Some providers send one very large piece of a streamed answer in a single go: a long reasoning trace, a code execution result, a turn with many tool calls, or a response echo carrying a big tool list. Anything past 128 KB in one line killed the chat mid-answer with a misleading `400, message: Got more than 131072 bytes when reading`. Nothing was rejected upstream, that is our own reader giving up on an oversized line.
Open WebUI already had code that assembles lines itself with no such limit, but it only ran when CHAT_STREAM_RESPONSE_CHUNK_MAX_BUFFER_SIZE was set. Unset is the default, and in that case the raw capped reader was used instead, so a default install always broke. That path now always assembles lines, and the setting goes back to being what its name says: an optional cap, off by default. It applies to the Ollama stream as well, since both now share the same reader.
The assembly loop only splits once a line actually completes, because the old one re-concatenated and re-split the whole buffer on every network chunk. Without that, allowing long lines would have traded an error for multi-second event loop stalls.
| | 20 MB in one line | 200k small lines |
| --- | --- | --- |
| before | 4249 ms | 27.3 ms |
| after | 37 ms | 25.2 ms |
* fix: use the pooled client timeout for the Anthropic Messages passthrough
The native `/api/v1/messages` passthrough still referenced `openai.AIOHTTP_CLIENT_TIMEOUT`, which stopped existing when `routers/openai.py` moved onto `session_pool.get_client_timeout()`. Every passthrough request therefore raised `AttributeError: module 'open_webui.routers.openai' has no attribute 'AIOHTTP_CLIENT_TIMEOUT'` before it was sent, and the surrounding handler turned that into a 502 "Open WebUI: Server Connection Error", so Anthropic-format clients such as Cline could not reach any model at all.
Use `get_client_timeout(stream=...)` like the OpenAI and Ollama proxies do, so the configured `AIOHTTP_CLIENT_TIMEOUT` applies and streaming requests additionally get the idle-read timeout.
Fixes#27595
* fix: authenticate native Anthropic requests with x-api-key
The Anthropic Messages passthrough and the token-count forwarding both build their upstream request through `get_anthropic_request_target`, which sends the connection key as `Authorization: Bearer <key>`. Anthropic's OpenAI-compatible `/chat/completions` endpoint accepts that, which is why the model works in the chat UI, but the native `/v1/messages` and `/v1/messages/count_tokens` endpoints do not: they require the key in `x-api-key` and reject a bearer token with 401 `Invalid bearer token` (and `jwt auth is not yet supported on count_tokens`). They also require an `anthropic-version` header, which was never sent.
For `api.anthropic.com` connections, send `anthropic-version` and move the key into `x-api-key`, dropping the bearer header. Connections using session, OAuth or Entra ID auth keep their token untouched, LiteLLM passthrough connections are unaffected, and admin-configured custom headers still win over both defaults.
Fixes#27695
The /openai/responses endpoint forwarded the prefixed model id (e.g.
"myprovider.gpt-4o") to the upstream provider instead of the stripped
native name, causing "model not found" errors when a connection has a
Prefix ID configured.
generate_chat_completion() already strips the prefix before forwarding;
apply the same strip_provider_model_prefix() call in responses() after
the urlIdx routing (which needs the prefixed id) and re-serialize the
body afterwards.
Also fixes the Azure non-v1 deployment path, which built the deployment
URL from the prefixed model name.
Co-authored-by: Claude <noreply@anthropic.com>
GLOBAL_LOG_LEVEL defaults to INFO, so every log.debug(...) in the backend is discarded, but the message is built first: 187 call sites interpolate their payload into an f-string before the logging call runs, so the work happens on every request and the result is thrown away. The worst one sits in process_chat_payload and stringifies the whole request body, full conversation history included, once per chat completion.
That one line with DEBUG disabled, CPython 3.12:
| conversation | payload | before | after |
| ------------ | ------- | -------- | ------- |
| 4 messages | 1.2 kB | 3.4 us | 0.07 us |
| 20 messages | 17 kB | 24.8 us | 0.07 us |
| 60 messages | 123 kB | 216.6 us | 0.07 us |
The lazy form log.debug('form_data: %s', form_data) hands the payload to record.getMessage(), which the InterceptHandler only reaches once a record has passed the level check. With DEBUG enabled the emitted lines are byte-identical, f'{x=}' sites included: those map to %r. MistralLoader._debug_log callers get the same treatment, since that wrapper already forwards *args.
