| name | add-new-model |
| description | Add support for a newly-released LLM model in pydantic-ai (e.g. openai:gpt-5.6, anthropic:claude-sonnet-5). Use when a provider ships a new model id and you need to wire literals, profile flags, and tests to recognize it. Handles SDK-lag, gateway list conventions, and capability probing. |
| user-invocable | true |
| allowed-tools | Bash, Read, Edit, Write, Glob, Grep, WebFetch, WebSearch, AskUserQuestion |
Add New Model
Wire a newly-released provider model into pydantic-ai. Optimized for the common case (mirror an existing sibling); flags the cases where it's not a mirror and needs deeper work.
Reference docs (read once before scoping)
agent_docs/pydantic-ai-slim.md — the Ownership section, plus pydantic_ai_slim/pydantic_ai/native_tools/AGENTS.md, for the user-visible surface this model needs to land on.pydantic_ai_slim/pydantic_ai/profiles/AGENTS.md, providers/AGENTS.md, models/AGENTS.md, and pydantic_ai_slim/pydantic_ai/AGENTS.md (the capability-flag and Provider.model_profile() rules), plus the Design Rules section of agent_docs/pydantic-ai-slim.md. These tell you where capability facts belong (profile vs. provider vs. model class) when the new id has non-mirror behavior.
Inputs
User invokes with provider + model id (e.g. openai gpt-5.6). If missing, ask via AskUserQuestion.
Step 1 — Verify the model exists at the provider
Never trust marketing names, news articles, or guesses. Hit the provider's model-listing endpoint:
| Provider | Verification call |
|---|
| OpenAI | curl -s https://api.openai.com/v1/models -H "Authorization: Bearer $OPENAI_API_KEY" |
| Anthropic | curl -s https://api.anthropic.com/v1/models -H "x-api-key: $ANTHROPIC_API_KEY" -H "anthropic-version: 2023-06-01" |
| xAI | curl -s https://api.x.ai/v1/models -H "Authorization: Bearer $XAI_API_KEY" |
| Google | curl -s https://generativelanguage.googleapis.com/v1beta/models -H "x-goog-api-key: $GOOGLE_API_KEY" |
| Groq | curl -s https://api.groq.com/openai/v1/models -H "Authorization: Bearer $GROQ_API_KEY" |
| Bedrock | aws bedrock list-foundation-models --region "$AWS_REGION" |
Load credentials from the repo-root .env with source .env && <cmd>. list-foundation-models is region-scoped, so query the region your models are actually deployed in (not a hard-coded default). List every id the provider exposes for this release — base, dated snapshot, -pro, -mini, -nano, -codex, -chat-latest. Add only what actually exists; do not extrapolate sibling variants.
If the user-given id is not in the listing, stop and confirm with the user before proceeding.
Step 2 — Mirror the most recent add-model PR for this provider
git log --all --oneline --grep="<previous-version-pattern>" -20
Pick the smallest, most recent "add model X" PR for the same provider. Pull its file list with gh pr view <num> --json files --jq '.files[].path'. That file list is the floor of what you'll touch. It is rarely the ceiling.
Step 3 — Enumerate (load-bearing step)
For every variable, tuple, and literal you're about to touch, grep its readers across the repo. This step is what catches the snapshot/enumeration tests that ratchet on every model add. Skipping it pushes work onto CI and produces broken PRs.
Specifically, for a typical model add, grep for:
- The previous model id literal you're mirroring (e.g.
gpt-5.4, claude-opus-4-5) — rg '<prev-id>' --glob '!**/*.yaml' --glob '!**/cassettes/**'
- Every prefix/membership key in the profile module you're editing (e.g. OpenAI's
_REASONING_SUPPORT_BY_PREFIX keys, Anthropic's inline model_name.startswith((...)) tuples, xAI's _GROK_43_REASONING_MODELS)
KnownModelName and its provider-block neighbours- Snapshot test files:
tests/models/test_model_names.py, tests/test_capabilities.py
Classify each hit:
- must update — model-name lists, dispatch tuples
- snapshot to refresh —
inline_snapshot blocks needing pytest --inline-snapshot=fix
- skip — VCR cassettes, docs about an unrelated model
If rg output looks mangled (unicode/regex artifacts), drop to grep -n — don't push past garbled output.
