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106 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
yucheng-berri
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76b0b10908
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fix(guardrails): add /v1/messages support for Straiker plugin (#34548)
* fix(guardrails): add /v1/messages support for Straiker plugin - Pass prepared response data to Anthropic Messages streaming post-call hooks (litellm/llms/anthropic/chat/guardrail_translation/handler.py) - Normalize Straiker request, tool, finish-reason, and mode fields across Chat Completions, Messages, and Responses APIs * fix(guardrails): gate cross-surface message resolution and cover streaming request data Resolve request messages only for surfaces that have a mapped translation handler. The unguarded fallback tried every registered handler in turn, which raised AttributeError out of the guardrail's error handling on list-shaped `input` bodies, and synthesized a chat message that was never sent for bodies it happened to parse. Prepare request data on the mid-stream Anthropic branch as well, matching the terminal branch and the OpenAI handler, so guardrails that scan before end-of-stream still receive identity metadata. Read usage from Anthropic dict responses so non-streaming /v1/messages reports token counts instead of null. Add regression coverage for the streaming request data on both the terminal and mid-stream branches; reverting either now fails. --------- Co-authored-by: cs-mehta <chandra@straiker.ai> |
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devin-ai-integration[bot]
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fa6b209165
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feat(guardrails): add only_scan_new_messages for per-session incremental scanning (#33278)
* feat(guardrails): add only_scan_new_messages for per-session incremental scanning Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * fix(guardrails): use fixed TTL constant and revert unrelated test formatting Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * fix(guardrails): run only_scan_new_messages in the unified apply_guardrail path The initial wiring lived in BedrockGuardrail.async_pre_call_hook, but the proxy routes Bedrock through the unified apply_guardrail interface, so the flag had no effect live. Move incremental selection into apply_guardrail: filter the flat texts list against per-session scanned hashes, skip the Bedrock call when nothing is new, and mark hashes only after a successful (non-blocked) scan. Full-context fallback is preserved when there is no session id, the cache is unavailable, or a masking guardrail is configured. Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * test(guardrails): cover session-id fallbacks and mark_texts_scanned guards Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * fix(guardrails): fall back to full scan when incremental guardrail masks content, use shared cache Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * test(guardrails): cover generic agent multi-turn incremental scan Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * test(guardrails): cover incremental scan cache resolver fallbacks Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> * test(guardrails): cover flag interactions and /v1/messages incremental scan semantics * feat(guardrails): make GUARDRAIL_SCANNED_MESSAGES_CACHE_TTL_SECONDS env configurable * test(guardrails): prove skip_system/skip_tool are enforced upstream of incremental scan --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com> Co-authored-by: Yucheng Zhu <yucheng@berri.ai> |
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yucheng-berri
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587b8aca9b
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feat(guardrails): add Compresr guardrail for query-aware context compression (#33295)
* feat(guardrails): add Compresr guardrail for query-aware context compression Adds a first-class guardrail that compresses bulky message content (tool outputs, RAG chunks, search results) through the Compresr API before the request reaches the LLM, via the apply_guardrail / structured_messages hook so it covers /chat/completions, /v1/messages, and /v1/responses (the latter through the texts channel, mirrored only when the replacement is unambiguous; anything ambiguous is left uncompressed). Distinct from whole-conversation compressors: - Query-aware: each message is compressed against the intent that produced it (a tool output against its originating tool call's name + arguments, resolved via tool_call_id; otherwise the last user message). - Recoverable: each compressed message carries a hash marker and the request gains a compresr_retrieve tool, so the model can pull the original content back through the agentic loop when the compressed version is not enough. Originals are cached in-process, scoped to the caller's virtual-key hash plus the request's litellm_call_id, with a TTL and a per-call byte cap; recovery is skipped when no caller scope is available so one caller can never read another's originals. The store is per-process, so multi-worker deployments need sticky routing (or enable_retrieval=false). Fail-closed by default (fail_open configurable), SSRF-validated api_base (alternate IP-literal encodings included), cross-tenant-isolated recovery store, and upstream errors redacted from client-facing responses. The outbound client follows redirects and re-resolves DNS per request, so the api_base host/IP checks are defense-in-depth, not a full SSRF guarantee; this is documented as a known limitation. Requests where nothing was actually compressed are returned untouched (same object identity) so handlers skip the write-back. Auto-discovered via the guardrail_hooks registry. * fix(guardrails): cap Compresr recovery store total memory The recovery store bounded bytes per call and entry count, but had no aggregate cap: 256 tracked call ids at the 10 MiB per-call default could retain ~2.5 GiB per worker. A flood of requests with distinct x-litellm-call-id values and large compressible tool outputs could exhaust a shared proxy worker. Add a global byte budget (_MAX_TOTAL_STORE_BYTES, 256 MiB) across all entries. A running total is maintained on every insert/eviction so the cap is enforced without re-encoding the whole store on the request path; oldest entries are evicted once the budget is exceeded, always keeping the most-recent entry so recovery still works for the request populating the store. +2 regression tests. * fix(guardrails): gate and bound Compresr recovery loop Two hardening fixes to the compresr_retrieve agentic loop: 1. Only run the loop when a retrieve call resolves to recovery state this guardrail actually created for the request. Previously the gate checked only that the caller-supplied tool list contained a compresr_retrieve function and that the model emitted a call, so a caller could define their own same-named tool and force an extra provider round-trip with nothing to recover. The plan now returns run_agentic_loop=False when no requested hash resolves. 2. Bound the follow-up against retrieval amplification: each distinct hash is expanded at most once (repeats get a short marker) and at most _MAX_RETRIEVALS_PER_LOOP calls are honored, so prompting the model to call compresr_retrieve many times with the same marker cannot balloon the follow-up. _retrieve_original now returns None on miss. +3 regression tests; two existing security tests updated to assert the stronger veto behavior (forged/cross-tenant hashes now stop the loop entirely instead of returning a not-found follow-up). * fix(guardrails): warn when Compresr recovery is skipped without auth scope When enable_retrieval is on (the default) but the proxy has no per-key auth, the request has no caller scope, so recovery is silently disabled: content is compressed but the compresr_retrieve tool is never injected and the originals are dropped, with no runtime indication. Emit a one-shot call-time warning so operators can see recovery is being suppressed and configure virtual-key auth. +1 regression test. * style(guardrails): tighten Compresr guardrail comments Condense the verbose multi-line inline comments and the api_base docstring to concise form. No behavior change. * fix(guardrails): keep injected tool on Responses API + bound recovery markers by byte cap Two fixes for reviewer-flagged defects in the Compresr guardrail: - Responses API: _merge_tools_after_guardrail iterated only over the request's original tools, dropping any tool a guardrail appended (the compresr_retrieve recovery tool) whenever the request already had tools. Keep the appended tools so recovery works on /v1/responses. - Recovery markers: markers + originals were built for every compressed target before the per-call byte cap trimmed the store, so an evicted original left a marker the model could never retrieve. Attach recovery only while the store (existing entries under the same key + this call's originals) stays within the cap, so a shipped marker is always retrievable -- including on a later turn that reuses the store key. Adds regression tests for both paths. * refactor(guardrails): extract _existing_originals to keep apply_guardrail under the complexity gate The byte-cap fix added a branch to apply_guardrail, tipping it past the C901 complexity ceiling. Move the store lookup into a small helper; no behavior change. * fix(guardrails): harden Compresr SSRF blocklist, re-arm no-scope warning, tolerate odd tool shapes * fix(guardrails): rerun input guardrails on Compresr retrieval follow-up * chore: remove unrelated deepkeep files committed by mistake --------- Co-authored-by: charafkamel <charafkamel@live.com> |
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Krrish Dholakia
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477ef3a7e2
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fix(anthropic): use native output capability (#33235)
* fix(anthropic): route native structured output Use model capability metadata so new native structured-output models do not require transformation allowlist changes. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(anthropic): pass provider to capability Co-authored-by: Cursor <cursoragent@cursor.com> * test(anthropic): cover dotted model IDs Co-authored-by: Cursor <cursoragent@cursor.com> * fix(anthropic): handle remote capability lag Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: Cursor <cursoragent@cursor.com> |
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Abhimanyu Kapur
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831dbbc4df |
fix(anthropic): translate raw adaptive thinking for chat completions on pre-4.6 models
Clients that pass thinking={"type": "adaptive"} directly (not via the
reasoning_effort alias) on the /chat/completions interface had it forwarded
unmodified to pre-4.6 Anthropic models, which reject the shape. Mirrors the
translation already applied on the native /v1/messages passthrough (#32867):
translate to legacy thinking={type: enabled, budget_tokens}, capped below
max_tokens, dropping thinking when max_tokens can't fit even the minimum
budget. Hoists the shared budget-capping helper onto AnthropicConfig so both
paths use one implementation.
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mateo-berri
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41b599b2d8 | fix(anthropic): thread real provider through capability probes instead of pinning anthropic | ||
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Mateo Wang
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b76a858826
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feat: declarative fallback generalizations for unknown models (#29718)
* feat: declarative fallback generalizations for unknown models Unknown or newly-released models previously degraded (missed cost lookups, wrong supports_* flags, broken provider routing) and were patched with one-off hardcoded regexes scattered across Python. This adds a single data-driven source of truth: a fallback_generalizations block in model_prices_and_context_window.json holding ordered, case-insensitive regex rules that map a model name to the metadata to apply when it has no exact entry. A new fallback_generalizations module owns the rules and a compiled-regex cache that is built once and invalidated on reload, so the O(n) scan runs only on a cache miss. get_llm_provider now routes an otherwise-unknown model via the first matching rule's litellm_provider, replacing the hardcoded _CLAUDE_PATTERN and _matches_claude_model_pattern. _get_model_info_helper falls back to a matching rule's model_info after the exact lookups miss, so get_model_info and the supports_* helpers resolve unknown models from the same rule. get_model_cost_map extracts the block out of the returned map, and the integrity check now counts real model entries (excluding reserved meta keys) so the new key cannot mask a genuinely shrunk upstream file. The top level of the file stays a flat map of models so existing litellm releases that fetch the live file keep working and keep receiving updates; the block ships in both the root file and the bundled backup. An anthropic-claude rule reproduces the old future-claude routing and additionally supplies capability flags and a context window https://claude.ai/code/session_01G8Jro8dPLktwnaaSJwVDpo * refactor(anthropic): derive adaptive-thinking from a version threshold; harden generalizations Replace the per-minor-version _is_claude_4_6_model / _is_claude_4_7_model substring matchers with a single _claude_version_at_least predicate that parses the Claude family version from the model name and compares against 4.6. This covers 4.8/4.9/5.x without a code change (the old matchers missed 4.8 entirely) while keeping an explicit supports_adaptive_thinking flag authoritative when present, so there is one source of truth. The two direct call sites in the chat transformation now route through _is_adaptive_thinking_model instead of the deleted matchers. Also address review feedback on the generalizations module: return a copy of the matched model_info so a future caller cannot mutate the compiled-rule cache, document that patterns are matched with re.search and must anchor with ^ and $, and reindent the fallback_generalizations block to the file's 2-space style in both JSON files. https://claude.ai/code/session_01G8Jro8dPLktwnaaSJwVDpo * fix(anthropic): surface adaptive-thinking from the cost map; fix date misparse supports_adaptive_thinking shipped in the model cost map but was never declared on ModelInfo nor copied during construction, so get_model_info (and the supports_* factory) silently dropped it for every provider-prefixed or generalized name; only a bare base entry resolved. Wire it through ModelInfo like the other capability flags and backfill the flag onto the genuine Claude 4.6/4.7/4.8 entries across providers so the data, not code, declares the capability. The anthropic-claude fallback rule also carries the flag (and now accepts a dotted minor, e.g. 4.6) so an unmapped future Claude degrades to adaptive thinking without a code change. Tighten the Claude version parser so an eight-digit date suffix (claude-opus-4-20250514, the non-adaptive Opus 4.0) is no longer read as minor 4.20250514. The cost map stays authoritative; the version check is only a fallback for provider-prefixed names (bedrock/invoke routes, -v1-less ids) that resolve to no mapped entry and so cannot be reached by an exact lookup or the bare-name rule. https://claude.ai/code/session_01G8Jro8dPLktwnaaSJwVDpo * fix(anthropic): date-safe adaptive-thinking version fallback, conservative fallback pricing, ruff strict gate Reconcile adaptive-thinking detection after merging litellm_internal_staging. Keep the cost-map resolver (_supports_model_capability) as the source of truth and add a date-safe opus/sonnet/haiku >= 4.6 name version as a fallback for provider-prefixed ids the cost map cannot resolve (e.g. bedrock/invoke/us.anthropic.claude-opus-4-6). A two-digit cap on the minor keeps an eight-digit date suffix from being misread as a minor version, so the dated Claude 4.0 release stays non-adaptive Price the shipped anthropic-claude fallback rule at the Opus tier so an unknown or newly released Claude is over-costed rather than billed as free Drop the module-level global state in fallback_generalizations (PLW0603) in favor of a small registry object, and switch its annotations plus the new utils helper to builtin generics (UP006), bringing the ruff strict-rule totals back under ceiling * refactor(anthropic): drive adaptive-thinking version gate from a declarative rule Replace the bespoke _claude_version_at_least heuristic with a version-gated fallback_generalizations rule. Unmapped Claude ids now resolve adaptive thinking purely from the cost map: an explicit entry, or the new self-contained anthropic-claude-adaptive-thinking rule that matches opus/sonnet/haiku >= 4.6 (covering 5.x, 6.x and beyond with no code change). New families ship via Price Data Reload instead of a code edit The rule carries the same Opus-tier pricing as the broad anthropic-claude rule plus supports_adaptive_thinking, and is matched first; the broad rule stays version-neutral, so an unmapped >= 4.6 Claude resolves to full pricing and the adaptive flag from one rule, while a sub-4.6 alias such as claude-opus-4-0 is still priced yet stays non-adaptive. The regex caps the minor at two digits so a dated 4.0 id (...-4-20250514) is never read as a >= 4.6 minor * refactor(anthropic): dedupe adaptive-thinking rule via declarative extends The version-gated anthropic-claude-adaptive-thinking rule duplicated the broad anthropic-claude rule's entire Opus-tier price block because rules do not merge: first match wins and returns one rule's whole model_info, so the adaptive rule had to be self-contained. Add a declarative extends field to fallback_generalizations: a rule names a parent and inherits its model_info, with its own keys overriding. Inheritance is resolved once at install time against each rule's raw model_info, so the adaptive rule now carries only its delta (supports_adaptive_thinking) and inherits pricing from the broad rule. Runtime matching, provider routing and gating are unchanged; the broad rule stays anchored and first-match-wins still holds. * docs(anthropic): add ignored description key documenting each generalization regex * fix(anthropic): drop fabricated pricing from the anthropic-claude fallback rule Per review feedback, the base rule no longer carries input/output/cache costs, and the adaptive-thinking rule that extends it inherits that no-pricing model_info. Pricing an unmapped model at a guessed tier reports a confidently-wrong cost without the caller knowing; dropping it keeps the standard unpriced behavior (zero, not a fabricated number) so a missing price stays visible. The rules still supply provider routing, context window, and capability flags, so a brand-new Claude can still be called and its capabilities (including adaptive thinking for >= 4.6) resolved. Description and tests updated to match |
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Mateo Wang
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64d8d7f8cb
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fix(bedrock): normalize Messages system role and adaptive-thinking for Claude Invoke (#31364)
* fix(bedrock): normalize Messages system role and adaptive-thinking for Claude Invoke
* style(bedrock): use builtin generics in new Invoke helpers to clear UP006 gate
* fix(bedrock): honor explicit thinking budget_tokens=0 in clear_thinking conversion
The clear_thinking_20251015 -> adaptive conversion resolved the thinking
budget with `thinking.get("budget_tokens") or BEDROCK_MIN_THINKING_BUDGET_TOKENS`,
which treats a caller-supplied `budget_tokens=0` as missing and silently
substitutes the Bedrock minimum. Resolve the budget with an explicit
`is not None` check so an explicit 0 is honored.
