefore committing
Files:
autogpt_platform/backend/backend/api/features/library/triggers_test.pyautogpt_platform/backend/backend/copilot/tools/schedule_followup_test.pyautogpt_platform/backend/backend/executor/scheduler_unit_test.py
🧠 Learnings (21)
📚 Learning: 2026-02-26T17:02:22.448Z
Learnt from: Pwuts
Repo: Significant-Gravitas/AutoGPT PR: 12211
File: .pre-commit-config.yaml:160-179
Timestamp: 2026-02-26T17:02:22.448Z
Learning: Keep the pre-commit hook pattern broad for autogpt_platform/backend to ensure OpenAPI schema changes are captured. Do not narrow to backend/api/ alone, since the generated schema depends on Pydantic models across multiple directories (backend/data/, backend/blocks/, backend/copilot/, backend/integrations/, backend/util/). Narrowing could miss schema changes and cause frontend type desynchronization.
Applied to files:
autogpt_platform/backend/backend/data/db_manager.pyautogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers_test.pyautogpt_platform/backend/backend/copilot/tools/schedule_followup_test.pyautogpt_platform/backend/backend/executor/scheduler_unit_test.py
📚 Learning: 2026-03-05T15:42:08.207Z
Learnt from: ntindle
Repo: Significant-Gravitas/AutoGPT PR: 12297
File: .claude/skills/backend-check/SKILL.md:14-16
Timestamp: 2026-03-05T15:42:08.207Z
Learning: In Python files under autogpt_platform/backend (recursively), rely on poetry run format to perform formatting (Black + isort) and linting (ruff). Do not run poetry run lint as a separate step after poetry run format, since format already includes linting checks.
Applied to files:
autogpt_platform/backend/backend/data/db_manager.pyautogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers_test.pyautogpt_platform/backend/backend/copilot/tools/schedule_followup_test.pyautogpt_platform/backend/backend/executor/scheduler_unit_test.py
📚 Learning: 2026-03-16T16:35:40.236Z
Learnt from: majdyz
Repo: Significant-Gravitas/AutoGPT PR: 12440
File: autogpt_platform/backend/backend/api/features/workflow_import.py:54-63
Timestamp: 2026-03-16T16:35:40.236Z
Learning: Avoid using the word 'competitor' in public-facing identifiers and text. Use neutral naming for API paths, model names, function names, and UI text. Examples: rename 'CompetitorFormat' to 'SourcePlatform', 'convert_competitor_workflow' to 'convert_workflow', '/competitor-workflow' to '/workflow'. Apply this guideline to files under autogpt_platform/backend and autogpt_platform/frontend.
Applied to files:
autogpt_platform/backend/backend/data/db_manager.pyautogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers_test.pyautogpt_platform/backend/backend/copilot/tools/schedule_followup_test.pyautogpt_platform/backend/backend/executor/scheduler_unit_test.py
📚 Learning: 2026-03-31T15:37:38.626Z
Learnt from: majdyz
Repo: Significant-Gravitas/AutoGPT PR: 12623
File: autogpt_platform/backend/backend/copilot/tools/agent_generator/fixer.py:37-47
Timestamp: 2026-03-31T15:37:38.626Z
Learning: When validating/constructing Anthropic API model IDs in Significant-Gravitas/AutoGPT, allow the hyphen-separated Claude Opus 4.6 model ID `claude-opus-4-6` (it corresponds to `LlmModel.CLAUDE_4_6_OPUS` in `autogpt_platform/backend/backend/blocks/llm.py`). Do NOT require the dot-separated form in Anthropic contexts. Only OpenRouter routing variants should use the dot separator (e.g., `anthropic/claude-opus-4.6`); `claude-opus-4-6` should be treated as correct when passed to Anthropic, and flagged only if it’s used in the OpenRouter path where the dot form is expected.
