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6 commits
| Author | SHA1 | Message | Date | |
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Mateo Wang
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f628b41400
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feat(complexity_router): add custom_technical_keywords config (#32262) | ||
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Krrish Dholakia
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e7bc316db0
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Litellm krrish staging 04 20 2026 (#26138)
* feat(router): add auto_router/quality_router for quality-tier routing (#25987) * feat(router): add auto_router/quality_router for quality-tier routing Adds a new auto-router type that routes a request to a model at a target quality tier. The quality tier is inferred by re-using the existing ComplexityRouter's classification, then mapped through an admin-configured complexity_to_quality table. Each candidate model declares its own quality_tier in model_info.litellm_routing_preferences. Resolution strategy: exact tier match, else round up to the next higher tier, else fall back to default_model. Co-Authored-By: Claude Opus 4 (1M context) <noreply@anthropic.com> * feat(quality_router): add capability-based filtering Each deployment can declare a `capabilities: List[str]` field in `model_info.litellm_routing_preferences` (e.g. ["vision", "function_calling"]). Requests can pass `litellm_capabilities` in `request_kwargs` to require specific capabilities — the router will only route to deployments whose declared capabilities are a superset. Resolution still walks tier (exact → round up), but at each tier filters by capability before picking. Falls back to default_model only when it also satisfies the required capabilities; otherwise raises rather than silently routing to a model that lacks a required capability. Co-Authored-By: Claude Opus 4 (1M context) <noreply@anthropic.com> * feat(quality_router): expose routing decision in response headers For transparency, expose the QualityRouter's routing decision in the proxy response headers: x-litellm-quality-router-model → picked model_name (e.g. "haiku-vision") x-litellm-quality-router-tier → resolved quality tier (e.g. "1") x-litellm-quality-router-complexity → ComplexityTier name (e.g. "SIMPLE") Mechanism: the pre-routing hook stashes the decision in request_kwargs["metadata"]["quality_router_decision"]. After the call returns, Router.set_response_headers lifts the decision into response._hidden_params["additional_headers"] alongside the existing x-litellm-model-group / x-litellm-model-id headers. Existing metadata keys (trace_id, user_id, etc.) are preserved. Co-Authored-By: Claude Opus 4 (1M context) <noreply@anthropic.com> * feat(quality_router): replace capabilities with keyword override Drops the capability-based filtering in favor of a keyword-based override for v0: - RoutingPreferences.keywords: List[str] (replaces capabilities) — each deployment can declare substring keywords. - If any declared keyword (case-insensitive) appears in the user message, the router short-circuits the complexity-classification flow and routes to the matching deployment. - Tiebreaker for overlapping keyword matches: quality_tier DESC, then cheapest model_info.input_cost_per_token ASC. Unpriced models lose ties to priced ones. Decision metadata + headers now expose the override: x-litellm-quality-router-via → "keyword" | "quality_tier" x-litellm-quality-router-keyword → matched keyword (only on keyword route) x-litellm-quality-router-complexity → complexity tier (only on tier route) Removes: - request_kwargs["litellm_capabilities"] reading - _model_capabilities, _model_supports_capabilities, _first_capable_model_at_tier, capability filter in _resolve_model_for_quality_tier Co-Authored-By: Claude Opus 4 (1M context) <noreply@anthropic.com> * feat(quality_router): add explicit `order` to RoutingPreferences Adds an explicit priority field to RoutingPreferences for resolving collisions deterministically: RoutingPreferences.order: Optional[int] # lower wins; unset = +inf Used as the PRIMARY tiebreaker in two places: 1. Keyword overlap: when multiple deployments declare the same matching keyword, sort by (order ASC, quality_tier DESC, input_cost_per_token ASC, model_name ASC). Explicit always beats implicit. 2. Tier resolution: when multiple deployments share a quality tier, `_resolve_model_for_quality_tier` picks the one with the lowest order. The tier list is now sorted at index-build time. This lets admins make routing decisions explicit when the natural quality-and-price ordering would pick the wrong model. Co-Authored-By: Claude Opus 4 (1M context) <noreply@anthropic.com> * feat(quality_router): reorder tiebreak to (quality, order, price) Changes the tiebreak ordering so quality_tier always wins first, then explicit `order` is used to break ties within the same tier, then price breaks the rest: 1. quality_tier DESC ← best model wins first 2. order ASC ← explicit priority within a tier 3. input_cost_per_token ASC 4. model_name ASC Previously `order` was the primary key — that meant a tier-2 model with `order=1` would beat a tier-3 model with no `order`, which is the wrong default. Now `order` only resolves collisions among same-tier candidates. Tier resolution (within a single tier) keeps the same key minus quality: (order ASC, cost ASC, name). Test renames + flips: - test_explicit_order_overrides_quality_tier → test_quality_wins_over_explicit_order - new: test_order_breaks_tie_within_same_quality_tier Co-Authored-By: Claude Opus 4 (1M context) <noreply@anthropic.com> * fix(quality_router): resolve Greptile review feedback Addresses four P1 findings from PR review plus test coverage: 1. set_model_list missing quality_routers reset - Hot-reloading the Router would leave stale QualityRouter instances pointing at the old model_list. `set_model_list` now clears `self.quality_routers` alongside the other indices. 