6 commits

Author	SHA1	Message	Date
yuneng-jiang	6a0603e00f	feat(count_tokens)!: backport the count_tokens stack to rc/1.107.0 (#46839, #46843, #46583, #47057, #47064, #47163, #46777) (#47209) ... * fix(proxy)!: count prompt-only token_counter requests with the provider when call_endpoint is set (#46839) * fix(proxy): count prompt-only token_counter requests with the provider when call_endpoint is set * fix(proxy): keep the local count for a prompt-only token_counter request when the provider count fails * fix(proxy): log a prompt-only token count fallback as a one-line warning * fix(proxy): send a prompt-only token_counter request to the provider only on Gemini and Vertex AI * fix(proxy): count a token_counter prompt sent next to an empty messages or contents list * fix(proxy): count a prompt next to messages on the local token count instead of answering 500 * fix(proxy): answer 400 for a token count prompt next to a conversation unless drop_params is set With drop_params the conversation is counted without the prompt. On the local count, a prompt sent with system or tools is counted as a user message with them, like the messages form. * test(proxy): count tools with a prompt in the token counter fallback test * test(proxy): cover prompt counting on /utils/token_counter in the Gemini count wire tests * test(proxy): name the token counter fallback test by what it checks (cherry picked from commit 2dc6aafa6b) * fix(count_tokens): apply context_management edits in the Gemini /v1/messages/count_tokens count (#46843) * fix(count_tokens): apply context_management edits in the Gemini /v1/messages/count_tokens count /v1/messages runs clear_tool_uses context edits before it sends an Anthropic body to a Gemini or Vertex AI deployment, but /v1/messages/count_tokens counted the body unedited, so a request with older tool results cleared counted about 2.8x what inference bills. The count now runs the same history and context-edit step inference runs, honors additional_drop_params on the deployment, and skips compact edits so a count never makes a paid summary call. * fix(count_tokens): match the Gemini count to inference for compact edits and drop opt-outs Run compact edits in the count without the summary call so trigger checks and history slicing match /v1/messages, honor a request-level additional_drop_params, keep the full body when any deployment in the model group drops context_management, and answer 400 naming a block Gemini cannot count when an edit trigger fails on it * fix(count_tokens): count the unedited body when an edit fails and honor deployment ids in the drop check * fix(count_tokens): log a failed edit in the count at debug, since the send already logs it * fix(count_tokens): resolve a deployment id before wildcard routes in the drop check, as the router does * fix(count_tokens): honor the router-wide additional_drop_params opt-out in the drop check The router merges default_litellm_params between the deployment and the request, so a router-wide context_management opt-out makes /v1/messages send the full history. The count now reads it in the same order. Adds the audit's wire tests for the count's context edits. * fix(count_tokens): read the opt-out of a deployment the router serves by its litellm model name * fix(count_tokens): read the opt-out and drop_params from the deployments the router and completion_model send to * fix(messages): drop an invalid context_management spec under drop_params so the send matches the count * fix(count_tokens): give Claude Code and Codex counts the drop_params default /v1/messages gives them The proxy turns on drop_params for Claude Code and Codex user agents on /v1/messages. The count now applies the same default, so an invalid context_management spec drops on both endpoints instead of the count answering 400 while the send answers 200. The wire test also covers default_fallbacks, model_group_alias, and completion_model routes. * test(count_tokens): cover a coding agent that turns drop_params off and stop two count tests running as Claude Code (cherry picked from commit f8217d2a03) * fix(proxy)!: answer 400 for malformed tools on count_tokens instead of crashing (#46583) * fix(proxy): answer 400 for malformed tools on count_tokens instead of crashing A non-list tools value crashed the vector store lookup during auth and the built-in tool cost lookup in the failure logging path, so the proxy answered an unhandled 500 and closed the keep-alive connection. count_tokens now answers a 400 invalid_request_error for a malformed body, drops malformed tools when drop_params is on, and both lookups skip values of the wrong shape. * fix(proxy): skip non-list tools in the tool allowlist check Also validate the count_tokens body without recursion, which the recursion detector rejects. * test(integration): restore the int tools case on count_tokens The proxy now answers a non-list tools value with a 400, so the case is deterministic again. * fix(proxy): reject malformed tools at the LLM route entry and keep vector store checks on malformed ids * fix(proxy): apply drop_params like core when dropping malformed tools, and reject malformed tool vector store ids Check tools after request setup so the Claude Code default and the logged call type apply, read the