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Merge branch 'BerriAI:main' into main

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Esteban Zeller 2026-02-24 17:00:48 -03:00 • committed by GitHub
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20
Dockerfile
View file

@ -49,7 +49,7 @@ USER root
# Install runtime dependencies (libsndfile needed for audio processing on ARM64)
RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip libsndfile && \
npm install -g npm@latest tar@7.5.7 glob@11.1.0 @isaacs/brace-expansion@5.0.1 && \
npm install -g npm@latest tar@7.5.8 glob@11.1.0 @isaacs/brace-expansion@5.0.1 minimatch@10.2.1 diff@8.0.3 && \
# SECURITY FIX: npm bundles tar, glob, and brace-expansion at multiple nested
# levels inside its dependency tree. `npm install -g <pkg>` only creates a
# SEPARATE global package, it does NOT replace npm's internal copies.
@ -64,6 +64,12 @@ RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip libsndfile
find "$GLOBAL/npm" -type d -name "brace-expansion" -path "*/node_modules/@isaacs/brace-expansion" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \
done && \
find "$GLOBAL/npm" -type d -name "minimatch" -path "*/node_modules/minimatch" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/minimatch" "$d"; \
done && \
find "$GLOBAL/npm" -type d -name "diff" -path "*/node_modules/diff" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/diff" "$d"; \
done && \
npm cache clean --force
WORKDIR /app
@ -90,14 +96,20 @@ RUN find /usr/lib -type f -path "*/tornado/test/*" -delete && \
# npm with old vulnerable deps at /usr/lib/python3.*/site-packages/nodejs_wheel/.
# Patch every copy of tar, glob, and brace-expansion inside that tree.
RUN GLOBAL="$(npm root -g)" && \
find /usr/lib -path "*/nodejs_wheel/*/node_modules/tar" -type d | while read d; do \
find /usr/lib -type d -name "tar" -path "*/node_modules/tar" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \
done && \
find /usr/lib -path "*/nodejs_wheel/*/node_modules/glob" -type d | while read d; do \
find /usr/lib -type d -name "glob" -path "*/node_modules/glob" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/glob" "$d"; \
done && \
find /usr/lib -path "*/nodejs_wheel/*/node_modules/@isaacs/brace-expansion" -type d | while read d; do \
find /usr/lib -type d -name "brace-expansion" -path "*/node_modules/@isaacs/brace-expansion" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \
done && \
find /usr/lib -type d -name "minimatch" -path "*/node_modules/minimatch" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/minimatch" "$d"; \
done && \
find /usr/lib -type d -name "diff" -path "*/node_modules/diff" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/diff" "$d"; \
done
# Install semantic_router and aurelio-sdk using script

1
ci_cd/security_scans.sh
View file

@ -160,6 +160,7 @@ run_grype_scans() {
"CVE-2026-0775" # npm cli incorrect permission assignment - no fix available yet, npm is only used at build/prisma-generate time
"GHSA-3ppc-4f35-3m26" # minimatch ReDoS via repeated wildcards - from nodejs_wheel bundled npm, not used in application runtime code
"GHSA-83g3-92jg-28cx" # tar arbitrary file read/write via hardlink - from nodejs_wheel bundled npm, not used in application runtime code
"CVE-2026-25639" # axios - full fix requires 1.x major version bump; pinned to >=0.30.2 to clear other axios CVEs, upgrade to 1.x in follow-up
)
# Build JSON array of allowlisted CVE IDs for jq

4
deploy/charts/litellm-helm/README.md
View file

@ -36,6 +36,10 @@ If `db.useStackgresOperator` is used (not yet implemented):
| `serviceAccount.create` | Whether or not to create a Kubernetes Service Account for this deployment. The default is `false` because LiteLLM has no need to access the Kubernetes API. | `false` |
| `service.type` | Kubernetes Service type (e.g. `LoadBalancer`, `ClusterIP`, etc.) | `ClusterIP` |
| `service.port` | TCP port that the Kubernetes Service will listen on. Also the TCP port within the Pod that the proxy will listen on. | `4000` |
| `livenessProbe.*` | Liveness probe settings for the LiteLLM container (`path`, `periodSeconds`, `timeoutSeconds`, thresholds, and initial delay). | See `values.yaml` |
| `readinessProbe.*` | Readiness probe settings for the LiteLLM container (`path`, `periodSeconds`, `timeoutSeconds`, thresholds, and initial delay). | See `values.yaml` |
| `startupProbe.*` | Startup probe settings for the LiteLLM container (`path`, `periodSeconds`, `timeoutSeconds`, thresholds, and initial delay). | See `values.yaml` |
| `resources.*` | CPU/memory requests and limits for the LiteLLM container. | `{}` |
| `service.loadBalancerClass` | Optional LoadBalancer implementation class (only used when `service.type` is `LoadBalancer`) | `""` |
| `ingress.labels` | Additional labels for the Ingress resource | `{}` |
| `ingress.*` | See [values.yaml](./values.yaml) for example settings | N/A |

2
deploy/charts/litellm-helm/templates/configmap-litellm.yaml
View file

@ -6,4 +6,4 @@ metadata:
data:
config.yaml: |
{{ .Values.proxy_config | toYaml | indent 6 }}
{{- end }}
{{- end }}

25
deploy/charts/litellm-helm/templates/deployment.yaml
View file

@ -158,18 +158,31 @@ spec:
{{- end }}
livenessProbe:
httpGet:
path: /health/liveliness
path: {{ .Values.livenessProbe.path | quote }}
port: {{ if .Values.separateHealthApp }}"health"{{ else }}"http"{{ end }}
initialDelaySeconds: {{ .Values.livenessProbe.initialDelaySeconds }}
periodSeconds: {{ .Values.livenessProbe.periodSeconds }}
timeoutSeconds: {{ .Values.livenessProbe.timeoutSeconds }}
successThreshold: {{ .Values.livenessProbe.successThreshold }}
failureThreshold: {{ .Values.livenessProbe.failureThreshold }}
readinessProbe:
httpGet:
path: /health/readiness
path: {{ .Values.readinessProbe.path | quote }}
port: {{ if .Values.separateHealthApp }}"health"{{ else }}"http"{{ end }}
initialDelaySeconds: {{ .Values.readinessProbe.initialDelaySeconds }}
periodSeconds: {{ .Values.readinessProbe.periodSeconds }}
timeoutSeconds: {{ .Values.readinessProbe.timeoutSeconds }}
successThreshold: {{ .Values.readinessProbe.successThreshold }}
failureThreshold: {{ .Values.readinessProbe.failureThreshold }}
startupProbe:
httpGet:
path: /health/readiness
path: {{ .Values.startupProbe.path | quote }}
port: {{ if .Values.separateHealthApp }}"health"{{ else }}"http"{{ end }}
failureThreshold: 30
periodSeconds: 10
initialDelaySeconds: {{ .Values.startupProbe.initialDelaySeconds }}
periodSeconds: {{ .Values.startupProbe.periodSeconds }}
timeoutSeconds: {{ .Values.startupProbe.timeoutSeconds }}
successThreshold: {{ .Values.startupProbe.successThreshold }}
failureThreshold: {{ .Values.startupProbe.failureThreshold }}
resources:
{{- toYaml .Values.resources | nindent 12 }}
volumeMounts:
@ -235,4 +248,4 @@ spec:
{{- if .Values.topologySpreadConstraints }}
topologySpreadConstraints:
{{- toYaml .Values.topologySpreadConstraints | nindent 8 }}
{{- end }}
{{- end }}

148
deploy/charts/litellm-helm/tests/deployment_tests.yaml
View file

@ -159,4 +159,150 @@ tests:
value: -c
- equal:
path: spec.template.spec.containers[0].lifecycle.preStop.exec.command[2]
value: echo "Container stopping"
value: echo "Container stopping"
- it: should render background health check settings from proxy_config.general_settings
template: configmap-litellm.yaml
set:
proxy_config.general_settings.background_health_checks: true
proxy_config.general_settings.health_check_interval: 240
proxy_config.general_settings.health_check_concurrency: 16
proxy_config.general_settings.health_check_details: false
asserts:
- matchRegex:
path: data["config.yaml"]
pattern: '(?m)^\s*background_health_checks:\s*true$'
- matchRegex:
path: data["config.yaml"]
pattern: '(?m)^\s*health_check_interval:\s*240$'
- matchRegex:
path: data["config.yaml"]
pattern: '(?m)^\s*health_check_concurrency:\s*16$'
- matchRegex:
path: data["config.yaml"]
pattern: '(?m)^\s*health_check_details:\s*false$'
- it: should allow overriding liveness, readiness, and startup probes
template: deployment.yaml
set:
livenessProbe:
path: /custom/livez
initialDelaySeconds: 5
periodSeconds: 15
timeoutSeconds: 5
successThreshold: 1
failureThreshold: 5
readinessProbe:
path: /custom/readyz
initialDelaySeconds: 10
periodSeconds: 20
timeoutSeconds: 6
successThreshold: 1
failureThreshold: 6
startupProbe:
path: /custom/startupz
initialDelaySeconds: 15
periodSeconds: 25
timeoutSeconds: 7
successThreshold: 1
failureThreshold: 40
asserts:
- equal:
path: spec.template.spec.containers[0].livenessProbe.httpGet.path
value: /custom/livez
- equal:
path: spec.template.spec.containers[0].livenessProbe.timeoutSeconds
value: 5
- equal:
path: spec.template.spec.containers[0].readinessProbe.httpGet.path
value: /custom/readyz
- equal:
path: spec.template.spec.containers[0].readinessProbe.timeoutSeconds
value: 6
- equal:
path: spec.template.spec.containers[0].startupProbe.httpGet.path
value: /custom/startupz
- equal:
path: spec.template.spec.containers[0].startupProbe.failureThreshold
value: 40
- it: should render container resources from values
template: deployment.yaml
set:
resources:
limits:
cpu: 500m
memory: 2Gi
requests:
cpu: 250m
memory: 1Gi
asserts:
- equal:
path: spec.template.spec.containers[0].resources.limits.cpu
value: 500m
- equal:
path: spec.template.spec.containers[0].resources.limits.memory
value: 2Gi
- equal:
path: spec.template.spec.containers[0].resources.requests.cpu
value: 250m
- equal:
path: spec.template.spec.containers[0].resources.requests.memory
value: 1Gi
- it: should keep default probes and empty resources unchanged
template: deployment.yaml
asserts:
- equal:
path: spec.template.spec.containers[0].livenessProbe.httpGet.path
value: /health/liveliness
- equal:
path: spec.template.spec.containers[0].livenessProbe.initialDelaySeconds
value: 0
- equal:
path: spec.template.spec.containers[0].livenessProbe.periodSeconds
value: 10
- equal:
path: spec.template.spec.containers[0].livenessProbe.timeoutSeconds
value: 1
- equal:
path: spec.template.spec.containers[0].livenessProbe.successThreshold
value: 1
- equal:
path: spec.template.spec.containers[0].livenessProbe.failureThreshold
value: 3
- equal:
path: spec.template.spec.containers[0].readinessProbe.httpGet.path
value: /health/readiness
- equal:
path: spec.template.spec.containers[0].readinessProbe.initialDelaySeconds
value: 0
- equal:
path: spec.template.spec.containers[0].readinessProbe.periodSeconds
value: 10
- equal:
path: spec.template.spec.containers[0].readinessProbe.timeoutSeconds
value: 1
- equal:
path: spec.template.spec.containers[0].readinessProbe.successThreshold
value: 1
- equal:
path: spec.template.spec.containers[0].readinessProbe.failureThreshold
value: 3
- equal:
path: spec.template.spec.containers[0].startupProbe.httpGet.path
value: /health/readiness
- equal:
path: spec.template.spec.containers[0].startupProbe.initialDelaySeconds
value: 0
- equal:
path: spec.template.spec.containers[0].startupProbe.periodSeconds
value: 10
- equal:
path: spec.template.spec.containers[0].startupProbe.timeoutSeconds
value: 1
- equal:
path: spec.template.spec.containers[0].startupProbe.successThreshold
value: 1
- equal:
path: spec.template.spec.containers[0].startupProbe.failureThreshold
value: 30
- equal:
path: spec.template.spec.containers[0].resources
value: {}

25
deploy/charts/litellm-helm/values.yaml
View file

@ -84,6 +84,31 @@ service:
separateHealthApp: false
separateHealthPort: 8081
# Probe tuning for proxy container
livenessProbe:
path: /health/liveliness
initialDelaySeconds: 0
periodSeconds: 10
timeoutSeconds: 1
successThreshold: 1
failureThreshold: 3
readinessProbe:
path: /health/readiness
initialDelaySeconds: 0
periodSeconds: 10
timeoutSeconds: 1
successThreshold: 1
failureThreshold: 3
startupProbe:
path: /health/readiness
initialDelaySeconds: 0
periodSeconds: 10
timeoutSeconds: 1
successThreshold: 1
failureThreshold: 30
ingress:
enabled: false
className: "nginx"

23
docker/Dockerfile.custom_ui
View file

@ -5,8 +5,21 @@ FROM ghcr.io/berriai/litellm:litellm_fwd_server_root_path-dev
WORKDIR /app
# Install Node.js and npm (adjust version as needed)
RUN apt-get update && apt-get install -y nodejs npm && \
npm install -g npm@latest tar@7.5.7 glob@11.1.0 @isaacs/brace-expansion@5.0.1 && \
RUN apt-get update && apt-get upgrade -y \
libxml2 \
libexpat1 \
openssl \
libssl3 \
git \
libkrb5-3 \
libglib2.0-0 \
wget \
libaom3 \
libxslt1.1 \
libgnutls30 \
libc6 && \
apt-get install -y nodejs npm && \
npm install -g npm@latest tar@7.5.8 glob@11.1.0 @isaacs/brace-expansion@5.0.1 minimatch@10.2.1 diff@8.0.3 && \
GLOBAL="$(npm root -g)" && \
find "$GLOBAL/npm" -type d -name "tar" -path "*/node_modules/tar" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \
@ -17,6 +30,12 @@ RUN apt-get update && apt-get install -y nodejs npm && \
find "$GLOBAL/npm" -type d -name "brace-expansion" -path "*/node_modules/@isaacs/brace-expansion" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \
done && \
find "$GLOBAL/npm" -type d -name "minimatch" -path "*/node_modules/minimatch" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/minimatch" "$d"; \
done && \
find "$GLOBAL/npm" -type d -name "diff" -path "*/node_modules/diff" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/diff" "$d"; \
done && \
npm cache clean --force
# Copy the UI source into the container

20
docker/Dockerfile.database
View file

@ -50,7 +50,7 @@ USER root
# Install runtime dependencies
RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip libsndfile && \
npm install -g npm@latest tar@7.5.7 glob@11.1.0 @isaacs/brace-expansion@5.0.1 && \
npm install -g npm@latest tar@7.5.8 glob@11.1.0 @isaacs/brace-expansion@5.0.1 minimatch@10.2.1 diff@8.0.3 && \
GLOBAL="$(npm root -g)" && \
find "$GLOBAL/npm" -type d -name "tar" -path "*/node_modules/tar" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \
@ -61,6 +61,12 @@ RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip libsndfile
find "$GLOBAL/npm" -type d -name "brace-expansion" -path "*/node_modules/@isaacs/brace-expansion" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \
done && \
find "$GLOBAL/npm" -type d -name "minimatch" -path "*/node_modules/minimatch" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/minimatch" "$d"; \
done && \
find "$GLOBAL/npm" -type d -name "diff" -path "*/node_modules/diff" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/diff" "$d"; \
done && \
npm cache clean --force
WORKDIR /app
@ -79,14 +85,20 @@ RUN pip install *.whl /wheels/* --no-index --find-links=/wheels/ && rm -f *.whl
# npm with old vulnerable deps at /usr/lib/python3.*/site-packages/nodejs_wheel/.
# Patch every copy of tar, glob, and brace-expansion inside that tree.
RUN GLOBAL="$(npm root -g)" && \
find /usr/lib -path "*/nodejs_wheel/*/node_modules/tar" -type d | while read d; do \
find /usr/lib -type d -name "tar" -path "*/node_modules/tar" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \
done && \
find /usr/lib -path "*/nodejs_wheel/*/node_modules/glob" -type d | while read d; do \
find /usr/lib -type d -name "glob" -path "*/node_modules/glob" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/glob" "$d"; \
done && \
find /usr/lib -path "*/nodejs_wheel/*/node_modules/@isaacs/brace-expansion" -type d | while read d; do \
find /usr/lib -type d -name "brace-expansion" -path "*/node_modules/@isaacs/brace-expansion" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \
done && \
find /usr/lib -type d -name "minimatch" -path "*/node_modules/minimatch" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/minimatch" "$d"; \
done && \
find /usr/lib -type d -name "diff" -path "*/node_modules/diff" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/diff" "$d"; \
done
# Install semantic_router and aurelio-sdk using script

