Resolve fallbackModels as preset references or explicit inline provider configs so failover uses complete model settings without exposing fallback logic to the agent loop.
Co-authored-by: Cursor <cursoragent@cursor.com>
Bind fallback model chains to the active model configuration so defaults and presets do not inherit or merge fallback behavior implicitly. Require explicit fallback providers while preserving per-fallback generation overrides and context-window safety.
Co-authored-by: Cursor <cursoragent@cursor.com>
When the primary model returns a non-transient error and no content
has been streamed yet, the runner now tries each model listed in the
active preset's fallback_models in order. Each fallback model may
reside on a different provider — a temporary provider instance is
created on-the-fly via make_provider(config, model=...).
Key design:
- Failover is request-scoped (does not affect subagents/dream/consolidator)
- Provider is restored via try/finally after each fallback attempt
- Skipped when content was already streamed to avoid duplicate output
- Recursive failover prevented by clearing fallback_models on fallback spec
- Circuit breaker trips open after 3 consecutive primary failures (60s cooldown)
- Cross-provider routing: fallback model prefix (e.g. groq/) determines provider
Fixes: cross-provider fallback was broken because the factory passed the
original preset (with provider forced to primary's provider) when creating
fallback providers. Now uses provider="auto" so the model string prefix
correctly routes to the right provider.
Also fixes: log messages now distinguish between primary-failed,
previous-fallback-failed, and circuit-open scenarios.
closes: https://github.com/HKUDS/nanobot/issues/3376
- Add `ModelPresetConfig` schema for named model presets
- Add `model_presets` dict to `Config` and `model_preset` field to `AgentDefaults`
- Add `resolve_preset()` to return effective model params from preset or defaults
- Add `@model_validator` to reject unknown preset names
- Update `_match_provider()` to use resolved preset model/provider
- Update `make_provider()` and `provider_signature()` to use `resolve_preset()`
- Add `model_preset` property to `AgentLoop` for atomic runtime switching
- Update `AgentLoop.from_config()` to inject a runtime `default` preset
- Wire self-tool to inspect/clear preset state
- Update CLI display strings to show active preset
Add an `extra_body` field to `ProviderConfig` that merges arbitrary
key-value pairs into every OpenAI-compatible request body. This is the
escape hatch for provider-specific features that nanobot does not have
first-class fields for.
Real-world use cases this unblocks via config alone (no code changes):
- vLLM/TGI `chat_template_kwargs` (e.g. `enable_thinking: false`)
- vLLM guided decoding (`guided_json`, `guided_regex`)
- Local model sampling params (`repetition_penalty`, `top_k`, `min_p`)
- Any future provider-specific param without a new PR each time
The config extra_body is applied last via recursive deep-merge, so it
can extend or override provider-specific defaults (e.g. thinking
params) without clobbering sibling keys set by internal logic.
Changes:
- Add `extra_body: dict[str, Any] | None` to `ProviderConfig`
- Pass it through `factory.py` to `OpenAICompatProvider.__init__`
- Deep-merge into `_build_kwargs` after all internal extra_body entries
- Add `_deep_merge` helper (recursive dict merge, does not mutate inputs)
- 21 tests: deep-merge semantics, provider init, _build_kwargs
integration, thinking coexistence, real-world patterns (guided_json,
repetition_penalty), and schema validation
Xubin RenandCursor
chengyongru
hussein1362andXubin Ren