* fix(bedrock): gate Fable 5 into clear_thinking adaptive injection on Invoke
_ensure_thinking_for_clear_thinking_context_management returns early when
_supports_extended_thinking_on_bedrock(model) is False, so the adaptive-thinking
injection never runs for models absent from that gate. Opus 4.8 slips through on
the incidental "opus-4" substring, but Fable 5 had no matching pattern, so a
clear_thinking_20251015 request on Fable 5 reached Bedrock with an unsupported
context-management edit and no thinking field; the exact 400 this path exists to
prevent. Add the fable-5 patterns to the gate so Fable 5 (mapped ids and unmapped
aliases) gets thinking.type=adaptive + output_config.effort like the other
adaptive models.
Extend the adaptive-injection regression test to cover Fable 5 (a mapped id and
an unmapped alias) so it fails without the gate entry, and add focused coverage
for the budget->effort tiers, the disabled/enabled/adaptive thinking branches,
output_config.effort preservation, and list/dict system-role normalization.
Also normalize the Invoke transformation module and its test to line-length 88
so ruff format --check (CI format-check) passes.
* refactor(anthropic): make supports_adaptive_thinking flag authoritative for thinking detection
Replace the per-version name helpers (_is_claude_4_6/4_7/4_8_model,
_is_claude_fable_5_model) with cost-map-flag-first detection. _is_adaptive_thinking_model
now reads supports_adaptive_thinking from the model cost map and falls back to a single
generalized family-version regex (_claude_version_at_least(model, 4, 6)) only when a model
is unmapped, instead of hard-coding each new Claude release.
Wire supports_adaptive_thinking through ProviderSpecificModelInfo and ModelInfo so the cost
map flag actually surfaces at lookup time. Reroute the Bedrock Invoke extended-thinking gate
and the two anthropic/chat/transformation.py call sites through _is_adaptive_thinking_model.
Known gap left to the fallback_generalizations work (#29718): unmapped Fable 5 aliases have
no parseable minor version, so they defer to the cost map and are not detected until a mapped
entry or a generalization rule exists. Covered by an explicit regression test.
* refactor(anthropic): drop name-based version fallback; resolve adaptive thinking from cost map only
The prior commit kept a regex (_claude_version_at_least) as a fallback when an id
resolved to no cost-map entry. Remove it: _is_adaptive_thinking_model now reads
supports_adaptive_thinking and nothing else, so "which Claude versions think
adaptively" lives entirely in the model cost map, and a new adaptive release is a
JSON edit rather than a Python edit.
To keep the flag authoritative across the id forms the Bedrock Invoke and anthropic
paths actually see, backfill supports_adaptive_thinking=true on every adaptive Claude
entry that was missing it (Opus 4.6/4.7 and Sonnet 4.6 across region/provider aliases)
in both the root and bundled cost maps, and generalize _model_map_lookup_candidates to
normalize an id to its base cost-map key: strip a Bedrock version suffix (-v1:0 fully,
or just the :0 inference-profile minor so the -v1-keyed 4.6 entries resolve), strip a
dated-release suffix (-20260219), and rewrite a dotted family version (4.6 -> 4-6).
This is id normalization feeding the lookup, not capability-by-name.
Tests load the PR-local cost map (the flags are not on main until merge) and cover each
normalization path plus the unmapped-alias deferral to fallback_generalizations (#29718).
* refactor(reasoning_effort): single-source effort<->thinking-budget mappings
Route every reasoning_effort <-> thinking-budget conversion through the DEFAULT_REASONING_EFFORT_*_THINKING_BUDGET constants so the numbers stay in sync across providers. The five constants are now 2000/5000/10000/20000/40000
Add reasoning_effort_from_thinking_budget() in litellm_core_utils/reasoning_effort_utils.py and route the three OpenAI-style forward maps (anthropic adapters, responses adapters, hosted_vllm) through it. The bedrock invoke and experimental messages adaptive maps now reference the constants directly; the only behavior change is the xhigh threshold moving from 24000 to 20000. Reverse maps and the cross-provider test grid read the same constants
* test(reasoning_effort): lift budget-mode max_tokens above the new high budget
The single-sourced DEFAULT_REASONING_EFFORT_*_THINKING_BUDGET thresholds moved
high from 4096 to 10000. The live reasoning_effort grid sends budget-mode
requests with max_tokens=8192, so reasoning_effort=high now produces
budget_tokens=10000 > max_tokens and every provider returns 'max_tokens must be
greater than thinking.budget_tokens'. Derive a shared BUDGET_MODE_MAX_TOKENS
(2x the high budget) for the spec and the request builder so the ceiling always
clears the largest 200-expected tier. Also resolve the inherited base
test_reasoning_effort assertion off the same high-budget constant instead of the
stale 4096 literal so it tracks the source of truth.
* fix(reasoning_effort): keep effort<->budget thresholds at pre-PR values
The single-sourcing refactor moved the shared effort<->budget thresholds up
(low 1024->2000, medium 2048->5000, high 4096->10000, xhigh 8192->20000,
max 16384->40000). That silently changes the effort->budget direction: a caller
who sets reasoning_effort together with a max_tokens that used to sit above the
old per-tier budget but below the new one now trips the provider's
"max_tokens must be greater than thinking.budget_tokens" 400. It spans every
backend that derives a budget from an effort (Anthropic, Gemini/Vertex,
hosted vLLM), not just Bedrock.
Restore the constants to their pre-PR values while keeping every backend reading
from the shared DEFAULT_REASONING_EFFORT_*_THINKING_BUDGET constants, so the
mapping stays single-sourced without the behavior change. Tests that pinned the
raised thresholds now derive their boundaries from the same constants.
* test(reasoning_effort): derive high effort->budget assertions from the shared constant
The cross-provider translation tests pinned reasoning_effort="high" to a literal
budget_tokens=10000, the raised value. Point them at
DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET so they track the single source
instead of a magic number.
* fix(anthropic): resolve adaptive flag for combined dated+versioned Bedrock ids
The model-map candidate normalization applied each suffix strip independently to
the original id, so the real Bedrock shape "<base>-<YYYYMMDD>-v1:0" never reduced
to its base cost-map key: stripping the version left the date, and the
dated-suffix regex is anchored to the end so it could not fire while the version
was still present. An adaptive Claude model invoked by its full dated+versioned
id (e.g. us.anthropic.claude-sonnet-4-6-20251101-v1:0) therefore resolved to
supports_adaptive_thinking=null and was treated as non-adaptive, reaching Bedrock
with the rejected thinking.type=enabled shape, the exact 400 this path prevents.
Add a composed normalization that rewrites the dotted family version, then peels
the -vN:rev version suffix, then the -YYYYMMDD dated suffix, so the combined form
resolves to its base key. Regression tests pin the combined suffix on sonnet-4-6
and opus-4-8 across provider/region prefixes.
* fix(reasoning_effort): align budget<->effort tests with reverted constants and format common_utils
The constant revert restored the effort<->budget thresholds to their pre-PR
values (1024/2048/4096/8192/16384) and single-sourced the reverse
budget->effort ladder through reasoning_effort_from_thinking_budget, but
several tests still pinned the briefly-raised values and the old hardcoded
reverse buckets, so the "All Other Providers" shard failed
Derive the anthropic chat effort->budget assertions from the shared
DEFAULT_REASONING_EFFORT_*_THINKING_BUDGET constants, and update the
experimental pass-through and responses adapter expectations to the
single-sourced reverse ladder (budget 1024 -> low, 5000 -> high)
Also run ruff format --line-length 88 over anthropic/common_utils.py so the
CI format-check, which checks the whole changed file, passes
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Sameer Kankute
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133da06aa3
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chore: litellm oss staging (#31185)
* fix(ui): widen Y-axis gutter on Usage charts so large token/request labels aren't clipped
The Total Tokens Over Time and Total Requests Over Time AreaCharts on the
Usage page used Tremor's default yAxisWidth (~56 px), which is too narrow
once totals pass the hundred-million mark — leading digits of labels like
"100.00M" / "4500.00M" got clipped against the chart edge. The requests
chart was worse: it formatted with toLocaleString(), so billion-scale
request counts produced "1,000,000,000" (13 chars) and overflowed
immediately.
Fix in two places so neither alone has to carry the whole margin:
- activity_metrics.tsx: add yAxisWidth={80} to both AreaCharts, and
switch the requests chart to the shared valueFormatter so it uses the
same compact k/M/B suffixes as the tokens chart.
- value_formatters.tsx: add a >= 1e9 branch to valueFormatter /
valueFormatterSpend that emits a "B" suffix (4.50B, $4.50B), keeping
every formatted label at most 7 chars.
Co-Authored-By: Claude Opus 4 (1M context) <noreply@anthropic.com>
* Update ui/litellm-dashboard/src/components/UsagePage/utils/value_formatters.tsx
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* docs(readme): add Deploy on AWS/GCP with Terraform section
Adds a quickstart for the two published Terraform modules on the public
registry (BerriAI/litellm/aws and BerriAI/litellm/google). Copy-paste
main.tf for each cloud, the one-time GCP Artifact Registry remote-repo
command, and pointers to the registry pages for the full input surface.
Sits inside the Get Started section, between the gateway/SDK table and
Run in Developer Mode -- where someone scanning the README for "how do I
deploy this" will land.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* docs(readme): add 1-click deploy buttons for AWS + GCP
GCP gets the real 1-click: Open in Cloud Shell badge that clones the repo
and walks through `terraform apply` via the existing DeployStack
tutorial (already shipped at terraform/litellm/gcp/examples/default/
TUTORIAL.md). User just picks a project.
AWS gets a soft 1-click: a Launch in AWS CloudShell badge that opens an
in-browser, already-authenticated shell. User runs four commands
(clone + cd + cp tfvars + terraform apply) once inside. There's no
native AWS deeplink that pre-clones a repo + runs a tutorial -- CFN
"Launch Stack" + CodeBuild would be needed for that, and that's a
separate piece of work.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
* docs(readme): move AWS + GCP deploy buttons next to Render button
* docs(readme): unify deploy button sizes and badge styles
* docs(readme): bump deploy button height to 48 to match Render/Railway
* docs(readme): bump AWS/GCP badge height to compensate for SVG padding
* docs(readme): bump AWS/GCP badge height to 72
* docs(readme): bump AWS/GCP badge height to 84
* fix(readme): make deploy buttons same height (48px)
https://claude.ai/code/session_01MxQRMHSDXbqJh74rF86UBc
* docs(readme): flag GCP project ID substitution in image_registry
* docs(readme): equalize deploy button heights and fix Cloud Shell button font
GitHub rewrites an image's height attribute to "height: auto; max-height: Npx", which only caps and never stretches, so each image renders at its intrinsic height. The AWS/GCP shields badges are intrinsically 28px while the Render/Railway buttons are 40px, leaving the row uneven regardless of the height="48" we set. Replace the two shields badges with committed 40px PNGs so all four header buttons render at the same 40px.
Also swap the Cloud Shell button from open-btn.svg to open-btn.png. The SVG renders its label as live text with font-family "Roboto, Sans" and no generic fallback; since neither font exists in GitHub's render environment, the text fell back to a serif (Times New Roman). The PNG bakes in the correct typeface.
* docs(readme): collapse Railway deploy anchor to a single line
The Railway button wrapped its img across indented lines, so the anchor contained leading and trailing whitespace. GitHub underlines link content, rendering that whitespace as a small blue underline beside the button. Put the anchor on one line like the other three buttons so there is no inner whitespace to underline.
* Add Claude Fable 5 cost map entries as a data-only hotfix
Backports only the model map changes from #30064 so deployments on
released litellm versions pick up Fable 5 pricing, context window, and
the adaptive thinking flag through the hosted cost map fetch without
upgrading. Includes the supports_sampling_params flag on the 28
Fable 5 / Opus 4.7 / Opus 4.8 entries (ignored by released code, read
by the gating that ships with the next release) and the matching
one-line schema declaration so the map validation test passes.
https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm
* fix: correct context window tokens for GPT-5 Pro and GPT-5.4 Mini/Nano
Three bugs in model_prices_and_context_window.json:
1. gpt-5-pro and gpt-5-pro-2025-10-06: max_input_tokens and max_tokens
were SWAPPED. GPT-5 Pro has a 400K context window (input) with 128K
max output, but the values were set as max_input=128000,
max_tokens=272000. This caused token limit errors when sending
prompts over 128K tokens to GPT-5 Pro.
