* feat: add deterministic eval graders (AGI SDK + WebArena-Infinity)
Two new benchmark integrations with programmatic grading — no LLM judge.
AGI SDK / REAL Bench (52 tasks):
- 11 React/Next.js clones of consumer apps (DoorDash, Amazon, Gmail, etc.)
- Grader navigates browser to /finish, extracts state diff from <pre> tag
- Python verifier checks exact values via jmespath queries
WebArena-Infinity (50 hard tasks):
- 13 LLM-generated SaaS clones (Gmail, GitLab, Linear, Figma, etc.)
- InfinityAppManager starts fresh app server per task per worker
- Python verifier calls /api/state and asserts on JSON state
Infrastructure:
- GraderInput extended with mcpUrl + infinityAppUrl for parallel workers
- Each worker gets isolated ports (no cross-worker state contamination)
- CI workflow: pip install agisdk, clone webarena-infinity repo
* chore: switch eval configs back to kimi-k2p5
* fix: register deterministic graders in pass rate calculation
Add agisdk_state_diff and infinity_state to PASS_FAIL_GRADER_ORDER
in both runner types and weekly report script, so scores show correctly
in the dashboard.
* chore: temp switch to opus 4.6 for eval run
* chore: restore kimi-k2p5 as default eval config
* ci: add timeout and continue-on-error for trend report step
* feat(eval): switch to ubuntu-latest runner, add OE-Clado config
- Switch workflow from self-hosted Mac Studio to ubuntu-latest
- Install BrowserOS Linux .deb in CI (no self-hosted runner needed)
- Add browseros-oe-clado-weekly.json config for orchestrator-executor
- Fix report chart to show date+time (not just date)
- Make BROWSEROS_BINARY configurable via env var
* feat(eval): add NopeCHA captcha solver extension to eval runs
- Auto-load NopeCHA extension in eval Chrome instances
- Works in incognito + headless mode
- CI workflow downloads NopeCHA before eval
- extensions/ directory gitignored (downloaded at runtime)
* feat(eval): per-config concurrency — different configs run in parallel
* feat(eval): remove concurrency limit — all runs execute in parallel
* feat(eval): weekly eval pipeline with R2 uploads and trend dashboard
Add infrastructure for running weekly evaluations and tracking score
trends over time:
- Auto-generated output dirs: results/{config-name}/{timestamp}/
Each eval run gets its own timestamped folder, nothing is overwritten.
- upload-run.ts: uploads eval results to Cloudflare R2. Supports
uploading a specific run or all un-uploaded runs for a config.
- weekly-report.ts: generates an interactive HTML dashboard from R2
data. Config dropdown, trend chart with hover tooltips, searchable
runs table. Groups runs by config name.
- viewer.html: client-facing 3-column run viewer (task list,
screenshots with autoplay, agent stream with messages.jsonl).
Shows performance grader axis breakdown with per-axis scores.
- browseros-agent-weekly.json: weekly benchmark config (kimi-k2p5,
webbench-2of4-50, 10 workers, performance grader, headless).
- eval-weekly.yml: GitHub Actions workflow with cron (Saturday 6am)
and manual trigger. Runs on self-hosted Mac Studio runner.
Concurrency group ensures only one eval runs at a time.
- Dashboard updates: load previous runs, messages.jsonl viewer,
grade badges show percentages, async stream loading.
- Grader updates: timeout 30min, max turns 100, DOM content
verification guidance for performance grader.
* fix(eval): address Greptile review — injection, nested dirs, escaping
- Fix script injection in eval-weekly.yml: pass github.event.inputs
through env var instead of interpolating into shell
- Fix /api/runs to enumerate nested results/{config}/{timestamp}/ dirs
- Fix /api/load-run to allow single-slash run names (config/timestamp)
- Add HTML escaping for R2-sourced values in weekly-report.ts
- Escape axis names in viewer.html renderAxesBreakdown
* fix(eval): fix biome lint — non-null assertion, template literals
* fix(eval): fix biome errors — replace var with let, fix inner function declaration
* fix(eval): address Greptile P2 issues
- isRunDir: check all subdirs for metadata.json, not just first 3
- eval-runner: guard configPath for dashboard-driven runs (fallback to 'eval')
- load-run: default unknown termination_reason to 'failed' not 'completed'
* feat(eval): make BROWSEROS_BINARY configurable via env var
* feat: add GitHub Copilot as OAuth-based LLM provider
Add GitHub Copilot as a second OAuth provider using the Device Code flow
(RFC 8628). Users authenticate via github.com/login/device, and the server
polls for token completion. Supports 25+ models through a single Copilot
subscription.