Create and publish Docker images with specific build args / build (map[arch:linux/arm64 runner:ubuntu-24.04-arm], map[build_args:USE_CUDA=true
USE_CUDA_VER=cu126
free_disk:true name:cuda126 suffix:-cuda126]) (push) Has been cancelled
Create and publish Docker images with specific build args / build (map[arch:linux/arm64 runner:ubuntu-24.04-arm], map[build_args:USE_CUDA=true free_disk:true name:cuda suffix:-cuda]) (push) Has been cancelled
Create and publish Docker images with specific build args / build (map[arch:linux/arm64 runner:ubuntu-24.04-arm], map[build_args:USE_OLLAMA=true free_disk:false name:ollama suffix:-ollama]) (push) Has been cancelled
Create and publish Docker images with specific build args / build (map[arch:linux/arm64 runner:ubuntu-24.04-arm], map[build_args:USE_SLIM=true free_disk:false name:slim suffix:-slim]) (push) Has been cancelled
Create and publish Docker images with specific build args / build (map[arch:linux/amd64 runner:ubuntu-latest], map[build_args:USE_OLLAMA=true free_disk:false name:ollama suffix:-ollama]) (push) Has been cancelled
Create and publish Docker images with specific build args / build (map[arch:linux/amd64 runner:ubuntu-latest], map[build_args:USE_SLIM=true free_disk:false name:slim suffix:-slim]) (push) Has been cancelled
Create and publish Docker images with specific build args / build (map[arch:linux/arm64 runner:ubuntu-24.04-arm], map[build_args: free_disk:false name:main suffix:]) (push) Has been cancelled
Create and publish Docker images with specific build args / build (map[arch:linux/amd64 runner:ubuntu-latest], map[build_args: free_disk:false name:main suffix:]) (push) Has been cancelled
Create and publish Docker images with specific build args / build (map[arch:linux/amd64 runner:ubuntu-latest], map[build_args:USE_CUDA=true
USE_CUDA_VER=cu126
free_disk:true name:cuda126 suffix:-cuda126]) (push) Has been cancelled
Create and publish Docker images with specific build args / build (map[arch:linux/amd64 runner:ubuntu-latest], map[build_args:USE_CUDA=true free_disk:true name:cuda suffix:-cuda]) (push) Has been cancelled
Swap the JSON encoder/decoder used across the backend from stdlib json to
orjson when ENABLE_ORJSON is set — HTTP request bodies, JSONResponse
bodies, upstream provider responses, SSE chunks, and socket.io/Redis
payloads.
The flag defaults to off, in which case the app uses stdlib json and
engineio's codec verbatim, so default behaviour is unchanged.
- json_codec exports JSONCodec (stdlib json or the orjson codec) and
SOCKETIO_JSON (engineio's codec or the orjson codec); call sites import
JSONCodec and stay implementation-agnostic
- apply_orjson_http_json() is a no-op when the flag is off, leaving
starlette's Request.json / JSONResponse.render untouched
- the orjson codec falls back to the stdlib for inputs orjson rejects
(non-str dict keys, ints beyond 64 bits, NaN literals)
- orjson is imported only when the flag is on
- FastAPI(default_response_class=...) is deliberately not used: an
explicit default disables the Pydantic direct-to-bytes fast path for
response_model routes
stream_wrapper without a content handler iterates aiohttp's response.content, which reads line by line: every line costs a buffer scan, a slice, a bytes concat, a generator resume and its own ASGI response message. A typical SSE event is two lines (the data line and the blank separator), so every upstream token event became two yields and two transport writes even on routes where the body is never inspected.
stream_wrapper now takes passthrough=True, which iterates response.content.iter_any(): the exact same bytes, one yield per network read, no line scanning. It is applied only to routes no internal consumer parses line-by-line: the ollama pull/push/create/generate proxies and its v1 completions, chat completions, messages and responses endpoints, plus the openai embeddings, responses and catch-all proxies. The two internally consumed chat routes keep line iteration, which the streaming middleware and the Ollama-to-OpenAI converter require; the ollama send_request signature documents that constraint.
Benchmark (local aiohttp SSE server, 500 events, consumed through stream_wrapper):
| metric | before (readline) | after (iter_any) |
| --- | --- | --- |
| stream consumption time | 1.46 ms | 0.62 ms |
| generator yields + response writes per stream | 1000 | 1 |
The single yield is a loopback artifact (the whole body arrives in one buffered read); over a real network it becomes one yield per TCP read instead of two per SSE event.
Functionally verified: line mode and passthrough mode produce byte-identical output for the same stream, and passthrough always yields fewer, larger chunks.
Custom per-connection headers can now forward the user's groups to
upstream backends via two new template placeholders:
- {{USER_GROUPS}}: comma-separated group names
- {{USER_GROUP_IDS}}: comma-separated group ids
The group lookup is async, so get_custom_headers becomes an async
wrapper around the sync template substitution (parse_custom_headers)
and fetches groups lazily — only when a header value actually
references a groups placeholder. The external document loader path
runs in a worker thread without an event loop, so Loader.aload
prefetches the groups before offloading and passes them through to
ExternalDocumentLoader.