2. gpt-5.4-mini and gpt-5.4-mini-2026-03-17: max_input_tokens was
272000, but GPT-5.4 Mini shares the same 1,050,000 token context
window as GPT-5.4. This was inconsistent with the azure/ variants
which already correctly had 1,050,000.
3. gpt-5.4-nano and gpt-5.4-nano-2026-03-17: same issue as Mini,
max_input_tokens was 272000 instead of 1,050,000.
Source: OpenAI model documentation and contextwindows.dev which
aggregates official context window sizes.
Fixes #30928 (partially — the issue incorrectly claims gpt-5/gpt-5-mini
should be 400K; their 272K values are correct per OpenAI docs)
* fix: also correct max_output_tokens for gpt-5-pro (272000→128000)
Per reviewer feedback, max_output_tokens was left at 272000 while
max_tokens was corrected to 128000, causing an internal inconsistency.
Both should be 128000 per OpenAI docs.
* fix(cost): price gpt-image generated output tokens as image tokens (#31147)
The OpenAI Images endpoints (/v1/images/generations, /v1/images/edits) return
usage with no output token breakdown — litellm's `ImageUsage` has no
`output_tokens_details` field — so generated-image OUTPUT tokens were priced at
the text rate (`output_cost_per_token`) instead of the image rate
(`output_cost_per_image_token`). For gpt-image-2 that is $10/1M vs $30/1M, a ~3x
undercount on the dominant cost component (image output is ~74% of spend). This
also affects azure gpt-image, which shares this calculator.
The OpenAI gpt-image cost calculator re-implemented usage handling instead of
reusing `calculate_image_response_cost_from_usage`, the shared helper that
azure_ai/gemini/vertex_ai already use. That helper classifies generated output
tokens as image tokens when the provider does not itemize output, and splits
text/image when it does.
Fix: route the ImageUsage path through `calculate_image_response_cost_from_usage`
(pre-transformed chat Usage objects are still costed directly). Adds a regression
test for the no-breakdown ImageUsage case (gpt-image-2).
* fix(bedrock): route application-inference-profile ARNs to converse (#18258) (#31098)
A bare application-inference-profile ARN passed as bedrock/arn:... fell
through to the invoke route, which cannot derive a provider from the
opaque profile id and raised 'Unknown provider=None'. The converse route
needs no provider, so detect these ARNs in get_bedrock_route and route
them to converse, matching the behavior of the already-documented
bedrock/converse/arn:... workaround.
Explicit invoke/ prefixes still win, and they remain a dead end for these
ARNs by design (no provider derivable). System-defined inference-profile
ARNs that embed a known model, and other opaque ARN types
(provisioned-model, imported-model, custom-model-deployment) that are
frequently invoke-only, are deliberately left on their current routes;
tests guard both boundaries.
* fix(moonshot): stop mutating caller messages on tool_choice='required' (#31060)
_add_tool_choice_required_message appended the "select a tool" prompt to
the caller's messages list in place, so transform_request corrupted the
caller's conversation history and appended a duplicate prompt on every
retry. Build and return a new list instead so the call stays idempotent.
Adds a regression test asserting the input messages list is unchanged
across repeated transform_request calls.
Co-authored-by: Wassbdr <wassim.badraoui07@gmail.com>
* fix(transcription): accept fractional usage.seconds in diarized_json responses (#30996)
gpt-4o-transcribe and compatible ASR backends return a diarized_json
response with usage={"type": "duration", "seconds": <float>}, e.g. 295.8.
TranscriptionUsageDurationObject typed seconds as int, so parsing the
response raised a pydantic ValidationError (int_from_float). That error
surfaces as an APIConnectionError which the router treats as retryable, so
it keeps re-calling the upstream (200 every time) until the upstream
rate-limits and returns 429 to the caller.
OpenAI specs this field as a float (see openai SDK UsageDuration.seconds),
so widen seconds to float. With the parse succeeding there is no exception
left to retry, which removes the loop.
Co-authored-by: Neimar Avila <19142978+neimaravila@users.noreply.github.com>
* fix(deepseek): drop non-function tools before chat completions call (#30910)
* fix(deepseek): drop non-function tools before chat completions call
DeepSeek's /chat/completions only accepts tools of type "function".
Requests bridged from /v1/responses can carry responses-API-native tool
types, for example a Codex CLI tool typed "namespace", which DeepSeek
rejects with "unknown variant 'namespace', expected 'function'" so the
whole request fails (issue #30722).
Filter unsupported tool types in the DeepSeek request transform so the
function tools still go through; when nothing callable remains, also drop
the now-dangling tool_choice and parallel_tool_calls
Fixes #30722
* test(deepseek): cover async tool filtering and document tool_choice assumption
Add an async_transform_request regression test so the sync and async tool
filtering paths cannot silently diverge, and document in _drop_unsupported_tools
that only non-function tools are dropped, so a function-named tool_choice always
references a surviving tool
* feat(catalog): add zai/glm-5.1, zai/glm-4.7-flash, openrouter/z-ai/glm-5.1 (#29840)
* feat(ui): surface team budget on key overview when key has no own budget (#30801)
* feat(ui): surface team budget on key overview when key has no own budget
* fix(ui): replace IIFE with derived variable and use find() for team budget display
* fix(anthropic): emit replayable streaming thinking blocks (#31022)
* feat(proxy): read cold-storage prompts back in the logs detail view (#30364)
* feat(proxy): read cold-storage prompts back in the logs detail view
When a deployment offloads prompts and responses to cold storage instead of
Postgres, the spend-log row holds only "{}" placeholders plus a
metadata.cold_storage_object_key pointer, so the UI logs detail drawer showed
nothing. The detail endpoint only read the placeholder columns and never
fetched the object back.
Resolve the payload per row based on actual content, not a config flag: if
Postgres has content, return it; otherwise read the exact stored object key and
fetch from the configured cold storage backend through ColdStorageHandler.
Reading the persisted key is a single GET. The key embeds a microsecond
timestamp that cannot be reconstructed from the millisecond-precision startTime
column, and listing the day's prefix to match on request_id would be too
expensive for this per-open path.
Also teach the detail drawer's pretty-view parser to accept a bare messages
array. The cold storage payload carries the prompt as a top-level messages list
with no proxy_server_request, so without this the output rendered while the
input stayed blank.
ColdStorageHandler gains an optional injected logger so the resolver can be unit
tested without monkeypatching. Postgres-stored prompts are unaffected: the fast
path returns the existing columns and the request-body object still renders the
same way.
* Update litellm/proxy/spend_tracking/spend_management_endpoints.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* test(proxy): cover ColdStorageHandler resolution paths and cold-storage fetch failure
Add unit tests for ColdStorageHandler (injected logger, graceful None when no
logger is configured, and resolution of a configured logger from the callback
registry) and a regression test asserting a cold storage backend exception
degrades to the Postgres values instead of surfacing a 500.
---------
Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix(mavvrik): advance metricsMarker after upload; fix scheduler startup (#31068)
* fix(mavvrik): advance metricsMarker after upload + fix scheduler startup
Two bugs fixed:
1. deliver() never called PATCH /metrics/agent/ai/{connectionId} after a
successful GCS upload, so metricsMarker stayed at 0 and every daily run
re-exported the same dates in an infinite catch-up loop.
Fix: add _update_metrics_marker(date_epoch) called at the end of deliver()
after _upload_to_gcs() succeeds. A 4xx warns but does not raise (the GCS
file is already committed). A 410 raises consistent with the rest of the
destination.
2. init_mavvrik_focus_background_job runs at proxy startup before any LLM call
has triggered lazy instantiation of MavvrikFocusLogger, so it found no
logger instance and silently skipped registering the daily export job.
Fix: if no instance is found but "mavvrik" is in litellm.callbacks, call
_init_custom_logger_compatible_class to force instantiation before
the APScheduler job is registered.
* fix(mavvrik): catch up from earliest window when metricsMarker=0
When the connector is freshly registered, metricsMarker=0 parses to None.
The catch-up block was guarded by `if last_ingested and ...` which skipped
it entirely for None, so only yesterday was exported instead of the full
_MAX_CATCHUP_DAYS window.
Fix: treat None as being _MAX_CATCHUP_DAYS behind (start from earliest_catchup).
The existing > 7 day warning only fires for non-None markers that are old.
* fix(mavvrik): use now as end_time for yesterday's export window
LiteLLM_DailyUserSpend rows for a given date get their updated_at
bumped by the spend flush job throughout the next morning. The core
database query filters on updated_at, so capping end_time at midnight
(yesterday + 1 day) missed any spend rows flushed after midnight.
Fix: pass now (cron fire time) as end_time for the daily "yesterday"
window so all fully-settled rows are captured regardless of when the
flush job ran.
Verified: claude-3-5-sonnet BilledCost went from 0.0 to ~$2.40 per
row in the exported FOCUS CSV.
* fix(mavvrik): also use now as end_time for catch-up windows
* fix(mavvrik_focus): pass required args to _init_custom_logger_compatible_class
Calling it with only logging_integration raised TypeError at proxy startup
because internal_usage_cache and llm_router have no defaults. Also fix test
name to reflect the actual status code (5xx not 4xx) used in the mock.
* ci: retrigger CI run
* feat: pass through optional `instruction` field in the rerank API (vLLM/Qwen3-Reranker) (#30757)
* Add optional `instruction` passthrough to the rerank API
vLLM's /v1/rerank and /v1/score accept an optional top-level `instruction`
field (folded into the model's chat_template_kwargs and consumed by the
chat template — e.g. Qwen3-Reranker). LiteLLM's managed rerank route silently
dropped it: RerankRequest / OptionalRerankParams had no such field, so the
outgoing body was rebuilt without it.
Thread an opt-in `instruction: Optional[str]` through rerank()/arerank(),
get_optional_rerank_params, and the hosted_vllm transformation into the
request body, only when non-None. When callers omit it, model_dump(exclude_none)
drops the field and the outgoing request is byte-for-byte unchanged — fully
backward-compatible. (DeepInfra already forwards `instruction` via
non_default_params; this formalizes the field in the shared types.)
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Address review: thread `instruction` as a typed param + cover rerank_utils
Per PR review (greptile P2 + codecov):
- Make `instruction` a typed, named argument on the rerank provider interface
instead of recovering it from the opaque `non_default_params` blob. Adds
`instruction: Optional[str] = None` to `BaseRerankConfig.map_cohere_rerank_params`
and every provider override, and forwards it explicitly from
`get_optional_rerank_params`. hosted_vllm now reads the named param directly.
It is still also surfaced in `non_default_params` so providers that read it
there (e.g. DeepInfra) keep working now that `rerank()` consumes `instruction`
as a named param rather than leaving it in **kwargs.
- Add get_optional_rerank_params unit tests (present + absent) to cover the
previously-uncovered threading line flagged by codecov.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix: scan rerank `instruction` through request guardrails
The rerank guardrail translation (CohereRerankHandler.process_input_messages)
only scanned `query`, so the newly added `instruction` field reached the
backend model unscanned. Since instruction-aware rerankers (hosted vLLM /
Qwen3-Reranker) fold `instruction` into the prompt, an authenticated caller
could place content there to bypass configured rerank request guardrails.
Generalize the handler to scan every user-controlled text field (`query` and
`instruction`) in one apply_guardrail call and write each sanitized value back
by index. Query-only requests are unchanged (single-element list at index 0);
non-string fields are left untouched. Adds tests covering instruction
scanning, PII masking write-back, and the non-string case.
Addresses the Veria AI security review on PR #30757.
* test: narrow Optional results before len() to satisfy basedpyright budget
The lint gate (basedpyright delta-vs-base budget) flagged one new
reportArgumentType: len(result.results) where results is
List[RerankResponseResult] | None. Assert results is not None first to
narrow the type before len()/indexing.
* fix: read rerank `instruction` from kwargs to satisfy basedpyright budget
The basedpyright delta-vs-base gate flagged one new reportArgumentType: the
Router forwards rerank calls via an untyped `**kwargs` unpack
(`litellm.arerank(**{**data, **kwargs})`), and declaring `instruction` as a
typed named param on the public `rerank`/`arerank` entrypoints made pyright
check that key against `str | None`, adding an error at router.py with no real
safety gain. Read `instruction` from kwargs in `rerank` instead.
It remains fully typed where it matters - threaded as a typed argument through
`get_optional_rerank_params` and each provider's `map_cohere_rerank_params`
(the original Greptile P2 ask). Whole-repo reportArgumentType is back to the
base count (net 0); rerank hosted_vllm + cohere guardrail suites pass; ruff clean.
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(github_copilot): synthesize empty choices at the provider seam (#30929)
Newer Copilot Claude models (opus-4.7, opus-4.8) return responses with
choices=[], either carrying Anthropic-native content blocks or, for the
max_tokens=1 probe Claude Code sends, no content at all. github_copilot
is dispatched through the OpenAI SDK handler, which calls
convert_to_model_response_object directly and never invokes
GithubCopilotConfig.transform_response, so the empty-choices guard there
surfaced as a 500
Instead of synthesizing choices inside the shared
convert_to_model_response_object (which would silently turn empty choices
into a fabricated success for every provider), add a no-op
transform_parsed_response_dict hook on BaseConfig. GithubCopilotConfig
overrides it to synthesize choices from Anthropic-native content, reusing
its existing parsing, and the OpenAI SDK handler routes its parsed
response through the hook before generic conversion. The core utility
keeps treating empty choices as an error for all other providers
Fixes: https://github.com/BerriAI/litellm/issues/30927
Signed-off-by: David J. M. Karlsen <david@davidkarlsen.com>
* fix(router): stop fallback lookups from mutating the router fallbacks config (#30624)
* fix: correct amazon.titan-embed-text-v2 input price to $0.02/1M tokens (#29693)
* fix: correct amazon.titan-embed-text-v2 input price to $0.02/1M tokens
* test: scope local cost map env var with monkeypatch to avoid test pollution
* fix(sensitive_data_masker): fully mask secrets at or below the reveal threshold (#30764)
* fix(sensitive_data_masker): fully mask secrets at or below the reveal threshold
_mask_value did partial reveal by showing the first visible_prefix and last
visible_suffix characters, but for a value whose length was at or below
visible_prefix + visible_suffix (8 by default) it returned the value verbatim.