37
docker/Dockerfile.dev
View file

@ -56,13 +56,26 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime
USER root
# Install only runtime dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
libssl3 \
RUN apt-get update && apt-get upgrade -y \
libxml2 \
libexpat1 \
openssl \
libssl3 \
git \
libkrb5-3 \
libglib2.0-0 \
wget \
libaom3 \
libxslt1.1 \
libgnutls30 \
libc6 \
&& apt-get install -y --no-install-recommends \
libssl3 \
libatomic1 \
nodejs \
npm \
&& rm -rf /var/lib/apt/lists/* \
&& npm install -g npm@latest tar@7.5.7 glob@11.1.0 @isaacs/brace-expansion@5.0.1 \
&& npm install -g npm@latest tar@7.5.8 glob@11.1.0 @isaacs/brace-expansion@5.0.1 minimatch@10.2.1 diff@8.0.3 \
&& GLOBAL="$(npm root -g)" \
&& find "$GLOBAL/npm" -type d -name "tar" -path "*/node_modules/tar" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \
@ -73,6 +86,12 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
&& find "$GLOBAL/npm" -type d -name "brace-expansion" -path "*/node_modules/@isaacs/brace-expansion" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \
done \
&& find "$GLOBAL/npm" -type d -name "minimatch" -path "*/node_modules/minimatch" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/minimatch" "$d"; \
done \
&& find "$GLOBAL/npm" -type d -name "diff" -path "*/node_modules/diff" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/diff" "$d"; \
done \
&& npm cache clean --force
WORKDIR /app
@ -95,14 +114,20 @@ RUN pip install --no-cache-dir *.whl /wheels/* --no-index --find-links=/wheels/
# npm with old vulnerable deps at /usr/lib/python3.*/site-packages/nodejs_wheel/.
# Patch every copy of tar, glob, and brace-expansion inside that tree.
RUN GLOBAL="$(npm root -g)" && \
find /usr/lib -path "*/nodejs_wheel/*/node_modules/tar" -type d | while read d; do \
find /usr/lib -type d -name "tar" -path "*/node_modules/tar" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \
done && \
find /usr/lib -path "*/nodejs_wheel/*/node_modules/glob" -type d | while read d; do \
find /usr/lib -type d -name "glob" -path "*/node_modules/glob" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/glob" "$d"; \
done && \
find /usr/lib -path "*/nodejs_wheel/*/node_modules/@isaacs/brace-expansion" -type d | while read d; do \
find /usr/lib -type d -name "brace-expansion" -path "*/node_modules/@isaacs/brace-expansion" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \
done && \
find /usr/lib -type d -name "minimatch" -path "*/node_modules/minimatch" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/minimatch" "$d"; \
done && \
find /usr/lib -type d -name "diff" -path "*/node_modules/diff" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/diff" "$d"; \
done
# Generate prisma client and set permissions

23
docker/Dockerfile.non_root
View file

@ -80,7 +80,7 @@ ENV PRISMA_BINARY_CACHE_DIR=/app/.cache/prisma-python/binaries \
XDG_CACHE_HOME=/app/.cache \
PATH="/usr/lib/python3.13/site-packages/nodejs/bin:${PATH}"
RUN pip install --no-cache-dir prisma==0.11.0 nodejs-wheel-binaries==24.12.0 \
RUN pip install --no-cache-dir prisma==0.11.0 nodejs-wheel-binaries==24.13.1 \
&& mkdir -p /app/.cache/npm
RUN NPM_CONFIG_CACHE=/app/.cache/npm \
@ -105,7 +105,8 @@ RUN for i in 1 2 3; do \
&& for i in 1 2 3; do \
apk add --no-cache python3 py3-pip bash openssl tzdata nodejs npm supervisor && break || sleep 5; \
done \
&& npm install -g npm@latest tar@7.5.7 glob@11.1.0 @isaacs/brace-expansion@5.0.1 \
&& apk upgrade --no-cache nodejs \
&& npm install -g npm@latest tar@7.5.8 glob@11.1.0 @isaacs/brace-expansion@5.0.1 minimatch@10.2.1 diff@8.0.3 \
&& GLOBAL="$(npm root -g)" \
&& find "$GLOBAL/npm" -type d -name "tar" -path "*/node_modules/tar" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \
@ -116,6 +117,12 @@ RUN for i in 1 2 3; do \
&& find "$GLOBAL/npm" -type d -name "brace-expansion" -path "*/node_modules/@isaacs/brace-expansion" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \
done \
&& find "$GLOBAL/npm" -type d -name "minimatch" -path "*/node_modules/minimatch" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/minimatch" "$d"; \
done \
&& find "$GLOBAL/npm" -type d -name "diff" -path "*/node_modules/diff" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/diff" "$d"; \
done \
&& npm cache clean --force
# Copy artifacts from builder
@ -162,14 +169,20 @@ RUN pip install --no-index --find-links=/wheels/ -r requirements.txt && \
# npm with old vulnerable deps at /usr/lib/python3.*/site-packages/nodejs_wheel/.
# Patch every copy of tar, glob, and brace-expansion inside that tree.
RUN GLOBAL="$(npm root -g)" && \
find /usr/lib -path "*/nodejs_wheel/*/node_modules/tar" -type d | while read d; do \
find /usr/lib -type d -name "tar" -path "*/node_modules/tar" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \
done && \
find /usr/lib -path "*/nodejs_wheel/*/node_modules/glob" -type d | while read d; do \
find /usr/lib -type d -name "glob" -path "*/node_modules/glob" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/glob" "$d"; \
done && \
find /usr/lib -path "*/nodejs_wheel/*/node_modules/@isaacs/brace-expansion" -type d | while read d; do \
find /usr/lib -type d -name "brace-expansion" -path "*/node_modules/@isaacs/brace-expansion" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \
done && \
find /usr/lib -type d -name "minimatch" -path "*/node_modules/minimatch" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/minimatch" "$d"; \
done && \
find /usr/lib -type d -name "diff" -path "*/node_modules/diff" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/diff" "$d"; \
done
# Permissions, cleanup, and Prisma prep

145
docs/my-website/blog/gpt_5_3_codex/index.md Normal file
View file

@ -0,0 +1,145 @@
---
slug: gpt_5_3_codex
title: "Day 0 Support: GPT-5.3-Codex"
date: 2026-02-24T10:00:00
authors:
- name: Sameer Kankute
title: SWE @ LiteLLM (LLM Translation)
url: https://www.linkedin.com/in/sameer-kankute/
image_url: https://pbs.twimg.com/profile_images/2001352686994907136/ONgNuSk5_400x400.jpg
- name: Krrish Dholakia
title: "CEO, LiteLLM"
url: https://www.linkedin.com/in/krish-d/
image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg
- name: Ishaan Jaff
title: "CTO, LiteLLM"
url: https://www.linkedin.com/in/reffajnaahsi/
image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
description: "Day 0 support for GPT-5.3-Codex on LiteLLM, including phase parameter handling for Responses API."
tags: [openai, gpt-5.3-codex, codex, day 0 support]
hide_table_of_contents: false
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
LiteLLM now supports GPT-5.3-Codex on Day 0, including support for the new assistant `phase` metadata on Responses API output items.
## Why `phase` matters for GPT-5.3-Codex
`phase` appears on assistant output items and helps distinguish preamble/commentary turns from final closeout responses.
Reference: [Phase parameter docs](https://developers.openai.com/api/reference/overview)
Supported values:
- `null`
- `"commentary"`
- `"final_answer"`
Important:
- Persist assistant output items with `phase` exactly as returned.
- Send those assistant items back on the next turn.
- Do **not** add `phase` to user messages.
## Docker Image
```bash
docker pull ghcr.io/berriai/litellm:v1.81.12-stable.gpt-5.3
```
## Usage
<Tabs>
<TabItem value="proxy" label="LiteLLM Proxy">
**1. Setup config.yaml**
```yaml
model_list:
- model_name: gpt-5.3-codex
litellm_params:
model: openai/gpt-5.3-codex
```
**2. Start the proxy**
```bash
docker run -d \
-p 4000:4000 \
-e ANTHROPIC_API_KEY=$OPENAI_API_KEY \
-v $(pwd)/config.yaml:/app/config.yaml \
ghcr.io/berriai/litellm:v1.81.12-stable.gpt-5.3 \
--config /app/config.yaml
```
**3. Test it**
```bash
curl -X POST "http://0.0.0.0:4000/v1/responses" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $LITELLM_KEY" \
-d '{
"model": "gpt-5.3-codex",
"input": "Write a Python script that checks if a number is prime."
}'
```
</TabItem>
</Tabs>
## Python Example: Persist `phase` with OpenAI Client + LiteLLM Base URL
```python
from openai import OpenAI
client = OpenAI(
base_url="http://0.0.0.0:4000/v1", # LiteLLM Proxy
api_key="your-litellm-api-key",
)
items = [] # Persist this per conversation/thread
def _item_get(item, key, default=None):
if isinstance(item, dict):
return item.get(key, default)
return getattr(item, key, default)
def run_turn(user_text: str):
global items
# User message: no phase field
items.append(
{
"type": "message",
"role": "user",
"content": [{"type": "input_text", "text": user_text}],
}
)
resp = client.responses.create(
model="gpt-5.3-codex",
input=items,
)
# Persist assistant output items verbatim, including phase
for out_item in (resp.output or []):
items.append(out_item)
# Optional: inspect latest phase for UI/telemetry routing
latest_phase = None
for out_item in reversed(resp.output or []):
if _item_get(out_item, "type") == "output_item.done" and _item_get(out_item, "phase") is not None:
latest_phase = _item_get(out_item, "phase")
break
return resp, latest_phase
```
## Notes
- Use `/v1/responses` for GPT Codex models.
- Preserve full assistant output history for best multi-turn behavior.
- If `phase` metadata is dropped during history reconstruction, output quality can degrade on long-running tasks.