request, global, and model group drop_params the way core does, drop tool_choice and friends with tools, cover Gemini generateContent, and answer 400 for a malformed tools[].vector_store_ids with the strict flag off * fix(proxy): keep the request's call type when logging a gate rejection after request setup The body-shape fallback relabeled a /v1/messages rejection as acompletion and a list-input /v1/responses rejection as aembedding, so it now only applies when the logging object carries no known call type * fix(proxy): share the malformed tools check across tool routes and honor drop_params per entry * fix(proxy): accept null vector_store_ids, read every named store id in auth, check the responses websocket * refactor(proxy): leave the responses websocket out of the request-level tool check * fix(proxy): let a request's additional_drop_params replace the deployment list and keep compact's call type on rejections * fix(proxy): read router default drop lists and deployment ids in the malformed tools check * fix(proxy): leave malformed tools to the router when it can pick another deployment Fallbacks, auto-routers, model strings, comma batches and team model names let the router pick a deployment after the proxy checks the request, so the proxy now forwards a malformed tools field there as main does, and only rejects it when the deployment it checks is the one that serves. Token counting never reroutes, so count_tokens and input_tokens keep reading the group they count with. additional_drop_params names are read the way core reads them, so a list with a non-string entry still names its strings. * test(integration): cover malformed tools on reroutable models, router defaults and deployment ids * fix(proxy): resolve malformed tools at the deployment the router picks * fix(proxy): keep provider-repaired tool schemas, honor fallback_on_unsupported_params, skip cooldown on malformed tools Empty schemas and JSON-string schemas pass the tools check the way litellm's provider paths already read them. The rejection is a BadRequestError subclass carrying the deployment's provider, so fallback_on_unsupported_params: false keeps the 400, and the router never cools a deployment down for a request litellm rejected before calling it. Adds the audit's pre_call guardrail and cross-provider bridge cells. * test(integration): expect a 400 for an empty-string tools field on /v1/messages Real Anthropic rejects tools "" with a 400 on main too, so the case joins the malformed tools test * fix(router): keep web search routing for tools passed as a tuple * fix(router): keep a deployment's own tools when drop_params drops a malformed request tools * test(router): list _merged_tools as covered through the deployment tools merge tests * fix(router): drop the request's tool settings with its malformed tools when deployment tools replace them * fix(router): skip a tool whose type is not a string in the web search filter instead of raising TypeError * fix(prometheus): keep deployment health metrics clean on a malformed tools rejection * test: type the new malformed tools tests * test: mark new test locals Final and type kwargs dicts (cherry picked from commit d76ec3a47e) * ci(cost map): classify token_count_model as an object key in the schema generator (#47057) * ci(cost map): classify token_count_model as an object key in the schema generator * ci(cost map): require non-blank token_count_model strings and test the region rules (cherry picked from commit 6e191a58f1) * ci(cost map): remove the generated model prices JSON schema (#47064) * ci(cost map): remove the generated model prices JSON schema Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(cost map): keep schema-independent cost map tests after dropping the schema Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * ci(cost map): keep the schema file until main's guard stops requiring it Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * ci(cost map): let bot sync PRs keep touching the schema file until it is deleted Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> (cherry picked from commit ae6f7f6613) * fix(count_tokens)!: answer 400 for Vertex partner models without a count API and OpenAI-shaped provider errors on /v1/responses/input_tokens (#47163) * fix(count_tokens): answer 400 for Vertex partner models without a count API and OpenAI-shaped provider errors on /v1/responses/input_tokens * fix(count_tokens): answer 400 for Vertex Mistral counts and type the input_tokens error tests Vertex has no count-tokens route for Mistral or codestral models, so they now get the same 400 as other partner models without a count API instead of a 404 that points operators at IAM. * fix(count_tokens): unwrap prefixed provider errors on /v1/responses/input_tokens * fix(count_tokens): say LiteLLM, not Vertex, cannot count a Vertex partner model * fix(count_tokens): count a Vertex partner model with its configured custom_tokenizer and unwrap prefixed flat provider errors * fix(count_tokens): keep the 400 for contents-only Vertex partner counts with a custom_tokenizer * fix(count_tokens): pass through an OpenAI provider error that omits param or code * fix(count_tokens): keep the status-based type for Anthropic count errors on the Responses route * test(count_tokens): cover the