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whit3rabbitandClaude Sonnet 4.6 545bdbbbd3 feat: add bedrock native passthrough, generic passthrough, provider docs, and managed backend fixes

Adds bedrock_native.rs (Converse/InvokeModel with SigV4) and
generic_passthrough.rs catch-all for Translate mode. Adds comprehensive
provider reference docs (docs/providers/, docs/ENDPOINTS.md). Fixes
managed backend admin UI (BackendForm, ManagedBackendsSection) and
admin route/model handler issues. Adds automated model pricing update
workflow (scripts/update_pricing.py, .github/workflows/update-pricing.yml).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

2026-04-08 16:36:16 -05:00

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AWS SageMaker

AWS SageMaker — managed ML endpoints for custom and third-party models deployed in your AWS account.

Status: Not Yet Implemented SageMaker uses SigV4 request signing with a non-standard invocation format. No HTTP client is implemented for this backend. Requests routed to BACKEND=sagemaker will not succeed. For production AWS LLM routing today, use the bedrock backend instead.

LiteLLM prefix: sagemaker/
Status: Stub — Custom protocol (SigV4), no HTTP client implemented
Docs: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html

Authentication

Variable	Required	Description
`AWS_ACCESS_KEY_ID`	Yes	IAM access key ID
`AWS_SECRET_ACCESS_KEY`	Yes	IAM secret access key
`AWS_REGION_NAME`	Yes	AWS region where the endpoint is deployed, e.g. `us-east-1`

IAM credentials must have the sagemaker:InvokeEndpoint permission on the target endpoint ARN.

Quick Start

These examples show the intended configuration once the backend is implemented. They will not work today.

Single-Backend (env vars)

BACKEND=sagemaker \
  AWS_ACCESS_KEY_ID=AKIA... \
  AWS_SECRET_ACCESS_KEY=... \
  AWS_REGION_NAME=us-east-1 \
  PROXY_OPEN_RELAY=true \
  cargo run -p anyllm_proxy

LiteLLM YAML Config

model_list:
  - model_name: my-llama-endpoint
    litellm_params:
      model: sagemaker/my-llama3-endpoint
      aws_access_key_id: "env:AWS_ACCESS_KEY_ID"
      aws_secret_access_key: "env:AWS_SECRET_ACCESS_KEY"
      aws_region_name: us-east-1

Usage Examples

Anthropic Messages API

curl http://localhost:3000/v1/messages \
  -H "x-api-key: $PROXY_API_KEYS" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "my-llama3-endpoint",
    "max_tokens": 1024,
    "messages": [{"role": "user", "content": "Hello"}]
  }'

OpenAI Chat Completions API

curl http://localhost:3000/v1/chat/completions \
  -H "Authorization: Bearer $PROXY_API_KEYS" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "my-llama3-endpoint",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

Capabilities

Feature	Supported
Chat Completions	✓ (planned)
Streaming	✓ (planned)
Tool Use	—
Embeddings	✓ (planned)
Vision	—
Batch	—

Notes

SageMaker invocation endpoint format: https://runtime.sagemaker.{region}.amazonaws.com/endpoints/{endpoint-name}/invocations. The model name in the proxy request maps to the endpoint name.
SageMaker requires AWS SigV4 request signing, which is distinct from the OpenAI-compatible Bearer token auth used by most other providers. This is why a custom HTTP client is needed and has not yet been implemented.
The request/response payload format varies by the model container (e.g., TGI, vLLM, Triton). An OpenAI-compatible container (such as a vLLM-based endpoint) would require the least translation work.
For production AWS LLM routing, use BACKEND=bedrock — it has SigV4 signing implemented and supports Claude, Llama, and other managed models without the need to manage your own endpoints.
AWS_SESSION_TOKEN will also be required for temporary credentials once implementation is complete.

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