26
docs/my-website/docs/proxy/prometheus.md
View file

@ -122,7 +122,7 @@ Use this to track overall LiteLLM Proxy usage.
| Metric Name | Description |
|----------------------|--------------------------------------|
| `litellm_proxy_failed_requests_metric` | Total number of failed responses from proxy - the client did not get a success response from litellm proxy. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "user_email", "exception_status", "exception_class", "route", "model_id"` |
| `litellm_proxy_total_requests_metric` | Total number of requests made to the proxy server - track number of client side requests. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "status_code", "user_email", "route", "model_id"` |
| `litellm_proxy_total_requests_metric` | Total number of requests made to the proxy server - track number of client side requests. Labels: `"end_user", "hashed_api_key", "api_key_alias", "requested_model", "team", "team_alias", "user", "status_code", "user_email", "route", "model_id"`. Optionally includes `"stream"` — see [Emit Stream Label](#emit-stream-label). |
### Callback Logging Metrics
@ -214,9 +214,31 @@ litellm_settings:
```
### Emit Stream Label
Add a `stream` label to `litellm_proxy_total_requests_metric` to split requests by streaming vs. non-streaming. Disabled by default.
```yaml title="config.yaml"
litellm_settings:
callbacks: ["prometheus"]
prometheus_emit_stream_label: true
```
When enabled, `litellm_proxy_total_requests_metric` gains a `stream` label with values `"True"`, `"False"`, or `"None"`.
```
litellm_proxy_total_requests_metric{..., stream="True"} 42
litellm_proxy_total_requests_metric{..., stream="False"} 100
```
:::note
This label is opt-in because adding a new label to an existing metric changes its cardinality and breaks existing Prometheus queries / Grafana dashboards that target this metric. Enable it only on fresh deployments or when you are ready to update your dashboards.
:::
## [BETA] Custom Metrics
Track custom metrics on prometheus on all events mentioned above.
Track custom metrics on prometheus on all events mentioned above.
### Custom Metadata Labels

13
docs/my-website/package.json
View file

@ -62,6 +62,8 @@
"gray-matter": "4.0.3",
"glob": ">=11.1.0",
"tar": ">=7.5.8",
"minimatch": ">=10.2.1",
"diff": ">=8.0.3",
"@isaacs/brace-expansion": ">=5.0.1",
"node-forge": ">=1.3.2",
"mdast-util-to-hast": ">=13.2.1",
@ -81,6 +83,15 @@
"url-loader": {
"ajv": "6.14.0"
},
"minimatch": "10.2.1"
"@babel/traverse": ">=7.23.2",
"ws": ">=7.5.10",
"http-proxy-middleware": ">=2.0.9",
"tar-fs": ">=2.1.4",
"webpack-dev-middleware": ">=5.3.4",
"braces": ">=3.0.3",
"axios": ">=0.30.2",
"webpack": ">=5.94.0",
"serve-static": ">=1.16.0",
"path-to-regexp": ">=0.1.12"
}
}

18
litellm-js/spend-logs/package.json
View file

@ -12,7 +12,19 @@
},
"overrides": {
"glob": ">=11.1.0",
"tar": ">=7.5.7",
"@isaacs/brace-expansion": ">=5.0.1"
"tar": ">=7.5.8",
"minimatch": ">=10.2.1",
"diff": ">=8.0.3",
"@isaacs/brace-expansion": ">=5.0.1",
"@babel/traverse": ">=7.23.2",
"ws": ">=7.5.10",
"http-proxy-middleware": ">=2.0.9",
"tar-fs": ">=2.1.4",
"webpack-dev-middleware": ">=5.3.4",
"braces": ">=3.0.3",
"axios": ">=0.30.2",
"webpack": ">=5.94.0",
"serve-static": ">=1.16.0",
"path-to-regexp": ">=0.1.12"
}
}
}

1
litellm/__init__.py
View file

@ -374,6 +374,7 @@ enable_end_user_cost_tracking_prometheus_only: Optional[bool] = None
custom_prometheus_metadata_labels: List[str] = []
custom_prometheus_tags: List[str] = []
prometheus_metrics_config: Optional[List] = None
prometheus_emit_stream_label: bool = False
disable_add_prefix_to_prompt: bool = (
False # used by anthropic, to disable adding prefix to prompt
)

6
litellm/integrations/prometheus.py
View file

@ -974,6 +974,9 @@ class PrometheusLogger(CustomLogger):
),
client_ip=standard_logging_payload["metadata"].get("requester_ip_address"),
user_agent=standard_logging_payload["metadata"].get("user_agent"),
stream=str(standard_logging_payload.get("stream"))
if litellm.prometheus_emit_stream_label
else None,
)
if (
@ -1624,6 +1627,9 @@ class PrometheusLogger(CustomLogger):
client_ip=_metadata.get("requester_ip_address"),
user_agent=_metadata.get("user_agent"),
model_id=model_id,
stream=str(request_data.get("stream"))
if litellm.prometheus_emit_stream_label
else None,
)
_labels = prometheus_label_factory(
supported_enum_labels=self.get_labels_for_metric(

248
litellm/litellm_core_utils/realtime_streaming.py
View file

@ -203,6 +203,129 @@ class RealTimeStreaming:
return True
return False
async def _handle_provider_config_message(self, raw_response) -> None:
"""Process a backend message when a provider_config is set (transformed path)."""
returned_object = self.provider_config.transform_realtime_response( # type: ignore[union-attr]
raw_response,
self.model,
self.logging_obj,
realtime_response_transform_input={
"session_configuration_request": self.session_configuration_request,
"current_output_item_id": self.current_output_item_id,
"current_response_id": self.current_response_id,
"current_delta_chunks": self.current_delta_chunks,
"current_conversation_id": self.current_conversation_id,
"current_item_chunks": self.current_item_chunks,
"current_delta_type": self.current_delta_type,
},
)
transformed_response = returned_object["response"]
self.current_output_item_id = returned_object["current_output_item_id"]
self.current_response_id = returned_object["current_response_id"]
self.current_delta_chunks = returned_object["current_delta_chunks"]
self.current_conversation_id = returned_object["current_conversation_id"]
self.current_item_chunks = returned_object["current_item_chunks"]
self.current_delta_type = returned_object["current_delta_type"]
self.session_configuration_request = returned_object["session_configuration_request"]
events = (
transformed_response
if isinstance(transformed_response, list)
else [transformed_response]
)
for event in events:
## GUARDRAIL: inject create_response=false on session.created
if isinstance(event, dict) and event.get("type") == "session.created":
if self._has_realtime_guardrails():
await self.backend_ws.send(
json.dumps(
{
"type": "session.update",
"session": {
"turn_detection": {
"type": "server_vad",
"create_response": False,
}
},
}
)
)
for event in events:
event_str = json.dumps(event)
## GUARDRAIL: run on transcription events in provider_config path too
if (
isinstance(event, dict)
and event.get("type")
== "conversation.item.input_audio_transcription.completed"
):
transcript = event.get("transcript", "")
self.store_message(event_str)
await self.websocket.send_text(event_str)
blocked = await self.run_realtime_guardrails(
transcript, item_id=event.get("item_id")
)
if not blocked:
await self.backend_ws.send(
json.dumps({"type": "response.create"})
)
continue
## LOGGING
self.store_message(event_str)
await self.websocket.send_text(event_str)
async def _handle_raw_backend_message(self, raw_response) -> bool:
"""Process a backend message without provider_config (raw path).
Returns True if the caller should skip the default store+forward (i.e. continue the loop).
"""
try:
event_obj = json.loads(raw_response)
if event_obj.get("type") == "session.created":
# If any realtime guardrails are registered, proactively
# set create_response=false so the LLM never auto-responds
# before our guardrail has a chance to run.
if self._has_realtime_guardrails():
await self.backend_ws.send(
json.dumps(
{
"type": "session.update",
"session": {
"turn_detection": {
"type": "server_vad",
"create_response": False,
}
},
}
)
)
verbose_logger.debug(
"[realtime guardrail] injected create_response=false into session"
)
if (
event_obj.get("type")
== "conversation.item.input_audio_transcription.completed"
):
transcript = event_obj.get("transcript", "")
## LOGGING — must happen before continue below
self.store_message(raw_response)
# Forward transcript to client so user sees what they said
await self.websocket.send_text(raw_response)
blocked = await self.run_realtime_guardrails(
transcript,
item_id=event_obj.get("item_id"),
)
if not blocked:
# Clean — trigger LLM response
await self.backend_ws.send(
json.dumps({"type": "response.create"})
)
return True
except (json.JSONDecodeError, AttributeError):
pass
return False
async def backend_to_client_send_messages(self):
import websockets
@ -216,128 +339,11 @@ class RealTimeStreaming:
raw_response = await self.backend_ws.recv() # type: ignore[assignment]
if self.provider_config:
returned_object = self.provider_config.transform_realtime_response(
raw_response,
self.model,
self.logging_obj,
realtime_response_transform_input={
"session_configuration_request": self.session_configuration_request,
"current_output_item_id": self.current_output_item_id,
"current_response_id": self.current_response_id,
"current_delta_chunks": self.current_delta_chunks,
"current_conversation_id": self.current_conversation_id,
"current_item_chunks": self.current_item_chunks,
"current_delta_type": self.current_delta_type,
},
)
transformed_response = returned_object["response"]
self.current_output_item_id = returned_object[
"current_output_item_id"
]
self.current_response_id = returned_object["current_response_id"]
self.current_delta_chunks = returned_object["current_delta_chunks"]
self.current_conversation_id = returned_object[
"current_conversation_id"
]
self.current_item_chunks = returned_object["current_item_chunks"]
self.current_delta_type = returned_object["current_delta_type"]
self.session_configuration_request = returned_object[
"session_configuration_request"
]
events = (
transformed_response
if isinstance(transformed_response, list)
else [transformed_response]
)
for event in events:
## GUARDRAIL: inject create_response=false on session.created
if isinstance(event, dict) and event.get("type") == "session.created":
if self._has_realtime_guardrails():
await self.backend_ws.send(
json.dumps(
{
"type": "session.update",
"session": {
"turn_detection": {
"type": "server_vad",
"create_response": False,
}
},
}
)
)
for event in events:
event_str = json.dumps(event)
## GUARDRAIL: run on transcription events in provider_config path too
if (
isinstance(event, dict)
and event.get("type")
== "conversation.item.input_audio_transcription.completed"
):
transcript = event.get("transcript", "")
self.store_message(event_str)
await self.websocket.send_text(event_str)
blocked = await self.run_realtime_guardrails(
transcript, item_id=event.get("item_id")
)
if not blocked:
await self.backend_ws.send(
json.dumps({"type": "response.create"})
)
continue
## LOGGING
self.store_message(event_str)
await self.websocket.send_text(event_str)
await self._handle_provider_config_message(raw_response)
else:
## GUARDRAIL: intercept transcription events before triggering LLM
try:
event_obj = json.loads(raw_response)
if event_obj.get("type") == "session.created":
# If any realtime guardrails are registered, proactively
# set create_response=false so the LLM never auto-responds
# before our guardrail has a chance to run.
if self._has_realtime_guardrails():
await self.backend_ws.send(
json.dumps(
{
"type": "session.update",
"session": {
"turn_detection": {
"type": "server_vad",
"create_response": False,
}
},
}
)
)
verbose_logger.debug(
"[realtime guardrail] injected create_response=false into session"
)
if (
event_obj.get("type")
== "conversation.item.input_audio_transcription.completed"
):
transcript = event_obj.get("transcript", "")
## LOGGING — must happen before continue below
self.store_message(raw_response)
# Forward transcript to client so user sees what they said
await self.websocket.send_text(raw_response)
blocked = await self.run_realtime_guardrails(
transcript,
item_id=event_obj.get("item_id"),
)
if not blocked:
# Clean — trigger LLM response
await self.backend_ws.send(
json.dumps({"type": "response.create"})
)
continue
except (json.JSONDecodeError, AttributeError):
pass
handled = await self._handle_raw_backend_message(raw_response)
if handled:
continue
## LOGGING
self.store_message(raw_response)
await self.websocket.send_text(raw_response)

8
litellm/llms/bedrock/rerank/handler.py
View file

@ -29,12 +29,13 @@ class BedrockRerankHandler(BaseAWSLLM):
async def arerank(
self,
prepared_request: BedrockPreparedRequest,
timeout: Optional[Union[float, httpx.Timeout]] = None,
client: Optional[AsyncHTTPHandler] = None,
):
if client is None:
client = get_async_httpx_client(llm_provider=litellm.LlmProviders.BEDROCK)
try:
response = await client.post(url=prepared_request["endpoint_url"], headers=dict(prepared_request["prepped"].headers), data=prepared_request["body"])
response = await client.post(url=prepared_request["endpoint_url"], headers=dict(prepared_request["prepped"].headers), data=prepared_request["body"], timeout=timeout)
response.raise_for_status()
except httpx.HTTPStatusError as err:
error_code = err.response.status_code
@ -56,6 +57,7 @@ class BedrockRerankHandler(BaseAWSLLM):
return_documents: Optional[bool] = True,
max_chunks_per_doc: Optional[int] = None,
_is_async: Optional[bool] = False,
timeout: Optional[Union[float, httpx.Timeout]] = None,
api_base: Optional[str] = None,
extra_headers: Optional[dict] = None,
client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
@ -89,12 +91,12 @@ class BedrockRerankHandler(BaseAWSLLM):
)
if _is_async:
return self.arerank(prepared_request, client=client if client is not None and isinstance(client, AsyncHTTPHandler) else None) # type: ignore
return self.arerank(prepared_request, timeout=timeout, client=client if client is not None and isinstance(client, AsyncHTTPHandler) else None) # type: ignore
if client is None or not isinstance(client, HTTPHandler):
client = _get_httpx_client()
try:
response = client.post(url=prepared_request["endpoint_url"], headers=dict(prepared_request["prepped"].headers), data=prepared_request["body"])
response = client.post(url=prepared_request["endpoint_url"], headers=dict(prepared_request["prepped"].headers), data=prepared_request["body"], timeout=timeout)
response.raise_for_status()
except httpx.HTTPStatusError as err:
error_code = err.response.status_code

128
litellm/model_prices_and_context_window_backup.json
View file

@ -20562,6 +20562,39 @@
"supports_tool_choice": true,
"supports_vision": true
},
"gpt-5.3-codex": {
"cache_read_input_token_cost": 1.75e-07,
"cache_read_input_token_cost_priority": 3.5e-07,
"input_cost_per_token": 1.75e-06,
"input_cost_per_token_priority": 3.5e-06,
"litellm_provider": "openai",
"max_input_tokens": 272000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "responses",
"output_cost_per_token": 1.4e-05,
"output_cost_per_token_priority": 2.8e-05,
"supported_endpoints": [
"/v1/responses"
],
"supported_modalities": [
"text",
"image"
],
"supported_output_modalities": [
"text"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_parallel_function_calling": true,
"supports_pdf_input": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": true,
"supports_system_messages": false,
"supports_tool_choice": true,
"supports_vision": true
},
"gpt-5-mini": {
"cache_read_input_token_cost": 2.5e-08,
"cache_read_input_token_cost_flex": 1.25e-08,
@ -26555,65 +26588,124 @@
"supports_function_calling": true,
"supports_tool_choice": true
},
"perplexity/preset/fast-search": {
"litellm_provider": "perplexity",
"mode": "responses",
"supports_web_search": true,
"supports_preset": true,
"supports_function_calling": true
},
"perplexity/preset/pro-search": {
"litellm_provider": "perplexity",
"mode": "responses",
"supports_web_search": true,
"supports_preset": true
"supports_preset": true,
"supports_function_calling": true
},
"perplexity/openai/gpt-4o": {
"perplexity/preset/deep-research": {
"litellm_provider": "perplexity",
"mode": "responses",
"supports_web_search": true,
"supports_reasoning": false
"supports_preset": true,
"supports_function_calling": true
},
"perplexity/openai/gpt-4o-mini": {
"perplexity/preset/advanced-deep-research": {
"litellm_provider": "perplexity",
"mode": "responses",
"supports_web_search": true,
"supports_reasoning": false
"supports_preset": true,
"supports_function_calling": true
},
"perplexity/openai/gpt-5.2": {
"litellm_provider": "perplexity",
"mode": "responses",
"supports_web_search": true,
"supports_reasoning": true
"supports_reasoning": true,
"supports_function_calling": true
},
"perplexity/anthropic/claude-3-5-sonnet-20241022": {
"perplexity/openai/gpt-5.1": {
"litellm_provider": "perplexity",
"mode": "responses",
"supports_web_search": true,
"supports_reasoning": false
"supports_reasoning": false,
"supports_function_calling": true
},
"perplexity/anthropic/claude-3-5-haiku-20241022": {
"perplexity/openai/gpt-5-mini": {
"litellm_provider": "perplexity",
"mode": "responses",
"supports_web_search": true,
"supports_reasoning": false
"supports_reasoning": false,
"supports_function_calling": true
},
"perplexity/google/gemini-2.0-flash-exp": {
"perplexity/anthropic/claude-opus-4-6": {
"litellm_provider": "perplexity",
"mode": "responses",
"supports_web_search": true,
"supports_reasoning": false
"supports_reasoning": false,
"supports_function_calling": true
},
"perplexity/google/gemini-2.0-flash-thinking-exp": {
"perplexity/anthropic/claude-opus-4-5": {
"litellm_provider": "perplexity",
"mode": "responses",
"supports_web_search": true,
"supports_reasoning": true
"supports_reasoning": false,
"supports_function_calling": true
},
"perplexity/xai/grok-2-1212": {
"perplexity/anthropic/claude-sonnet-4-5": {
"litellm_provider": "perplexity",
"mode": "responses",
"supports_web_search": true,
"supports_reasoning": false
"supports_reasoning": false,
"supports_function_calling": true
},
"perplexity/xai/grok-2-vision-1212": {
"perplexity/anthropic/claude-haiku-4-5": {
"litellm_provider": "perplexity",
"mode": "responses",
"supports_web_search": true,
"supports_reasoning": false
"supports_reasoning": false,
"supports_function_calling": true
},
"perplexity/google/gemini-3-pro-preview": {