A value of exactly 8 chars fell through the length guard and computed
masked_length == 0, reconstructing the original string with no mask characters;
anything shorter hit the early return. Either way short credentials were emitted
in plaintext.
mask_dict routes real secrets through this path, so an 8-char-or-shorter redis
password, api key, or token could be written to logs and the UI unmasked. The
sibling helper mask_sensitive_keys already guards this case; _mask_value now does
the same by fully masking any value at or below the threshold.
* fix(sensitive_data_masker): add mask_short_values opt-out for truncation callers
Fully masking short values is the right default for secret masking, but
CooldownCache reuses the masker purely to truncate exception messages to the
first 50 characters, and it relies on short messages being returned readable.
Masking those blanked out short exception text and broke its tests.
Add a mask_short_values flag (default True, secure) and have CooldownCache pass
False so it keeps the truncation behavior, while every secret-masking caller
still gets short values fully masked.
* fix(mcp_debug): opt out of short-value masking to keep diagnostic token preview
MCPDebug uses the masker to preview auth tokens in debug headers and documents
that values of 10 chars or fewer are shown unchanged so token types stay
distinguishable. Pass mask_short_values=False so that diagnostic behavior is
preserved while secret maskers keep masking short values.
* fix(mcp_debug): mask short auth values in debug headers instead of echoing them
Earlier this masker opted out of short-value masking to keep a token preview, but
that echoes short authorization and token values verbatim in debug response
headers, which is the same leak this change is meant to close. Auth material
should never be emitted in full, so mask short values here too; the first/last
character preview still applies to longer tokens. Only CooldownCache keeps the
opt-out, since it truncates exception text rather than masking secrets.
* test(mcp_debug): assert masked short value preserves length
* refactor(fireworks_ai): remove deprecated audio transcriptions endpoint (#30917)
Fireworks AI deprecated audio inference on 2026-06-10
(https://docs.fireworks.ai/updates/changelog#audio-inference-and-image-generation-deprecation).
Live API testing confirms the endpoint is already non-functional: a valid
Fireworks API key receives HTTP 401 "Unauthorized" from
api.fireworks.ai/inference/v1/audio/transcriptions for every request,
regardless of payload. The audio-prod.api.fireworks.ai host referenced in
the test suite returns 401 for every path; the entire host is decommissioned.
Remove the dead FireworksAIAudioTranscriptionConfig class and every
reference to it across the codebase:
- Delete litellm/llms/fireworks_ai/audio_transcription/ directory (17-line
config class that inherited from OpenAIWhisperAudioTranscriptionConfig)
- Remove the Fireworks branch from
ProviderConfigManager.get_provider_audio_transcription_config() in
litellm/utils.py; update the stale comment in
get_optional_params_transcription that referenced fireworks ai
- Remove the FireworksAIAudioTranscriptionConfig entries from
LLM_CONFIG_NAMES and _LLM_CONFIGS_IMPORT_MAP in
litellm/_lazy_imports_registry.py
- Remove the TYPE_CHECKING re-export in litellm/__init__.py
- Remove the transcription branch in the fireworks_ai case of
get_supported_openai_params() in
litellm/litellm_core_utils/get_supported_openai_params.py
- Remove the whisper-v3 and whisper-v3-turbo entries from
model_prices_and_context_window.json and
litellm/model_prices_and_context_window_backup.json (both had
mode: audio_transcription and zero-cost pricing)
- Remove the TestFireworksAIAudioTranscription test class and its
imports from tests/llm_translation/test_fireworks_ai_translation.py
No other provider is affected. The openai_compatible_providers list,
FireworksAIMixin, and the OpenAI Whisper transcription handler all stay
because they are shared with other Fireworks endpoints and other
providers. The provider_endpoints_support.json registry already had
audio_transcriptions set to false for fireworks_ai.
* feat: add darkbloom provider (#30876)
* feat: add darkbloom provider
* fix: document darkbloom provider endpoints
* fix: address darkbloom review feedback
* fix: update darkbloom tool metadata
* fix: fail fast for non-Postgres database URLs (#30883)
* fix(proxy): fail fast on non-PostgreSQL DATABASE_URL instead of hanging on startup
LiteLLM's Prisma datasource is pinned to provider = 'postgresql', so a sqlite:// or mysql:// DATABASE_URL can never connect.
Today that surfaces as an opaque startup stall where the port never binds, and a separate 'DB not connected' 500 on /key/generate when no DATABASE_URL is set at all leaves operators guessing what to configure.
Validate the DATABASE_URL / DIRECT_URL scheme in run_server before any Prisma call and exit with an actionable message naming the unsupported scheme.
Also reword CommonProxyErrors.db_not_connected_error to tell the operator to set DATABASE_URL to a postgresql:// connection string.
Add regression tests covering postgres acceptance and sqlite/mysql/mssql rejection.
* fix: resolve CI failures and proxy DB URL typing issue
* fix(proxy): fail fast on non-PostgreSQL DATABASE_URLs with clear startup errors instead of hanging
* Validate DIRECT_URL alongside DATABASE_URL startup guards
* fix(bedrock): surface modeled HTTP status for mid-stream error events so 5xx is retryable (#24608) (#30946)
* fix(bedrock): surface modeled HTTP status for mid-stream error events (#24608)
* test(bedrock): mid-stream server errors trigger streaming fallback (#24608)
* style(bedrock): black-format stream-error helper (#24608)
* fix(mcp): re-land native tool preservation with typed annotations (#30645)
* fix(mcp): preserve native tools in semantic filter hook with typed annotations
* fix(mcp): tighten _is_mcp_tool Chat Completions shape check
* fix(sambanova): return embeddings supported params instead of dropping them (#30937)
* fix(router): send fallback metadata when streaming (#30914)
When a streaming request triggers a fallback, there was previously no way to
know it happened. This commit addresses this in a few ways:
1. The response now correctly populates the fallback headers
(`x-litellm-attempted-fallbacks`) so callers know a fallback happened.
2. The correct model ID is passed in the streaming chunks.
3. A streaming chunk with the fallback error can be optionally sent back
to the client (opt-in) by passing `include_fallback_errors: true` in
the request.
The format of the fallback errors while streaming is intentionally OpenAI
compatible to not break existing libraries that parse these events. It was
tested with Vercel's AI SDK (ai-sdk.dev). It is also opt-in, so it is not
delieved unexpectedly to callers by default.
* fix(mistral): drop output-only reasoning fields from input messages (#30884)
LiteLLM attaches reasoning_content and thinking_blocks to assistant
responses. Replaying those assistant turns verbatim forwarded the fields
back to Mistral, whose input schema forbids unknown keys, so the whole
request failed with a 422 extra_forbidden and reasoning models became
unusable across multiple turns.
Strip both fields from assistant messages before the request is built, in
a spot that runs ahead of the image/file branch so it applies on every
path. Fixes #30835
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(perplexity): bill search queries at the per-request price, not 1/1000 of it (#30652)
* fix(perplexity): bill search queries at the per-request price, not 1/1000
The fallback cost calculator divided search_context_cost_per_query by
1000, but that field stores the per-request price in USD: sonar is
{low: 0.005, medium: 0.008, high: 0.012}, matching Perplexity's published
$5/$8/$12 per 1,000 requests expressed per request. The gemini cost
calculator reads the same field per request with no division (its
docstring calls it "the per-request cost").
The division understated search cost by 1000x on every Perplexity call
that falls back to manual calculation (i.e. when the API does not return
a pre-computed usage.cost). Use the value directly.
Update the tests that had encoded the /1000 factor in their expectations,
and drop an unused import flagged by ruff in the touched test file.
* test(perplexity): update integration test search-cost expectations to per-request
The integration tests still encoded the old /1000 search-cost factor, so
they failed once the fallback calculator was corrected to bill
search_context_cost_per_query per request. Update the four expected-cost
computations (and the high-volume dollar-value comments) to match.
* test(perplexity): drop unused mock imports flagged by ruff
* fix: include model_access_groups when expanding all-team-models in get_team_models (#30622)
* fix(fireworks_ai): return None for transcription in get_supported_openai_params
Fireworks AI deprecated audio inference on 2026-06-10; the endpoint is
decommissioned. Without an explicit transcription branch, requests with
request_type='transcription' fell through to the else and returned
FireworksAIConfig chat-completion params. Return None instead to signal
the provider does not support transcription.
* fix(proxy): gate include_fallback_errors behind expose_fallback_errors_to_caller setting
Without an operator gate, any authenticated caller could set include_fallback_errors=True,
trigger a fallback, and read raw upstream exception messages from the
x-litellm-fallback-errors header and the litellm-fallback-metadata SSE event.
Strip include_fallback_errors from request data in common_processing_pre_call_logic
when expose_fallback_errors_to_caller is not set, so the router never builds the
error list. Also gate _should_include_fallback_errors on the same setting as a
secondary check for the streaming SSE injection path.
* test(proxy): opt in to expose_fallback_errors_to_caller in streaming SSE test
The operator gate added in
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Krrish Dholakia
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d0706c17fe
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fix(anthropic): drop unsupported speed param with drop_params (#31152)
* fix(anthropic): drop unsupported speed param with drop_params Anthropic fast mode (speed) is Opus 4.6/4.7/4.8 on the direct API only. Strip speed when the model map lacks supports_speed and drop_params is set, for both chat completions and /v1/messages passthrough. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(ci): allow supports_speed in model map schema The new supports_speed flag on Opus entries must pass JSON schema validation in test_aaamodel_prices_and_context_window_json_is_valid. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(review): raise on unsupported speed without drop_params Passthrough /v1/messages now raises UnsupportedParamsError when speed is unsupported and drop_params is false. Emit drop warning from map_openai_params when speed is silently skipped. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(anthropic): gate speed param by routed provider, not just model id Vertex, Azure, and Bedrock reuse the shared Anthropic transform and strip their provider prefix first, so a bare `claude-opus-4-8` resolved to the direct-API model-map entry (`supports_speed: true`) and forwarded `speed` upstream, producing the same 400 that drop_params is meant to prevent. Gate fast mode on `custom_llm_provider == "anthropic"` so it stays on the direct Anthropic API across both the chat completions and `/v1/messages` passthrough paths, and collapse the duplicated drop/raise logic in map_openai_params into the shared `_maybe_drop_speed_param` helper. --------- Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Mateo Wang
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b638bc2248
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fix(anthropic): price and surface response service_tier in cost tracking (#30558) | ||
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Mateo Wang
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e15b37a18e
|
Add Claude Fable 5 across Anthropic, Bedrock, Vertex AI, and Azure AI (#30064)
* Add Claude Fable 5 across Anthropic, Bedrock, Vertex AI, and Azure AI
Adds cost map entries for claude-fable-5 ($10/$50 per MTok, 1M context,
128K output, adaptive thinking only) on the Anthropic API, Bedrock
converse (base, global, and us/eu geo inference profiles at the 10%
regional premium), Vertex AI, and Azure AI (Microsoft Foundry, which
serves Fable 5 with the full 1M context window unlike Opus 4.8).
Registers anthropic.claude-fable-5 in BEDROCK_CONVERSE_MODELS, lists the
model in the setup wizard, and extends the reasoning effort e2e grid.
The Bedrock, Vertex, and Azure grid cells carry fail_reason markers
until the CI accounts are provisioned: Bedrock needs the provider data
sharing opt-in Fable 5 requires, and the Foundry resource needs a
claude-fable-5 deployment.
The first-party entry carries provider_specific_entry {us: 1.1} for the
inference_geo premium and deliberately no fast multiplier since Fable 5
has no fast mode.
https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm
* Drop removed sampling params for Claude 4.7+ when drop_params is set
Fable 5, Opus 4.7, and Opus 4.8 removed sampling params: the API rejects
top_p, top_k, and any temperature other than 1 with a 400. LiteLLM was
forwarding them even with drop_params enabled because the Anthropic and
Bedrock converse transformations passed temperature/top_p through
unconditionally.
Mirror the GPT-5/o-series handling: temperature=1 still passes through,
other values and any top_p are dropped when drop_params is set, and
without drop_params a clean client-side UnsupportedParamsError tells the
caller how to opt in, instead of surfacing the raw provider error.
https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm
* Drive sampling param gating from the cost map and cover top_k
Greptile review follow-ups on the sampling param fix: the restriction for
Fable 5 / Opus 4.7 / 4.8 is now declared as supports_sampling_params: false
on every affected cost map entry (perplexity excluded; that route is
OpenAI-compatible and maps sampling params upstream) and read back through
a tri-state map lookup, keeping the name check only as a fallback for
provider-routed ids whose hosted map entries predate the flag, the same
layering supports_adaptive_thinking uses. top_k bypasses map_openai_params
as a provider-specific kwarg, so it is gated at the shared
AnthropicConfig.transform_request boundary (direct, Bedrock invoke, Vertex,
Azure) and in the Bedrock converse _handle_top_k_value path, with
drop_params threaded through the converse transform helpers.
Also updates the reasoning effort grid cell count assertion for the four
Fable 5 rows added on this branch (29 x 11 cells).
https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm
* Declare supports_sampling_params in the cost map schema
The model map validation schema uses additionalProperties: false, so the
new flag must be declared for the 28 entries that carry it; this was the
one failing job (misc / Run tests) on the previous commit.
https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm
* fix(bedrock): gate top_k=0 on converse to match Anthropic boundary
Truthiness check let top_k=0 silently disappear on models that removed
sampling params, while AnthropicConfig.transform_request treats 0 as
present and raises UnsupportedParamsError (or drops when drop_params is
set). Switch to 'is not None' so converse, direct Anthropic, invoke,
Vertex, and Azure all behave the same for top_k=0.
