Gemini 3+ models may throw errors if a default value is set for
parameters like temperature, top-p, etc.
The 3-series models use 1.0 as the default temperature, so this
should be a backwards-compatible change.
The 400 status chosen for truncation errors (deliberately outside provider
retry lists — an identical retry truncates identically) also fell outside
Agent._try_switch_to_fallback_llm's allowlist, so a configured fallback_llm
could no longer rescue a truncated run. Before this PR the downstream parse
failure was wrapped as a 502 ModelProviderError, which did allow the switch.
Introduce ModelOutputTruncatedError(ModelProviderError, status 400): the
three providers raise it, provider retry loops still skip it, and the
agent's fallback check treats it as switchable explicitly — a fallback with
a different output cap can succeed where the primary truncated.
max_completion_tokens (OpenAI) and max_output_tokens (Google) are optional;
when set to None the truncation message printed 'truncated at
max_output_tokens=None'. Fall back to "the model's output token limit" —
a MAX_TOKENS/length finish reason means some server-side cap fired even
with no client-side cap configured. Anthropic's max_tokens is non-optional
and unaffected.
Structured output cut off at the completion-token cap was never detected:
OpenAI's finish_reason='length', Anthropic's stop_reason='max_tokens', and
Gemini's MAX_TOKENS finish reason all produced JSON cut mid-string, which
surfaced as an opaque parse error ('Unterminated string starting at...') —
or worse, a valid-but-chopped prefix. The actual cause (output token cap)
was never mentioned, and with defaults like max_completion_tokens=4096 this
regularly hits long done()/extract outputs.
Each provider now checks the finish/stop reason before parsing structured
output and raises a clear ModelProviderError ('Model output was truncated
at max_*_tokens=N; increase it or request shorter output'). Status code 400
is used deliberately: it is not in any retry list, and retrying the same
request would truncate identically.
Also adds an 'except ModelProviderError: raise' guard in the Anthropic
handler so the new error is not re-wrapped by the generic catch-all.
Adds a cached_content field on ChatGoogle (and a per-call kwarg on
ainvoke) that gets threaded into the GenerateContentConfigDict before
each generate_content call. This lets callers point Gemini at an
explicit CachedContent resource (e.g. "cachedContents/abc123") instead
of relying on implicit caching, which is constrained by the ~5-minute
TTL window.
Token accounting already pulls cached_content_token_count from
response.usage_metadata into ChatInvokeUsage.prompt_cached_tokens, so
the savings show up in usage stats without further work.
Cache creation itself (client.caches.create) is left to the caller —
this PR only adds the forwarding hook so explicit caching becomes
opt-in usable. A follow-up can wire agent-side cache lifecycle if
useful.
- add gemini-3-flash-preview-lite to VerifiedGeminiModels and token mappings
- list both gemini-3-flash-preview[-lite] alongside existing -latest aliases in CLOUD.md and api-v2.md SupportedLLMs
- swap example code and recommendations (examples/, AGENTS.md, quickstart.md, bug report placeholder) to gemini-3-flash-preview / -lite
- update Google CI test + evaluate_tasks judge LLM to gemini-3-flash-preview / -lite
Mark McDonald