"litellm_provider": "perplexity",
"mode": "responses",
"supports_web_search": true,
"supports_reasoning": false,
"supports_function_calling": true
},
"perplexity/google/gemini-3-flash-preview": {
"litellm_provider": "perplexity",
"mode": "responses",
"supports_web_search": true,
"supports_reasoning": false,
"supports_function_calling": true
},
"perplexity/google/gemini-2.5-pro": {
"litellm_provider": "perplexity",
"mode": "responses",
"supports_web_search": true,
"supports_reasoning": false,
"supports_function_calling": true
},
"perplexity/google/gemini-2.5-flash": {
"litellm_provider": "perplexity",
"mode": "responses",
"supports_web_search": true,
"supports_reasoning": false,
"supports_function_calling": true
},
"perplexity/xai/grok-4-1-fast-non-reasoning": {
"litellm_provider": "perplexity",
"mode": "responses",
"supports_web_search": true,
"supports_reasoning": false,
"supports_function_calling": true
},
"perplexity/perplexity/sonar": {
"litellm_provider": "perplexity",
"mode": "responses",
"supports_web_search": true,
"supports_reasoning": false,
"supports_function_calling": true
},
"publicai/aisingapore/Qwen-SEA-LION-v4-32B-IT": {
"input_cost_per_token": 0.0,

7
litellm/proxy/_types.py
View file

@ -2079,6 +2079,13 @@ class ConfigGeneralSettings(LiteLLMPydanticObjectBase):
health_check_interval: int = Field(
300, description="background health check interval in seconds"
)
health_check_concurrency: Optional[int] = Field(
None,
description=(
"limit concurrent health checks per cycle; when unset, "
"health checks run without a concurrency cap"
),
)
alerting: Optional[List] = Field(
None,
description="List of alerting integrations. Today, just slack - `alerting: ['slack']`",

224
litellm/proxy/health_check.py
View file

@ -3,12 +3,15 @@
import asyncio
import logging
import random
import sys
import threading
import time
from typing import List, Optional
import litellm
logger = logging.getLogger(__name__)
from litellm.constants import HEALTH_CHECK_TIMEOUT_SECONDS, DEFAULT_HEALTH_CHECK_PROMPT
from litellm.constants import DEFAULT_HEALTH_CHECK_PROMPT, HEALTH_CHECK_TIMEOUT_SECONDS
ILLEGAL_DISPLAY_PARAMS = [
"messages",
@ -23,6 +26,29 @@ ILLEGAL_DISPLAY_PARAMS = [
MINIMAL_DISPLAY_PARAMS = ["model", "mode_error"]
def _get_process_rss_mb() -> Optional[float]:
"""
Get process RSS memory in MB.
On Linux, ru_maxrss is in KB. On macOS, ru_maxrss is in bytes.
"""
try:
import resource
ru_maxrss = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
if sys.platform == "darwin":
return float(ru_maxrss) / (1024 * 1024)
return float(ru_maxrss) / 1024
except Exception:
return None
def _rss_mb_for_log() -> str:
rss_mb = _get_process_rss_mb()
if rss_mb is None:
return "unknown"
return f"{rss_mb:.2f}"
def _get_random_llm_message():
"""
Get a random message from the LLM.
@ -67,48 +93,115 @@ async def run_with_timeout(task, timeout):
try:
return await asyncio.wait_for(task, timeout)
except asyncio.TimeoutError:
task.cancel()
# Only cancel child tasks of the current task
current_task = asyncio.current_task()
for t in asyncio.all_tasks():
if t != current_task:
t.cancel()
try:
await asyncio.wait_for(task, 0.1) # Give 100ms for cleanup
except (asyncio.TimeoutError, asyncio.CancelledError, Exception):
pass
# `asyncio.wait_for()` already cancels only the awaited task on timeout.
# Do not cancel unrelated sibling health check tasks.
return {"error": "Timeout exceeded"}
async def _perform_health_check(model_list: list, details: Optional[bool] = True):
async def _run_model_health_check(model: dict):
litellm_params = model["litellm_params"]
model_info = model.get("model_info", {})
mode = model_info.get("mode", None)
litellm_params = _update_litellm_params_for_health_check(model_info, litellm_params)
timeout = model_info.get("health_check_timeout") or HEALTH_CHECK_TIMEOUT_SECONDS
return await run_with_timeout(
litellm.ahealth_check(
litellm_params,
mode=mode,
prompt=DEFAULT_HEALTH_CHECK_PROMPT,
input=["test from litellm"],
),
timeout,
)
async def _run_health_checks_with_bounded_concurrency(
models: list, concurrency_limit: int
) -> tuple[list, int]:
"""
Run health checks with at most `concurrency_limit` active tasks.
Preserves result ordering to match `models`.
"""
results: list = [None] * len(models)
tasks_to_index: dict[asyncio.Task, int] = {}
model_iter = iter(enumerate(models))
peak_in_flight = 0
def _schedule_next() -> bool:
nonlocal peak_in_flight
try:
idx, next_model = next(model_iter)
except StopIteration:
return False
task = asyncio.create_task(_run_model_health_check(next_model))
tasks_to_index[task] = idx
peak_in_flight = max(peak_in_flight, len(tasks_to_index))
return True
for _ in range(min(concurrency_limit, len(models))):
_schedule_next()
while tasks_to_index:
done, _ = await asyncio.wait(
set(tasks_to_index.keys()),
return_when=asyncio.FIRST_COMPLETED,
)
for task in done:
idx = tasks_to_index.pop(task)
try:
results[idx] = task.result()
except Exception as e:
results[idx] = e
_schedule_next()
return results, peak_in_flight
async def _perform_health_check(
model_list: list,
details: Optional[bool] = True,
max_concurrency: Optional[int] = None,
instrumentation_context: Optional[dict] = None,
):
"""
Perform a health check for each model in the list.
max_concurrency: Optional limit on concurrent health check requests.
"""
tasks = []
for model in model_list:
litellm_params = model["litellm_params"]
model_info = model.get("model_info", {})
mode = model_info.get("mode", None)
litellm_params = _update_litellm_params_for_health_check(
model_info, litellm_params
instrumentation_context = instrumentation_context or {}
instrumentation_enabled = bool(instrumentation_context.get("enabled", False))
cycle_id = instrumentation_context.get("cycle_id", "unknown")
source = instrumentation_context.get("source", "unknown")
dispatch_mode = "unbounded"
peak_in_flight = 0
if isinstance(max_concurrency, int) and max_concurrency > 0:
dispatch_mode = "bounded"
results, peak_in_flight = await _run_health_checks_with_bounded_concurrency(
model_list, max_concurrency
)
timeout = model_info.get("health_check_timeout") or HEALTH_CHECK_TIMEOUT_SECONDS
else:
tasks = [
asyncio.create_task(_run_model_health_check(model)) for model in model_list
]
peak_in_flight = len(tasks)
results = await asyncio.gather(*tasks, return_exceptions=True)
task = run_with_timeout(
litellm.ahealth_check(
model["litellm_params"],
mode=mode,
prompt=DEFAULT_HEALTH_CHECK_PROMPT,
input=["test from litellm"],
),
timeout,
if instrumentation_enabled:
logger.debug(
"health_check_dispatch_summary source=%s cycle_id=%s mode=%s model_count=%d max_concurrency=%s peak_in_flight=%d thread_count=%d rss_mb=%s",
source,
cycle_id,
dispatch_mode,
len(model_list),
max_concurrency,
peak_in_flight,
threading.active_count(),
_rss_mb_for_log(),
)
tasks.append(task)
results = await asyncio.gather(*tasks, return_exceptions=True)
healthy_endpoints = []
unhealthy_endpoints = []
@ -190,6 +283,8 @@ async def perform_health_check(
model: Optional[str] = None,
cli_model: Optional[str] = None,
details: Optional[bool] = True,
max_concurrency: Optional[int] = None,
instrumentation_context: Optional[dict] = None,
):
"""
Perform a health check on the system.
@ -197,14 +292,28 @@ async def perform_health_check(
Returns:
(bool): True if the health check passes, False otherwise.
"""
instrumentation_context = instrumentation_context or {}
instrumentation_enabled = bool(instrumentation_context.get("enabled", False))
cycle_id = instrumentation_context.get("cycle_id", "unknown")
source = instrumentation_context.get("source", "unknown")
if not model_list:
if cli_model:
model_list = [
{"model_name": cli_model, "litellm_params": {"model": cli_model}}
]
else:
if instrumentation_enabled:
logger.debug(
"health_check_cycle_skipped source=%s cycle_id=%s reason=no_models",
source,
cycle_id,
)
return [], []
cycle_start_time = time.monotonic()
requested_model_count = len(model_list)
if model is not None:
_new_model_list = [
x for x in model_list if x["litellm_params"]["model"] == model
@ -213,11 +322,56 @@ async def perform_health_check(
_new_model_list = [x for x in model_list if x["model_name"] == model]
model_list = _new_model_list
post_filter_model_count = len(model_list)
model_list = filter_deployments_by_id(
model_list=model_list
) # filter duplicate deployments (e.g. when model alias'es are used)
healthy_endpoints, unhealthy_endpoints = await _perform_health_check(
model_list, details
)
deduped_model_count = len(model_list)
if instrumentation_enabled:
logger.debug(
"health_check_cycle_start source=%s cycle_id=%s requested_model_count=%d post_model_filter_count=%d deduped_model_count=%d max_concurrency=%s thread_count=%d rss_mb=%s",
source,
cycle_id,
requested_model_count,
post_filter_model_count,
deduped_model_count,
max_concurrency,
threading.active_count(),
_rss_mb_for_log(),
)
try:
healthy_endpoints, unhealthy_endpoints = await _perform_health_check(
model_list,
details,
max_concurrency=max_concurrency,
instrumentation_context=instrumentation_context,
)
except Exception:
if instrumentation_enabled:
logger.exception(
"health_check_cycle_failed source=%s cycle_id=%s model_count=%d duration_ms=%.2f thread_count=%d rss_mb=%s",
source,
cycle_id,
deduped_model_count,
(time.monotonic() - cycle_start_time) * 1000,
threading.active_count(),
_rss_mb_for_log(),
)
raise
if instrumentation_enabled:
logger.debug(
"health_check_cycle_complete source=%s cycle_id=%s model_count=%d healthy_count=%d unhealthy_count=%d duration_ms=%.2f thread_count=%d rss_mb=%s",
source,
cycle_id,
deduped_model_count,
len(healthy_endpoints),
len(unhealthy_endpoints),
(time.monotonic() - cycle_start_time) * 1000,
threading.active_count(),
_rss_mb_for_log(),
)
return healthy_endpoints, unhealthy_endpoints

96
litellm/proxy/health_check_utils/shared_health_check_manager.py
View file

@ -16,7 +16,7 @@ from litellm.proxy.health_check import perform_health_check
class SharedHealthCheckManager:
"""
Manager for coordinating health checks across multiple pods using Redis.
This class implements a shared health check state mechanism that:
- Prevents duplicate health checks across pods
- Caches health check results with configurable TTL
@ -58,7 +58,7 @@ class SharedHealthCheckManager:
async def acquire_health_check_lock(self) -> bool:
"""
Attempt to acquire the global health check lock.
Returns:
bool: True if lock was acquired, False otherwise
"""
@ -74,7 +74,7 @@ class SharedHealthCheckManager:
nx=True, # Only set if key doesn't exist
ttl=self.lock_ttl,
)
if acquired:
verbose_proxy_logger.info(
"Pod %s acquired health check lock", self.pod_id
@ -83,12 +83,10 @@ class SharedHealthCheckManager:
verbose_proxy_logger.debug(
"Pod %s failed to acquire health check lock", self.pod_id
)
return acquired
except Exception as e:
verbose_proxy_logger.error(
"Error acquiring health check lock: %s", str(e)
)
verbose_proxy_logger.error("Error acquiring health check lock: %s", str(e))
return False
async def release_health_check_lock(self) -> None:
@ -106,14 +104,12 @@ class SharedHealthCheckManager:
"Pod %s released health check lock", self.pod_id
)
except Exception as e:
verbose_proxy_logger.error(
"Error releasing health check lock: %s", str(e)
)
verbose_proxy_logger.error("Error releasing health check lock: %s", str(e))
async def get_cached_health_check_results(self) -> Optional[Dict[str, Any]]:
"""
Get cached health check results from Redis.
Returns:
Optional[Dict]: Cached health check results or None if not found/expired
"""
@ -123,7 +119,7 @@ class SharedHealthCheckManager:
try:
cache_key = self.get_health_check_cache_key()
cached_data = await self.redis_cache.async_get_cache(cache_key)
if cached_data is None:
return None
@ -136,7 +132,7 @@ class SharedHealthCheckManager:
# Check if the cache is still valid
cache_timestamp = cached_results.get("timestamp", 0)
current_time = time.time()
if current_time - cache_timestamp > self.health_check_ttl:
verbose_proxy_logger.debug("Cached health check results expired")
return None
@ -151,13 +147,13 @@ class SharedHealthCheckManager:
return None
async def cache_health_check_results(
self,
healthy_endpoints: List[Dict[str, Any]],
unhealthy_endpoints: List[Dict[str, Any]]
self,
healthy_endpoints: List[Dict[str, Any]],
unhealthy_endpoints: List[Dict[str, Any]],
) -> None:
"""
Cache health check results in Redis.
Args:
healthy_endpoints: List of healthy endpoints
unhealthy_endpoints: List of unhealthy endpoints
@ -181,7 +177,7 @@ class SharedHealthCheckManager:
safe_dumps(cache_data),
ttl=self.health_check_ttl,
)
verbose_proxy_logger.info(
"Cached health check results for %d healthy and %d unhealthy endpoints",
len(healthy_endpoints),
@ -189,29 +185,29 @@ class SharedHealthCheckManager:
)
except Exception as e:
verbose_proxy_logger.error(
"Error caching health check results: %s", str(e)
)
verbose_proxy_logger.error("Error caching health check results: %s", str(e))
async def perform_shared_health_check(
self,
model_list: List[Dict[str, Any]],
details: bool = True
self,
model_list: List[Dict[str, Any]],
details: bool = True,
max_concurrency: Optional[int] = None,
) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
"""
Perform health check with shared state coordination.
This method:
1. First checks if there are recent cached results
2. If no recent cache, tries to acquire lock to run health check
3. If lock acquired, runs health check and caches results
4. If lock not acquired, waits briefly and tries to get cached results again
5. Falls back to running health check locally if no cache available
Args:
model_list: List of models to check
details: Whether to include detailed information
max_concurrency: Optional limit on concurrent health check requests
Returns:
Tuple of (healthy_endpoints, unhealthy_endpoints)
"""
@ -225,27 +221,29 @@ class SharedHealthCheckManager:
# No recent cache, try to acquire lock
lock_acquired = await self.acquire_health_check_lock()
if lock_acquired:
try:
# We have the lock, run health check
verbose_proxy_logger.info(
"Pod %s running health check for %d models",
self.pod_id,
len(model_list)
"Pod %s running health check for %d models",
self.pod_id,
len(model_list),
)
healthy_endpoints, unhealthy_endpoints = await perform_health_check(
model_list=model_list, details=details
model_list=model_list,
details=details,
max_concurrency=max_concurrency,
)
# Cache the results
await self.cache_health_check_results(
healthy_endpoints, unhealthy_endpoints
)
return healthy_endpoints, unhealthy_endpoints
finally:
# Always release the lock
await self.release_health_check_lock()
@ -254,10 +252,10 @@ class SharedHealthCheckManager:
verbose_proxy_logger.debug(
"Pod %s waiting for other pod to complete health check", self.pod_id
)
# Wait a bit for the other pod to complete
await asyncio.sleep(2)
# Try to get cached results again
cached_results = await self.get_cached_health_check_results()
if cached_results is not None:
@ -265,19 +263,23 @@ class SharedHealthCheckManager:
cached_results.get("healthy_endpoints", []),
cached_results.get("unhealthy_endpoints", []),
)
# Still no cache, fall back to local health check
verbose_proxy_logger.warning(
"Pod %s falling back to local health check (no cache available)",
self.pod_id
"Pod %s falling back to local health check (no cache available)",
self.pod_id,
)
return await perform_health_check(
model_list=model_list,
details=details,
max_concurrency=max_concurrency,
)
return await perform_health_check(model_list=model_list, details=details)
async def is_health_check_in_progress(self) -> bool:
"""
Check if a health check is currently in progress by another pod.
Returns:
bool: True if health check is in progress, False otherwise
"""
@ -297,7 +299,7 @@ class SharedHealthCheckManager:
async def get_health_check_status(self) -> Dict[str, Any]:
"""
Get the current status of health check coordination.
Returns:
Dict containing status information
"""
@ -320,7 +322,9 @@ class SharedHealthCheckManager:
cached_results = await self.get_cached_health_check_results()
status["cache_available"] = cached_results is not None
if cached_results:
status["cache_age_seconds"] = time.time() - cached_results.get("timestamp", 0)
status["cache_age_seconds"] = time.time() - cached_results.get(
"timestamp", 0
)
status["last_checked_by"] = cached_results.get("checked_by")
except Exception as e:

114
litellm/proxy/health_endpoints/_health_endpoints.py
View file

@ -110,26 +110,31 @@ def _resolve_os_environ_variables(params: dict) -> dict:
def get_callback_identifier(callback):
"""
Get the callback identifier string, handling both strings and objects.
This function extracts a string identifier from a callback, which can be:
- A string (returned as-is)
- An object with a callback_name attribute
- An object registered in CustomLoggerRegistry
- Falls back to callback_name() helper function
Args:
callback: The callback to identify (can be str or object)
Returns:
str: The callback identifier string
"""
if isinstance(callback, str):
return callback
if hasattr(callback, 'callback_name') and callback.callback_name:
if hasattr(callback, "callback_name") and callback.callback_name:
return callback.callback_name
if hasattr(callback, '__class__'):
callback_strs = CustomLoggerRegistry.get_all_callback_strs_from_class_type(callback.__class__)
if hasattr(callback, 'callback_name') and callback.callback_name in callback_strs:
if hasattr(callback, "__class__"):
callback_strs = CustomLoggerRegistry.get_all_callback_strs_from_class_type(
callback.__class__
)
if (
hasattr(callback, "callback_name")
and callback.callback_name in callback_strs
):
return callback.callback_name
if callback_strs:
return callback_strs[0]
@ -151,7 +156,7 @@ services = Union[
"datadog_llm_observability",
"generic_api",
"arize",
"sqs"
"sqs",
],
str,
]
@ -224,7 +229,7 @@ async def health_services_endpoint( # noqa: PLR0915
"datadog_llm_observability",
"generic_api",
"arize",