Applied to files:
autogpt_platform/backend/backend/data/db_manager.pyautogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers_test.pyautogpt_platform/backend/backend/copilot/tools/schedule_followup_test.pyautogpt_platform/backend/backend/executor/scheduler_unit_test.py
📚 Learning: 2026-04-15T02:43:36.890Z
Learnt from: ntindle
Repo: Significant-Gravitas/AutoGPT PR: 12780
File: autogpt_platform/backend/backend/copilot/tools/workspace_files.py:0-0
Timestamp: 2026-04-15T02:43:36.890Z
Learning: When reviewing Python exception handlers, do not flag `isinstance(e, X)` checks as dead/unreachable if the caught exception `X` is a subclass of the exception type being handled. For example, if `X` (e.g., `VirusScanError`) inherits from `ValueError` (directly or via an intermediate class) and it can be raised within an `except ValueError:` block, then `isinstance(e, X)` inside that handler is reachable and should not be treated as dead code.
Applied to files:
autogpt_platform/backend/backend/data/db_manager.pyautogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers_test.pyautogpt_platform/backend/backend/copilot/tools/schedule_followup_test.pyautogpt_platform/backend/backend/executor/scheduler_unit_test.py
📚 Learning: 2026-05-23T05:29:43.085Z
Learnt from: majdyz
Repo: Significant-Gravitas/AutoGPT PR: 13200
File: autogpt_platform/backend/backend/executor/scheduler.py:590-593
Timestamp: 2026-05-23T05:29:43.085Z
Learning: When reviewing Python code that uses Pydantic discriminated/tagged unions (e.g., `Annotated[Union[...], Field(discriminator="kind")]`), recognize that using `isinstance(x, SomeVariantInfo)` to narrow the union is an intentional and correct runtime guard and should also enable static type narrowing in tools like Pyright. Do not recommend replacing such `isinstance`-based narrowing with `cast(...)` when the check already proves the variant at runtime.
Applied to files:
autogpt_platform/backend/backend/data/db_manager.pyautogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers_test.pyautogpt_platform/backend/backend/copilot/tools/schedule_followup_test.pyautogpt_platform/backend/backend/executor/scheduler_unit_test.py
📚 Learning: 2026-04-21T04:35:34.710Z
Learnt from: majdyz
Repo: Significant-Gravitas/AutoGPT PR: 12865
File: autogpt_platform/backend/backend/data/credit.py:1584-1584
Timestamp: 2026-04-21T04:35:34.710Z
Learning: When reviewing this codebase, don’t flag snake_case attribute names (e.g., `subscription_tier`, `stripe_customer_id`, `top_up_config`) on the app-layer Pydantic `User` model as “wrong” field names. These are correct for the app-layer model and are expected to be mapped from the Prisma-layer camelCase fields (e.g., `subscriptionTier`, `stripeCustomerId`) inside methods like `User.from_db()`. Only Prisma-returned/raw objects would use camelCase, but functions like `get_user_by_id(user_id: str)` are expected to return the Pydantic app-layer model.
Applied to files:
autogpt_platform/backend/backend/data/db_manager.py
📚 Learning: 2026-05-07T15:32:39.703Z
Learnt from: majdyz
Repo: Significant-Gravitas/AutoGPT PR: 13033
File: autogpt_platform/backend/backend/data/generate_data.py:111-117
Timestamp: 2026-05-07T15:32:39.703Z
Learning: When reviewing the Python data-generation layer, do not treat missing `user_id`/user filtering in calls to graph-metadata resolvers as a security issue if the `graph_id` inputs are already guaranteed to be user-scoped by earlier upstream SQL (e.g., `WHERE "userId" = ...`). In particular, `_resolve_agent_name(graph_id)` in `generate_data.py` correctly calls `get_graph_metadata(graph_id=graph_id)` without a `user_id` parameter by design, because name resolution must also work for user-executed shared/marketplace agents that the user may not own.
Applied to files:
autogpt_platform/backend/backend/data/db_manager.py
📚 Learning: 2026-04-22T11:46:04.431Z
Learnt from: majdyz
Repo: Significant-Gravitas/AutoGPT PR: 12881
File: autogpt_platform/backend/backend/copilot/config.py:0-0
Timestamp: 2026-04-22T11:46:04.431Z
Learning: Do not flag the Claude Sonnet 4.6 model ID as incorrect when it uses the project’s established hyphenated convention: `anthropic/claude-sonnet-4-6`. This hyphen form is the intentional, production convention and should be treated as valid (including in files like llm.py, blocks tests, reasoning.py, `_is_anthropic_model` tests, and config defaults). Note that OpenRouter also accepts the dot variant `anthropic/claude-sonnet-4.6`, so either form may be tolerated, but `anthropic/claude-sonnet-4-6` should be considered the standard to match project usage.