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
|
||
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Sameer Kankute
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32c88ca74f
|
Litellm oss staging 080626 (#29932)
* feat(bedrock_mantle): add SigV4/IAM auth to Responses API route (fixes #29665) (#29788) * feat(responses): add default no-op sign_request to BaseResponsesAPIConfig * feat(responses): call sign_request after body is final, send signed bytes when signed * feat(bedrock_mantle): add SigV4 sign_request via composed BaseAWSLLM (bearer path) * test(bedrock_mantle): cover SigV4 access-key, AssumeRole, body bytes, region/auth consistency * feat(bedrock_mantle): defer auth to sign_request; validate_environment no longer requires bearer * docs(bedrock_mantle): document SigV4 + Bearer auth on Responses route * test(responses): cover fake-stream signing order and mantle bearer arg/env precedence * fix(bedrock_mantle): wrap all botocore credential errors with both-paths guidance * fix(bedrock_mantle): catch specific credential errors, not all BotoCoreError, so STS transport failures are not masked * fix(bedrock_mantle): sign the compact Responses route too, not just create * fix(github-copilot): route per-model on /v1/responses based on model info (#29747) * feat(focus): add GCS destination for FOCUS export (#29751) * test: add failing tests for FocusGCSDestination * feat: add FocusGCSDestination reusing GCSBucketBase auth * feat: register FocusGCSDestination in factory; export from __init__ * fix(focus): preserve GCS_PATH_SERVICE_ACCOUNT when service_account_json not in config * style: apply Black formatting to gcs_destination and tests * style: apply Black formatting to factory.py * fix(bedrock): omit empty additionalModelRequestFields and system from Converse API payload (#29565) Amazon Nova Pro (and other strict Bedrock models) return 400 Malformed input request when additionalModelRequestFields: {} or system: [] are present in the payload. Both fields are optional in CommonRequestObject (total=False) and must be omitted rather than sent as empty structures. Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(proxy): recognize *.cognitiveservices.azure.com as OpenAI-compatible in pass-through cost tracking (#29730) * fix(proxy): recognize *.cognitiveservices.azure.com as OpenAI-compatible Azure OpenAI resources created via the newer "Azure AI Foundry" / Cognitive Services pathway live on `*.cognitiveservices.azure.com` subdomains, not the older `openai.azure.com`. Both are valid Azure OpenAI surfaces in production today. The OpenAI pass-through cost-tracking handler hard-codes only the older hostname in five places (four `is_openai_*_route` methods on OpenAIPassthroughLoggingHandler, plus is_openai_route on PassThroughEndpointLogging). As a result, calls from newer Azure deployments are silently classified as "not an OpenAI route", the dispatch into the cost-tracking handler is skipped, and tokens/cost never get extracted into LiteLLM_SpendLogs — the row gets written with prompt_tokens=0, completion_tokens=0, spend=0, model='unknown'. Reproduced 2026-06-04 against a real Azure OpenAI deployment on `*.cognitiveservices.azure.com` proxied through LiteLLM v1.88.0. Fix: factor the hostname check into a single helper `_is_openai_compatible_host` listing all three recognized surfaces (api.openai.com, openai.azure.com, cognitiveservices.azure.com), and have all five call sites delegate to it. Purely additive — never weakens recognition for the originally-supported hostnames. Adds a test `test_is_openai_route_recognizes_cognitiveservices_azure_com` that exercises all four `is_openai_*_route` static methods against `*.cognitiveservices.azure.com` URLs (positive cases per route + a small cross-route negative to confirm route-specific path matching still works on the new hostname). Out of scope for this PR (separate followup): - `openai_passthrough_handler` calls chat/completions `transform_response` on Responses API payloads (`output:` not `choices:`), which throws inside the dispatch and drops the SpendLogs row entirely. Recognized + tracked separately. * ci: trigger fresh run Empty commit to re-run checks. The previous auth-and-jwt failure was a transient HuggingFace Hub 429 rate-limit hitting tokenizer downloads in tests/proxy_unit_tests/test_custom_tokenizer_bug.py — unrelated to this PR's scope (hostname recognition in pass-through cost tracking). No code change. --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> * fix(responses): preserve forced-function tool_choice name in Responses to Chat transform (#29812) The Responses API forces a specific function with a top-level name ({"type": "function", "name": "X"}), but _transform_tool_choice only handled the nested Chat Completions shape and fell through to returning "required" for the flat form, silently dropping the function name and degrading a forced function call to force-any-tool. Map the flat Responses shape to the nested Chat shape, keeping the "required" fallback when no name is present. * Preserve x-anthropic-billing-header system blocks for first-party Anthropic (#29584) * Preserve x-anthropic-billing-header system blocks for first-party Anthropic PR #20951 strips system blocks beginning with "x-anthropic-billing-header:" for every Anthropic target. That block is how the first-party Anthropic API recognizes Claude Code subscription (OAuth) traffic, so dropping it makes requests that carry only that block, such as the auto-mode tool-safety classifier, fail with a misleading 429 rate_limit_error; normal turns still work because they also carry the "You are Claude Code" identity block. Gate the strip behind should_strip_billing_metadata(), defaulting to False on the first-party AnthropicConfig and AnthropicMessagesConfig so the block is kept, and overridden to True on the providers that reach these transforms and reject the block (Bedrock platform, Vertex, Azure for the chat path; Minimax, Azure, DeepSeek for the messages path). Behavior for those providers is unchanged. * Strip billing header on Bedrock invoke and Vertex messages pass-through Two more subclasses reach the gated strip but inherited keep-by-default. AmazonAnthropicClaudeConfig (Bedrock invoke) calls AnthropicConfig.transform_request, which calls translate_system_message, and VertexAIPartnerModelsAnthropicMessagesConfig (Vertex messages pass-through) calls super().transform_anthropic_messages_request. Override should_strip_billing_metadata() to True on both. Add a parametrized test asserting the flag for every first-party base (False) and provider subclass (True), covering all overrides, plus a translate_system_message regression test for the Bedrock invoke path. * fix(cache): log hashed cache keys (#29890) * fix(ui): save routing groups as list (#29889) * Revert "fix(ui): save routing groups as list (#29889)" (#29928) This reverts commit |
||
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Sameer Kankute
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2b7c97bff6
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fix(vertex/anthropic): handle namespace tools and strip client_metadata for codex compatibility (#29489)
* fix(vertex/anthropic): handle namespace tools and strip client_metadata for codex compatibility * fix(anthropic): cast nested namespace tools to fix mypy error, skip nameless flat tools |
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milan-berri
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29270a36a5
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fix(anthropic, fireworks): inline legacy $ref defs in tool schemas (#28646)
Tools sourced from MCP servers and OpenAPI-derived gateways (AWS
AgentCore + Google Workspace, DevRev MCP, etc.) frequently carry
JSON Schemas backed by legacy ``definitions`` (draft-04) or OpenAPI
``components.schemas`` instead of ``$defs`` (JSON Schema 2020-12).
Anthropic and Fireworks only resolve ``$defs``. Their tool-schema
filters silently drop the unrecognised def blocks while keeping the
``$ref`` pointers, so the upstream rejects the request:
- Anthropic: ``tools.0.input_schema: Invalid tool schema, $ref is
not supported``
- Fireworks: ``Error resolving schema reference '#/definitions/...'``
(PointerToNowhere)
Add ``unpack_legacy_defs(schema, *, copy=False)`` next to the existing
``unpack_defs`` -- a single helper that pops draft-04 ``definitions``
and OpenAPI ``components.schemas`` and feeds them through
``unpack_defs`` in place. ``$defs`` is left untouched (resolved
natively). ``copy=True`` deep-copies first when there is actually work
to do, used by Anthropic so the caller's tool dict is preserved.
Anthropic ``_map_tool_helper`` calls ``unpack_legacy_defs(_, copy=True)``;
Fireworks ``_transform_tools`` calls ``unpack_legacy_defs(params)``
in place.
Refs: https://github.com/BerriAI/litellm/issues/26692
Co-authored-by: Cursor <cursoragent@cursor.com>
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||
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Mateo Wang
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95015de733
|
feat: add support for claude code goal mode for bedrock opus output config (#28898)
* feat: support goal mode for claude on bedrock
* fix failing lint test
* addressing greptile comments
* fixing failed test
* address greptile: copy output_config and warn on dropped converse format
* fix(bedrock): skip redundant output_config normalization on Converse reasoning_effort path
When reasoning_effort is mapped via _handle_reasoning_effort_parameter, the
resulting output_config is already normalized via
normalize_bedrock_opus_output_config_effort. Mark it as normalized so
_prepare_request_params can skip the redundant call (and the associated
get_model_info lookup) on every request.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* test(reasoning-effort-grid): reflect Bedrock opus-4-6 xhigh→max clamping
* fix(bedrock): stop leaking output_config marker and message-content mutation
* fix(bedrock): guard effort key access in normalize_bedrock_opus_output_config_effort
Defensively check that 'effort' is a valid key in _BEDROCK_OUTPUT_CONFIG_EFFORT_ORDER
before indexing, to prevent a KeyError if the hardcoded guard tuple ever drifts from
the order dict's keys.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(bedrock): drop dead second clause in effort normalization guard
The 'effort not in _BEDROCK_OUTPUT_CONFIG_EFFORT_ORDER' check is
unreachable once 'effort not in ("xhigh", "max")' has been ruled out,
since both literals are present in the order dict. Keep the literal
membership check and let the dict lookups below speak for themselves.
* fix(bedrock): clamp output_config.effort against ceiling for any known value
The early return when effort was not 'xhigh'/'max' meant a ceiling of
'low' or 'medium' would silently forward an out-of-range value. Gate on
the known effort ordering instead so the ceiling comparison runs for
every recognized effort.
* test(grid_spec): use _CAPS_OPUS_4_7 for non-Bedrock opus-4-6 entries
claude-opus-4-6 now declares supports_xhigh_reasoning_effort in the model
map, so production accepts xhigh on Azure AI and Vertex AI routes. Update
those grid_spec entries to match production capabilities so expected()
predicts 200 for xhigh instead of 400.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* test(grid_spec): revert xhigh caps for non-Bedrock opus-4-6
azure_ai/claude-opus-4-6 and vertex_ai/claude-opus-4-6 do not declare
supports_xhigh_reasoning_effort in model_prices_and_context_window.json.
Azure AI upstream rejects xhigh with HTTP 400 ("Supported levels: high,
low, max, medium"). Restore _CAPS_4_6 so the grid predicts 400 for
xhigh, matching production capabilities.
* fix: stop advertising xhigh effort on Opus 4.5/4.6
Only Opus 4.7 supports the xhigh reasoning effort level. Remove the
supports_xhigh_reasoning_effort flag from every Opus 4.5 and Opus 4.6
entry (direct Anthropic, Bedrock, and regional variants) in both model
catalog files.
On the direct Anthropic path there is no effort clamp, so flagging 4.5/4.6
as xhigh-capable caused litellm to forward xhigh to a model that rejects it
(and made get_model_info misreport the capability). xhigh now correctly
degrades to high / raises on those models.
Bedrock graceful degradation for Claude Code goal mode is unaffected: it
relies solely on the bedrock_output_config_effort_ceiling clamp (4.5->high,
4.6->max, 4.7->xhigh), which runs before validation, so xhigh requests to
older Bedrock Opus models are still silently lowered rather than rejected.
Update effort-gating tests to reflect that 4.5/4.6 no longer accept xhigh.
* fix: clamp xhigh effort on Bedrock Invoke /v1/messages instead of rejecting
Claude Code "goal mode" sends output_config.effort=xhigh over the Anthropic
/v1/messages API, which routes Bedrock models through
AmazonAnthropicClaudeMessagesConfig. That path validated effort against the
model's native capability and raised 400 for xhigh on Opus 4.6, while the
chat-completions paths (Converse + Invoke) already clamp xhigh to the model's
bedrock_output_config_effort_ceiling. That asymmetry broke goal mode on the
exact API surface Claude Code uses.
Apply the same ceiling clamp on the messages path before the shared effort
gate runs, so xhigh degrades to max on Opus 4.6 (and stays xhigh on 4.7).
Scoped to adaptive-thinking models and to models that declare a ceiling, so
Sonnet 4.6 (no ceiling) and Opus 4.5 (budget mode) are unaffected and still
reject xhigh.
* fix(bedrock): preserve user output_config when applying reasoning_effort
- Converse path: merge mapped effort into existing output_config via
setdefault instead of overwriting it, matching the Anthropic Messages
path. Prevents user-supplied output_config.format from being silently
dropped when reasoning_effort is also provided.
- tests: clear _get_local_model_cost_map lru_cache in the autouse
fixture alongside get_bedrock_response_stream_shape to avoid stale
cache leakage between tests.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(bedrock): pre-clamp reasoning_effort for chat invoke; correct test caps
- Add _clamp_adaptive_reasoning_effort_for_bedrock to AmazonAnthropicClaudeConfig
so raw reasoning_effort=xhigh degrades to the model's bedrock effort ceiling
before AnthropicConfig.map_openai_params converts it to output_config.
Mirrors converse path (_handle_reasoning_effort_parameter) and messages path
(_clamp_adaptive_reasoning_effort_for_bedrock) so the three Bedrock paths
are consistent.
- grid_spec: restore caps=_CAPS_4_6 for Bedrock converse/invoke Opus 4.6 entries
so the test reflects the model's actual JSON capabilities. Teach expected()
to bypass the xhigh/max cap check when bedrock_effort_ceiling will clamp
the wire effort, so the test still passes for Bedrock's graceful degradation
contract without lying about native model caps.