"sqs"
"sqs",
]:
raise HTTPException(
status_code=400,
@ -238,14 +243,14 @@ async def health_services_endpoint( # noqa: PLR0915
service_in_success_callbacks = True
else:
for cb in litellm.success_callback:
if hasattr(cb, 'callback_name') and cb.callback_name == service:
if hasattr(cb, "callback_name") and cb.callback_name == service:
service_in_success_callbacks = True
break
cb_id = get_callback_identifier(cb)
if cb_id == service:
service_in_success_callbacks = True
break
if (
service == "openmeter"
or service == "braintrust"
@ -320,6 +325,7 @@ async def health_services_endpoint( # noqa: PLR0915
)
elif service == "sqs":
from litellm.integrations.sqs import SQSLogger
sqs_logger = SQSLogger()
response = await sqs_logger.async_health_check()
return {
@ -518,12 +524,12 @@ async def _save_health_check_to_db(
def _build_model_param_to_info_mapping(model_list: list) -> dict:
"""
Build a mapping from model parameter to model info (model_name, model_id).
Multiple models might share the same model parameter, so we use a list.
Args:
model_list: List of model configurations
Returns:
Dictionary mapping model parameter to list of model info dicts
"""
@ -534,14 +540,16 @@ def _build_model_param_to_info_mapping(model_list: list) -> dict:
model_id = model_info.get("id")
litellm_params = model.get("litellm_params", {})
model_param = litellm_params.get("model")
if model_param and model_name:
if model_param not in model_param_to_info:
model_param_to_info[model_param] = []
model_param_to_info[model_param].append({
"model_name": model_name,
"model_id": model_id,
})
model_param_to_info[model_param].append(
{
"model_name": model_name,
"model_id": model_id,
}
)
return model_param_to_info
@ -552,19 +560,19 @@ def _aggregate_health_check_results(
) -> dict:
"""
Aggregate health check results per unique model.
Uses (model_id, model_name) as key, or (None, model_name) if model_id is None.
Args:
model_param_to_info: Mapping from model parameter to model info
healthy_endpoints: List of healthy endpoint results
unhealthy_endpoints: List of unhealthy endpoint results
Returns:
Dictionary mapping (model_id, model_name) to aggregated health check results
"""
model_results = {}
# Process healthy endpoints
for endpoint in healthy_endpoints:
model_param = endpoint.get("model")
@ -580,7 +588,7 @@ def _aggregate_health_check_results(
"error_message": None,
}
model_results[key]["healthy_count"] += 1
# Process unhealthy endpoints
for endpoint in unhealthy_endpoints:
model_param = endpoint.get("model")
@ -600,7 +608,7 @@ def _aggregate_health_check_results(
# Use the first error message encountered
if not model_results[key]["error_message"] and error_message:
model_results[key]["error_message"] = str(error_message)[:500]
return model_results
@ -613,14 +621,14 @@ async def _save_health_check_results_if_changed(
):
"""
Save health check results to database, but only if status changed or >1 hour since last save.
OPTIMIZATION: Only saves to database if the status has changed from the last saved check.
This dramatically reduces database writes when health status remains stable.
- Stable systems: ~1 write/hour per model (instead of 12 writes/hour with 5-min intervals)
- Status changes: Immediate write (no delay)
- Result: ~92% reduction in DB writes for stable systems, while maintaining real-time updates on changes
Args:
prisma_client: Database client
model_results: Dictionary of aggregated health check results per model
@ -630,7 +638,7 @@ async def _save_health_check_results_if_changed(
"""
for result in model_results.values():
new_status = "healthy" if result["healthy_count"] > 0 else "unhealthy"
# Check if we should save this result
should_save = True
lookup_key = result["model_id"] if result["model_id"] else result["model_name"]
@ -641,6 +649,7 @@ async def _save_health_check_results_if_changed(
# Check if last check was recent (within 1 hour)
if last_check.checked_at:
from datetime import datetime, timezone
time_since_last_check = (
datetime.now(timezone.utc) - last_check.checked_at
).total_seconds()
@ -648,7 +657,7 @@ async def _save_health_check_results_if_changed(
# This ensures we still get periodic updates even if status is stable
if time_since_last_check < 3600: # 1 hour threshold
should_save = False
if should_save:
asyncio.create_task(
prisma_client.save_health_check_result(
@ -675,27 +684,27 @@ async def _save_background_health_checks_to_db(
):
"""
Save background health check results to database for each model.
Maps health check endpoints back to their original models to get model_name and model_id.
Aggregates results per unique model (by model_id if available, otherwise model_name).
OPTIMIZATION: Only saves to database if the status has changed from the last saved check.
This dramatically reduces database writes when health status remains stable.
"""
if prisma_client is None:
return
try:
# Step 1: Build mapping from model parameter to model info
model_param_to_info = _build_model_param_to_info_mapping(model_list)
# Step 2: Aggregate health check results per unique model
model_results = _aggregate_health_check_results(
model_param_to_info,
healthy_endpoints,
unhealthy_endpoints,
)
# Step 3: Get latest health checks for all models in one query to compare status
latest_checks = await prisma_client.get_all_latest_health_checks()
latest_checks_map = {}
@ -704,7 +713,7 @@ async def _save_background_health_checks_to_db(
key = check.model_id if check.model_id else check.model_name
if key not in latest_checks_map:
latest_checks_map[key] = check
# Step 4: Save aggregated results, but only if status changed
await _save_health_check_results_if_changed(
prisma_client,
@ -729,10 +738,15 @@ async def _perform_health_check_and_save(
start_time,
user_id,
model_id=None,
max_concurrency=None,
):
"""Helper function to perform health check and save results to database"""
healthy_endpoints, unhealthy_endpoints = await perform_health_check(
model_list=model_list, cli_model=cli_model, model=target_model, details=details
model_list=model_list,
cli_model=cli_model,
model=target_model,
details=details,
max_concurrency=max_concurrency,
)
# Optionally save health check result to database (non-blocking)
@ -789,6 +803,7 @@ async def health_endpoint(
import time
from litellm.proxy.proxy_server import (
health_check_concurrency,
health_check_details,
health_check_results,
llm_model_list,
@ -841,6 +856,7 @@ async def health_endpoint(
start_time=start_time,
user_id=user_api_key_dict.user_id,
model_id=None, # CLI model doesn't have model_id
max_concurrency=health_check_concurrency,
)
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
@ -864,6 +880,7 @@ async def health_endpoint(
start_time=start_time,
user_id=user_api_key_dict.user_id,
model_id=model_id,
max_concurrency=health_check_concurrency,
)
except Exception as e:
verbose_proxy_logger.error(
@ -1420,11 +1437,11 @@ async def test_model_connection(
status_code=500,
detail={"error": CommonProxyErrors.db_not_connected_error.value},
)
# Get model name from litellm_params
request_litellm_params = litellm_params or {}
model_name = request_litellm_params.get("model")
# Look up model configuration from router if model name is provided
# This gets the litellm_params from proxy config (with resolved env vars)
config_litellm_params: dict = {}
@ -1432,34 +1449,39 @@ async def test_model_connection(
try:
# First try to find by proxy model_name (e.g., "gpt-4o")
deployments = llm_router.get_model_list(model_name=model_name)
# If not found, try to find by litellm model name (e.g., "azure/gpt-4o")
if not deployments or len(deployments) == 0:
all_deployments = llm_router.get_model_list(model_name=None)
if all_deployments:
for deployment in all_deployments:
if deployment.get("litellm_params", {}).get("model") == model_name:
if (
deployment.get("litellm_params", {}).get("model")
== model_name
):
deployments = [deployment]
break
if deployments and len(deployments) > 0:
# Use the first deployment's litellm_params as base config
# These already have resolved environment variables from proxy config
config_litellm_params = dict(deployments[0].get("litellm_params", {}))
config_litellm_params = dict(
deployments[0].get("litellm_params", {})
)
except Exception as e:
verbose_proxy_logger.debug(
f"Could not find model {model_name} in router: {e}. "
"Proceeding with request params only."
)
# Merge: config params (from proxy config) as base, request params override
# This allows users to override specific params while using config for credentials
merged_litellm_params = {**config_litellm_params, **request_litellm_params}
# Resolve os.environ/ environment variables in any remaining request params
# This handles cases where user explicitly passes os.environ/ values to override config
litellm_params = _resolve_os_environ_variables(merged_litellm_params)
## Auth check
await ModelManagementAuthChecks.can_user_make_model_call(
model_params=Deployment(

332
litellm/proxy/proxy_server.py
View file

@ -9,6 +9,7 @@ import secrets
import shutil
import subprocess
import sys
import threading
import time
import traceback
import warnings
@ -658,7 +659,7 @@ _description = (
def cleanup_router_config_variables():
global master_key, user_config_file_path, otel_logging, user_custom_auth, user_custom_auth_path, user_custom_key_generate, user_custom_sso, user_custom_ui_sso_sign_in_handler, use_background_health_checks, use_shared_health_check, health_check_interval, prisma_client
global master_key, user_config_file_path, otel_logging, user_custom_auth, user_custom_auth_path, user_custom_key_generate, user_custom_sso, user_custom_ui_sso_sign_in_handler, use_background_health_checks, use_shared_health_check, health_check_interval, health_check_concurrency, prisma_client
# Set all variables to None
master_key = None
@ -672,6 +673,7 @@ def cleanup_router_config_variables():
use_background_health_checks = None
use_shared_health_check = None
health_check_interval = None
health_check_concurrency = None
prisma_client = None
@ -711,7 +713,7 @@ async def _initialize_shared_aiohttp_session():
try:
from aiohttp import ClientSession, TCPConnector
connector_kwargs = {
connector_kwargs: Dict[str, Any] = {
"keepalive_timeout": AIOHTTP_KEEPALIVE_TIMEOUT,
"ttl_dns_cache": AIOHTTP_TTL_DNS_CACHE,
}
@ -822,7 +824,9 @@ async def proxy_startup_event(app: FastAPI): # noqa: PLR0915
verbose_proxy_logger.debug("About to initialize semantic tool filter")
_config = proxy_config.get_config_state()
_litellm_settings = _config.get("litellm_settings", {})
verbose_proxy_logger.debug(f"litellm_settings keys = {list(_litellm_settings.keys())}")
verbose_proxy_logger.debug(
f"litellm_settings keys = {list(_litellm_settings.keys())}"
)
await ProxyStartupEvent._initialize_semantic_tool_filter(
llm_router=llm_router,
litellm_settings=_litellm_settings,
@ -1468,7 +1472,9 @@ redis_usage_cache: Optional[
RedisCache
] = None # redis cache used for tracking spend, tpm/rpm limits
polling_via_cache_enabled: Union[Literal["all"], List[str], bool] = False
native_background_mode: List[str] = [] # Models that should use native provider background mode instead of polling
native_background_mode: List[
str
] = [] # Models that should use native provider background mode instead of polling
polling_cache_ttl: int = 3600 # Default 1 hour TTL for polling cache
user_custom_auth = None
user_custom_key_generate = None
@ -1478,8 +1484,11 @@ use_background_health_checks = None
use_shared_health_check = None
use_queue = False
health_check_interval = None
health_check_concurrency = None
health_check_details = None
health_check_results: Dict[str, Union[int, List[Dict[str, Any]]]] = {}
background_health_check_loop_active = False
background_health_check_cycle_seq = 0
queue: List = []
litellm_proxy_budget_name = "litellm-proxy-budget"
litellm_proxy_admin_name = LITELLM_PROXY_ADMIN_NAME
@ -1927,6 +1936,88 @@ def run_ollama_serve():
)
def _get_process_rss_mb() -> Optional[float]:
"""
Get process RSS memory in MB.
On Linux, ru_maxrss is in KB. On macOS, ru_maxrss is in bytes.
"""
try:
import resource
ru_maxrss = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
if sys.platform == "darwin":
return float(ru_maxrss) / (1024 * 1024)
return float(ru_maxrss) / 1024
except Exception:
return None
def _rss_mb_for_log() -> str:
rss_mb = _get_process_rss_mb()
if rss_mb is None:
return "unknown"
return f"{rss_mb:.2f}"
async def _run_direct_health_check_with_instrumentation(
model_list: list,
details: Optional[bool],
max_concurrency: Optional[int],
instrumentation_context: dict,
):
try:
return await perform_health_check(
model_list=model_list,
details=details,
max_concurrency=max_concurrency,
instrumentation_context=instrumentation_context,
)
except TypeError as e:
if "instrumentation_context" not in str(e):
raise
# Backward compatibility for monkeypatched or wrapped callables
# that do not accept instrumentation_context.
return await perform_health_check(
model_list=model_list,
details=details,
max_concurrency=max_concurrency,
)
def _schedule_background_health_check_db_save(
prisma_client,
shared_health_manager,
model_list: list,
healthy_endpoints: list,
unhealthy_endpoints: list,
):
"""Fire-and-forget: persist health check results to DB if prisma is available."""
if prisma_client is None:
return
import time as time_module
from litellm.proxy.health_endpoints._health_endpoints import (
_save_background_health_checks_to_db,
)
checked_by = (
shared_health_manager.pod_id
if shared_health_manager is not None
else "background_health_check"
)
start_time = time_module.time()
asyncio.create_task(
_save_background_health_checks_to_db(
prisma_client,
model_list,
healthy_endpoints,
unhealthy_endpoints,
start_time,
checked_by=checked_by,
)
)
async def _run_background_health_check():
"""
Periodically run health checks in the background on the endpoints.
@ -1934,7 +2025,10 @@ async def _run_background_health_check():
Update health_check_results, based on this.
Uses shared health check state when Redis is available to coordinate across pods.
"""
global health_check_results, llm_model_list, health_check_interval, health_check_details, use_shared_health_check, redis_usage_cache, prisma_client
global health_check_results, llm_model_list, health_check_interval
global health_check_concurrency, health_check_details, use_shared_health_check
global redis_usage_cache, prisma_client
global background_health_check_loop_active, background_health_check_cycle_seq
if (
health_check_interval is None
@ -1943,6 +2037,24 @@ async def _run_background_health_check():
):
return
if background_health_check_loop_active:
verbose_proxy_logger.warning(
"background_health_check_loop_overlap_detected existing_loop_active=true interval_seconds=%s max_concurrency=%s shared=%s",
health_check_interval,
health_check_concurrency,
use_shared_health_check,
)
background_health_check_loop_active = True
verbose_proxy_logger.info(
"background_health_check_loop_started interval_seconds=%s max_concurrency=%s shared=%s details=%s thread_count=%d rss_mb=%s",
health_check_interval,
health_check_concurrency,
use_shared_health_check,
health_check_details,
threading.active_count(),
_rss_mb_for_log(),
)
# Initialize shared health check manager if Redis is available and feature is enabled
shared_health_manager = None
if use_shared_health_check and redis_usage_cache is not None:
@ -1958,8 +2070,13 @@ async def _run_background_health_check():
verbose_proxy_logger.info("Initialized shared health check manager")
while True:
background_health_check_cycle_seq += 1
cycle_id = f"bg-{background_health_check_cycle_seq}"
cycle_start_time = time.monotonic()
# make 1 deep copy of llm_model_list on every health check iteration
_llm_model_list = copy.deepcopy(llm_model_list) or []
model_count_total = len(_llm_model_list)
# filter out models that have disabled background health checks
_llm_model_list = [
@ -1967,6 +2084,33 @@ async def _run_background_health_check():
for m in _llm_model_list
if not m.get("model_info", {}).get("disable_background_health_check", False)
]
model_count_enabled = len(_llm_model_list)
expected_peak_in_flight = model_count_enabled
if (
isinstance(health_check_concurrency, int)
and health_check_concurrency > 0
and model_count_enabled > 0
):
expected_peak_in_flight = min(model_count_enabled, health_check_concurrency)
verbose_proxy_logger.debug(
"background_health_check_cycle_start cycle_id=%s model_count_total=%d model_count_enabled=%d interval_seconds=%s max_concurrency=%s expected_peak_in_flight=%d shared=%s thread_count=%d rss_mb=%s",
cycle_id,
model_count_total,
model_count_enabled,
health_check_interval,
health_check_concurrency,
expected_peak_in_flight,
shared_health_manager is not None,
threading.active_count(),
_rss_mb_for_log(),
)
instrumentation_context = {
"enabled": True,
"source": "proxy_background_loop",
"cycle_id": cycle_id,
}
# Use shared health check if available, otherwise fall back to direct health check
# Convert health_check_details to bool for perform_shared_health_check (defaults to True if None)
@ -1980,19 +2124,31 @@ async def _run_background_health_check():
healthy_endpoints,
unhealthy_endpoints,
) = await shared_health_manager.perform_shared_health_check(
model_list=_llm_model_list, details=details_bool
model_list=_llm_model_list,
details=details_bool,
max_concurrency=health_check_concurrency,
)
except Exception as e:
verbose_proxy_logger.error(
"Error in shared health check, falling back to direct health check: %s",
str(e),
)
healthy_endpoints, unhealthy_endpoints = await perform_health_check(
model_list=_llm_model_list, details=health_check_details
healthy_endpoints, unhealthy_endpoints = (
await _run_direct_health_check_with_instrumentation(
_llm_model_list,
health_check_details,
health_check_concurrency,
instrumentation_context,
)
)
else:
healthy_endpoints, unhealthy_endpoints = await perform_health_check(
model_list=_llm_model_list, details=health_check_details
healthy_endpoints, unhealthy_endpoints = (