Applied to files:
autogpt_platform/backend/backend/data/db_manager.pyautogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers_test.pyautogpt_platform/backend/backend/copilot/tools/schedule_followup_test.pyautogpt_platform/backend/backend/executor/scheduler_unit_test.py
📚 Learning: 2026-04-22T11:46:12.892Z
Learnt from: majdyz
Repo: Significant-Gravitas/AutoGPT PR: 12881
File: autogpt_platform/backend/backend/copilot/baseline/service.py:322-332
Timestamp: 2026-04-22T11:46:12.892Z
Learning: In this codebase (Significant-Gravitas/AutoGPT), OpenRouter-routed Anthropic model IDs should use the hyphen-separated convention (e.g., `anthropic/claude-sonnet-4-6`, `anthropic/claude-opus-4-6`). Although OpenRouter may accept both hyphen and dot variants, treat the hyphen-separated form as the intended, correct codebase-wide convention and do not flag it as an error. Only flag the dot-separated variant (e.g., `anthropic/claude-sonnet-4.6`) as incorrect when reviewing/validating model ID strings for OpenRouter-routed Anthropic models.
Applied to files:
autogpt_platform/backend/backend/data/db_manager.pyautogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers_test.pyautogpt_platform/backend/backend/copilot/tools/schedule_followup_test.pyautogpt_platform/backend/backend/executor/scheduler_unit_test.py
📚 Learning: 2026-05-07T18:48:14.242Z
Learnt from: majdyz
Repo: Significant-Gravitas/AutoGPT PR: 13040
File: autogpt_platform/backend/backend/blocks/llm.py:0-0
Timestamp: 2026-05-07T18:48:14.242Z
Learning: In this repository, isort may split imports from the same module into separate blocks when some imports are aliased (e.g., `from module import X as Y`) and others are not. Preserve the two-block layout when it results from isort (such as keeping `from openai.types.chat import ChatCompletion as OpenAIChatCompletion` separate from non-aliased imports from `openai.types.chat`). Do not treat that split as a style issue during review; merging them into a single block can fail CI with `Imports are incorrectly sorted and/or formatted`.
Applied to files:
autogpt_platform/backend/backend/data/db_manager.pyautogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers_test.pyautogpt_platform/backend/backend/copilot/tools/schedule_followup_test.pyautogpt_platform/backend/backend/executor/scheduler_unit_test.py
📚 Learning: 2026-05-26T14:24:34.866Z
Learnt from: Abhi1992002
Repo: Significant-Gravitas/AutoGPT PR: 13217
File: autogpt_platform/backend/backend/api/features/search/service.py:137-137
Timestamp: 2026-05-26T14:24:34.866Z
Learning: In the Significant-Gravitas/AutoGPT backend, treat `user_id` (an opaque UUID used only for correlation/tracing) as non-PII. Do not flag direct logging of `user_id` in `logger.warning`/`logger.info` statements as a PII exposure issue, as the established convention is to log `user_id` for tracing while reserving PII for fields like email or display name.
Applied to files:
autogpt_platform/backend/backend/data/db_manager.pyautogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers_test.pyautogpt_platform/backend/backend/copilot/tools/schedule_followup_test.pyautogpt_platform/backend/backend/executor/scheduler_unit_test.py
📚 Learning: 2026-06-11T19:39:10.493Z
Learnt from: ntindle
Repo: Significant-Gravitas/AutoGPT PR: 13337
File: autogpt_platform/backend/backend/copilot/graphiti/reranker.py:0-0
Timestamp: 2026-06-11T19:39:10.493Z
Learning: In the Significant-Gravitas/AutoGPT Python backend, when calling the OpenAI Python client `chat.completions.create`, construct the `messages` payload using the concrete typed-dict variants from `openai.types.chat` (e.g., `ChatCompletionSystemMessageParam`, `ChatCompletionUserMessageParam`, etc.) rather than trying to instantiate `ChatCompletionMessageParam` directly. `ChatCompletionMessageParam` is a `Union` alias and is not constructible, so `ChatCompletionMessageParam(role=..., content=...)` should fail type checking. Build each message element with the appropriate concrete typed dict and then annotate the resulting list as `list[ChatCompletionMessageParam]` (e.g., `messages: list[ChatCompletionMessageParam] = [ChatCompletionSystemMessageParam(...), ...]`).