2. Round-down fallback before default_model - `_resolve_model_for_quality_tier` now rounds DOWN to the closest lower tier after round-up fails, before falling back to `default_model`. Degrades gracefully rather than jumping straight off-tier. 3. RoutingPreferences validation bypass - `_build_tier_index` now instantiates `RoutingPreferences(**prefs)` so invalid shapes (e.g. non-int quality_tier) raise a clear ValueError instead of silently succeeding. 4. Config-ordering dependency - `_tier_to_models` is now built lazily on first access. Previously, eager construction in `__init__` meant a QualityRouter deployment had to appear AFTER all its referenced models in config.yaml, because `Router._create_deployment` populates `model_list` incrementally. Any `available_models` defined after the router entry would silently be reported as missing. Also adds 6 new tests covering each fix: - test_invalid_quality_tier_type_raises_clear_error - test_router_can_be_instantiated_before_its_targets_exist - test_set_model_list_clears_quality_routers_registry - test_rounds_down_when_no_higher_tier_exists - test_rounds_down_prefers_closest_lower_tier - test_prefers_round_up_over_round_down Co-Authored-By: Claude Opus 4 (1M context) <noreply@anthropic.com> * style: apply black 24.10.0 formatting to pre-existing offenders Unblocks the LiteLLM Linting check for this PR — these 12 files are already failing `black --check` on main (the lint workflow only runs on PRs, so main drifts). No behavior changes; formatting-only. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * Update litellm/router.py Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> --------- Co-authored-by: Claude Opus 4 (1M context) <noreply@anthropic.com> Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * Support /v1/responses in complexity router (#26137) * feat(proxy): add --reload flag for uvicorn hot reload (dev only) Opt-in CLI flag, off by default, no env var. Only affects the uvicorn run path; gunicorn/hypercorn paths and prod (which doesn't pass the flag) are unaffected. * Feature/add audio support for scaleway (#26110) * feat(scaleway): add SCALEWAY to LlmProviders enum * feat(scaleway): add audio transcription config and dispatch wiring Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * test(scaleway): add behavior tests for audio transcription config Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * chore(scaleway): advertise audio_transcriptions in endpoint-support JSON * docs(scaleway): document audio transcription support * fix(scaleway): address PR review — plain-text response_format + missing-key fail-fast Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * test(scaleway): cover new response paths, drop gettysburg.wav coupling Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> * Prompt Compression - add it to the proxy (#25729) * refactor: new agentic loop event hook simplifies how to create logic for tool based multi llm calls * fix: compress - make it work on anthropic input as well * fix(compress.py): working prompt compression for claude code ensures claude code messages can run through proxy easily * docs: add agentic loop hook guide * docs: add agentic_loop_hook to sidebar * fix: fix multiple arguments error * fix: fix tool call loop for compression on streaming /v1/messages * fix: fix linting errors * fix: fix ci/cd errors * feat(litellm_pre_call_utils.py): use claude code session for litellm session id allows claude code logs to be stitched together, making it easy to know they were all part of the same conversation * fix: suppress incorrect mypy warning rE: module * revert: drop PR's changes to litellm/proxy/_experimental/out/ Restores the 34 HTML files under _experimental/out/ to their pre-PR paths (X/index.html -> X.html). All renames are R100 (content unchanged); no other files are touched. * fix: address greptile review comments on PR #25729 - Skip ``kwargs["tools"] = []`` injection when compression is a no-op — Anthropic Messages rejects empty tool arrays on requests that did not originally declare tools. - Move agentic-loop safety guards (fingerprint cycle / max depth) out of the per-callback try/except so they propagate instead of being swallowed by the generic exception handler. Extracted _check_agentic_loop_safety. - Gate generic ``x-<vendor>-session-id`` capture behind the LITELLM_CAPTURE_VENDOR_SESSION_HEADERS env var (off by default) to preserve backwards compatibility; explicit x-litellm-* headers are unaffected. - Fix monkeypatch target in pre-call-hook test to patch the actual module-level binding (litellm.integrations.compression_interception.handler.compress). - Add regression tests for empty-tools skip and opt-in session capture. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * revert: drop LITELLM_CAPTURE_VENDOR_SESSION_HEADERS flag Generic x-<vendor>-session-id header capture is a new feature and only runs *after* the explicit x-litellm-trace-id / x-litellm-session-id checks, so it does not change behavior for any existing caller that was already using the LiteLLM headers — no backwards-incompatibility to gate. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * refactor(compress): replace input_type with CallTypes call_type Drop the bespoke ``CompressionInputType`` literal and use the existing ``litellm.types.utils.CallTypes`` enum instead. ``litellm.compress()`` now takes ``call_type: Union[CallTypes, str]`` (default ``CallTypes.completion``) — no new concept to learn, and the enum is already the way the rest of the codebase talks about request shapes. Supported values: ``completion`` / ``acompletion`` (OpenAI chat-completions shape) and ``anthropic_messages`` (Anthropic structured content blocks). Updated: compress(), the compression_interception handler, tests, docs, and the two eval scripts. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> * Support /v1/responses in complexity router Adds cross-format support to the complexity router via the guardrail translation handler dispatch. Adds get_structured_messages to base translation plus OpenAI chat, Responses, and Anthropic handlers. Auto-router helper _extract_text_from_messages handles tool-call and multimodal messages. Widens async_pre_routing_hook messages type to Dict[str, Any]. Fixes https://github.com/BerriAI/litellm/issues/25134 * chore: apply black formatting * fix: fallback to trying each handler when route inference fails --------- Co-authored-by: Ryan Crabbe <ryan@berri.ai> Co-authored-by: nhyy244 <106547304+nhyy244@users.noreply.github.com> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> * test: cover _is_quality_router_deployment and init_quality_router_deployment * fix: reset auto_routers on set_model_list to prevent hot-reload ValueError * style: apply black formatting to websearch_interception and agentic_streaming_iterator --------- Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Claude Opus 4 (1M context) <noreply@anthropic.com> Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Co-authored-by: Ryan Crabbe <ryan@berri.ai> Co-authored-by: nhyy244 <106547304+nhyy244@users.noreply.github.com> |
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Ishaan Jaffer
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e8461b5b97
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style: run black formatter on files from main merge | ||
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tombii
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28fe9fabae
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fix: complexity_router crashes on list-format message content (OpenAI multi-part messages) (#22761)
* fix: complexity_router fails on list-format message content (OpenAI multi-part messages)
When a client sends messages with list-format content
(e.g. [{"type": "text", "text": "..."}] as used by the OpenAI JS SDK
and other clients), the complexity_router's async_pre_routing_hook
skipped those messages because it only handled str content. This caused
user_message to be None, the hook returned None, and the router fell
through to selecting the complexity_router deployment itself
(model="auto_router/complexity_router") which litellm cannot dispatch,
resulting in LiteLLMUnknownProvider.
Fixes:
- Extract text from list-format content parts (type=text) before
classifying
- Return default_model instead of None when no user message can be
extracted, preventing the crash fallthrough
- Loosen PreRoutingHookResponse.messages type from Dict[str, str] to
Dict[str, Any] to accommodate list-format content values
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: update messages type annotation in async_pre_routing_hook to Dict[str, Any]
Consistent with PreRoutingHookResponse.messages type change and the
list-format content support added in the previous commit.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: normalize None content to empty string in complexity_router message parsing
msg.get("content", "") returns None when the key exists with value None
(e.g. assistant messages with tool calls). Use `or ""` to normalize
None to an empty string explicitly.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: strip whitespace from joined list content parts in complexity_router
Prevents leading/trailing spaces when some content parts have empty
text values (e.g. " ".join(["", "hello"]) → " hello").
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
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Ishaan Jaff
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f74a1c94df
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test(router): add coverage tests for _is_complexity_router_deployment and init_complexity_router_deployment (#21848) | ||
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shin-bot-litellm
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1be30f5129
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feat(router): Add complexity-based auto routing strategy (#21789)
* feat(router): Add complexity-based auto routing strategy
Adds a rule-based routing strategy that classifies requests by complexity
and routes them to appropriate models - with zero API calls and sub-millisecond
latency.