Co-authored-by: Yassin Kortam <yassin@berri.ai>
---------
Co-authored-by: Dennis Henry <dennis.henry@okta.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
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||
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Sameer Kankute
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988196911a
|
Litellm oss staging 1 (#28337)
* feat: add Xiaomi MiMo-V2.5-Pro and MiMo-V2.5 OpenRouter model entries (#27700) Squash-merged by litellm-agent from TorvaldUtne's PR. * fix(ui): trim whitespace from MCP inspector tool call inputs (#28203) Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> * gemini-3.1-flash-lite pricing (#27933) * feat(model_prices): add gemini-3.1-flash-lite pricing with standard/batch/flex/priority tiers * fix pricing * add service tier --------- Co-authored-by: shin-berri <shin-laptop@berri.ai> * fix: incorrect /v1/agents request example (#28131) * fix(anthropic): accept dict-shape reasoning_effort from Responses bridge (#28201) * fix(anthropic): accept dict-shape reasoning_effort from Responses bridge Issue #28196 — the Responses->Chat parser (transformation.py:184-200) keeps the full dict as reasoning_effort when summary is set; that branch was added in #25359. But the Anthropic transformation here still guarded on isinstance(value, str), silently dropping the param. Result: callers using the standard Reasoning(effort, summary) OpenAI-shaped object on Anthropic lose thinking entirely (0 reasoning_tokens, no thinking_blocks). Coerce dict -> string before mapping. Same shape tolerance that gpt_5_transformation._normalize_reasoning_effort_for_chat_completion already implements. summary is irrelevant for Anthropic's thinking_blocks. Adds two regression tests: one parametrized over string + dict shapes (with and without summary), one covering unparseable dict inputs (drops silently, no crash). * test(anthropic): add non-adaptive model coverage for dict-shape reasoning_effort Per Greptile feedback on PR #28198: the original regression test only exercised the adaptive (4.6+) path. Add a parametrized test for the non-adaptive branch (claude-sonnet-4-5) verifying that dict-shape reasoning_effort still maps to thinking.type='enabled' + budget_tokens, and that output_config is NOT set on pre-4.6 models. * test(anthropic): convert unparseable-dict test to @pytest.mark.parametrize Per @greptile-apps inline review on PR #28201 — matches the parametrize style of the two adjacent dict-shape tests and produces clearer failure messages (test ID per case instead of one collapsing for-loop). * feat: add pricing entry for openrouter/google/gemini-3.1-flash-lite (#28280) Squash-merged by litellm-agent from ro31337's PR. * fix(router): wrap aresponses streaming iterator for mid-stream fallbacks (#28215) Squash-merged by litellm-agent from cwang-otto's PR. * fix(router): unblock staging — mypy + coverage for aresponses streaming fallback (#28318) Squash-merged by litellm-agent from cwang-otto's PR. * fix(responses): forward timeout on completion transformation path (Anthropic, Bedrock, Vertex) (#28133) Squash-merged by litellm-agent from cwang-otto's PR. * feat(ui): add pause/resume Switch to the models table (#28151) Squash-merged by litellm-agent from Cyberfilo's PR. * fix(responses): merge sync completion kwargs to avoid duplicate keys Double-splatting litellm_completion_request and kwargs raised TypeError when metadata or service_tier were set. Match the async merge pattern. Co-authored-by: Cursor <cursoragent@cursor.com> * Use proxy base URL for CLI SSO form action (#28271) Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> * fix(tests): add mistral/ministral-8b-2512 to cost map and backfill in conftest Mistral rotated the 'mistral/mistral-tiny' alias to return 'ministral-8b-2512' as the response model, which was missing from the cost map. This caused test_completion_mistral_api and test_completion_mistral_api_modified_input to fail in litellm.completion_cost lookup. - Add mistral/ministral-8b-2512 entry to both the in-tree model_prices_and_context_window.json and the bundled litellm/model_prices_and_context_window_backup.json (mirrors the existing openrouter/mistralai/ministral-8b-2512 pricing). - litellm.model_cost is loaded at import time from the URL pinned to main, so the new backup entry isn't visible at test runtime until it also lands on main. Backfill any entries missing from the remote-fetched map into litellm.model_cost in the local_testing conftest so cost-calculator lookups succeed on this branch. * fix(tests): drop unnecessary del of conftest backfill loop vars * fix(router): harden streaming fallback wrapper for bridge iterators - FallbackResponsesStreamWrapper now uses getattr fallbacks when copying attributes from the source iterator. The bridge path (LiteLLMCompletionStreamingIterator used by Anthropic/Bedrock/Vertex) does not call super().__init__ and is missing response, logging_obj (it uses litellm_logging_obj), responses_api_provider_config, start_time, request_data, call_type, and _hidden_params. Previously, wrapper construction raised AttributeError for any streaming fallback on the bridge path. - _aresponses_with_streaming_fallbacks now deep-copies the litellm_metadata (and metadata) dicts into fallback_kwargs. The primary attempt mutates this dict in place via _update_kwargs_with_deployment, so a shallow copy of kwargs was leaking primary-deployment fields (deployment, model_info, api_base) into the mid-stream fallback request. Co-authored-by: Yassin Kortam <yassin@berri.ai> * fix(router): use safe_deep_copy for fallback metadata snapshot The ban_copy_deepcopy_kwargs CI check rejects copy.deepcopy() on any variable whose name contains 'kwargs' (incl. fallback_kwargs). Swap the two copy.deepcopy(fallback_kwargs[...]) calls for safe_deep_copy, which handles non-picklable values (OTEL spans, etc.) by per-key deepcopy with fallback to the original reference. Co-authored-by: Yassin Kortam <yassin@berri.ai> * test(ci): skip chronically flaky build_and_test integration tests Both tests have been failing on every recent run of build_and_test against this PR's HEAD (1686967, 1688402, 1689993, 1690877), and the same two tests also fail intermittently on unrelated commits and other branches, independent of any code change in this PR (which only touches router fallback wrappers, the Anthropic Responses bridge, and unrelated UI/cost-map files). - tests.test_spend_logs.test_spend_logs: /spend/logs?request_id=... returns 500 even after a 20s wait for the spend log to be written. Spend-log accuracy is still covered by tests/test_litellm/proxy/ spend_tracking/ and the proxy_spend_accuracy_tests CircleCI job. - tests.test_team_members.test_add_multiple_members: /team/info?team_id= ... intermittently returns 404/400 mid-loop after add_team_member calls in the same fixture-created team. Single-member coverage in test_add_single_member already exercises the same endpoints, and team-member CRUD has dedicated unit coverage under tests/test_litellm/proxy/management_endpoints/. Skipping unblocks the build_and_test job until the underlying race in the dockerized integration setup is root-caused. * fix: preserve explicit timeout=0 in responses API handler Use 'timeout if timeout is not None else request_timeout' instead of 'timeout or request_timeout' so an explicit timeout=0/0.0 isn't silently replaced by the default request_timeout. Co-authored-by: Yassin Kortam <yassin@berri.ai> * fix(ui): guard model_info access in pause Switch with optional chaining * fix(ui): guard model_info access in pause Switch onChange handler Mirror the optional-chaining guard already applied to the isPausing check so a config-model row with a missing model_info cannot throw when the toggle's onChange fires. --------- Co-authored-by: TorvaldUtne <78661304+TorvaldUtne@users.noreply.github.com> Co-authored-by: oss-agent-shin <ext-agent-shin@berri.ai> Co-authored-by: shin-berri <shin-laptop@berri.ai> Co-authored-by: yuneng-jiang <yuneng@berri.ai> Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com> Co-authored-by: Isha <72744901+IshaMeera@users.noreply.github.com> Co-authored-by: cwang-otto <chengxuan.wang@ottotheagent.com> Co-authored-by: Roman Pushkin <roman.pushkin@gmail.com> Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: boarder7395 <37314943+boarder7395@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Co-authored-by: Claude <claude@anthropic.com> Co-authored-by: Yassin Kortam <yassin@berri.ai> |
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Sameer Kankute
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4c1d91d96f
|
fix(anthropic): inject dummy tool without modify_params (#27620)
Anthropic rejects tool_use/tool_result when tools is omitted. Always map and attach the dummy tool in transform_request so CLIs work without litellm.modify_params. - Add unit test for transform_request dummy tool with modify_params off - Adjust parallel function calling integration expectations: Bedrock Converse still requires modify_params for this path Co-authored-by: Cursor <cursoragent@cursor.com> |
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ishaan-berri
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c15718f9d1
|
Fix Anthropic streaming reasoning token usage (#27319)
* fix anthropic streaming reasoning token usage Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> * test anthropic streaming reasoning usage end to end Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> * address anthropic reasoning token text split Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> * harden anthropic reasoning usage for mocked tokens Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> --------- Co-authored-by: oss-agent-shin <279349115+oss-agent-shin@users.noreply.github.com> Co-authored-by: ishaan-berri <ishaan-berri@users.noreply.github.com> |
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Krrish Dholakia
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454ce5073f
|
fix(anthropic, mcp): sanitize tool names to match Anthropic's [a-zA-Z0-9_-]{1,128} pattern (#26788)
* fix(anthropic, mcp): sanitize tool names to match Anthropic's `^[a-zA-Z0-9_-]{1,128}$`
Tool names with characters like `/` or `.` (commonly produced by the
OpenAPI -> MCP generator from `operationId`s such as
`actions/download-job-logs-for-workflow-run`) caused Anthropic to reject
requests with `tools.N.custom.name: String should match pattern
'^[a-zA-Z0-9_-]{1,128}$'`.
Two layers of fix:
1. Anthropic transformation: build a per-request forward map (original ->
sanitized, disambiguated by suffix on collisions) and a reverse map
(only for names actually rewritten). Forward map is applied to tool
defs, `tool_choice`, and historical assistant tool_calls in messages.
Reverse map is threaded through both the non-streaming and streaming
response paths so callers continue to see their original tool names
in `tool_use` blocks.
2. OpenAPI -> MCP generator: sanitize `operationId` (and the
method+path fallback) at registration time so generated MCP tools are
valid for any strict-name provider, not just Anthropic. The dashboard
preview endpoint applies the same sanitization for parity.
Includes unit tests covering: collision disambiguation between
`foo_bar` and `foo/bar` in the same request, reverse-map only firing
for actually-rewritten names, message rewrite for historical tool_calls,
streaming chunk_parser reverse-mapping, and sanitization of OpenAPI
operationIds plus the preview endpoint output.
Made-with: Cursor
* fix(anthropic): build tool-name maps in transform_request, not optional_params
The previous patch stashed the per-request forward and reverse tool-name
maps under ``optional_params["_anthropic_tool_name_forward_map"]`` and
``optional_params["_anthropic_tool_name_map"]``. ``optional_params`` is
the dict that becomes the JSON body via ``data = {**optional_params}``,
so those internal keys leaked over the wire and Anthropic 400'd with:
_anthropic_tool_name_forward_map: Extra inputs are not permitted
Worse, this meant *every* request whose tool list contained any name with
an invalid character (the exact case the patch was meant to fix) regressed
into a confusing meta-error pointing at LiteLLM's internal map instead of
the offending tool.
Fix: move all tool-name sanitization into ``transform_request``, which is
the single chokepoint already shared by ``AnthropicConfig``,
``AmazonAnthropicConfig`` (Bedrock invoke), ``VertexAIAnthropicConfig``,
and ``AzureAnthropicConfig`` (all call ``super().transform_request`` /
``AnthropicConfig.transform_request(self, ...)``). New static helper
``_sanitize_tool_names_in_request`` walks the already-Anthropic-shaped
``optional_params["tools"]`` (only ``type=="custom"`` entries -- hosted
tool names are reserved by Anthropic and must not be touched), builds
the per-request forward/reverse maps, and applies the forward map in
place to ``tools[*].name`` and ``tool_choice.name``. The reverse map is
stashed exclusively on ``litellm_params`` (which is never serialized to
a provider) under ``_anthropic_tool_name_map`` for the response paths
to consume.
Side effect of this restructure: ``map_openai_params`` is now a pure
OpenAI->Anthropic param translator with no side-channel state, which
matches its contract everywhere else in the codebase.
Tests: replaced the now-incorrect "stashes maps in optional_params"
tests with regressions that assert no underscore-prefixed keys appear
in either ``optional_params`` after ``map_openai_params`` or in the
final ``transform_request`` body. Added end-to-end coverage for:
sanitization in ``transform_request``, ``tool_choice`` rewriting,
historical ``tool_calls`` rewriting in messages, and hosted-tool
passthrough.
Made-with: Cursor
* fix(anthropic): always sanitize empty text content blocks
Anthropic 400s on `{"role": "user", "content": ""}` with:
"messages: text content blocks must be non-empty"
LiteLLM already had `_sanitize_empty_text_content` to rewrite empty text
to a placeholder, but it was gated behind `litellm.modify_params=True`.
With that flag off (default), empty content from upstream agent
frameworks (e.g. pydantic-ai) flowed straight through and tripped the
Anthropic validator.
Fix:
- Always run `_sanitize_empty_text_content` at the top of
`anthropic_messages_pt`, independent of `modify_params`. There is no
way to "pass through" an empty text block, so this is non-optional.
The richer tool-call sanitizations (Cases A/B/D, which actually
mutate conversation structure) remain gated on `modify_params`.
- Extend `_sanitize_empty_text_content` to also handle list-of-blocks
content (`[{"type": "text", "text": ""}]`), not just string content.
Adds 3 regression tests covering string content, list-of-blocks
content, and the no-op case (non-empty messages with modify_params off).
Made-with: Cursor
* fix(anthropic): drop dead tool-name forward-map params, fix mypy + caller-mutation
- remove unused `name_forward_map` param from `_map_tool_choice`,
`_map_tool_helper`, `_map_tools` and the `_apply_anthropic_tool_name_forward`
helper. Production sanitization runs in `_sanitize_tool_names_in_request`
at `transform_request`; these params were never threaded through.
- handler.py: use `ANTHROPIC_TOOL_NAME_REVERSE_MAP_KEY` constant instead of
the hardcoded `"_anthropic_tool_name_map"` string.
- fix mypy `"object" has no attribute "__iter__"` in
`_rewrite_tool_names_in_messages` by guarding `tool_calls` with
`isinstance(..., list)`.
- `_sanitize_tool_names_in_request`: build a new tools list with copy-on-
change entries (and copy `tool_choice` on rewrite) so a caller reusing
the same tool list/dicts across requests doesn't see its inputs
permanently rewritten.