Applied to files:
autogpt_platform/backend/backend/data/db_manager.pyautogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers_test.pyautogpt_platform/backend/backend/copilot/tools/schedule_followup_test.pyautogpt_platform/backend/backend/executor/scheduler_unit_test.py
📚 Learning: 2026-08-13T05:22:22.032Z
Learnt from: Abhi1992002
Repo: Significant-Gravitas/AutoGPT PR: 14017
File: autogpt_platform/backend/backend/copilot/briefing/outcome_test.py:161-163
Timestamp: 2026-08-13T05:22:22.032Z
Learning: In the AutoGPT backend Python code, do not flag naive datetime.datetime(...) constructors solely for omitting tzinfo: autogpt_platform/backend/pyproject.toml does not enable Ruff rule DTZ001, so these constructors do not fail the backend Ruff check for that reason alone.
Applied to files:
autogpt_platform/backend/backend/data/db_manager.pyautogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers_test.pyautogpt_platform/backend/backend/copilot/tools/schedule_followup_test.pyautogpt_platform/backend/backend/executor/scheduler_unit_test.py
📚 Learning: 2026-08-13T22:09:30.099Z
Learnt from: dexhunter
Repo: Significant-Gravitas/AutoGPT PR: 13749
File: autogpt_platform/backend/backend/util/test_json.py:767-771
Timestamp: 2026-08-13T22:09:30.099Z
Learning: In the AutoGPT backend, do not report missing mutable class-attribute annotations as required lint fixes for Ruff rule RUF012, because Ruff is pinned to version 0.15.0 and the repository configuration does not select RUF012. Reconsider this guidance if the pinned Ruff version or configured rule selection changes.
Applied to files:
autogpt_platform/backend/backend/data/db_manager.pyautogpt_platform/backend/backend/api/features/experts/experts_db.pyautogpt_platform/backend/backend/api/features/library/triggers_test.pyautogpt_platform/backend/backend/copilot/tools/schedule_followup_test.pyautogpt_platform/backend/backend/executor/scheduler_unit_test.py
📚 Learning: 2026-08-13T09:22:20.942Z
Learnt from: Abhi1992002
Repo: Significant-Gravitas/AutoGPT PR: 14017
File: autogpt_platform/backend/backend/api/features/home/service_test.py:196-200
Timestamp: 2026-08-13T09:22:20.942Z
Learning: In backend API feature tests, use snapshot testing for API route responses exercised through fastapi.testclient with configured_snapshot. Service-level tests, such as backend/api/features/home/service_test.py, do not require snapshots when deterministic direct assertions and composer-level tests provide equivalent coverage.
Applied to files:
autogpt_platform/backend/backend/api/features/library/triggers_test.py
📚 Learning: 2026-03-04T08:04:35.881Z
Learnt from: majdyz
Repo: Significant-Gravitas/AutoGPT PR: 12273
File: autogpt_platform/backend/backend/copilot/tools/workspace_files.py:216-220
Timestamp: 2026-03-04T08:04:35.881Z
Learning: In the AutoGPT Copilot backend, ensure that SVG images are not treated as vision image types by excluding 'image/svg+xml' from INLINEABLE_MIME_TYPES and MULTIMODAL_TYPES in tool_adapter.py; the Claude API supports PNG, JPEG, GIF, and WebP for vision. SVGs (XML text) should be handled via the text path instead, not the vision path.