Key changes:
- Device Code OAuth flow in token manager (poll with safety margin)
- Custom fetch wrapper injecting Copilot headers + vision detection
- Provider factory using createOpenAICompatible for Chat Completions API
- Extension UI with template card, auto-create on auth, and disconnect
* fix: address PR review comments for GitHub Copilot OAuth
- Validate device code response for error fields (GitHub can return 200
with error payload)
- Store empty refreshToken instead of access token for GitHub tokens
- Add closeButton to Toaster for dismissing device code toast
* fix: add github-copilot to agent provider factory
The chat route uses a separate provider-factory.ts (agent layer) from the
test-provider route (llm/provider.ts). Added createGitHubCopilotFactory
to the agent factory so chat works with GitHub Copilot.
* fix: add github-copilot to provider icons, models, and dialog
- Add Github icon from lucide-react to providerIcons map
- Add 8 Copilot models (GPT-4o, Claude, Gemini, Grok) to models.ts
- Add github-copilot to NewProviderDialog zod enum, validation skip,
canTest check, and OAuth credential message
* fix: reorder copilot models with free-tier models first
Put models available on Copilot Free at the top (gpt-4o, gpt-4.1,
gpt-5-mini, claude-haiku-4.5, grok-code-fast-1), followed by
premium models that require paid Copilot subscription.
* fix: set correct 64K context window for Copilot models
Copilot API enforces a 64K input token limit regardless of the
underlying model's native context window. Updated all model entries
and the default template to 64000 so compaction triggers correctly.
* fix: use actual per-model prompt limits from Copilot /models API
Queried api.githubcopilot.com/models for real max_prompt_tokens values.
GPT-4o/4.1 have 64K, Claude/gpt-5-mini have 128K, GPT-5.x have 272K.
Also updated model list to match what's actually available on the API
(e.g. claude-sonnet-4.6 instead of 4.5, added gpt-5.4/5.2-codex).
* feat: resize images for Copilot using VS Code's algorithm
Large screenshots cause 413 errors on Copilot's API. Resize images
following VS Code's approach: max 2048px longest side, 768px shortest
side, re-encode as JPEG at 75% quality. Uses sharp for server-side
image processing.
* fix: address all Greptile P1 review comments
- Add .catch() on fire-and-forget pollDeviceCode to prevent unhandled
rejection crashes (Node 15+)
- Add deduplication guard (activeDeviceFlows Set) to prevent concurrent
device code flows for the same provider
- Add runtime validation of server response in frontend before calling
window.open() and showing toast
- Remove dead GITHUB_DEVICE_VERIFICATION constant from urls.ts
* fix: upgrade biome to 2.4.8, fix all lint errors, and address review bugs
- Upgrade biome from 2.4.5 to 2.4.8 (matches CI) and migrate configs
- Fix image resize: only re-encode when dimensions actually change
- Fix device code polling: retry on transient network errors instead of aborting
- Allow restarting device code flow (clear old flow instead of throwing 500)
- Fix pre-existing noNonNullAssertion and noExplicitAny lint errors globally
* fix: address Greptile P2 review — image resize and config guard
- Fix early-return guard: check max/min sides against their respective
limits (MAX_LONG_SIDE/MAX_SHORT_SIDE) instead of both against SHORT
- Preserve PNG alpha: detect hasAlpha and keep PNG format instead of
unconditionally converting to lossy JPEG
- Keep browserosId guard in resolveGitHubCopilotConfig consistent with
ChatGPT Pro pattern (safety check that caller context is valid)
* feat: update Copilot models to full list from pricing page, default to gpt-5-mini
Added all 23 models from GitHub Copilot pricing page. Ordered with
free-tier models first (gpt-5-mini, claude-haiku-4.5), then premium.
Changed default from gpt-4o to gpt-5-mini since it's unlimited on
Pro plan and has 128K context (vs gpt-4o's 64K limit).
- Add hover_at, type_at, drag_at coordinate tools to server
- Add hoverAt, typeAt, dragAt methods to Browser class
- Export server internals (browser, tool-loop, registry) for eval imports
- Copy eval app from enterprise repo with agents, graders, runner, dashboard
- Nest eval-targets inside apps/eval
- Adapt sessionExecutionDir → workingDir for current server API
- Add biome ignore for dashboard HTML to prevent lint breaking onclick handlers