await _run_direct_health_check_with_instrumentation(
_llm_model_list,
health_check_details,
health_check_concurrency,
instrumentation_context,
)
)
# Update the global variable with the health check results
@ -2000,34 +2156,34 @@ async def _run_background_health_check():
health_check_results["unhealthy_endpoints"] = unhealthy_endpoints
health_check_results["healthy_count"] = len(healthy_endpoints)
health_check_results["unhealthy_count"] = len(unhealthy_endpoints)
cycle_duration_ms = (time.monotonic() - cycle_start_time) * 1000
verbose_proxy_logger.debug(
"background_health_check_cycle_complete cycle_id=%s model_count_enabled=%d healthy_count=%d unhealthy_count=%d duration_ms=%.2f interval_seconds=%s thread_count=%d rss_mb=%s",
cycle_id,
model_count_enabled,
len(healthy_endpoints),
len(unhealthy_endpoints),
cycle_duration_ms,
health_check_interval,
threading.active_count(),
_rss_mb_for_log(),
)
if cycle_duration_ms > (health_check_interval * 1000):
verbose_proxy_logger.warning(
"background_health_check_cycle_duration_exceeded_interval cycle_id=%s duration_ms=%.2f interval_seconds=%s",
cycle_id,
cycle_duration_ms,
health_check_interval,
)
# Save background health checks to database (non-blocking)
if prisma_client is not None:
import time as time_module
from litellm.proxy.health_endpoints._health_endpoints import (
_save_background_health_checks_to_db,
)
# Use pod_id or a system identifier for checked_by if shared health check is enabled
checked_by = None
if shared_health_manager is not None:
checked_by = shared_health_manager.pod_id
else:
# Use a system identifier for background health checks
checked_by = "background_health_check"
start_time = time_module.time()
asyncio.create_task(
_save_background_health_checks_to_db(
prisma_client,
_llm_model_list,
healthy_endpoints,
unhealthy_endpoints,
start_time,
checked_by=checked_by,
)
)
_schedule_background_health_check_db_save(
prisma_client,
shared_health_manager,
_llm_model_list,
healthy_endpoints,
unhealthy_endpoints,
)
await asyncio.sleep(health_check_interval)
@ -2480,7 +2636,7 @@ class ProxyConfig:
"""
Load config values into proxy global state
"""
global master_key, user_config_file_path, otel_logging, user_custom_auth, user_custom_auth_path, user_custom_key_generate, user_custom_sso, user_custom_ui_sso_sign_in_handler, use_background_health_checks, use_shared_health_check, health_check_interval, use_queue, proxy_budget_rescheduler_max_time, proxy_budget_rescheduler_min_time, ui_access_mode, litellm_master_key_hash, proxy_batch_write_at, disable_spend_logs, prompt_injection_detection_obj, redis_usage_cache, store_model_in_db, premium_user, open_telemetry_logger, health_check_details, proxy_batch_polling_interval, config_passthrough_endpoints
global master_key, user_config_file_path, otel_logging, user_custom_auth, user_custom_auth_path, user_custom_key_generate, user_custom_sso, user_custom_ui_sso_sign_in_handler, use_background_health_checks, use_shared_health_check, health_check_interval, health_check_concurrency, use_queue, proxy_budget_rescheduler_max_time, proxy_budget_rescheduler_min_time, ui_access_mode, litellm_master_key_hash, proxy_batch_write_at, disable_spend_logs, prompt_injection_detection_obj, redis_usage_cache, store_model_in_db, premium_user, open_telemetry_logger, health_check_details, proxy_batch_polling_interval, config_passthrough_endpoints
config: dict = await self.get_config(config_file_path=config_file_path)
@ -2905,7 +3061,18 @@ class ProxyConfig:
health_check_interval = general_settings.get(
"health_check_interval", DEFAULT_HEALTH_CHECK_INTERVAL
)
health_check_concurrency = general_settings.get(
"health_check_concurrency", None
)
health_check_details = general_settings.get("health_check_details", True)
verbose_proxy_logger.info(
"background_health_check_config enabled=%s shared=%s interval_seconds=%s max_concurrency=%s details=%s",
use_background_health_checks,
use_shared_health_check,
health_check_interval,
health_check_concurrency,
health_check_details,
)
### RBAC ###
rbac_role_permissions = general_settings.get("role_permissions", None)
@ -2999,7 +3166,7 @@ class ProxyConfig:
for k, v in router_settings.items():
if k in available_args:
router_params[k] = v
elif k == "health_check_interval":
elif k in {"health_check_interval", "health_check_concurrency"}:
raise ValueError(
f"'{k}' is NOT a valid router_settings parameter. Please move it to 'general_settings'."
)
@ -4201,9 +4368,7 @@ class ProxyConfig:
)
if self._should_load_db_object(object_type="semantic_filter_settings"):
await self._init_semantic_filter_settings_in_db(
prisma_client=prisma_client
)
await self._init_semantic_filter_settings_in_db(prisma_client=prisma_client)
async def _init_semantic_filter_settings_in_db(self, prisma_client: PrismaClient):
"""
@ -5259,30 +5424,38 @@ class ProxyStartupEvent:
):
"""Initialize MCP semantic tool filter if configured"""
from litellm.proxy.hooks.mcp_semantic_filter import SemanticToolFilterHook
mcp_semantic_filter_config = litellm_settings.get("mcp_semantic_tool_filter", None)
mcp_semantic_filter_config = litellm_settings.get(
"mcp_semantic_tool_filter", None
)
# Only proceed if the feature is configured and enabled
if not mcp_semantic_filter_config or not mcp_semantic_filter_config.get("enabled", False):
verbose_proxy_logger.debug("Semantic tool filter not configured or not enabled, skipping initialization")
if not mcp_semantic_filter_config or not mcp_semantic_filter_config.get(
"enabled", False
):
verbose_proxy_logger.debug(
"Semantic tool filter not configured or not enabled, "
"skipping initialization"
)
return
verbose_proxy_logger.debug(
f"Initializing semantic tool filter: llm_router={llm_router is not None}, "
f"config={mcp_semantic_filter_config}"
)
hook = await SemanticToolFilterHook.initialize_from_config(
config=mcp_semantic_filter_config,
llm_router=llm_router,
)
if hook:
verbose_proxy_logger.debug("Semantic tool filter hook registered")
litellm.logging_callback_manager.add_litellm_callback(hook)
else:
# Only warn if the feature was configured but failed to initialize
verbose_proxy_logger.warning("Semantic tool filter hook was configured but failed to initialize")
verbose_proxy_logger.warning(
"Semantic tool filter hook was configured but failed to initialize"
)
@classmethod
def _initialize_jwt_auth(
@ -8706,7 +8879,8 @@ async def _apply_search_filter_to_models(
# Fetch database models if we need more for the current page
if router_models_count < models_needed_for_page:
models_to_fetch = min(
models_needed_for_page - router_models_count, db_models_total_count
models_needed_for_page - router_models_count,
db_models_total_count,
)
if models_to_fetch > 0:
@ -8742,21 +8916,21 @@ async def _apply_search_filter_to_models(
def _normalize_datetime_for_sorting(dt: Any) -> Optional[datetime]:
"""
Normalize a datetime value to a timezone-aware UTC datetime for sorting.
This function handles:
- None values: returns None
- String values: parses ISO format strings and converts to UTC-aware datetime
- Datetime objects: converts naive datetimes to UTC-aware, and aware datetimes to UTC
Args:
dt: Datetime value (None, str, or datetime object)
Returns:
UTC-aware datetime object, or None if input is None or cannot be parsed
"""
if dt is None:
return None
if isinstance(dt, str):
try:
# Handle ISO format strings, including 'Z' suffix
@ -8770,14 +8944,14 @@ def _normalize_datetime_for_sorting(dt: Any) -> Optional[datetime]:
return parsed_dt
except (ValueError, AttributeError):
return None
if isinstance(dt, datetime):
# If naive, assume UTC and make it aware
if dt.tzinfo is None:
return dt.replace(tzinfo=timezone.utc)
# If aware, convert to UTC
return dt.astimezone(timezone.utc)
return None
@ -8797,46 +8971,60 @@ def _sort_models(
Returns:
Sorted list of models
"""
if not sort_by or sort_by not in ["model_name", "created_at", "updated_at", "costs", "status"]:
if not sort_by or sort_by not in [
"model_name",
"created_at",
"updated_at",
"costs",
"status",
]:
return all_models
reverse = sort_order.lower() == "desc"
def get_sort_key(model: Dict[str, Any]) -> Any:
model_info = model.get("model_info", {})
if sort_by == "model_name":
return model.get("model_name", "").lower()
elif sort_by == "created_at":
created_at = model_info.get("created_at")
normalized_dt = _normalize_datetime_for_sorting(created_at)
if normalized_dt is None:
# Put None values at the end for asc, at the start for desc
return (datetime.max.replace(tzinfo=timezone.utc) if not reverse else datetime.min.replace(tzinfo=timezone.utc))
return (
datetime.max.replace(tzinfo=timezone.utc)
if not reverse
else datetime.min.replace(tzinfo=timezone.utc)
)
return normalized_dt
elif sort_by == "updated_at":
updated_at = model_info.get("updated_at")
normalized_dt = _normalize_datetime_for_sorting(updated_at)
if normalized_dt is None:
return (datetime.max.replace(tzinfo=timezone.utc) if not reverse else datetime.min.replace(tzinfo=timezone.utc))
return (
datetime.max.replace(tzinfo=timezone.utc)
if not reverse
else datetime.min.replace(tzinfo=timezone.utc)
)
return normalized_dt
elif sort_by == "costs":
input_cost = model_info.get("input_cost_per_token", 0) or 0
output_cost = model_info.get("output_cost_per_token", 0) or 0
total_cost = input_cost + output_cost
# Put 0 or None costs at the end for asc, at the start for desc
if total_cost == 0:
return (float("inf") if not reverse else float("-inf"))
return float("inf") if not reverse else float("-inf")
return total_cost
elif sort_by == "status":
# False (config) comes before True (db) for asc
db_model = model_info.get("db_model", False)
return db_model
return None
try:
@ -9032,9 +9220,7 @@ async def _find_model_by_id(
)
if db_model:
# Convert database model to router format
decrypted_models = proxy_config.decrypt_model_list_from_db(
[db_model]
)
decrypted_models = proxy_config.decrypt_model_list_from_db([db_model])
if decrypted_models:
found_model = decrypted_models[0]
except Exception as e:
@ -9208,13 +9394,13 @@ async def model_info_v2(
)
verbose_proxy_logger.debug("all_models: %s", all_models)
# Append A2A agents to models list
all_models = await append_agents_to_model_info(
models=all_models,
user_api_key_dict=user_api_key_dict,
)
# Update total count to include agents
search_total_count = len(all_models)
@ -10057,7 +10243,7 @@ async def model_group_info(
model_groups: List[ModelGroupInfoProxy] = _get_model_group_info(
llm_router=llm_router, all_models_str=all_models_str, model_group=model_group
)
# Append A2A agents to model groups
model_groups = await append_agents_to_model_group(
model_groups=model_groups,

1
litellm/rerank_api/main.py
View file

@ -365,6 +365,7 @@ def rerank( # noqa: PLR0915
max_chunks_per_doc=max_chunks_per_doc,
_is_async=_is_async,
optional_params=optional_params.model_dump(exclude_unset=True),
timeout=optional_params.timeout,
api_base=api_base,
extra_headers=merged_headers,
logging_obj=litellm_logging_obj,

26
litellm/router.py
View file

@ -163,7 +163,11 @@ from litellm.types.utils import (
)
from litellm.types.utils import ModelInfo
from litellm.types.utils import ModelInfo as ModelMapInfo
from litellm.types.utils import ModelResponseStream, StandardLoggingPayload, Usage
from litellm.types.utils import (
ModelResponseStream,
StandardLoggingPayload,
Usage,
)
from litellm.utils import (
CustomStreamWrapper,
EmbeddingResponse,
@ -1555,6 +1559,9 @@ class Router:
logging_obj=model_response.logging_obj,
)
self._async_generator = async_generator
# Preserve hidden params (including litellm_overhead_time_ms) from original response
if hasattr(model_response, "_hidden_params"):
self._hidden_params = model_response._hidden_params.copy()
def __aiter__(self):
return self
@ -6978,9 +6985,9 @@ class Router:
raise ValueError("Deployment not found")
## GET BASE MODEL
base_model = deployment.get("model_info", {}).get("base_model", None)
base_model = (deployment.get("model_info") or {}).get("base_model", None)
if base_model is None:
base_model = deployment.get("litellm_params", {}).get("base_model", None)
base_model = (deployment.get("litellm_params") or {}).get("base_model", None)
model = base_model
@ -6995,7 +7002,7 @@ class Router:
raise ValueError(
f"Deployment missing valid litellm_params. "
f"Got: {type(litellm_params_data).__name__}, "
f"deployment_id: {deployment.get('model_info', {}).get('id', 'unknown')}"
f"deployment_id: {(deployment.get('model_info') or {}).get('id', 'unknown')}"
)
_model, custom_llm_provider, _, _ = litellm.get_llm_provider(
model=litellm_params.model,
@ -7015,10 +7022,10 @@ class Router:
if potential_models is not None:
for potential_model in potential_models:
try:
if potential_model.get("model_info", {}).get(
if (potential_model.get("model_info") or {}).get(
"id"
) == deployment.get("model_info", {}).get("id"):
model = potential_model.get("litellm_params", {}).get(
) == (deployment.get("model_info") or {}).get("id"):
model = (potential_model.get("litellm_params") or {}).get(
"model"
)
break
@ -7039,9 +7046,10 @@ class Router:
model_info = litellm.get_model_info(model=model_info_name)
## CHECK USER SET MODEL INFO
user_model_info = deployment.get("model_info", {})
user_model_info = deployment.get("model_info") or {}
model_info.update(user_model_info)
if model_info is not None:
model_info.update(user_model_info)
return model_info

25
litellm/types/integrations/prometheus.py
View file

@ -55,22 +55,21 @@ def _sanitize_prometheus_label_value(value: Optional[Any]) -> Optional[str]:
return None
# Coerce non-string values (int, bool, etc.) to str before sanitizing
if not isinstance(value, str):
value = str(value)
str_value: str = value if isinstance(value, str) else str(value)
# Remove Unicode line/paragraph separators that break text format
value = value.replace("\u2028", "").replace("\u2029", "")
str_value = str_value.replace("\u2028", "").replace("\u2029", "")
# Remove carriage returns
value = value.replace("\r", "")
str_value = str_value.replace("\r", "")
# Replace newlines with spaces
value = value.replace("\n", " ")
str_value = str_value.replace("\n", " ")
# Escape backslashes and double quotes per Prometheus exposition format
value = value.replace("\\", "\\\\").replace('"', '\\"')
str_value = str_value.replace("\\", "\\\\").replace('"', '\\"')
return value
return str_value
@dataclass
@ -185,6 +184,7 @@ class UserAPIKeyLabelNames(Enum):
CLIENT_IP = "client_ip"
USER_AGENT = "user_agent"
CALLBACK_NAME = "callback_name"
STREAM = "stream"
DEFINED_PROMETHEUS_METRICS = Literal[
@ -638,6 +638,14 @@ class PrometheusMetricLabels:
]
)
# Conditionally add stream label to litellm_proxy_total_requests_metric
if (
label_name == "litellm_proxy_total_requests_metric"
and litellm.prometheus_emit_stream_label is True
and UserAPIKeyLabelNames.STREAM.value not in default_labels
):
custom_labels.append(UserAPIKeyLabelNames.STREAM.value)
return default_labels + custom_labels
@ -709,6 +717,9 @@ class UserAPIKeyLabelValues(BaseModel):
user_agent: Annotated[
Optional[str], Field(..., alias=UserAPIKeyLabelNames.USER_AGENT.value)
] = None
stream: Annotated[
Optional[str], Field(..., alias=UserAPIKeyLabelNames.STREAM.value)
] = None
class PrometheusMetricsConfig(BaseModel):

3
litellm/types/responses/main.py
View file

@ -6,6 +6,7 @@ from typing_extensions import Any, List, Optional, TypedDict
from litellm.types.llms.base import BaseLiteLLMOpenAIResponseObject
Phase = Optional[Literal["commentary", "final_answer"]] # TODO: Once openai sdk has updated, we can remove this and use the openai sdk type
class GenericResponseOutputItemContentAnnotation(BaseLiteLLMOpenAIResponseObject):
"""Annotation for content in a message"""
@ -35,6 +36,7 @@ class OutputFunctionToolCall(BaseLiteLLMOpenAIResponseObject):
type: Optional[str] # "function_call"
id: Optional[str]
status: Literal["in_progress", "completed", "incomplete"]
phase: Phase = None
class OutputImageGenerationCall(BaseLiteLLMOpenAIResponseObject):
@ -57,6 +59,7 @@ class GenericResponseOutputItem(BaseLiteLLMOpenAIResponseObject):
status: str # "completed", "in_progress", etc.
role: str # "assistant", "user", etc.
content: List[OutputText]
phase: Phase = None
class DeleteResponseResult(BaseLiteLLMOpenAIResponseObject):

33
model_prices_and_context_window.json
View file

@ -20562,6 +20562,39 @@
"supports_tool_choice": true,
"supports_vision": true
},
"gpt-5.3-codex": {
"cache_read_input_token_cost": 1.75e-07,
"cache_read_input_token_cost_priority": 3.5e-07,
"input_cost_per_token": 1.75e-06,
"input_cost_per_token_priority": 3.5e-06,
"litellm_provider": "openai",
"max_input_tokens": 272000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "responses",
"output_cost_per_token": 1.4e-05,
"output_cost_per_token_priority": 2.8e-05,
"supported_endpoints": [
"/v1/responses"
],
"supported_modalities": [
"text",
"image"
],
"supported_output_modalities": [
"text"
],
"supports_function_calling": true,
"supports_native_streaming": true,
"supports_parallel_function_calling": true,
"supports_pdf_input": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": true,
"supports_system_messages": false,
"supports_tool_choice": true,
"supports_vision": true
},
"gpt-5-mini": {
"cache_read_input_token_cost": 2.5e-08,
"cache_read_input_token_cost_flex": 1.25e-08,

18
package.json
View file

@ -12,7 +12,19 @@
},
"overrides": {
"glob": ">=11.1.0",
"tar": ">=7.5.7",
"@isaacs/brace-expansion": ">=5.0.1"
"tar": ">=7.5.8",
"minimatch": ">=10.2.1",
"diff": ">=8.0.3",
"@isaacs/brace-expansion": ">=5.0.1",
"@babel/traverse": ">=7.23.2",
"ws": ">=7.5.10",
"http-proxy-middleware": ">=2.0.9",
"tar-fs": ">=2.1.4",
"webpack-dev-middleware": ">=5.3.4",
"braces": ">=3.0.3",
"axios": ">=0.30.2",
"webpack": ">=5.94.0",
"serve-static": ">=1.16.0",
"path-to-regexp": ">=0.1.12"
}
}
}

4
requirements.txt
View file

@ -3,6 +3,8 @@
urllib3>=2.6.0 # CVE-2025-66471, CVE-2025-66418, CVE-2026-21441
tornado>=6.5.3 # CVE-2025-67725, CVE-2025-67726, CVE-2025-67724