Vertex partner count gate and Responses count error shapes on a real proxy * test(count_tokens): add regression tests for the Vertex partner count gate and Responses count error shapes (cherry picked from commit ef1d3959a0) * feat(count_tokens)!: configurable token_count_framing and token_count_model, count source in the response (#46777) * feat(count_tokens): configurable token_count_framing and token_count_fallback_model, count source in the response Count endpoints now say where each number came from in litellm_count_source (provider, fallback_model, local_estimate) and name the fallback model in litellm_count_fallback_model. When the provider answers 400 or 404 for a model it cannot count, the proxy counts the same request with token_count_fallback_model. token_count_framing adds a per-model constant to local estimates. Both settings ship in the cost map for Bedrock Claude Sonnet/Haiku 5.5, Bedrock Kimi K3, and Databricks Kimi K3 and DeepSeek V4 Flash, and can be overridden in model_info. * test(count_tokens): classify the new cost map keys in the schema generator and expect count_source in exact-shape tests * test(count_tokens): e2e matrix against real inference and integration wire tests for count source, fallback and framing * fix(count_tokens): keep the provider error when local counting is disabled, name the fallback error, keep settings in cooldown Moves the cost map lookup off the event loop * ci(cost map): bound token_count_framing to non-negative and token_count_fallback_model to non-empty * fix(count_tokens): keep a failed fallback's own status, ship fallbacks only where they count, add system.ai framing A fallback model that answers 404 returns the requested model's error unchanged, and any other fallback failure answers with its own status and message. The eu, apac, au and jp Sonnet and Haiku 5.5 entries no longer ship the Opus 5.5 fallback, which AWS serves only in us-east-1. A configured empty token_count_fallback_model turns the shipped fallback off. Config framing accepts whole-number floats and rejects negatives. The Databricks system.ai Kimi K3 and DeepSeek V4 Flash names carry the same framing as their serving endpoint names, and the e2e matrix runs on Bedrock Haiku 5.5 too. * test(databricks): list the system.ai Kimi K3 and DeepSeek V4 Flash names with their published DBU rates * fix(count_tokens): skip a token count fallback model for an hour after it answers 404 on that deployment A us. or global. Sonnet 5.5 deployment outside us-east-1 paid two extra Bedrock calls and about 200 ms on every count, since Opus 5.5 cannot count there either. The Kimi K3 e2e also runs the large system prompt shape now. * test(count_tokens): use the shared integration port allocator in the fallback wire test * fix(cost map): keep the Databricks system.ai entries to framing only, so their spend stays as today * test(count_tokens): cover the fallback 404 skip, inference profile base_model, shipped framing, and deployment-scoped token count settings Adds integration cells for a fallback model that answers 404, an application inference profile ARN deployment whose base_model carries the shipped fallback, and the shipped Kimi K3 framing with token_count_framing 0 turning it off. Adds unit tests that keep token count settings off the shared backend cost map key and that store a token count setting on PATCH only when the operator changed it * fix(count_tokens): keep the counter's own 400 and the provider's error when a framed estimate cannot read the body A 400 the OpenAI or Gemini counter returns before calling the provider is the caller's answer, so it no longer tries the fallback model or a framed estimate. When the framed local estimate cannot read a content block, the client gets the provider's error instead of a 500. Adds /audit round 2's integration tests for the fallback leg's converted body, the Anthropic envelope on fallback failures, and both framing cases. * fix(count_tokens): answer the provider's error whenever a framed estimate raises A tool schema the local estimate cannot format, such as a boolean subschema or an array with no items, raised AttributeError or KeyError and answered a 500 on a framed deployment whose provider had already answered 400. Any estimate failure now answers the provider's own error. * fix(count_tokens): load the custom tokenizer inside the guarded framed estimate A framed deployment whose custom_tokenizer could not load answered a 500 or 503 where the provider had already answered 400, because the tokenizer was selected outside the estimate's error handling. Selecting it inside answers the provider's error, logs a warning with the estimate's error, and still raises the Rust extension's fork-guard errors. * fix(count_tokens): look up framing under the deployment's custom_llm_provider * fix(count_tokens): share one tokenizer download per worker and bound the wait after a provider error A framed deployment whose custom_tokenizer is not cached locally made every count wait out the Hugging Face hub's whole retry cycle when the hub could not be reached, and a burst of those loads held the default thread limiter that every count's cost map lookup also needs. Counts on a worker now share one download per minute, a failed download is reused for the rest of that