Step 4 — SDK pin check
Snapshot/enumeration tests in this repo often tie KnownModelName to a literal set defined in the provider SDK. The provider SDK frequently lags the model release by days.
For OpenAI, check the broad union the repo actually consumes (OpenAIModelName = str | AllModels), not the chat-only ChatModel Literal — AllModels also carries Responses-API-only and embeddings ids that the enumeration test walks:
uv run python -c "from openai.types import AllModels; from typing import get_args; print([m for m in get_args(AllModels) if '<new-version>' in m])"
Anthropic and xAI do not follow this OpenAI flow — the repo bridges their SDK lag with a local Literal and lands green immediately, no split. See the SDK-lag bridge notes in their landmine sections below (Anthropic checks ModelParam, not Model).
If a provider with no bridge (e.g. OpenAI) doesn't yet list the new id, the literals PR cannot land green on CI. Surface this to the user with the choice:
- Split the PR — land the profile/handler change now (capability flip is harmless without
KnownModelName literals because runtime accepts plain strings). Open a separate draft PR for the literals; promote it once the SDK ships and the pin is bumped.
- Hold the whole PR — wait for SDK release, bump pin, refresh snapshots with
pytest --inline-snapshot=fix, push.
- Bump SDK pin now — only if the new SDK is already released.
Default recommendation: option 1 (split). Use AskUserQuestion.
Step 5 — Probe capabilities (only if not a pure mirror)
If the new model is just another sibling in an existing family (e.g. gpt-5.5 after gpt-5.4), skip to Step 6 — the existing profile branch covers it once you add the prefix to the dispatch tuple.
If the model is a new family or has unclear capabilities, write a small comparison script (local-notes/probe_<model>.py) that hits the new model AND its closest neighbour with:
temperature / top_p (does the API reject sampling params?)reasoning.effort values (none, low, medium, high, xhigh) — note which the API accepts- New parameters mentioned in the release notes
- Streaming / tool calls if the family is new
Diff the responses. Anything that diverges from the neighbour belongs in the profile.
Step 6 — Edit (minimal diff matching the mirrored PR)
Make only the changes the enumeration step surfaced. Resist scope creep. If you discover a pre-existing bug in a sibling model's profile, flag it in the PR description; do not fix it in this PR.
After edits:
make format && make lint
PYRIGHT_PYTHON_IGNORE_WARNINGS=1 uv run pyright <changed-python-files>
Run the tests directly touching the changed surface — the profile test plus any enumeration tests you updated. CI is the safety net for the long tail; locally you only need to verify the surface area of your change.
If snapshot tests changed: uv run pytest <file> --inline-snapshot=fix then verify the diff is the expected literal addition only.
Step 7 — VCR / integration tests
Default for mirror-only adds: skip recording a new VCR. Repo convention uses one representative model per family for VCR (e.g. gpt-5.2 covers the gpt-5.x reasoning family). The profile unit test added in Step 6 is sufficient.
When the new model introduces meaningful changes to pydantic_ai_slim/pydantic_ai/models/<provider>.py (new request shape, new response field, new handler branch):
- Look for an existing parametrized VCR test that covers the changed feature.
rg -l '<feature-name>' tests/models/. If one exists and it parametrizes over model ids, tag the new id onto the parametrize list rather than writing a new test.
- If no parametrized coverage exists and you need a new VCR test, place it:
- Prefer
tests/models/<provider>/test_<feature>.py only if the file already exists (e.g. tests/models/anthropic/test_output.py).
- Otherwise add it to
tests/models/test_<provider>.py. Do not create a new tests/models/<provider>/ subdirectory if one doesn't already exist for this provider.
- Record using the
testing-skill skill workflow.
Step 8 — PR
Terse, not botty. Structure:
- One sentence: what model(s) were added.
- Bulleted file list with one-line "what changed" per file.
- "Verified via probe / mirror of #NNNN" — explicit about which changes were API-verified vs assumed-by-mirror.
- Flag pre-existing latent bugs found but deliberately not fixed.
- Link the prior add-model PR for context.
Include the PR template, fill in the issue number, and check the "AI generated code" box in the GitHub UI yourself — gh pr create cannot set it.