filelock>=3.20.1 # CVE-2025-68146
h11>=0.16.0 # CVE-2025-43859, GHSA-vqfr-h8mv-ghfj — HTTP request smuggling
wheel>=0.46.2 # CVE-2026-24049 — path traversal
Pillow==12.1.1 #GHSA-cfh3-3jmp-rvhc
cryptography==46.0.5 #GHSA-r6ph-v2qm-q3c2
@ -21,7 +23,7 @@ boto3==1.40.53 # aws bedrock/sagemaker calls (has bedrock-agentcore-control, com
redis==5.2.1 # redis caching
redisvl==0.4.1 ## redis semantic caching
prisma==0.11.0 # for db
nodejs-wheel-binaries==24.12.0 ## required by prisma for migrations, prevents runtime download (updated from nodejs-bin for security fixes)
nodejs-wheel-binaries==24.13.1 ## required by prisma for migrations, prevents runtime download (updated from nodejs-bin for security fixes)
mangum==0.17.0 # for aws lambda functions
pynacl==1.6.2 # for encrypting keys
google-cloud-aiplatform==1.133.0 # for vertex ai calls

119
tests/litellm_utils_tests/test_health_check.py
View file

@ -92,7 +92,7 @@ async def test_azure_img_gen_health_check():
litellm._turn_on_debug()
max_retries = 3
retry_delay = 1 # Start with 1 second delay
for attempt in range(max_retries):
response = await litellm.ahealth_check(
model_params={
@ -103,11 +103,11 @@ async def test_azure_img_gen_health_check():
mode="image_generation",
prompt="cute baby sea otter",
)
# Check if response is successful (no error)
if isinstance(response, dict) and "error" not in response:
return response
# Check if error is a transient Azure internal server error
error_str = str(response.get("error", "")).lower()
is_transient_error = (
@ -116,16 +116,18 @@ async def test_azure_img_gen_health_check():
or "internalfailure" in error_str
or "internal failure" in error_str
)
# If it's the last attempt or not a transient error, fail the test
if attempt == max_retries - 1 or not is_transient_error:
assert isinstance(response, dict) and "error" not in response, f"Health check failed: {response.get('error', 'Unknown error')}"
assert (
isinstance(response, dict) and "error" not in response
), f"Health check failed: {response.get('error', 'Unknown error')}"
return response
# Wait before retrying with exponential backoff
await asyncio.sleep(retry_delay)
retry_delay *= 2 # Exponential backoff
# Should not reach here, but just in case
assert False, "Health check failed after all retries"
@ -562,6 +564,99 @@ async def test_health_check_bad_model():
), "Health check took longer than health_check_timeout"
@pytest.mark.asyncio
async def test_health_check_respects_concurrency_limit():
from litellm.proxy.health_check import _perform_health_check
model_list = [
{"litellm_params": {"model": f"openai/gpt-4o-mini-{i}", "api_key": "fake-key"}}
for i in range(6)
]
active = 0
max_active = 0
async def mock_health_check(litellm_params, **kwargs):
nonlocal active, max_active
active += 1
max_active = max(max_active, active)
await asyncio.sleep(0.05)
active -= 1
return {"status": "healthy"}
with patch("litellm.ahealth_check", side_effect=mock_health_check):
await _perform_health_check(model_list, max_concurrency=2)
assert max_active <= 2
@pytest.mark.asyncio
async def test_health_check_creates_only_bounded_initial_tasks():
from litellm.proxy.health_check import _perform_health_check
model_list = [
{"litellm_params": {"model": f"openai/gpt-4o-mini-{i}", "api_key": "fake-key"}}
for i in range(10)
]
release_event = asyncio.Event()
create_task_call_count = 0
real_create_task = asyncio.create_task
async def mock_health_check(litellm_params, **kwargs):
await release_event.wait()
return {"status": "healthy"}
def tracked_create_task(coro):
nonlocal create_task_call_count
create_task_call_count += 1
return real_create_task(coro)
with patch("litellm.ahealth_check", side_effect=mock_health_check), patch(
"litellm.proxy.health_check.asyncio.create_task", side_effect=tracked_create_task
):
perform_task = real_create_task(
_perform_health_check(model_list, max_concurrency=2)
)
await asyncio.sleep(0.05)
assert create_task_call_count == 2
release_event.set()
await perform_task
@pytest.mark.asyncio
async def test_timeout_does_not_cancel_other_health_checks():
from litellm.proxy.health_check import _perform_health_check
model_list = [
{
"litellm_params": {"model": "openai/slow-model", "api_key": "fake-key"},
"model_info": {"health_check_timeout": 0.05},
},
{
"litellm_params": {"model": "openai/fast-model", "api_key": "fake-key"},
"model_info": {"health_check_timeout": 1},
},
]
async def mock_health_check(litellm_params, **kwargs):
if litellm_params["model"] == "openai/slow-model":
await asyncio.sleep(0.2)
return {"status": "healthy"}
await asyncio.sleep(0.01)
return {"status": "healthy"}
with patch("litellm.ahealth_check", side_effect=mock_health_check):
healthy_endpoints, unhealthy_endpoints = await _perform_health_check(
model_list, max_concurrency=1
)
healthy_models = {endpoint["model"] for endpoint in healthy_endpoints}
unhealthy_models = {endpoint["model"] for endpoint in unhealthy_endpoints}
assert "openai/fast-model" in healthy_models
assert "openai/slow-model" in unhealthy_models
@pytest.mark.asyncio
async def test_ahealth_check_ocr():
litellm._turn_on_debug()
@ -643,20 +738,20 @@ async def test_image_generation_health_check_prompt(monkeypatch):
async def test_health_check_with_custom_llm_provider():
"""
Test that ahealth_check correctly uses custom_llm_provider from model_params.
This test verifies the fix for the issue where the UI's "Test connect" button
failed with "LLM Provider NOT provided" error for OpenAI-compatible self-hosted
providers, even when a provider was selected in the dropdown.
The fix ensures that when custom_llm_provider is passed in model_params,
it's properly forwarded to get_llm_provider() to identify the correct provider.
"""
from unittest.mock import MagicMock
# Mock the completion call to avoid making real API calls
mock_response = MagicMock()
mock_response._hidden_params = {"headers": {"x-ratelimit-remaining-tokens": "1000"}}
with patch("litellm.acompletion", return_value=mock_response):
# Test with a custom model name that wouldn't be recognized without custom_llm_provider
response = await litellm.ahealth_check(
@ -668,7 +763,7 @@ async def test_health_check_with_custom_llm_provider():
},
mode="chat",
)
# Should succeed without "LLM Provider NOT provided" error
assert "error" not in response
assert isinstance(response, dict)

18
tests/proxy_admin_ui_tests/package.json
View file

@ -13,7 +13,19 @@
},
"overrides": {
"glob": ">=11.1.0",
"tar": ">=7.5.7",
"@isaacs/brace-expansion": ">=5.0.1"
"tar": ">=7.5.8",
"minimatch": ">=10.2.1",
"diff": ">=8.0.3",
"@isaacs/brace-expansion": ">=5.0.1",
"@babel/traverse": ">=7.23.2",
"ws": ">=7.5.10",
"http-proxy-middleware": ">=2.0.9",
"tar-fs": ">=2.1.4",
"webpack-dev-middleware": ">=5.3.4",
"braces": ">=3.0.3",
"axios": ">=0.30.2",
"webpack": ">=5.94.0",
"serve-static": ">=1.16.0",
"path-to-regexp": ">=0.1.12"
}
}
}

16
tests/proxy_admin_ui_tests/ui_unit_tests/package.json
View file

@ -25,7 +25,19 @@
},
"overrides": {
"glob": ">=11.1.0",
"tar": ">=7.5.7",
"@isaacs/brace-expansion": ">=5.0.1"
"tar": ">=7.5.8",
"minimatch": ">=10.2.1",
"diff": ">=8.0.3",
"@isaacs/brace-expansion": ">=5.0.1",
"@babel/traverse": ">=7.23.2",
"ws": ">=7.5.10",
"http-proxy-middleware": ">=2.0.9",
"tar-fs": ">=2.1.4",
"webpack-dev-middleware": ">=5.3.4",
"braces": ">=3.0.3",
"axios": ">=0.30.2",
"webpack": ">=5.94.0",
"serve-static": ">=1.16.0",
"path-to-regexp": ">=0.1.12"
}
}

8
tests/proxy_unit_tests/test_proxy_server.py
View file

@ -2330,7 +2330,9 @@ async def test_run_background_health_check_reflects_llm_model_list(monkeypatch):
test_model_list_2 = [{"model_name": "model-b"}]
called_model_lists = []
async def fake_perform_health_check(model_list, details):
async def fake_perform_health_check(
model_list, details, max_concurrency=None
):
called_model_lists.append(copy.deepcopy(model_list))
return (["healthy"], ["unhealthy"])
@ -2378,7 +2380,9 @@ async def test_background_health_check_skip_disabled_models(monkeypatch):
]
called_model_lists = []
async def fake_perform_health_check(model_list, details):
async def fake_perform_health_check(
model_list, details, max_concurrency=None
):
called_model_lists.append(copy.deepcopy(model_list))
return (["healthy"], [])

81
tests/test_litellm/integrations/test_prometheus_stream_label.py Normal file
View file

@ -0,0 +1,81 @@
"""
Unit tests for prometheus_emit_stream_label opt-in setting.
Tests that:
- stream label is NOT added to litellm_proxy_total_requests_metric by default
- stream label IS added when litellm.prometheus_emit_stream_label = True
- stream value is populated correctly from standard_logging_payload
"""
import pytest
import litellm
from litellm.types.integrations.prometheus import (
PrometheusMetricLabels,
UserAPIKeyLabelNames,
)
def test_stream_label_not_present_by_default():
"""stream label should NOT appear in litellm_proxy_total_requests_metric unless opted in"""
litellm.prometheus_emit_stream_label = False
labels = PrometheusMetricLabels.get_labels("litellm_proxy_total_requests_metric")
assert UserAPIKeyLabelNames.STREAM.value not in labels
def test_stream_label_present_when_opted_in():
"""stream label SHOULD appear in litellm_proxy_total_requests_metric when opted in"""
litellm.prometheus_emit_stream_label = True
try:
labels = PrometheusMetricLabels.get_labels("litellm_proxy_total_requests_metric")
assert UserAPIKeyLabelNames.STREAM.value in labels
finally:
litellm.prometheus_emit_stream_label = False
def test_stream_label_not_in_other_metrics_when_opted_in():
"""stream label should NOT be added to other metrics even when opted in"""
litellm.prometheus_emit_stream_label = True
try:
other_metrics = [
"litellm_proxy_failed_requests_metric",
"litellm_spend_metric",
"litellm_input_tokens_metric",
"litellm_output_tokens_metric",
"litellm_llm_api_latency_metric",
]
for metric in other_metrics:
labels = PrometheusMetricLabels.get_labels(metric)
assert UserAPIKeyLabelNames.STREAM.value not in labels, (
f"stream label should not be in {metric}"
)
finally:
litellm.prometheus_emit_stream_label = False
def test_stream_label_name():
"""STREAM label name should be 'stream'"""
assert UserAPIKeyLabelNames.STREAM.value == "stream"
def test_user_api_key_label_values_has_stream_field():
"""UserAPIKeyLabelValues should accept stream field"""
from litellm.types.integrations.prometheus import UserAPIKeyLabelValues
values = UserAPIKeyLabelValues(stream="True")
assert values.stream == "True"
values_false = UserAPIKeyLabelValues(stream="False")
assert values_false.stream == "False"
values_none = UserAPIKeyLabelValues()
assert values_none.stream is None
def test_stream_label_in_model_dump():
"""stream field appears in model_dump() output for use in prometheus_label_factory"""
from litellm.types.integrations.prometheus import UserAPIKeyLabelValues
values = UserAPIKeyLabelValues(stream="True")
dumped = values.model_dump()
assert "stream" in dumped
assert dumped["stream"] == "True"

77
tests/test_litellm/llms/bedrock/rerank/test_bedrock_rerank_header_forwarding.py
View file

@ -237,6 +237,83 @@ async def test_bedrock_rerank_header_forwarding_async(model):
pytest.fail(f"Failed to forward headers to {model}: {str(e)}")
def test_bedrock_rerank_timeout_sync():
"""
Test that the timeout parameter is passed through to the HTTP client for Bedrock rerank (sync).
"""
client = HTTPHandler()
model = "bedrock/arn:aws:bedrock:us-east-1::foundation-model/cohere.rerank-v3-5:0"
mock_credentials_info = create_mock_credentials()
with patch.object(client, "post") as mock_post, \
patch("litellm.llms.bedrock.rerank.handler.BedrockRerankHandler._get_boto_credentials_from_optional_params", return_value=mock_credentials_info), \
patch("botocore.auth.SigV4Auth") as mock_sigv4:
mock_sigv4.return_value = MagicMock()
mock_response = Mock()
mock_response.status_code = 200
mock_response.text = json.dumps(bedrock_rerank_response)
mock_response.json = lambda: json.loads(mock_response.text)
mock_response.raise_for_status = lambda: None
mock_post.return_value = mock_response
litellm.rerank(
model=model,
query=test_query,
documents=test_documents,
top_n=3,
client=client,
timeout=0.001,
aws_region_name="us-east-1",
aws_bedrock_runtime_endpoint="https://bedrock-runtime.us-east-1.amazonaws.com",
)
assert mock_post.called
call_kwargs = mock_post.call_args.kwargs
assert call_kwargs.get("timeout") == 0.001, (
f"Expected timeout=0.001, got timeout={call_kwargs.get('timeout')}"
)
@pytest.mark.asyncio
async def test_bedrock_rerank_timeout_async():
"""
Test that the timeout parameter is passed through to the HTTP client for Bedrock rerank (async).
"""
client = AsyncHTTPHandler()
model = "bedrock/arn:aws:bedrock:us-east-1::foundation-model/cohere.rerank-v3-5:0"
mock_credentials_info = create_mock_credentials()
with patch.object(client, "post", new_callable=AsyncMock) as mock_post, \
patch("litellm.llms.bedrock.rerank.handler.BedrockRerankHandler._get_boto_credentials_from_optional_params", return_value=mock_credentials_info), \
patch("botocore.auth.SigV4Auth") as mock_sigv4:
mock_sigv4.return_value = MagicMock()
mock_response = AsyncMock()
mock_response.status_code = 200
mock_response.text = json.dumps(bedrock_rerank_response)
mock_response.json = lambda: json.loads(mock_response.text)
mock_response.raise_for_status = lambda: None
mock_post.return_value = mock_response
await litellm.arerank(
model=model,
query=test_query,
documents=test_documents,
top_n=3,
client=client,
timeout=0.001,
aws_region_name="us-east-1",
aws_bedrock_runtime_endpoint="https://bedrock-runtime.us-east-1.amazonaws.com",
)
assert mock_post.called
call_kwargs = mock_post.call_args.kwargs
assert call_kwargs.get("timeout") == 0.001, (
f"Expected timeout=0.001, got timeout={call_kwargs.get('timeout')}"
)
def test_bedrock_rerank_extra_headers_and_headers_merge():
"""
Test that both extra_headers and headers parameters are correctly merged for Bedrock rerank.

316
tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py
View file

@ -925,4 +925,318 @@ def test_get_supported_openai_params():
assert "temperature" in params
assert "stream" in params
assert "background" in params
assert "stream" in params
assert "stream" in params
class TestPhaseParameter:
"""Tests for the `phase` parameter on assistant output items (gpt-5.3-codex)."""
def setup_method(self):
self.config = OpenAIResponsesAPIConfig()
self.model = "gpt-5.3-codex"
self.logging_obj = MagicMock()
@staticmethod
def _make_output_text(text: str):
from litellm.types.responses.main import OutputText
return OutputText(type="output_text", text=text, annotations=[])
def test_generic_response_output_item_accepts_phase_commentary(self):
from litellm.types.responses.main import GenericResponseOutputItem
item = GenericResponseOutputItem(
type="message",
id="msg_001",
status="completed",
role="assistant",
content=[self._make_output_text("Thinking...")],
phase="commentary",
)
assert item.phase == "commentary"
def test_generic_response_output_item_accepts_phase_final_answer(self):
from litellm.types.responses.main import GenericResponseOutputItem
item = GenericResponseOutputItem(
type="message",
id="msg_002",
status="completed",
role="assistant",
content=[self._make_output_text("The answer is 42.")],
phase="final_answer",
)
assert item.phase == "final_answer"
def test_generic_response_output_item_phase_defaults_to_none(self):
from litellm.types.responses.main import GenericResponseOutputItem
item = GenericResponseOutputItem(
type="message",
id="msg_003",
status="completed",
role="assistant",
content=[self._make_output_text("Hello")],
)
assert item.phase is None
def test_output_function_tool_call_accepts_phase(self):
from litellm.types.responses.main import OutputFunctionToolCall
item = OutputFunctionToolCall(
type="function_call",
id="fc_001",
arguments='{"query": "test"}',
call_id="call_001",
name="search",
status="completed",
phase="commentary",
)
assert item.phase == "commentary"
def test_input_passthrough_dict_preserves_phase(self):
"""Dict input items (the normal HTTP flow) must preserve phase verbatim."""
input_items = [
{
"type": "message",
"role": "user",
"content": [{"type": "input_text", "text": "Hi"}],
},
{
"type": "message",
"role": "assistant",
"content": [{"type": "output_text", "text": "Preamble..."}],
"phase": "commentary",
},
{
"type": "message",
"role": "assistant",
"content": [{"type": "output_text", "text": "Done."}],
"phase": "final_answer",
},
{
"type": "message",
"role": "assistant",
"content": [{"type": "output_text", "text": "Neutral."}],
"phase": None,
},
]
result = self.config._validate_input_param(input_items)
assert isinstance(result, list)
assert "phase" not in result[0]
assert result[1]["phase"] == "commentary"
assert result[2]["phase"] == "final_answer"
assert result[3]["phase"] is None
def test_input_passthrough_pydantic_preserves_non_null_phase(self):
"""Pydantic input items must preserve non-null phase values."""
from litellm.types.responses.main import GenericResponseOutputItem
item = GenericResponseOutputItem(
type="message",
id="msg_010",
status="completed",
role="assistant",
content=[self._make_output_text("commentary")],
phase="commentary",
)
result = self.config._validate_input_param([item])
assert isinstance(result, list)
assert result[0]["phase"] == "commentary"
def test_response_parsing_preserves_phase_on_output(self):
"""Non-streaming response must preserve phase on output items."""
raw_json = {
"id": "resp_001",
"created_at": 1700000000,
"model": "gpt-5.3-codex",
"object": "response",
"status": "completed",
"output": [
{
"type": "message",
"id": "msg_001",
"status": "completed",
"role": "assistant",
"content": [{"type": "output_text", "text": "preamble"}],
"phase": "commentary",
},
{
"type": "message",
"id": "msg_002",
"status": "completed",
"role": "assistant",
"content": [{"type": "output_text", "text": "answer"}],
"phase": "final_answer",
},
],
"usage": {"input_tokens": 10, "output_tokens": 20, "total_tokens": 30},