Claude-Session: https://claude.ai/code/session_01EbBEfTyu8fFJmC13rnQthT
Co-authored-by: Claude <noreply@anthropic.com>
Adds a {{USER_AGENT}} custom-header placeholder that relays the inbound
client's User-Agent to upstream model backends, so providers see the real
client instead of Open WebUI's internal aiohttp UA. This makes upstream
usage/cost attribution and backend telemetry possible, and is opt-in
per-connection (no global flag): admins add {{USER_AGENT}} to a connection's
custom headers in Admin > Settings > Connections.
The placeholder is sourced from the live inbound request (with a metadata
fallback for detached RAG/tool calls), so it resolves on every prompt-sending
path, not just chat completions:
- OpenAI completions, Responses API, and proxy — all route through
get_headers_and_cookies, which now passes the request into get_custom_headers.
- Anthropic Messages API (/api/v1/messages) — already covered, it delegates
to the chat completion handler.
- Ollama (/api/chat, /v1/completions, /v1/chat/completions, /v1/messages,
/v1/responses) — previously had no custom-header support at all; send_request
now applies per-connection custom headers (with templating) for every
Ollama prompt endpoint.
Custom headers are applied after the built-in user-info headers so explicit
admin-configured headers take precedence. The other existing placeholders
({{CHAT_ID}}, {{USER_ID}}, ...) now also work on the newly covered paths.
Frontend: the connection editor's Headers field is now shown for Ollama
connections too (previously gated to non-Ollama), so the placeholder can be
configured there.
Ref: open-webui/open-webui#26159
routers/openai.py and routers/ollama.py decorated get_all_models with
`@cached(key=lambda _, user: ...)`. In aiocache 0.12, `key=` is a STATIC
cache key: get_cache_key returns `self.key` verbatim (the lambda object)
without calling it, so every caller collides to ONE shared entry within
the TTL. The intended per-user namespacing never happened — one user's
permission-filtered model list could be served to another user (or an
anonymous caller) during the cache window.
The per-call hook is `key_builder=` (called as key_builder(func, *args,
**kwargs)). Switch both sites to key_builder with a (func, request,
user=None) signature so the key is built per call. user=None mirrors
ollama's optional-user signature and stays correct whether user is passed
positionally, as a kwarg, or omitted.
Verified offline: old form returns the same key object for distinct users
(collision); new form yields distinct openai_all_models_<id> /
ollama_all_models_<id> keys, and the unauthenticated base key when no user.
These two were the only @cached(key=lambda ...) sites in the backend.
bypass_system_prompt is an internal flag used by utils/middleware.py and utils/chat.py to skip applying the model system prompt on recursive base-model calls, but it was still declared as a positional argument on the openai/ollama chat-completion route handlers, so FastAPI bound it from the query string. Move it to request.state so external clients cannot set it, matching how bypass_filter is handled.
Drop the argument from both route signatures and read getattr(request.state, 'bypass_system_prompt', False); utils/chat.py sets request.state.bypass_system_prompt alongside bypass_filter and drops the kwarg from the two route-handler calls (the recursive self-calls keep it). Mirrors c0385f60b.
Co-authored-by: anishgirianish <161533316+anishgirianish@users.noreply.github.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The catch-all /{path:path} proxy forwards any request to the upstream OpenAI-compatible API with the admin's API key and no access control. This is an intentional proxy but should be opt-in.
Adds ENABLE_OPENAI_API_PASSTHROUGH env var (defaults to False). When disabled, the catch-all returns 403. No other routers (Ollama, responses) have catch-all proxies.
The model name from user input was interpolated directly into Azure deployment URL paths without validation. A user could send a model name like '../../management/foo' to traverse the URL path and hit unintended Azure endpoints with the admin's API key.
Adds _sanitize_model_for_url that rejects path separators and traversal sequences, and percent-encodes the name. Applied at convert_to_azure_payload (covers chat completions + proxy) and the responses endpoint's direct URL construction.
The /responses proxy endpoint only required authentication via
get_verified_user but did not check per-model access grants. This
allowed any authenticated user to access any model through this
endpoint, bypassing the access control system.
Extract a shared check_model_access helper into utils/access_control
and replace all inline access control blocks across openai.py and
ollama.py (7 locations) with calls to this helper. This eliminates
code duplication and prevents future policy drift between endpoints.
CWE-862: Missing Authorization
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:H (6.5 Medium)
Azure offers two URL formats: the legacy deployment-based format
(/openai/deployments/{model}/...) and the newer v1 format
(/openai/v1/...) where the model stays in the payload body and no
api-version query parameter is needed.
Previously, the code always ran convert_to_azure_payload which
rewrites the URL to the deployment format, causing 404 errors for
users with v1-style base URLs. Now, when the base URL contains
'/openai/v1', we skip deployment URL construction and route
directly.
Applied consistently across all three Azure routing paths:
generate_chat_completion, /responses proxy, and generic proxy.