minute, and after a provider error a count waits at most 5 s before answering the provider's error. The swallowed-estimate warning logs only the error type. * fix(count_tokens): keep one tokenizer download per key running and load the local route per request * test(count_tokens): type the scripted counter and wait on events instead of wall time where possible * fix(count_tokens): run framed tokenizer loads on their own capped threads and answer a tokenizer panic with the provider error Framed loads used anyio's default 40 threads, so a hub that drops packets let about 40 hanging loads stall every count on the worker. Loads now run on a per-worker pool of 4, and a count that finds 4 loads running answers the provider's error at once. Any finished load is reused for a minute after it finishes, so a load that fell back to tiktoken is not retried on every count. A pyo3 PanicException from the tokenizer, while loading or counting, now answers the provider's error instead of a plain 500 * feat(proxy): count tokens with token_count_model in its pinned region token_count_model {model, region} replaces token_count_fallback_model. Bedrock Sonnet and Haiku 5.5 count with Opus 5.5 in us-east-1, or Melbourne for au. profiles, and a failure names the region to enable * fix(count_tokens): use the renamed count model constant in the e2e test and list token_count_model with the object keys * fix(proxy): keep the upstream error envelope when adding the region hint * test(proxy): annotate the token count setting echo test * ci(cost map): require non-blank token_count_model strings and test the region rules * fix(count_tokens): answer the token_count_model's error even when framing is set * fix(count_tokens): keep the provider error for Google contents instead of a framed estimate * fix(count_tokens): estimate Google contents the route translated to messages * fix(count_tokens): keep the provider error for Google contents with non-text parts * test(proxy): drive tokenizer wait and cost map tests by events and a skippable loop clock * fix(count_tokens): check Google text-only parts without a KeysView comparison basedpyright flags * feat(proxy): count Bedrock Kimi K3 tokens exactly with its pinned tokenizer * test(bedrock): keep the Kimi K3 image check in-process * fix(count_tokens): join the region hint to an upstream message that already ends with a period Also pins that a blank token_count_model in config falls through to the cost map default * fix(count_tokens): count Gemini tool-call turns and Responses tools, and stop tokenizer retries from stalling counts The Google countTokens route estimates contents with functionCall, functionResponse and thoughtSignature parts again, and still answers the provider error for media, thought and unknown parts The Kimi K3 exact count renders Responses API and Anthropic tools through the chat tool translation the Vertex count already used, now shared from the prompt factory, and falls back to the estimate when the Converse body cannot carry a tool A failed Kimi K3 tokenizer load is retried in the background without making counts wait, and a load still running after the wait logs a warning * fix(count_tokens): count Kimi K3 tools the way Bedrock inference sends them A flat Responses API function or custom tool becomes the chat tool the Responses route sends to inference, and every other tool goes to the Converse body as is, so hosted tools Bedrock drops are left out of the count instead of being counted or turning it into an estimate. * test(count_tokens): annotate new token counter test locals with Final * fix(count_tokens): keep shipped count regions inside the deployment's geography A plain Bedrock Sonnet or Haiku 5.5 deployment in a non-US region no longer sends its count prompt to us-east-1. Global count models and configured token_count_model keep their pinned region. Also expect litellm_count_source in the Vertex Llama count test main added * fix(count_tokens): resolve count settings off the event loop (cherry picked from commit 75faaeab64) --------- Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-17 18:20:22 -07:00
yuneng-jiang	1738b8a169	fix(vector-stores): keep proxy context out of provider vector store metadata (#47021) (#47159) ... * fix(vector-stores): keep proxy context out of provider vector store metadata The proxy merged its request context (user_api_key_* fields, endpoint, user_agent, requester_ip_address, litellm_api_version) and the router's deployment info into the vector store request's metadata. For vector stores that field is also OpenAI's own metadata, which is capped at 16 keys, so a store created with a team key was full on creation and every later metadata update returned 400. Vector store routes now keep that context in litellm_metadata, like batches, files and responses, and the router does the same for avector_store_create. Provider-side metadata holds only what the caller sent. Adds db_scripts/strip_vector_store_internal_metadata.py, an opt-in script that removes the proxy-written keys from stores created before this change. It is a dry run unless --apply is passed. * fix(vector-stores): page the metadata cleanup script without recursion and tolerate non-string values Listing every store recursed once per page, which overflows the