}
response = ResponsesAPIResponse(**raw_json)
assert len(response.output) == 2
for idx, output_item in enumerate(response.output):
if isinstance(output_item, dict):
phase = output_item.get("phase")
else:
phase = getattr(output_item, "phase", None)
expected = "commentary" if idx == 0 else "final_answer"
assert phase == expected, (
f"output[{idx}] phase={phase!r}, expected {expected!r}"
)
def test_streaming_output_item_done_preserves_phase(self):
"""OutputItemDoneEvent must preserve phase on its item."""
from litellm.types.llms.openai import (
OutputItemDoneEvent,
ResponsesAPIStreamEvents,
)
chunk = {
"type": "response.output_item.done",
"output_index": 0,
"sequence_number": 3,
"item": {
"type": "message",
"id": "msg_100",
"status": "completed",
"role": "assistant",
"content": [{"type": "output_text", "text": "done"}],
"phase": "final_answer",
},
}
result = self.config.transform_streaming_response(
model=self.model, parsed_chunk=chunk, logging_obj=self.logging_obj
)
assert isinstance(result, OutputItemDoneEvent)
assert result.type == ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE
assert getattr(result.item, "phase", None) == "final_answer"
def test_streaming_output_item_added_preserves_phase(self):
"""OutputItemAddedEvent must preserve phase on its item."""
from litellm.types.llms.openai import (
OutputItemAddedEvent,
ResponsesAPIStreamEvents,
)
chunk = {
"type": "response.output_item.added",
"output_index": 0,
"item": {
"type": "message",
"id": "msg_200",
"role": "assistant",
"phase": "commentary",
},
}
result = self.config.transform_streaming_response(
model=self.model, parsed_chunk=chunk, logging_obj=self.logging_obj
)
assert isinstance(result, OutputItemAddedEvent)
assert result.type == ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED
assert getattr(result.item, "phase", None) == "commentary"
def test_streaming_response_completed_preserves_phase(self):
"""ResponseCompletedEvent must preserve phase on output items inside the response."""
completed_chunk = {
"type": "response.completed",
"response": {
"id": "resp_300",
"created_at": 1700000000,
"model": "gpt-5.3-codex",
"object": "response",
"status": "completed",
"output": [
{
"type": "message",
"id": "msg_300",
"status": "completed",
"role": "assistant",
"content": [{"type": "output_text", "text": "final"}],
"phase": "final_answer",
}
],
"usage": {
"input_tokens": 5,
"output_tokens": 10,
"total_tokens": 15,
},
},
}
result = self.config.transform_streaming_response(
model=self.model,
parsed_chunk=completed_chunk,
logging_obj=self.logging_obj,
)
assert result.type == ResponsesAPIStreamEvents.RESPONSE_COMPLETED
output_item = result.response.output[0]
if isinstance(output_item, dict):
assert output_item["phase"] == "final_answer"
else:
assert getattr(output_item, "phase", None) == "final_answer"
def test_phase_roundtrip_output_to_input(self):
"""Simulate full round-trip: parse response output, then send items back as input."""
raw_json = {
"id": "resp_rt",
"created_at": 1700000000,
"model": "gpt-5.3-codex",
"object": "response",
"status": "completed",
"output": [
{
"type": "message",
"id": "msg_rt1",
"status": "completed",
"role": "assistant",
"content": [{"type": "output_text", "text": "preamble"}],
"phase": "commentary",
},
{
"type": "message",
"id": "msg_rt2",
"status": "completed",
"role": "assistant",
"content": [{"type": "output_text", "text": "answer"}],
"phase": "final_answer",
},
],
"usage": {"input_tokens": 10, "output_tokens": 20, "total_tokens": 30},
}
response = ResponsesAPIResponse(**raw_json)
input_items = []
for item in response.output:
if isinstance(item, dict):
input_items.append(item)
else:
input_items.append(
item.model_dump() if hasattr(item, "model_dump") else dict(item)
)
input_items.append(
{
"type": "message",
"role": "user",
"content": [{"type": "input_text", "text": "next question"}],
}
)
validated = self.config._validate_input_param(input_items)
assert isinstance(validated, list)
assert validated[0]["phase"] == "commentary"
assert validated[1]["phase"] == "final_answer"
assert "phase" not in validated[2]

201
tests/test_litellm/proxy/test_common_request_processing.py
View file

@ -1,4 +1,6 @@
import copy
import datetime
from typing import AsyncGenerator
from unittest.mock import AsyncMock, MagicMock
import pytest
@ -1348,3 +1350,202 @@ class TestOverrideOpenAIResponseModel:
# Verify the model was not changed
assert response_obj.model == fallback_model
class TestStreamingOverheadHeader:
"""
Tests that x-litellm-overhead-duration-ms is emitted in streaming responses.
Regression tests for: streaming requests not including overhead header.
"""
def test_get_custom_headers_includes_overhead_when_set(self):
"""
get_custom_headers() returns x-litellm-overhead-duration-ms
when litellm_overhead_time_ms is in hidden_params.
"""
mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth)
mock_user_api_key_dict.tpm_limit = None
mock_user_api_key_dict.rpm_limit = None
mock_user_api_key_dict.max_budget = None
mock_user_api_key_dict.spend = 0.0
mock_user_api_key_dict.allowed_model_region = None
hidden_params = {
"litellm_overhead_time_ms": 42.5,
"_response_ms": 500.0,
"model_id": "test-model-id",
"api_base": "https://api.openai.com",
}
headers = ProxyBaseLLMRequestProcessing.get_custom_headers(
user_api_key_dict=mock_user_api_key_dict,
call_id="test-call-id",
model_id="test-model-id",
cache_key="",
api_base="https://api.openai.com",
version="1.0.0",
response_cost=0.001,
model_region="",
hidden_params=hidden_params,
)
assert "x-litellm-overhead-duration-ms" in headers
assert headers["x-litellm-overhead-duration-ms"] == "42.5"
def test_get_custom_headers_omits_overhead_when_none(self):
"""
get_custom_headers() omits x-litellm-overhead-duration-ms
when litellm_overhead_time_ms is not in hidden_params.
"""
mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth)
mock_user_api_key_dict.tpm_limit = None
mock_user_api_key_dict.rpm_limit = None
mock_user_api_key_dict.max_budget = None
mock_user_api_key_dict.spend = 0.0
mock_user_api_key_dict.allowed_model_region = None
hidden_params = {
"_response_ms": 500.0,
"model_id": "test-model-id",
}
headers = ProxyBaseLLMRequestProcessing.get_custom_headers(
user_api_key_dict=mock_user_api_key_dict,
call_id="test-call-id",
model_id="test-model-id",
cache_key="",
api_base="https://api.openai.com",
version="1.0.0",
response_cost=0.001,
model_region="",
hidden_params=hidden_params,
)
# Should be absent (None gets filtered by exclude_values)
assert "x-litellm-overhead-duration-ms" not in headers
def test_update_response_metadata_sets_overhead_on_stream_wrapper(self):
"""
update_response_metadata() sets litellm_overhead_time_ms on
a streaming response's _hidden_params when llm_api_duration_ms is available.
"""
from litellm.litellm_core_utils.llm_response_utils.response_metadata import (
update_response_metadata,
)
# Mock the logging object with llm_api_duration_ms set
mock_logging_obj = MagicMock()
mock_logging_obj.model_call_details = {
"llm_api_duration_ms": 200.0,
"litellm_params": {},
}
mock_logging_obj.caching_details = None
mock_logging_obj.callback_duration_ms = None
mock_logging_obj.litellm_call_id = "test-call-id"
mock_logging_obj._response_cost_calculator = MagicMock(return_value=0.001)
# Simulate a streaming result object with _hidden_params (like CustomStreamWrapper)
stream_result = MagicMock()
stream_result._hidden_params = {
"model_id": "test-model-id",
"api_base": "https://api.openai.com",
"additional_headers": {},
}
start_time = datetime.datetime.now() - datetime.timedelta(milliseconds=300)
end_time = datetime.datetime.now()
update_response_metadata(
result=stream_result,
logging_obj=mock_logging_obj,
model="gpt-4o",
kwargs={},
start_time=start_time,
end_time=end_time,
)
assert "litellm_overhead_time_ms" in stream_result._hidden_params
overhead = stream_result._hidden_params["litellm_overhead_time_ms"]
assert overhead is not None
assert isinstance(overhead, float)
# overhead = total_response_ms (~300ms) - llm_api_duration_ms (200ms) = ~100ms
assert overhead > 0
@pytest.mark.asyncio
async def test_streaming_response_includes_overhead_header(self):
"""
StreamingResponse returned by create_response() includes
x-litellm-overhead-duration-ms in its headers.
"""
async def mock_generator() -> AsyncGenerator[str, None]:
yield 'data: {"id":"chatcmpl-test","choices":[{"delta":{"content":"hi"}}]}\n\n'
yield "data: [DONE]\n\n"
headers = {
"x-litellm-overhead-duration-ms": "42.5",
"x-litellm-call-id": "test-call-id",
"x-litellm-model-id": "test-model-id",
}
response = await create_response(
generator=mock_generator(),
media_type="text/event-stream",
headers=headers,
)
assert isinstance(response, StreamingResponse)
assert response.headers.get("x-litellm-overhead-duration-ms") == "42.5"
def test_streaming_overhead_header_in_custom_headers_from_stream_hidden_params(
self,
):
"""
Verifies that when get_custom_headers() is called with a streaming
response's hidden_params (containing litellm_overhead_time_ms),
the x-litellm-overhead-duration-ms header is correctly populated.
This tests the critical path: update_response_metadata sets the value
→ get_custom_headers reads it → StreamingResponse header is set.
"""
mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth)
mock_user_api_key_dict.tpm_limit = None
mock_user_api_key_dict.rpm_limit = None
mock_user_api_key_dict.max_budget = None
mock_user_api_key_dict.spend = 0.0
mock_user_api_key_dict.allowed_model_region = None
# This is what CustomStreamWrapper._hidden_params looks like after
# update_response_metadata() has been called on it
hidden_params = {
"model_id": "openai-gpt4o-deployment",
"api_base": "https://api.openai.com",
"additional_headers": {},
"litellm_overhead_time_ms": 55.3, # set by update_response_metadata
"_response_ms": 280.0,
"litellm_call_id": "test-call-id",
"response_cost": 0.002,
"cache_key": None,
"fastest_response_batch_completion": None,
"callback_duration_ms": None,
}
custom_headers = ProxyBaseLLMRequestProcessing.get_custom_headers(
user_api_key_dict=mock_user_api_key_dict,
call_id="test-call-id",
model_id=hidden_params.get("model_id"),
cache_key=hidden_params.get("cache_key") or "",
api_base=hidden_params.get("api_base") or "",
version="1.0.0",
response_cost=hidden_params.get("response_cost"),
model_region="",
hidden_params=hidden_params,
)
# The overhead header must be present and correct
assert "x-litellm-overhead-duration-ms" in custom_headers, (
"x-litellm-overhead-duration-ms header must be emitted during streaming. "
"It was missing — this is the streaming overhead header regression."
)
assert custom_headers["x-litellm-overhead-duration-ms"] == "55.3"

57
tests/test_litellm/test_router.py
View file

@ -1297,6 +1297,61 @@ async def test_acompletion_streaming_iterator_edge_cases():
print("✓ Edge case tests passed!")
@pytest.mark.asyncio
async def test_acompletion_streaming_iterator_preserves_hidden_params():
"""
Regression test: FallbackStreamWrapper must copy _hidden_params from the
original CustomStreamWrapper so that x-litellm-overhead-duration-ms (and
other hidden params) are present in the proxy response headers for streaming.
"""
from unittest.mock import MagicMock
router = litellm.Router(
model_list=[
{
"model_name": "gpt-4",
"litellm_params": {"model": "gpt-4", "api_key": "fake-key"},
}
],
)
# Simulate a CustomStreamWrapper that already has timing metadata set by
# update_response_metadata (litellm_overhead_time_ms, _response_ms, etc.)
mock_response = MagicMock()
mock_response.model = "gpt-4"
mock_response.custom_llm_provider = "openai"
mock_response.logging_obj = MagicMock()
mock_response._hidden_params = {
"litellm_overhead_time_ms": 12.34,
"_response_ms": 500.0,
"litellm_call_id": "test-call-id",
"api_base": "https://api.openai.com",
"additional_headers": {},
}
# Make the mock iterable (yields nothing — we only care about hidden_params)
async def _empty():
return
yield # make it an async generator
mock_response.__aiter__ = lambda self: _empty().__aiter__()
result = await router._acompletion_streaming_iterator(
model_response=mock_response,
messages=[{"role": "user", "content": "hi"}],
initial_kwargs={"model": "gpt-4", "stream": True},
)
# The returned FallbackStreamWrapper must carry the original _hidden_params
assert hasattr(result, "_hidden_params"), "result must have _hidden_params"
assert result._hidden_params.get("litellm_overhead_time_ms") == 12.34, (
"litellm_overhead_time_ms must be preserved — "
"this is what drives x-litellm-overhead-duration-ms in streaming responses"
)
assert result._hidden_params.get("litellm_call_id") == "test-call-id"
assert result._hidden_params.get("_response_ms") == 500.0
@pytest.mark.asyncio
async def test_async_function_with_fallbacks_common_utils():
"""Test the async_function_with_fallbacks_common_utils method"""
@ -1858,7 +1913,7 @@ def test_get_deployment_credentials_with_provider_resolves_credential_name():
litellm_credential_name to actual credential values (for UI-created models).
"""
from litellm.types.utils import CredentialItem
# Setup credential list with a test credential
litellm.credential_list = [
CredentialItem(

17
ui/litellm-dashboard/package.json
View file

@ -86,14 +86,25 @@
"mermaid": ">=11.10.0",
"js-yaml": ">=4.1.1",
"glob": ">=11.1.0",
"tar": ">=7.5.7",
"tar": ">=7.5.8",
"minimatch": ">=10.2.1",
"@isaacs/brace-expansion": ">=5.0.1",
"node-forge": ">=1.3.2",
"lodash-es": ">=4.17.23",
"lodash": ">=4.17.23"
"lodash": ">=4.17.23",
"@babel/traverse": ">=7.23.2",
"ws": ">=7.5.10",
"http-proxy-middleware": ">=2.0.9",
"tar-fs": ">=2.1.4",
"webpack-dev-middleware": ">=5.3.4",
"braces": ">=3.0.3",
"axios": ">=0.30.2",
"webpack": ">=5.94.0",
"serve-static": ">=1.16.0",
"path-to-regexp": ">=0.1.12"
},
"engines": {
"node": ">=18.17.0",
"npm": ">=8.3.0"
}
}
}

108
ui/litellm-dashboard/src/components/templates/key_info_view.test.tsx
View file

@ -363,31 +363,101 @@ describe("KeyInfoView", () => {
});
it("should show edit button in settings tab when user has write access", async () => {
vi.mocked(useAuthorized).mockReturnValue({
...baseUseAuthorizedMock,
userRole: "Admin",
describe("'Edit Settings' button visibility in the Settings tab", () => {
const renderAndOpenSettingsTab = async (keyData = MOCK_KEY_DATA) => {
render(
<KeyInfoView
keyData={keyData}
onClose={() => {}}
keyId="test-key-id"
onKeyDataUpdate={() => {}}
teams={[]}
/>,
);
await waitFor(() => {
expect(screen.getByRole("tab", { name: /settings/i })).toBeInTheDocument();
});
await userEvent.click(screen.getByRole("tab", { name: /settings/i }));
};
it("should show the Edit Settings button when the user is a proxy admin for a key they do not own", async () => {
vi.mocked(useAuthorized).mockReturnValue({
...baseUseAuthorizedMock,
userId: "proxy-admin-user-id",
userRole: "proxy_admin",
});
await renderAndOpenSettingsTab({ ...MOCK_KEY_DATA, user_id: "someone-else-id" });
expect(screen.getByRole("button", { name: /edit settings/i })).toBeInTheDocument();
});
render(
<KeyInfoView
keyData={MOCK_KEY_DATA}
onClose={() => { }}
keyId={"test-key-id"}
onKeyDataUpdate={() => { }}
teams={[]}
/>,
);
it("should show the Edit Settings button when the user is the key owner", async () => {
vi.mocked(useAuthorized).mockReturnValue({
...baseUseAuthorizedMock,
userId: "owner-user-id",
userRole: "Internal User",
});
await waitFor(() => {
const settingsTab = screen.getByRole("tab", { name: /settings/i });
expect(settingsTab).toBeInTheDocument();
await renderAndOpenSettingsTab({ ...MOCK_KEY_DATA, user_id: "owner-user-id" });
expect(screen.getByRole("button", { name: /edit settings/i })).toBeInTheDocument();
});
const settingsTab = screen.getByRole("tab", { name: /settings/i });
await userEvent.click(settingsTab);
it("should not show the Edit Settings button when an Internal User does not own the key", async () => {
vi.mocked(useAuthorized).mockReturnValue({
...baseUseAuthorizedMock,
userId: "non-owner-user-id",
userRole: "Internal User",
});
await renderAndOpenSettingsTab({ ...MOCK_KEY_DATA, user_id: "owner-user-id" });
expect(screen.queryByRole("button", { name: /edit settings/i })).not.toBeInTheDocument();
});
it("should not show the Edit Settings button when the user is an Internal Viewer even if they own the key", async () => {
vi.mocked(useAuthorized).mockReturnValue({
...baseUseAuthorizedMock,
userId: "owner-user-id",
userRole: "Internal Viewer",
});
await renderAndOpenSettingsTab({ ...MOCK_KEY_DATA, user_id: "owner-user-id" });
expect(screen.queryByRole("button", { name: /edit settings/i })).not.toBeInTheDocument();
});
it("should show the Edit Settings button when the user is a team admin for the key's team", async () => {
const teamId = "test-team-id";
const teamAdminUserId = "team-admin-user";
vi.mocked(useTeams).mockReturnValue({
teams: [
{
team_id: teamId,
team_alias: "Test Team",
models: [],
max_budget: null,
budget_duration: null,
tpm_limit: null,
rpm_limit: null,
organization_id: "org-1",
created_at: "2025-01-01T00:00:00Z",
keys: [],
members_with_roles: [{ user_id: teamAdminUserId, role: "admin" }],
spend: 0,
},
],
setTeams: vi.fn(),
});
vi.mocked(useAuthorized).mockReturnValue({
...baseUseAuthorizedMock,
userId: teamAdminUserId,
userRole: "user",
});
await renderAndOpenSettingsTab({ ...MOCK_KEY_DATA, team_id: teamId, user_id: "other-user-id" });
await waitFor(() => {
expect(screen.getByRole("button", { name: /edit settings/i })).toBeInTheDocument();
});
});

2
ui/litellm-dashboard/src/components/templates/key_info_view.tsx
View file

@ -595,7 +595,7 @@ export default function KeyInfoView({
<Card className="overflow-y-auto max-h-[65vh]">
<div className="flex justify-between items-center mb-4">
<Title>Key Settings</Title>
{!isEditing && userRole && rolesWithWriteAccess.includes(userRole) && (
{!isEditing && canModifyKey && (
<Button onClick={() => setIsEditing(true)}>Edit Settings</Button>
)}
</div>