Provider-specific landmines
OpenAI
_REASONING_SUPPORT_BY_PREFIX in pydantic_ai_slim/pydantic_ai/profiles/openai.py — a dict keyed by model-name prefix ('gpt-5.6', 'gpt-5.3-chat', 'gpt-5', 'o', …) → _ReasoningSupport(enabled_by_default, can_be_disabled, supports_mode), resolved first-match-wins by _reasoning_support(). A new gpt-5.N family MUST be added here, and ordering matters: a more specific prefix ('gpt-5.3-chat') must precede the broader one it would otherwise shadow ('gpt-5.3'), and every newer gpt-5.x family must precede the plain 'gpt-5' catch-all. Miss it and the model falls through to the _NO_REASONING default (thinking_always_enabled=False, openai_supports_reasoning_effort_none=False) — wrong defaults, no error. The resolved matrix is pinned in tests/profiles/test_openai.py.KnownModelName lives in pydantic_ai_slim/pydantic_ai/models/_known_model_names.py (a TypeAliasType), not models/__init__.py. It has split openai: and gateway/openai: blocks. Don't assume the gateway block omits -pro/-chat-latest — for the gpt-5.x series it enumerates them (gateway/openai:gpt-5.2-pro, gateway/openai:gpt-5.3-chat-latest, …). Mirror the exact enumeration of the most recent series across both blocks rather than guessing a convention.- Most
gpt-5.x-chat variants DO reason (_ALWAYS_ON_REASONING: reason at a fixed effort, reject reasoning_effort='none' and sampling parameters). The non-reasoning exception is the original gpt-5-chat/gpt-5-chat-latest (_NO_REASONING). Verify each -chat/-chat-latest variant against the live Responses API; don't copy a sibling's reasoning class blindly.
-pro variants map to _ALWAYS_ON_REASONING (, , ) — they reason and reject . The three-fact model doesn't encode per-effort- rejection, so if a new rejects a specific value (e.g. ), flag it rather than assuming the enum covers it.
Anthropic
xAI (Grok)
- Strict enumeration despite
XaiModelName = str | ChatModel. The str arm looks permissive but the enumeration test's get_model_names recurses into the union and yields nothing for a bare str type — so KnownModelName's xai: block is strictly enforced against the SDK's ChatModel Literal, exactly like OpenAI. tests/models/test_model_names.py::test_known_model_names fails with "Extra/Missing names" on any mismatch. Confirm parity: xai: + get_args(ChatModel) must equal the xai: entries in models/_known_model_names.py.
- SDK-lag bridge (Anthropic-style, and it's needed for xAI too).
xai_sdk's ChatModel frequently lags a release — as of 1.17.0 it still lacked grok-4.5, so bumping the floor won't help (check newer wheels first: download from PyPI and grep xai_sdk/types/model.py for ChatModel: TypeAlias = Literal[). Bridge with a local Literal: XaiModelName = str | ChatModel | Literal['grok-4.5', 'grok-4.5-latest'], docstring-note to drop it when the floor is bumped past the release that adds the id. This makes the enumeration test's generated side include the new id, matching the hand-added _known_model_names.py literal — lands green immediately. (Historically xAI bumped the SDK floor — commits e3f6e3c54/58f394aea — but that only works when the SDK already ships the id.)
- A new
grok-4.x is NOT a pure mirror. Reasoning-effort support lives in profiles/grok.py as membership sets (_GROK_43_REASONING_MODELS + a per-family effort frozenset), not startswith prefixes. The grok-4 prefix auto-grants grok_supports_builtin_tools=True but leaves grok_reasoning_efforts empty (→ supports_thinking=False) unless you add the id to a reasoning-models set. Forgetting this silently ships a reasoning model with thinking off. Add a _GROK_<ver>_REASONING_MODELS set + effort frozenset and an elif branch in grok_model_profile.
- Probe reasoning efforts via the OpenAI-compatible REST endpoint, comparing against the closest neighbour: with . A rejected value returns 400 . (rejected → always-on). CAVEAT: REST silently accepts / even though the gRPC (in ) is — don't over-read REST acceptance; is those four and collapses →, →. Grok 4.5 example: accepts , rejects → always-on; Grok 4.3 accepts too.
