Model Refresh

Model Catalog Refresh

Keep src/schema/models.py (the AllModelEnum catalog) current: add models a provider shipped since the last refresh, remove ones the provider has deprecated or is actively steering users away from, and make sure src/core/settings.py's per-provider DEFAULT_MODEL fallbacks point at a model that still exists.

Where model config lives

Workflow

1. Survey. For each provider block in src/schema/models.py, fetch the docs URL already in that class's docstring and diff its current model list against the enum values present. These docstring URLs are the single canonical source for where to look — don't hardcode a second copy of them elsewhere, since provider doc URLs shift over time and duplicated links silently rot.
   • If a docstring URL 404s, redirects to a generic landing page, or otherwise no longer points at a model listing: find the current canonical URL for that provider's model docs and update the docstring to it as part of this same change, then continue the survey from the corrected page. Don't skip the provider just because the old link broke.
   • A few providers need a second check beyond the docstring URL: Azure OpenAI lags OpenAI's own releases (it's deployment-based — check what base models Azure currently supports), Anthropic has a separate deprecations page worth checking for retirement dates, and Vertex AI model paths sometimes differ from the Gemini API's own names for the same model (e.g. models/gemini-2.5-flash vs gemini-2.5-flash) — check both columns. Ollama takes a user-supplied model name at runtime, so there's nothing to add there; just confirm the generic pass-through in llm.py still works.
   • docs.aws.amazon.com returns HTTP 403 to WebFetch (it blocks the fetcher's user agent — this is not an egress-policy denial). Use WebSearch for Bedrock model IDs instead; it reads the same doc pages server-side and surfaces the model-card and inference-profile-support pages. The per-model card pages (.../model-card-anthropic-claude-<model>.html) list the exact modelId and profile IDs.
2. Classify each gap:
   • New model, generally available → add it.
   • New model, preview/experimental → use judgment; this repo has taken preview models before (e.g. gemini-3-pro-preview) when there's no GA alternative yet, but prefer GA when one exists.
   • Existing model deprecated or sunset by the provider → remove it, unless it's the only model left for that provider (flag that case instead of leaving the catalog empty).
   • Existing model merely superseded but still served → leave it unless the provider's docs explicitly say to migrate off it.
3. Match existing naming conventions when adding a member:
   • Enum member names are SCREAMING_SNAKE_CASE, usually family+size, e.g. SONNET_45, GEMINI_25_PRO, LLAMA_33_70B. Version numbers drop dots (4.5 → 45).
   • Enum values are the exact string the provider's API expects (claude-sonnet-4-5, gemini-2.5-pro, gpt-5.1) — copy verbatim from the provider docs, don't guess.
   • Values must be unique across all the enums in AllModelEnum — tests/schema/test_models.py enforces this. Where two providers serve the same upstream model one side needs a distinguishing form: VertexAIModelName uses the models/ prefix, so keep it on new Vertex entries.
   • Keep provider families grouped and roughly ordered by size/generation within a class, matching how they read today.
4. Apply changes across every coupled location — do not edit only the enum:
   • src/schema/models.py: add/remove the StrEnum member. Keep the docstring URL current (see step 1).
   • src/core/settings.py model_post_init: if the removed model was a DEFAULT_MODEL fallback, repoint it at a remaining (ideally cheap/fast) model for that provider — match the intent of the current defaults (e.g. Haiku/Flash/Nano-tier, not the flagship).
   • src/core/llm.py: nothing to do for a plain rename/add (the _MODEL_TABLE and if model_name in ...Name dispatch are enum-driven), but check for any provider-specific special-casing (see the Groq safeguard-model branch) that might need to apply to the new model too.
   • tests/core/test_llm.py, tests/core/test_settings.py: update any test that hardcodes a model value being removed; add a case for a notable new model if it has special handling (temperature override, tool-binding quirk, etc).
   • .env.example, other docs: update only if they name a specific model that changed (most don't).
5. Do not touch provider credentials or add live network calls during the survey step. This step is pure research + code edit, no API keys needed.
6. Live-test what you changed (needs API keys — see "Live testing" below).
7. Summarize what was added/removed/repointed and why, citing the provider doc for each change. Flag any model rename explicitly — it's a breaking change for existing deployments pinning the old enum value in DEFAULT_MODEL/ AVAILABLE_MODELS env config, not something to swap silently. Commit and push per the repo's normal git workflow; open a PR only if asked.