interpreter stack on large projects. Pages are now walked iteratively. Metadata values are no longer required to be strings, and a malformed response exits 2 with a fixed message instead of a traceback that could echo metadata values. * fix(vector-stores): read proxy agent id, tags and spend_logs_metadata from litellm_metadata Vector store routes now keep proxy context in litellm_metadata, but the v3 limiter only read agent_id from metadata, and logging let provider metadata tags or spend_logs_metadata replace the proxy's values. A string tags value also crashed get_request_tags. The limiter now prefers the proxy-resolved agent_id in litellm_metadata. Logging combines list tags from both buckets (metadata first, deduplicated) and merges dict spend_logs_metadata with litellm_metadata winning, ignoring values of the wrong type. * fix(vector-stores): narrow the logging fallback to mistyped caller values The tag union and spend_logs_metadata merge changed logged tags and spend_logs_metadata on every route that has both metadata buckets, deduped tags quadratically, and stopped background interaction polling from keeping string tags as sent. Caller values now win exactly as before, and the proxy's litellm_metadata tags or spend_logs_metadata only replace a caller value of the wrong type. The limiter reads agent ids through a pydantic model instead of a cast and ignores non-string ids. * fix(vector-stores): log the proxy's full tag list on litellm_metadata routes On routes that keep proxy context in litellm_metadata, the proxy builds litellm_metadata.tags from the caller's tags plus key, team and header tags, but get_request_tags preferred the caller's metadata.tags. A caller sending metadata.tags on a vector store call dropped the key's tags from request_tags, which tag spend tracking reads. get_request_tags now prefers a non-empty litellm_metadata tag list, and falls back to metadata.tags as before. * fix(vector-stores): strip LiteLLM-written keys at the provider boundary instead of moving metadata buckets Moving vector store proxy context into litellm_metadata broke shared readers (agent limits, request tags, merged logging metadata), and fixing them widened the change to every litellm_metadata route. This reverts the bucket move, the router change and the reader fixes, so proxy context stays in metadata exactly as on main. Vector store create and update now drop the keys LiteLLM itself writes (user_api_key*, requester and deployment keys, agent ids) from the metadata sent to the provider, in the shared optional-param helper both paths use. The cleanup script reuses the same key list. * fix(vector-stores): pass non-string metadata keys through the provider metadata filter * fix(vector-stores): strip LiteLLM-written metadata keys only on proxy and router calls * fix(vector-stores): send the provider exactly the metadata the caller sent through the proxy * fix(vector-stores): snapshot caller metadata from the raw request body and keep it out of logged request bodies * fix(vector-stores): copy the parsed-body metadata when the raw request body cannot be reparsed (cherry picked from commit 4846cfabc7)	2026-10-17 13:53:28 -07:00
Priyansh Nandwana	391296ffdd	fix(vector_stores): stop sharing one list across default-constructed registries (#38879) ... * fix(vector_stores): stop sharing one list across default-constructed registries VectorStoreIndexRegistry and VectorStoreRegistry took a mutable list as their default argument. Python evaluates that once at class definition, so every registry built without arguments shared one list. The proxy builds one that way during startup, so an upsert into it leaked into every registry created after, and into the class default itself. Default to None and build a fresh list per instance. A caller-supplied list is still used as given, including an empty one, so nothing that passes its own list changes behaviour. Fixes #38874 * test(vector_stores): annotate locals Final and mutate registries through their public API The leak tests now go through add_vector_store_to_registry and upsert_vector_store_index rather than appending to the registry's list directly, which is closer to how the proxy hits the bug. * fix(vector_stores): drop the last mutable list default and identity-only tests --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>	2026-10-15 13:33:55 -07:00
devin-ai-integration[bot]	c3a23fe499	refactor: expose core private helpers under public names (#44871) ... * refactor: expose core private symbols with compatibility aliases Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * types: narrow core migration diagnostics Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix: preserve runtime behavior in core symbol migration Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix: preserve core private usage migration behavior Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix: preserve private value rebinding compatibility Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix: restore optional imports and cover public helpers Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test: exempt router property from call coverage Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test: align recursive detector ignore names Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: mateo <mateo@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-10-07 00:05:27 +00:00