## Features
- **Zero external API calls** - all scoring is local
- **Sub-millisecond latency** - typically <1ms per classification
- **Weighted multi-dimensional scoring** across 7 dimensions:
- Token count (short=simple, long=complex)
- Code presence (code keywords → complex)
- Reasoning markers ("step by step" → reasoning tier)
- Technical terms (domain complexity)
- Simple indicators ("what is" → simple, negative weight)
- Multi-step patterns (numbered steps)
- Question complexity (multiple questions)
- **Configurable tier boundaries** and model mappings
- **Reasoning override** - 2+ reasoning markers force REASONING tier
## Usage
```yaml
model_list:
- model_name: smart-router
litellm_params:
model: auto_router/complexity_router
complexity_router_config:
tiers:
SIMPLE: gpt-4o-mini
MEDIUM: gpt-4o
COMPLEX: claude-sonnet-4
REASONING: o1-preview
```
Inspired by ClawRouter: https://github.com/BlockRunAI/ClawRouter
## Files Added
- litellm/router_strategy/complexity_router/complexity_router.py - Main router class
- litellm/router_strategy/complexity_router/config.py - Configuration and defaults
- litellm/router_strategy/complexity_router/__init__.py - Package exports
- litellm/router_strategy/complexity_router/README.md - Documentation
- tests/test_litellm/router_strategy/test_complexity_router.py - Test suite (37 tests)
## Files Modified
- litellm/router.py - Integration with pre_routing_hook
- litellm/types/router.py - New config params
* feat(router): Add complexity-based auto routing strategy
Adds a new rule-based routing strategy that classifies requests by complexity
and routes them to appropriate models - without any external API calls.
## Features
- Weighted scoring across 7 dimensions: token count, code presence, reasoning
markers, technical terms, simple indicators, multi-step patterns, questions
- Maps to 4 tiers: SIMPLE, MEDIUM, COMPLEX, REASONING
- Each tier configurable to a different model
- Zero API calls, <1ms latency
- Inspired by ClawRouter
## Configuration
```yaml
model_list:
- model_name: smart_router
litellm_params:
model: auto_router/complexity_router
complexity_router_config:
tiers:
SIMPLE: gemini-2.0-flash
MEDIUM: gpt-4o-mini
COMPLEX: claude-sonnet-4
REASONING: claude-opus-4
```
## Use Cases
- Cost optimization: route simple queries to cheaper models
- Quality optimization: route complex queries to capable models
- Zero configuration: works out of the box with sensible defaults
* feat(router): Add complexity-based auto routing strategy
Adds a new rule-based routing strategy that classifies requests by complexity
and routes them to appropriate models - without any external API calls.
- Weighted scoring across 7 dimensions: token count, code presence, reasoning
markers, technical terms, simple indicators, multi-step patterns, questions
- Maps to 4 tiers: SIMPLE, MEDIUM, COMPLEX, REASONING
- Each tier configurable to a different model
- Zero API calls, <1ms latency
- Inspired by ClawRouter
```yaml
model_list:
- model_name: smart_router
litellm_params:
model: auto_router/complexity_router
complexity_router_config:
tiers:
SIMPLE: gemini-2.0-flash
MEDIUM: gpt-4o-mini
COMPLEX: claude-sonnet-4
REASONING: claude-opus-4
```
- Cost optimization: route simple queries to cheaper models
- Quality optimization: route complex queries to capable models
- Zero configuration: works out of the box with sensible defaults
* feat: add enterprise presets for complexity router
Adds preset configurations for different cloud providers:
- bedrock: AWS Bedrock (Claude models)
- vertex: Google Vertex AI (Gemini models)
- azure: Azure OpenAI (GPT + o1)
- standard: Direct API (OpenAI + Anthropic)
- cost_optimized: Maximum savings (Gemini Flash + cheaper models)
Usage:
```yaml
complexity_router_config:
preset: bedrock # or vertex, azure, standard, cost_optimized
```
* feat(ui): update auto router submit handler for complexity router
- Handle complexity_router model type in submit handler
- Generate correct litellm_params for complexity router:
- model: auto_router/complexity_router
- complexity_router_config: { tiers: { SIMPLE, MEDIUM, COMPLEX, REASONING } }
- Keep existing semantic router handling intact
- Add success notification with router type name
* docs: update PR description with UI changes
* chore: remove preset feature, keep simple tier config
* fix: exclude complexity_router from auto_router check
The _is_auto_router_deployment() was matching all auto_router/* models,
causing complexity_router to fail initialization. Now it explicitly
excludes auto_router/complexity_router which has its own handler.