Providers you can't live-verify (Bedrock, Azure, Vertex service-account, DeepSeek, OpenRouter)

Not having a key for a provider is not a reason to skip it — a stale or broken catalog entry is worse than a doc-sourced one. Update these from docs just like the rest, and in the PR mark them explicitly as doc-only / unverified, citing the provider page and calling out any caveat below so the next person with a key knows exactly what to spot-check. Only leave a provider untouched when the docs themselves are ambiguous and the change would be a product decision (e.g. adding a whole new pricing tier), not a freshness update.

• AWS Bedrock — the enum value is passed straight to ChatBedrock(model_id=...), so it must be a real Bedrock ID, not a friendly label. Two gotchas:
  1. The latest Claude models are not invocable on-demand by their base model ID — a bare anthropic.claude-... call 400s with "on-demand throughput isn't supported." They must go through a cross-region inference profile: the base ID prefixed with a geo (us./eu./apac.) or global.. Prefer global. (routes dynamically, region-agnostic — the best fit for a catalog value with no region context) and note in the PR that single-region deployments not enrolled in Global CRIS should swap the prefix for their geo.
  2. Bedrock inherits the same sampling-parameter restrictions as the direct Anthropic API — e.g. a Sonnet-5-class model rejects temperature. If you point a Bedrock entry at such a model, mirror the no-temperature branch that already exists for it in llm.py's Anthropic and Bedrock dispatch. If AWS credentials happen to be present, boto3.client("bedrock").list_foundation_models() is the fastest way to confirm real IDs — but note that Bedrock access is a separate enablement from plain AWS creds, so this can fail with an auth error even when other AWS calls would work.
• Azure OpenAI is the one genuinely heavier lift, because it's deployment-based and the catalog is coupled in more places than the enum:
  • settings.py model_post_init hardcodes a required_models set (currently {"gpt-4o", "gpt-4o-mini"}) that it validates the AZURE_OPENAI_DEPLOYMENT_MAP against — bumping the enum means bumping that set and the .env.example deployment-map sample and the ~7 Azure cases in tests/core/test_settings.py.
  • llm.py hardcodes temperature=0.5 for the Azure path, but Azure's GPT-5-era reasoning variants reject temperature (400). If you move Azure onto one, add a no-temperature branch like the Anthropic/Bedrock Sonnet-5 handling.
  • Changing an Azure enum value is a breaking change to every user's deployment map (they name deployments after these keys). Treat an Azure generation bump as its own reviewed change, and flag the deployment-map break loudly — don't fold it silently into a routine refresh.

Live testing

scripts/check_live_models.py sends a trivial one-word prompt to every model of every provider that has credentials configured in the environment, and reports PASS/FAIL/SKIP per model. It is deliberately outside the pytest suite (real network calls, tiny real cost) — run it by hand or from a scheduled trigger with keys populated:

PYTHONPATH=src uv run python scripts/check_live_models.py                    # all configured providers
PYTHONPATH=src uv run python scripts/check_live_models.py --provider anthropic google

A SKIP line means no credentials were present for that provider — that's expected and not a failure. Only treat FAIL rows as build-blocking. Cost is negligible (a handful of few-token completions per provider) but it's real spend against real keys — don't wire it into CI or run it on every commit.

If no provider credentials are configured in the current environment, skip this step entirely rather than failing — the survey/edit step is still fully useful on its own.