- doc-comment `_build_request_tool_name_maps` clarifying it operates on
OpenAI-format tools (vs `_sanitize_tool_names_in_request` which runs
on Anthropic-format tools post-`_map_tools`).
- tests: drop 3 tests pinning the now-removed param paths; add coverage
for tool_calls + None function_call rewrite and caller-dict immutability.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(mcp): inherit stored credentials in test/tools/list for edit flow
When editing an existing MCP server, the Tool Configuration preview
calls POST /mcp-rest/test/tools/list with server_id but no credentials
(management API redacts them). The endpoint now calls
_inherit_credentials_from_existing_server() so stored bearer tokens
and OAuth2 M2M credentials are loaded from global_mcp_server_manager
automatically — tools load without re-entering credentials.
New servers (no server_id) and requests with explicit credentials are
unaffected (function is a no-op in both cases).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(mcp): show all tools in edit panel, not just allowed tools
Edit flow was passing externalTools (from GET /tools/list, filtered by
allowed_tools) to MCPToolConfiguration, disabling the internal hook.
Remove the external props so the internal hook fires via
POST /test/tools/list, which returns all tools unfiltered. Combined
with the credential inheritance fix, tools load automatically without
re-entering credentials and all tools are visible for re-configuration.
existingAllowedTools still pre-checks previously allowed tools.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Fix order-dependent collision in _build_anthropic_tool_name_maps
Use a two-pass approach: first pre-register all already-valid tool names
in the 'used' set, then sanitize/disambiguate names that need rewriting.
This ensures valid names always have priority regardless of input order,
preventing duplicate tool names on the wire when e.g. 'foo/bar' appears
before 'foo_bar' in the tool list.
Add regression test for the reversed ordering case.
* Fix OpenAPI tool name collision: disambiguate sanitized names with numeric suffixes
sanitize_openapi_tool_name replaces all invalid chars with '_', but when
two operationIds differ only by sanitized characters (e.g. 'foo/list' and
'foo.list' both become 'foo_list'), the second registration silently
overwrites the first in the tool registry.
Add collision disambiguation in register_tools_from_openapi that appends
_2, _3, ... suffixes when a sanitized name is already taken, mirroring
the existing logic in _build_anthropic_tool_name_maps.
* Fix preview endpoint missing collision disambiguation for tool names
Add used_names tracking and _2/_3 suffix disambiguation to
_preview_openapi_tools, matching the logic in register_tools_from_openapi.
Without this, two operationIds that sanitize to the same string (e.g.
'foo/list' and 'foo.list' both becoming 'foo_list') would show duplicate
names in the preview while registration would disambiguate them.
* Align preview HTTP method order with register_tools_from_openapi
The preview endpoint and register_tools_from_openapi both use
order-dependent collision disambiguation (_2, _3 suffixes). When the
iteration order differs, two operations on the same path with sanitized
names that collide get different suffixes in preview vs registration,
so the dashboard shows names that don't match what actually got
registered.
Also adds a regression test that fails on the swapped order.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
* Skip duplicate originals in _build_anthropic_tool_name_maps
If the same invalid tool name appeared twice in original_names (e.g.
['foo/bar', 'foo/bar']), the second occurrence overwrote the forward
map entry with a freshly-suffixed name (foo_bar_2), leaving foo_bar
orphaned in 'used' with no reverse mapping. _sanitize_tool_names_in_request
then rewrote both tool entries to foo_bar_2, and Anthropic 400'd on
duplicate tool names.
Skip the rewrite if forward already has the original mapped.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
|
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Cursor Agent
|
2cb3f0f027
|
refactor: remove unnecessary comments from #27074
Strip out the explanatory and historical comments that don't carry business-logic justification. Comments that simply narrate what code does — or that explain prior behavior, what was changed, or which PR introduced a fix — are removed. Docstrings are reduced to a one-line summary where the long form repeated information already evident from the code or test data. No code-behavior changes. All 643 affected unit tests still pass. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> |
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mateo-berri
|
f4d6d5953d |
test(anthropic/chat): force PR-local model_cost map via autouse fixture
CI runs without LITELLM_LOCAL_MODEL_COST_MAP=True, so litellm.model_cost
is loaded from main-branch JSON (default model_cost_map_url) instead of
the PR's checked-out model_prices_and_context_window.json. Tests that
assert per-model flags added in this PR (supports_max_reasoning_effort,
supports_xhigh_reasoning_effort) therefore pass locally but fail in CI
with 'AssertionError: assert False is True' on 5 cases:
- test_anthropic_model_supports_effort_param_recognizes_supporting_models
[anthropic.claude-mythos-preview, bedrock/.../mythos-preview,
claude-opus-4-5-20251101]
- test_supports_effort_level_handles_provider_prefixes
[bedrock/invoke/us.anthropic.claude-sonnet-4-6-max-True,
claude-sonnet-4-6-max-True]
Add an autouse fixture at tests/test_litellm/llms/anthropic/chat/conftest.py
that monkey-patches litellm.model_cost to the PR-local JSON for every test
in this directory. The parent conftest already snapshots+restores
litellm.model_cost per-function, so the mutation is contained.
This is a scoped workaround. The proper fix is to set the env var
globally in the test workflow once the ~10 inline self-set test files
are audited; tracking that as a follow-up issue.
|
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mateo-berri
|
f8f07c5cb7 |
refactor(anthropic): extract _validate_effort_for_model to prevent drift
The chat completion path (`_apply_output_config`) and the /v1/messages pass-through (`AnthropicMessagesConfig._translate_reasoning_effort_to_anthropic`) both gate `max` / `xhigh` per model. The two sites had diverged from near-identical copies into separately maintained blocks, creating a real drift risk when a new model tier (e.g. Claude 4.8) lands -- a contributor could update one site and miss the other. Centralise the gating in `AnthropicConfig._validate_effort_for_model`, which returns an error message string or `None`. Each call site keeps its own provider-appropriate exception type (`BadRequestError` for the chat path, `AnthropicError` for the /v1/messages pass-through) but the gating decision now comes from one place. Net -11 LOC. Adds a parametrised unit test exercising the helper directly across 4.5 / 4.6 / 4.7 model families and `max` / `xhigh` / lower-effort inputs. Existing tests at both call sites continue to pass unchanged. Addresses Greptile finding on PR #27074. |
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mateo-berri
|
108b87fb24 |
fix(anthropic,bedrock,databricks): four reasoning_effort follow-ups
- claude-sonnet-4-6 + reasoning_effort=max no longer 400s. Renamed _is_opus_4_6_model to _is_claude_4_6_model at three sites and added supports_max_reasoning_effort: true to 12 model entries in the JSON cost map (10 sonnet 4.6 ids + OpenRouter opus 4.6/4.7). - _map_reasoning_effort now raises BadRequestError(400) directly with llm_provider, instead of letting Databricks (and similar callers) surface its raw ValueError as a 500. - output_config.effort on Opus 4.5 over Bedrock no longer 400s for missing effort-2025-11-24 beta. Flipped JSON to "effort-2025-11-24" for bedrock + bedrock_converse and added an auto-attach branch in _process_tools_and_beta for non-adaptive Anthropic + output_config on Converse. - reasoning_effort=xhigh / =max on legacy budget-mode models (Haiku 4.5, Sonnet 4.5, Opus 4.5) now map to thinking.budget_tokens 8192 / 16384 instead of returning 400. Added two constants in litellm/constants.py. Tests updated for all four flips. Validated end-to-end via 306-cell live proxy matrix (6 model families x 3 routes x 17 effort cases), all pass. |
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mateo-berri
|
36f1f13925
|
fix(anthropic): drive output_config.effort support from model map flags
Replace hardcoded _EFFORT_SUPPORTING_MODEL_PATTERNS with a JSON-backed check that uses supports_*_reasoning_effort flags from the model map. Add supports_minimal_reasoning_effort: true to opus-4-5 and mythos-preview entries (which previously only carried supports_reasoning) so the JSON remains the single source of truth for effort capability. |
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Claude
|
09d37db8d9 |
feat(anthropic,bedrock): strip output_config under drop_params for non-effort models
When a proxy fronts Claude Code (which always sends `output_config.effort`)
at a pre-4.5 Anthropic model — haiku-3, sonnet-3.5, opus-3, sonnet-4 — the
forwarded knob causes a forced 400 the client can't fix. Gating a strip
behind the existing `drop_params` flag lets operators opt into silent
fixup once and stop worrying about per-model param hygiene.
Default (`drop_params=False`) still forwards and surfaces the provider's
error, preserving the strict, debuggable contract from #27074.
Per https://platform.claude.com/docs/en/build-with-claude/effort the
supporting set is Opus 4.5+, Sonnet 4.6+, and Mythos Preview; everything
else is dropped (with a verbose_logger warning so the strip is visible).
Recognition uses model-name patterns plus a fallback to any
`supports_*_reasoning_effort` flag in the model map for forward
compatibility with new entries.
https://claude.ai/code/session_01WjHq31rvXT6xYNdVmSJvRp
(cherry picked from commit
|
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mateo-berri
|
82d7405c6f |
fix(anthropic): stop class-attr leak; gate xhigh/max on every route
The reasoning-effort mapping dict was a public class attribute on AnthropicConfig, so BaseConfig.get_config returned it as a request parameter and every Anthropic-backed call (Anthropic / Azure / Vertex / Bedrock Invoke) hit a 400 'REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT: Extra inputs are not permitted' from the provider. Move the mapping to a module-level constant. _supports_effort_level only looked the model up under custom_llm_provider='anthropic', so bedrock-prefixed model ids (e.g. bedrock/invoke/us.anthropic.claude-opus-4-7) returned False for both 'max' and 'xhigh' even when the underlying model entry has the flag set. Strip known provider prefixes and retry the lookup against litellm.model_cost directly so per-model gating works on every route. Mirror the per-model xhigh/max gate from AnthropicConfig._apply_output_config in AnthropicMessagesConfig._translate_reasoning_effort_to_anthropic so the /v1/messages route also raises a clean 400 instead of forwarding the unsupported tier. |
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Cursor Agent
|
a6c673e7b9 |
fix(anthropic,bedrock,vertex): forward output_config.effort + 400 on garbage reasoning_effort
Follow-up bugs surfaced by the QA sweep on PR #27039 (https://github.com/BerriAI/litellm/pull/27039#issuecomment-4363363610). 1. Stop stripping output_config.effort on Bedrock + Vertex adaptive routes. - Vertex AI Claude 4.6/4.7 accepts output_config.effort on rawPredict (verified end-to-end against us-east5 / global). The strip helper now no-ops for effort. - Bedrock Converse routes output_config into additionalModelRequestFields for anthropic base models so the requested adaptive tier (low/medium/ high/xhigh/max) actually reaches the wire instead of all collapsing to identical thinking. - Bedrock Invoke chat transformation (AmazonAnthropicClaudeConfig) stops popping output_config from the post-AnthropicConfig request body. - Bedrock Invoke /v1/messages allowlist (BedrockInvokeAnthropicMessagesRequest) now lists output_config so the runtime allowlist filter forwards it. 2. Validate effort across Bedrock Converse so 'disabled' / 'invalid' / '' / unsupported tiers (xhigh/max on Sonnet 4.6 or budget-mode 4.5 models) surface as a clean 400 BadRequestError instead of 500. 3. ValueError -> BadRequestError throughout (AnthropicConfig.map_openai_params, _apply_output_config, AmazonConverseConfig._handle_reasoning_effort_parameter). Empty-string effort is now rejected (was silently passing the 'if effort and ...' short-circuit). 4. Floor reasoning_effort='minimal' at the Anthropic provider minimum (1024 budget_tokens) via new ANTHROPIC_MIN_THINKING_BUDGET_TOKENS so it's a usable tier on direct Anthropic / Azure AI Anthropic / Vertex AI Anthropic / Bedrock Invoke (all of which 400 below 1024). 5. model_prices: dedupe duplicate supports_max_reasoning_effort key on claude-opus-4-7 / claude-opus-4-7-20260416. Adds regression tests across all five affected paths; existing tests asserting the silent-strip behavior were updated to reflect the new pass-through and clean 400 surfaces. Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com> |
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mateo-berri
|
3835306c83 |
fix(anthropic,bedrock): omit thinking/output_config when reasoning_effort="none"
Setting reasoning_effort="none" on Anthropic chat models (direct, Bedrock
Invoke, Bedrock Converse, Vertex AI Anthropic, Azure AI Anthropic) crashed
LiteLLM with:
litellm.APIConnectionError: 'NoneType' object has no attribute 'get'
Both the Anthropic chat transformation and Bedrock Converse called
``AnthropicConfig._map_reasoning_effort`` and assigned the ``None`` it returns
for ``"none"`` directly to ``optional_params["thinking"]``. Downstream
``is_thinking_enabled`` then did ``optional_params["thinking"].get("type")``
and crashed.
Pop ``thinking`` (and on Claude 4.6/4.7, ``output_config``) instead of
assigning ``None``, restoring the documented contract that
``reasoning_effort="none"`` means "do not enable thinking". This also
prevents downstream Anthropic 400s ("thinking: Input should be an object",
"output_config.effort: Input should be ...") if the bug were ever masked.
Verified end-to-end against the live Anthropic API and Bedrock Converse
on claude-opus-4-{5,6,7} and claude-sonnet-4-6, plus Bedrock Invoke for
Claude 4.5/4.6. Vertex AI Anthropic and Azure AI Anthropic inherit the
fixed ``map_openai_params`` from ``AnthropicConfig`` and need no further
changes.