Bedrock
- Bedrock Mantle is a separate provider from Bedrock Runtime.
bedrock: (the BedrockProvider, boto3-only) talks to the Converse API; bedrock-mantle: (the BedrockMantleProvider, an openai-backed Provider[AsyncOpenAI] built on AsyncBedrockOpenAI) talks to Mantle's OpenAI-compatible API. They have separate model catalogs and separate optional extras (bedrock vs bedrock-mantle); don't fold Mantle deps into the bedrock group.
- Mantle model families use different endpoints, keyed off the profile.
BedrockMantleProvider.model_profile stamps bedrock_mantle_interface: Literal['chat','responses','openai-responses'] on the profile (GPT-5.4+ → openai-responses at /openai/v1; GPT-OSS → responses at /v1; GPT-OSS Safeguard → chat at /v1). infer_model reads that (via the profile, not a separate interface method) to pick BedrockMantleResponsesModel vs BedrockMantleChatModel, and the Responses model overrides client to pick the base URL. Add a family only after verifying its endpoint against the AWS model card + a live request.
bedrock: stays on Converse; it does NOT auto-route to Mantle. A GPT-5.4+ model on bedrock: raises from BedrockProvider.model_profile pointing users to bedrock-mantle: (there's a TODO(v3) to flip the default with a deprecation later). Only add bedrock-mantle: names to KnownModelName — no bedrock:openai.gpt-5.* names, and hence no UNSUPPORTED_GATEWAY_MODEL_NAMES entries for them.- Response-scoped tool-call IDs are a profile flag, not a Mantle-wide behavior.
openai_responses_tool_call_ids_are_response_scoped (on OpenAIModelProfile) is enabled only for Mantle GPT-5.6 Responses; OpenAIResponsesModel qualifies call IDs with the response ID in both request and streaming ingestion so history stays uniquely keyed (#6536).
Google (Gemini)
- TWO places for the id, FOUR
KnownModelName blocks. Add to:
LatestGoogleModelNames in models/google.py (GoogleModelName = str | LatestGoogleModelNames — the str arm is permissive at typecheck time, but the enumeration test only walks the Literal arm).models/_known_model_names.py — four blocks: gateway/google-cloud:, gateway/google:, google-cloud:, google: (older add-model PRs that only edit three blocks or models/__init__.py are stale; KnownModelName moved in #5803).
- No SDK-lag bridge needed.
google-genai does not ship a model-id Literal the enumeration test consumes — the local LatestGoogleModelNames Literal is the source of truth. Adding the id lands green immediately.
- Profile is substring-gated, not per-id.
profiles/google.py keys off 'gemini-3' in model_name (thinking level, tool combination, server-side tool invocations, MIME types in tool returns) and 'pro' in model_name and 'flash' not in model_name (always-on thinking). A new gemini-3.x-flash* id is almost always a pure literal add — the existing Gemini-3 branch already covers it. Only probe if the release notes claim a capability divergence (e.g. no thinking, image-only, Pro always-on).
- API verification:
curl -s "https://generativelanguage.googleapis.com/v1beta/models?pageSize=200&key=$GOOGLE_API_KEY" (key is often in the main worktree .env, not every linked worktree). Confirm exact ids; do not invent dated snapshots or -preview suffixes. Specialized / limited-access models (e.g. Flash Cyber via CodeMender) are out of scope unless they appear in that public listing.
- Gateway support is opt-out, not opt-in. The enumeration test generates
gateway/{google,google-cloud}:* for every LatestGoogleModelNames entry except those listed in UNSUPPORTED_GATEWAY_MODEL_NAMES in tests/models/test_model_names.py. Mirror the most recent sibling series: if gemini-3.5-flash is in the gateway KnownModelName blocks (not in the unsupported set), new flash siblings go there too. Only add to when the gateway actually rejects the id.
Others
Not yet documented here. When you add the next model for one of these providers, add the landmines you encountered to this section before closing the session (see Step 9).
Step 9 — Update this skill
After completing the model-add, before closing the session: if anything came up that isn't already documented in this skill — a new test that ratcheted, a provider-specific dispatch tuple, a misleading SDK behavior, a corrected misconception, an iteration the user had to walk you through — add it to this SKILL.md.
Specifically:
- Provider-specific landmines → the matching subsection (or create it).
- Generic process gaps → the relevant numbered step.
- Workflow shape errors → restructure the steps.
This skill exists to compound learnings. A model-add that surfaced new friction and didn't update this file wasted that friction.