Applied to files:
autogpt_platform/backend/backend/copilot/tools/schedule_followup_test.py
📚 Learning: 2026-04-01T04:17:41.600Z
Learnt from: majdyz
Repo: Significant-Gravitas/AutoGPT PR: 12632
File: autogpt_platform/backend/backend/copilot/tools/workspace_files.py:0-0
Timestamp: 2026-04-01T04:17:41.600Z
Learning: When reviewing AutoGPT Copilot tool implementations, accept that `readOnlyHint=True` (provided via `ToolAnnotations`) may be applied unconditionally to *all* tools—even tools that have side effects (e.g., `bash_exec`, `write_workspace_file`, or other write/save operations). Do **not** flag these tools for having `readOnlyHint=True`; this is intentional to enable fully-parallel dispatch by the Anthropic SDK/CLI and has been E2E validated. Only flag `readOnlyHint` issues if they conflict with the established `ToolAnnotations` behavior (e.g., missing/incorrect propagation relative to the intended annotation mechanism).
Applied to files:
autogpt_platform/backend/backend/copilot/tools/schedule_followup_test.py
📚 Learning: 2026-06-06T12:22:37.648Z
Learnt from: anvyle
Repo: Significant-Gravitas/AutoGPT PR: 13302
File: autogpt_platform/backend/backend/copilot/tools/agent_search.py:579-583
Timestamp: 2026-06-06T12:22:37.648Z
Learning: When writing LLM-facing instruction strings that trigger tool calls in this AutoGPT codebase, use the exact registered tool name `view_agent_output` (as defined in `backend/copilot/tools/agent_output.py` via its `name` property and exported via `TOOL_REGISTRY`). Do not reference the bare name `agent_output`, since it is not a valid tool name and will cause tool invocation to fail.
Applied to files:
autogpt_platform/backend/backend/copilot/tools/schedule_followup_test.py
📚 Learning: 2026-03-04T12:19:39.243Z
Learnt from: majdyz
Repo: Significant-Gravitas/AutoGPT PR: 12279
File: autogpt_platform/backend/backend/copilot/tools/base.py:184-188
Timestamp: 2026-03-04T12:19:39.243Z
Learning: In autogpt_platform/backend/backend/copilot/tools/, ensure that anonymous users always pass user_id=None to tool execution methods. The anon_ prefix (e.g., anon_123) is used only for PostHog/analytics distinct_id and must not be used as an actual user_id. Use a simple truthiness check on user_id (e.g., if user_id: ... else: ... or a dedicated is_authenticated flag) to distinguish anonymous from authenticated users, and review all tool execution call sites within this directory to prevent accidentally forwarding an anon_ user_id to tools.
Applied to files:
autogpt_platform/backend/backend/copilot/tools/schedule_followup_test.py
📚 Learning: 2026-03-31T14:22:26.566Z
Learnt from: majdyz
Repo: Significant-Gravitas/AutoGPT PR: 12622
File: autogpt_platform/backend/backend/copilot/tools/agent_search.py:223-236
Timestamp: 2026-03-31T14:22:26.566Z
Learning: In files under autogpt_platform/backend/backend/copilot/tools/, ensure agent graph enrichment uses the typed Pydantic model `backend.data.graph.Graph` for `AgentInfo.graph` (i.e., `Graph | None`), not `dict[str, Any]`. When enriching with graph data (e.g., `_enrich_agents_with_graph`), prefer calling `graph_db().get_graph(graph_id, version=None, user_id=user_id)` directly to retrieve the typed `Graph` object rather than routing through JSON conversions like `get_agent_as_json()` / `graph_to_json()`.
Applied to files:
autogpt_platform/backend/backend/copilot/tools/schedule_followup_test.py
🔇 Additional comments (4)
autogpt_platform/backend/backend/data/db_manager.py (1)
500-500: LGTM!Also applies to: 821-821
autogpt_platform/backend/backend/api/features/library/triggers_test.py (1)
83-96: LGTM!Also applies to: 101-126
autogpt_platform/backend/backend/executor/scheduler_unit_test.py (1)
249-251: LGTM!Also applies to: 263-264, 280-282, 298-320
autogpt_platform/backend/backend/copilot/tools/schedule_followup_test.py (1)
159-188: LGTM!