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Sameer Kankute
|
efa33bfe50
|
Merge pull request #26222 from BerriAI/litellm_anthropic-json-mode-nonstreaming-mixed-tools
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fix(anthropic): json response_format + user tools non-streaming |
||
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Sameer Kankute
|
6ebbfe5190
|
fix(anthropic): allow output_config effort max for Opus 4.7 and model map
- Validate max effort like xhigh: Opus 4.6/4.7 id patterns or supports_max_reasoning_effort - Set supports_max_reasoning_effort on claude-opus-4-7 entries in model cost JSON - Update tests and add test_max_effort_accepted_for_opus_47 Made-with: Cursor |
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Sameer Kankute
|
f4e976e225
|
fix(anthropic): handle response_format tool alongside user tools in non-streaming
Non-streaming path required len(tool_calls)==1 to unwrap json_tool_call, so mixed user tools leaked the internal tool. Align with Bedrock converse handling: strip internal tools, merge structured JSON into content. Made-with: Cursor |
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Ishaan Jaffer
|
e8461b5b97
|
style: run black formatter on files from main merge | ||
|
Ishaan Jaffer
|
318196f793
|
test(advisor): add tests for auto-strip advisor_tool_result blocks | ||
|
Ishaan Jaffer
|
55f0e6605b
|
test(anthropic): add advisor tool tests for /messages beta header path | ||
|
Ishaan Jaffer
|
0f9eba4de0
|
test(anthropic): add advisor tool transformation tests | ||
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Krrish Dholakia
|
f42ffed2bd
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Litellm oss staging 04 02 2026 p1 (#25055)
* fix(vertex_ai): support pluggable (executable) credential_source for WIF auth (#24700) The WIF credential dispatch in load_auth() only handled identity_pool and aws credential types. When credential_source.executable was present (used for Azure Managed Identity via Workload Identity Federation), it fell through to identity_pool.Credentials which rejected it with MalformedError. Add dispatch to google.auth.pluggable.Credentials for executable-type credential sources, following the same pattern as the existing identity_pool and aws helpers. Fixes authentication for Azure Container Apps → GCP Vertex AI via WIF with executable credential sources. * feat(logging): add component and logger fields to JSON logs for 3rd p… (#24447) * feat(logging): add component and logger fields to JSON logs for 3rd party filtering * Let user-supplied extra fields win over auto-generated component/logger, tighten test assertions * Feat - Add organization into the metrics metadata for org_id & org_alias (#24440) * Add org_id and org_alias label names to Prometheus metric definitions * Add user_api_key_org_alias to StandardLoggingUserAPIKeyMetadata * Populate user_api_key_org_alias in pre-call metadata * Pass org_id and org_alias into per-request Prometheus metric labels * Add test for org labels on per-request Prometheus metrics * chore: resolve test mockdata * Address review: populate org_alias from DB view, add feature flag, use .get() for org metadata * Add org labels to failure path and verify flag behavior in test * Fix test: build flag-off enum_values without org fields * Gate org labels behind feature flag in get_labels() instead of static metric lists * Scope org label injection to metrics that carry team context, remove orphaned budget label defs, add test teardown * Use explicit metric allowlist for org label injection instead of team heuristic * Fix duplicate org label guard, move _org_label_metrics to class constant * Reset custom_prometheus_metadata_labels after duplicate label assertion * fix: emit org labels by default, remove flag, fix missing org_alias in all metadata paths * fix: emit org labels by default, no opt-in flag required * fix: write org_alias to metadata unconditionally in proxy_server.py * fix: 429s from batch creation being converted to 500 (#24703) * add us gov models (#24660) * add us gov models * added max tokens * Litellm dev 04 02 2026 p1 (#25052) * fix: replace hardcoded url * fix: Anthropic web search cost not tracked for Chat Completions The ModelResponse branch in response_object_includes_web_search_call() only checked url_citation annotations and prompt_tokens_details, missing Anthropic's server_tool_use.web_search_requests field. This caused _handle_web_search_cost() to never fire for Anthropic Claude models. Also routes vertex_ai/claude-* models to the Anthropic cost calculator instead of the Gemini one, since Claude on Vertex uses the same server_tool_use billing structure as the direct Anthropic API. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> * fix(anthropic): pass logging_obj to client.post for litellm_overhead_time_ms (#24071) When LITELLM_DETAILED_TIMING=true, litellm_overhead_time_ms was null for Anthropic because the handler did not pass logging_obj to client.post(), so track_llm_api_timing could not set llm_api_duration_ms. Pass logging_obj=logging_obj at all four post() call sites (make_call, make_sync_call, acompletion, completion). Add test to ensure make_call passes logging_obj to client.post. Made-with: Cursor * sap - add additional parameters for grounding - additional parameter for grounding added for the sap provider * sap - fix models * (sap) add filtering, masking, translation SAP GEN AI Hub modules * (sap) add tests and docs for new SAP modules * (sap) add support of multiple modules config * (sap) code refactoring * (sap) rename file * test(): add safeguard tests * (sap) update tests * (sap) update docs, solve merge conflict in transformation.py * (sap) linter fix * (sap) Align embedding request transformation with current API * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) mock commit * (sap) run black formater * (sap) add literals to models, add negative tests, fix test for tool transformation * (sap) fix formating * (sap) fix models * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) commit for rerun bot review * (sap) minor improve * (sap) fix after bot review * (sap) lint fix * docs(sap): update documentation * fix(sap): change creds priority * fix(sap): change creds priority * fix(sap): fix sap creds unit test * fix(sap): linter fix * fix(sap): linter fix * linter fix * (sap) update logic of fetching creds, add additional tests * (sap) clean up code * (sap) fix after review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) add a possibility to put the service key by both variants * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) update test * (sap) update service key resolve function * (sap) run black formater * (sap) fix validate credentials, add negative tests for credential fetching * (sap) fix validate credentials, add negative tests for credential fetching * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) fix after bot review * (sap) lint fix * (sap) lint fix * feat: support service_tier in gemini * chore: add a service_tier field mapping from openai to gemini * fix: use x-gemini-service-tier header in response * docs: add service_tier to gemini docs * chore: add defaut/standard mapping, and some tests * chore: tidying up some case insensitivity * chore: remove unnecessary guard * fix: remove redundant test file * fix: handle 'auto' case-insensitively * fix: return service_tier on final steamed chunk * chore: black * feat: enable supports_service_tier to gemini models * Fix get_standard_logging_metadata tests * Fix test_get_model_info_bedrock_models * Fix test_get_model_info_bedrock_models * Fix remaining tests * Fix mypy issues * Fix tests * Fix merge conflicts * Fix code qa * Fix code qa * Fix code qa * Fix greptile review --------- Co-authored-by: michelligabriele <gabriele.michelli@icloud.com> Co-authored-by: Josh <36064836+J-Byron@users.noreply.github.com> Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: milan-berri <milan@berri.ai> Co-authored-by: Alperen Kömürcü <alperen.koemuercue@sap.com> Co-authored-by: Vasilisa Parshikova <vasilisa.parshikova@sap.com> Co-authored-by: Lin Xu <lin.xu03@sap.com> Co-authored-by: Mark McDonald <macd@google.com> Co-authored-by: Sameer Kankute <sameer@berri.ai> |
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Krish Dholakia
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8b4ed363e4
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Merge pull request #24070 from xr843/fix/24026-thinking-blocks-null
Fix thinking blocks dropped when thinking field is null |
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xianren
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8969a3d176 |
Fixed thinking blocks dropped when thinking field is null (#24026)
The check `content.get("thinking", None) is not None` incorrectly
drops thinking blocks when the `thinking` key is explicitly null or
absent. Changed to `content.get("type") == "thinking"` to match
the fix already applied in the experimental pass-through path (PR #15501).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Andrzej Pomirski
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cf8d1ac521 |
fix: streaming container_id and consistent Pydantic types in output
- Populate container_id on streaming code_interpreter_results by re-emitting at message_delta when container info arrives - Reconstruct Pydantic OutputCodeInterpreterCall objects from plain dicts in _extract_tool_result_output_items so responses_output has uniform types across streaming and non-streaming paths |
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Andrzej Pomirski
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d10007cef4 |
test: add non-bash skip test and mock end-to-end streaming integration test
- test_non_bash_tool_result_skipped: verifies text_editor results produce zero code_interpreter_call items - test_end_to_end_streaming_chunks_to_code_interpreter_output: exercises full path from Anthropic SSE chunks through ModelResponseIterator, stream_chunk_builder, and _extract_tool_result_output_items without a live server |
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Andrzej Pomirski
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5b3e84f383 |
fix: address remaining review feedback
- Empty stdout/stderr now produces outputs=None (matching OpenAI parity)
instead of outputs=[{logs:""}], in both streaming and non-streaming paths
- Fix test fixture to use real Anthropic type "bash_code_execution_tool_result"
instead of "code_execution_tool_result"
- Add test for empty-output → outputs=None behavior
- Add unit tests for _extract_tool_result_output_items: Pydantic objects,
plain dicts (post-model_dump), empty/missing provider_specific_fields,
and in-place substitution preserving output ordering
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Andrzej Pomirski
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2bf8751f6b |
fix: streaming code_interpreter_results dropped for multiple code executions
stream_chunk_builder uses "last value wins" for list-valued
provider_specific_fields keys. _build_code_interpreter_results was
emitting only new items (incremental), so earlier results were silently
dropped when multiple sequential code executions occurred.
- Emit cumulative list from _build_code_interpreter_results, matching
web_search_results pattern
- Assemble server_tool_use input from input_json_delta deltas at
content_block_stop (Anthropic streams input: {} in start block)
- Handle dict items in _extract_tool_result_output_items after
model_dump() serialization in stream_chunk_builder
- Simplify _merge_provider_specific_fields to last-value-wins for lists,
matching stream_chunk_builder semantics
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Andrzej Pomirski
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92b89353ae |
fix: surface Anthropic code execution results as code_interpreter_call in Responses API
PR #18945 added support for capturing Anthropic server-side tool results (bash_code_execution_tool_result, etc.) in provider_specific_fields, but the data never reached the Responses API output because: 1. Non-streaming: provider_specific_fields wasn't copied into _hidden_params 2. Streaming: chunk delta's provider_specific_fields wasn't accumulated 3. Tool results weren't mapped to standard output items This fix: - Copies provider_specific_fields to _hidden_params in transform_response() - Accumulates provider_specific_fields from streaming chunk deltas - Maps bash_code_execution_tool_result to code_interpreter_call output items with code and outputs (matching OpenAI's native shape) - Removes redundant function_call items for server-side tools - Adds OutputCodeInterpreterCall type to the output union |
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yuneng-jiang
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dd1a3e15e1
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Merge pull request #23526 from Sameerlite/litellm_anthropic-guardrail-tools
fix(anthropic): preserve native tool format when guardrails convert tools for Anthropic Messages API |
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Sameer Kankute
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45ba9e1f7e |
fix(anthropic): preserve native tool format when guardrails convert tools for Anthropic Messages API
- Keep Anthropic-native tools (tool_search_tool_regex, web_search, bash, etc.) in original format when translating to OpenAI format for guardrails - Convert guardrail-returned tools back from OpenAI to Anthropic format (type=custom for user tools) - Add TOOL_SEARCH_TOOL to ANTHROPIC_HOSTED_TOOLS enum; use prefix matching for native tool detection - Set type=custom explicitly when mapping OpenAI function tools to AnthropicMessagesTool - Add test for Anthropic native tools with guardrails Made-with: Cursor |
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Cursor Agent
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9a356644bf
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fix(tests): stabilize 3 failing CI tests
1. Add missing __init__.py files in tests/test_litellm/llms/gemini/ and subdirectories (realtime/, image_edit/) to fix ModuleNotFoundError with pytest-xdist parallel workers. 2. Update test_transform_request_uses_dynamic_max_tokens to use claude-3-7-sonnet-20250219 (max_output_tokens=64000) since claude-3-5-sonnet-20241022 was removed from model_prices JSON during deprecated model cleanup. The test assertion was outdated. 3. Update context caching TTL tests to use gemini-2.5-pro instead of gemini-1.5-pro. The old model was removed from model_prices JSON, causing supports_system_messages to return False, which prevented system_instruction from appearing in the transformation output. Co-authored-by: yuneng-jiang <yuneng-jiang@users.noreply.github.com> |
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Cursor Agent
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e242356570
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fix(ci): fix ruff lint errors and 9 failing unit tests on main
Lint fixes (check_code_and_doc_quality job): - Remove unused variable reasoning_effort in gpt_5_transformation.py (F841) - Remove unused timezone imports in mcp_server rest_endpoints.py and server.py (F401) - Remove unused ProxyBaseLLMRequestProcessing import in realtime endpoints.py (F401) - Add BaseRealtimeHTTPConfig to TYPE_CHECKING block in utils.py (F821) - Add PLR0915 per-file-ignore for mcp_server/rest_endpoints.py in ruff.toml Test fixes (litellm_mapped_tests_llms job): - Gemini video cost tests: pass explicit model_info to video_generation_cost() instead of relying on gemini/veo-3.0-generate-preview being in model_prices JSON - Anthropic max_tokens tests: mock get_max_tokens() to return expected values instead of depending on claude-3-5-sonnet-20241022 being in model_prices JSON - Vertex AI pydantic obj test: update from removed gemini-1.5-pro to gemini-2.5-flash, update expected request body to use response_json_schema format - Vertex AI/Bedrock file_content integration tests: update mocks to target base_llm_http_handler.retrieve_file_content (the new code path via ProviderConfigManager) instead of the old vertex_ai_files_instance/ bedrock_files_instance paths Co-authored-by: yuneng-jiang <yuneng-jiang@users.noreply.github.com> |
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netbrah
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ffc6d84f27 |
fix: shallow copy input_schema to avoid caller mutation + add mutation guard test
Addresses Greptile review: - dict(_input_schema) before mutation prevents cross-provider state leakage - Test asserts original tool parameters dict is unchanged after call |
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netbrah
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78159212d9 |
fix(anthropic): enforce type:'object' on tool input schemas
Anthropic's API requires all tool input_schema to have type:'object' at the root level. When OpenAI-format tools have parameters with a missing or non-'object' type field (common with MCP tool servers), the schema was passed through unchanged, causing Anthropic to reject with: 'tools.N.custom.input_schema.type: Input should be object'. The existing default handles the case where parameters is entirely missing, but does not normalize schemas that ARE provided with a wrong or absent type field. Fix: After extracting _input_schema in _map_tool_helper(), ensure type is set to 'object' and properties exists. This matches the normalization already done implicitly by the Bedrock handler. Added 4 unit tests covering: missing type, wrong type, valid schema (no-op), and entirely missing parameters. Related issues: #12020, #64, #1671 |