Mateo Wang	d80f8c28ca	refactor(types): replace Any with proven types in 137 files (#44478) ... * refactor(types): prove runtime types at harness, search, rag and client boundaries Replace Any with adapter-validated types in the litellm.agent() harness, the search provider transformations, RAG ingestion and query, the vector store pre-call hook and registry, the galileo and opik logging integrations and the proxy client CLI. Each boundary gets unit tests for well-formed and malformed payloads. * chore(typing): prove types at more provider boundaries and restore search transformations Second pass over a2a, embedding, rerank, image, audio and small provider modules. Search transformations go back to their previous form because validating their response bodies would change the proxy status for malformed upstream bodies from 400 to 500. * refactor(types): prove types at logging, files, rerank, image, audio and management boundaries Replace Any with validated or annotation-only types in 37 more files: logging integrations, token counters, provider files/rerank/image generation/audio transcription transformations, pass-through logging handlers and management endpoints. No proxy HTTP status or error type changes. * refactor(types): prove types at repository, spend, files and router boundaries Replace Any with repository table accessors, validated mappings and annotation-only types in 33 more files: Prisma repositories, the enterprise batch and responses cost checkers, budget reservation, files endpoints, management endpoints, the policy registry, the adaptive and complexity routers and the secret managers. No proxy HTTP status or error type changes. * test(types): run the aiohttp transformation test in-process and cover repository row conversion The aiohttp chat transformation test no longer starts a server. It feeds the transformation a response whose json() returns the body under test. The proxy unit shards now exercise stored model rows whose params are JSON strings and the object permission create and update paths.	2026-10-05 11:01:45 -07:00
yuneng-jiang	f6882246d4	test: move tests/test_litellm root and small trees into tests/unit (#43186) ... * ci: run the unit_selection.sh shard files on every event instead of only fork pull requests Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * ci: rename fork-flag to unit-flag now that it applies on every event * test: move tests/test_litellm root and small trees into tests/unit Pure renames, no content changes. Follow-up commits in this PR fix references, merge the three files that already existed in tests/unit, keep live-provider tests in tests/test_litellm and wire CI. * test: carry tests/test_litellm conftest isolation into tests/unit Callback lists, routing fallbacks, cached HTTP clients, logger state, AWS, proxy-URL and keychain env, and session-end client cleanup now reset for unit tests too. The environment isolation owns its MonkeyPatch so a test's own monkeypatch is undone before the model-cost teardown runs. * test: merge, split and prune the moved root and small-tree tests Merge batches/test_batch_utils.py and the chat_completions and messages dispatch tests into the files that already existed in tests/unit. Keep the live Gemini interactions tests, the async image-fetch format test and the OpenAI embedding scorer test in tests/test_litellm since they need real network or keys. Put test_router.py under tests/unit/test_router so the existing package no longer shadows it. Delete eight tests the audit found superseded by stronger ones kept in this move. * ci: run the moved root and small-tree tests under their legacy flags Add the misc and responses-caching-types flags to unit_selection.sh and CircleCI, extend enterprise-routing and mcp-integration, and point the legacy GHA shards, Makefile, redis-compat workflow, merge smoke manifest and change classifier at the new paths. * test: make the new tests/unit directories packages tests/unit/test_package_layout.py requires every directory to carry an __init__.py, and without one the moved and retained test_litellm_responses_bridge.py modules collide on import. * test: scope the unit socket block to tests/unit in shared sessions The GHA shards collect the legacy test-path and the unit selection in one pytest session. The unit conftest's loopback-only block leaked into legacy modules that reach the network at import. The legacy conftest now lifts the restriction at collect and setup time, and the unit conftest re-applies it when collecting its own modules. * test: give the shard-script tests their own GITHUB_OUTPUT They only passed where the runner set it. The CircleCI unit job's env allowlist drops it, so the script's redirect failed there. * test: point the router and module-deletion checks at tests/unit router_code_coverage and code_qa_check_tests only searched tests/test_litellm, so the moved router tests no longer counted. The two silent-experiment tests the audit deleted were the only direct callers of those methods; they are replaced with tests that assert the forwarded shadow request and the recursion guard. --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>	2026-09-25 11:30:43 -07:00