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2 participants
Why / What / How
Why: Only
run_agent(session-scoped) and the v1/schedulesendpoint (resolve_expert_for_graph, graph-scoped) attribute executions to experts. Webhook triggers and scheduled copilot follow-ups created from inside an expert chat session were left un-attributed — so their runs never surfaced on the expert's thread, never counted toward her briefing, and bypassed her weekly spend guardrail (found in a code audit; SECRT-2547 context).What: Thread the chat session's
expert_idthrough the two creation paths that previously dropped it, so a resource created while talking to an expert is attributed to that expert.How: Mirror the existing
run_agent._schedule_agentpattern (expert_id=session.expert_id). Attribution happens only when the session is expert-scoped; plain sessions (and the non-chat/presetsroute) are byte-identical to before.setup_agent_webhook_trigger→setup_triggered_preset): the shared helper now takes an optionalexpert_id. The session's expert wins; when absent it falls back to the existing graph-match (resolve_expert_for_graph), so the/presets/setup-triggerroute and plain chats are unchanged. Attribution lands on the existingAgentPreset.expertIdcolumn.schedule_followup→add_copilot_turn_schedule):CopilotTurnJobArgsgains anexpert_id(captured at schedule time, exactly like the existing org/team fields). At fire time_execute_copilot_turnscopes the freshly-minted session to that expert, so agent runs inside the scheduled turn attribute correctly.Consumers pick it up automatically: expert-attributed scheduled graph runs already stamp
expert_idontoAgentGraphExecutionvia the existing scheduling path, which is what the budget gate (executor/utils.py) and thread post (executor/expert_posts.py) read.Changes 🏗️
library/triggers.py—setup_triggered_presetacceptsexpert_id; session expert preferred over graph-match fallback.copilot/tools/setup_agent_webhook_trigger.py— passsession.expert_idinto preset creation.copilot/tools/schedule_followup.py— passsession.expert_idinto the copilot-turn schedule.executor/scheduler.py—CopilotTurnJobArgs.expert_id;add_copilot_turn_schedule(expert_id=...);_execute_copilot_turnscopes the fresh session to the expert.manage_schedules.py/manage_presets.pyare list/update/delete-only (no creation path) and are intentionally left untouched.Out of scope (intentional): backfilling
expert_idon schedules/presets/triggers created before this change — only new creations are attributed.User-ID note: no new
data/*.pyquery is added.setup_triggered_presetalready scopes byuser_id(andresolve_expert_for_graphfiltersownerUserId=user_id); the copilot-turn schedule storesexpert_idfrom the session, whose expert ownership was already validated at session creation (same trust model asrun_agent).Checklist 📋
For code changes:
setup_agent_webhook_trigger: expert session →setup_triggered_preset(expert_id=<expert>); plain session →expert_id=Nonesetup_triggered_preset: session expert wins (graph-match not consulted); falls back to graph-match when no session expertschedule_followup: expert session →add_copilot_turn_schedule(expert_id=<expert>); plain session →Noneadd_copilot_turn_schedulepersistsexpert_idinto the job args (expert + plain)_execute_copilot_turnforwardsargs.expert_idto the freshcreate_chat_sessionpoetry run format(ruff + isort + black + pyright) clean; touched pytest modules green (93 passed + reverted integration test)Review follow-up (round 2) — commit e64cec7
_reschedule_one_shot_after_capforwardsexpert_idalongside timezone/tenancy._execute_copilot_turnre-checks the captured expert is still active + owned (experts_db().get_expert) before minting the fresh session; archived/unowned degrades to a plain session with a warning.setup_triggered_presetapplies the same active+owned check to a supplied session expert before provisioning; failures fall back to the graph-match, so an archived expert's historical chat can't mint a permanently budget-blocked trigger.Note: #14034 rewrites
setup_agent_webhook_trigger.py/schedule_followup.py. This PR's hunks in those two files are 1–3 lines each, so it may need a trivial rebase over #14034 depending on merge order.Review follow-up (round 3) — commit 44dcfa6
experts_db.resolve_attributable_expert(RPC-exposed), used by both the trigger setup path and the fire-time scheduler path./presetsroute, plain chats) keeps the pre-existing graph-match behaviour.schedule_followup) and existing-session no-revalidation test (_execute_copilot_turn).Review follow-up (round 4) — commit 1661662