* fix(complexity_router): Address Greptile review feedback
Fixes 5 issues flagged in code review:
1. **Mutable singleton mutation bug** - Now always creates a new
ComplexityRouterConfig instance instead of reusing DEFAULT_COMPLEXITY_CONFIG
singleton, preventing cross-instance config pollution.
2. **Substring matching false positives** - Added word boundaries (spaces)
to short keywords like 'ok', 'try', 'api', 'git', 'node', 'java', 'vue'
to prevent matching within longer words (e.g., 'capital' matching 'api').
3. **Redundant message extraction** - Simplified to single reverse loop that
extracts both last user message and last system prompt efficiently.
4. **Unused imports** - Removed unused DEFAULT_CREATIVE_KEYWORDS and
DEFAULT_MULTI_STEP_PATTERNS imports.
5. **Missing async_pre_routing_hook tests** - Added comprehensive tests for:
- Multi-turn conversations
- List-type content handling
- No user message case
- Empty string content
- Message preservation
- Singleton mutation prevention
* fix(complexity_router): Address Greptile review feedback
- Use word boundary matching for short keywords (<5 chars) to avoid
false positives (e.g., 'api' matching 'capital', 'git' matching 'digital')
- Remove 'ok' from simple keywords (too many false positives)
- Add tests for keyword false positive prevention
- Fix test expectations for edge cases (empty string content, list content)
Addresses: 2/5 Greptile score feedback on PR #21789
* docs(auto_routing): Add complexity router documentation
- Add Complexity Router section to auto_routing.md
- Include comparison table with semantic auto router
- Add Python SDK and Proxy Server configuration examples
- Document all configuration options (tier boundaries, token thresholds, dimension weights)
- Explain how complexity scoring works
* feat(complexity_router): Add eval suite + tune scoring parameters
Added comprehensive evaluation suite with 29 test cases covering:
- SIMPLE tier: greetings, definitions, factual questions
- MEDIUM tier: technical explanations, comparisons, debugging
- COMPLEX tier: architecture design, complex coding
- REASONING tier: explicit reasoning requests
- Regression tests: substring false positive prevention
Tuned scoring parameters based on eval results:
- Lowered tier boundaries (0.15/0.35/0.60) for better tier distribution
- Increased code/technical weights (0.30/0.25) for complex prompts
- Reduced simple indicator weight (0.05) to avoid over-penalizing
- Fixed 'hey'/'hi' keywords to require leading space
Eval results: 29/29 passed (100%)
* fix(complexity_router): Address Greptile review round 2
1. **Empty user message handling** - Changed from falsy check to None check
to properly distinguish 'no user message' from 'empty string message'
2. **ReDoS prevention** - Changed 'first.*then' to 'first.*?then' (non-greedy)
to prevent regex backtracking on pathological inputs
3. **Documentation sync** - Updated README.md to match actual config values:
- Tier boundaries: 0.15/0.35/0.60 (not 0.25/0.50/0.75)
- Dimension weights: tokenCount=0.10, codePresence=0.30, technicalTerms=0.25,
simpleIndicators=0.05, multiStepPatterns=0.03, questionComplexity=0.02
4. **Missing UI component** - Added ComplexityRouterConfig.tsx with:
- Tier-to-model dropdown selectors
- Descriptions and examples for each tier
- How classification works explanation
5. **Inline import comment** - Added explanation for why ComplexityRouter
import is inline (matches AutoRouter pattern, avoids circular imports)
* docs(auto_routing): fix dimension weights and tier boundaries to match config.py defaults
* fix(complexity_router): skip empty string content in async_pre_routing_hook
* fix(router): remove or {} masking None complexity_router_config
* fix(config): remove unused DEFAULT_MULTI_STEP_PATTERNS and DEFAULT_CREATIVE_KEYWORDS exports
* fix(complexity_router): use word boundary matching for all single-word keywords, avoid double-scanning reasoning keywords
* fix(router): clarify circular import comment for ComplexityRouter
* docs(README): fix token thresholds to match config.py defaults
* test(complexity_router): add false positive tests for error/class/merge keyword matching
* fix(complexity_router): align .get() fallbacks with config.py defaults, document system prompt scoring
* fix(config): deduplicate keywords across code and technical lists
---------
Co-authored-by: OpenClaw Assistant <assistant@openclaw.ai>
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
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