Files
coder/aibridge
Yevhenii Shcherbina 63ec93a7ce feat: add AWS Bedrock mantle endpoint to AI Gateway (#26745)
Implements
https://linear.app/codercom/issue/AIGOV-213/add-bedrock-provider

# AWS Bedrock mantle support in AI Gateway

## Summary

Add support for the AWS Bedrock **mantle** endpoint
(`bedrock-mantle.{region}.api.aws/anthropic/v1/messages`) to AI Gateway.
Mantle serves Claude through the native Anthropic Messages API. We model
it as a `protocol` field on the existing Bedrock provider settings
(`invoke-model` default, or `mantle`) rather than as a new provider
type, and we treat mantle as a pure passthrough: SigV4-sign and forward,
no body translation.

## Background

Claude on AWS Bedrock is reachable through two endpoints, each speaking
exactly one wire protocol:

1. **InvokeModel** (existing): `bedrock-runtime.{region}.amazonaws.com`.
Model id in the URL path, request translated into Bedrock's InvokeModel
format, responses returned as a binary AWS eventstream. This is what AI
Gateway already supported for Bedrock.
2. **Mantle** (this doc):
`bedrock-mantle.{region}.api.aws/anthropic/v1/messages`. Native
Anthropic Messages API: model in the body, plain SSE streaming.

## Why a `protocol` field, not a new provider type

The alternative is to model mantle as its own `ai_provider_type`
(`bedrock-mantle`) alongside `bedrock`. I chose the `protocol` field
instead for two reasons:

1. Mantle reads more like a protocol of Bedrock than a separate
provider. It is the same AWS account, credentials, region, and IAM,
reached over a different wire protocol and host. One Bedrock provider
with two protocols (`invoke-model` default and `mantle`) models that
more organically than two provider types.
2. It avoids a database migration. The `protocol` field lives in the
settings JSON blob (empty resolves to `invoke-model`, so existing
providers are unaffected), whereas a new type means an enum value and
the `ALTER TYPE ... ADD VALUE` migration that goes with it.

## Why passthrough, not translation

The client already emits Bedrock-legal requests in mantle mode:

```sh
export CLAUDE_CODE_USE_MANTLE=1
export CLAUDE_CODE_SKIP_MANTLE_AUTH=1
export ANTHROPIC_BEDROCK_MANTLE_BASE_URL=https://<coder>/api/v2/aibridge/<provider-name>
```

So the gateway just forwards the body and SigV4-signs it (service
`bedrock-mantle`), and skips all the InvokeModel body-translation (model
remap, thinking conversion, beta-flag allowlist, field stripping). This
keeps the mantle path thin and avoids a second copy of translation logic
to maintain.

## Consequences

- Protocol-dependent fields: `model` / `small_fast_model` are used by
InvokeModel but ignored by mantle (the client sends the model), and
`base_url` is required for mantle but optional for InvokeModel.
Validation is protocol-aware.
- No central model control on mantle: because it is a passthrough, the
operator cannot pin the model.
- `region` and the `base_url` host must name the same region (the SigV4
scope must match the endpoint); a mismatch surfaces as `Credential
should be scoped to a valid region`.

## Draft UI

<img width="1100" height="579" alt="image"
src="https://github.com/user-attachments/assets/37bab46d-8958-4a96-9f47-1fef3493e1b6"
/>

## Follow-up PRs:
- https://github.com/coder/coder/pull/27156
2026-07-13 19:44:36 -04:00
..

aibridge

aibridge provides an HTTP handler that intercepts AI client requests bound for upstream AI providers (Anthropic, OpenAI, Copilot). It records token usage, prompts, and tool invocations per user. Optionally supports centralized MCP tool injection with allowlist/denylist filtering.

The handler is mounted by a host process. Today that host is coderd, which mounts the handler at /api/v2/ai-gateway/<provider>/*. Running aibridge as a separate process is planned for the future.

Architecture

┌─────────────────┐     ┌───────────────────────────────────────────┐
│    AI Client    │     │                    aibridge               │
│  (Claude Code,  │────▶│  ┌─────────────────┐    ┌─────────────┐   │
│   Cursor, etc.) │     │  │  RequestBridge  │───▶│  Providers  │   │
└─────────────────┘     │  │  (http.Handler) │    │  (Anthropic │   │
                        │  └─────────────────┘    │   OpenAI)   │   │
                        │                         └──────┬──────┘   │
                        │                                │          │
                        │                                ▼          │    ┌─────────────┐
                        │  ┌─────────────────┐    ┌─────────────┐   │    │  Upstream   │
                        │  │    Recorder     │◀───│ Interceptor │─── ───▶│    API      │
                        │  │ (tokens, tools, │    │ (streaming/ │   │    │ (Anthropic  │
                        │  │  prompts)       │    │  blocking)  │   │    │   OpenAI)   │
                        │  └────────┬────────┘    └──────┬──────┘   │    └─────────────┘
                        │           │                    │          │
                        │           ▼             ┌──────▼──────┐   │
                        │  ┌ ─ ─ ─ ─ ─ ─ ─ ┐      │  MCP Proxy  │   │
                        │  │    Database   │      │   (tools)   │   │
                        │  └ ─ ─ ─ ─ ─ ─ ─ ┘      └─────────────┘   │
                        └───────────────────────────────────────────┘

Components

  • RequestBridge: The main http.Handler that routes requests to providers
  • Provider: Defines bridged routes (intercepted) and passthrough routes (proxied)
  • Interceptor: Handles request/response processing and streaming
  • Recorder: Interface for capturing usage data (tokens, prompts, tools)
  • MCP Proxy (optional): Connects to MCP servers to list tool, inject them into requests, and invoke them in an inner agentic loop

Request Flow

  1. Client sends request to /anthropic/v1/messages or /openai/v1/chat/completions
  2. Actor extraction: Request must have an actor in context (via AsActor()). The host is responsible for authenticating the caller before invoking the handler.
  3. Upstream call: Request forwarded to the AI provider
  4. Response relay: Response streamed/sent to client
  5. Recording: Token usage, prompts, and tool invocations recorded

With MCP enabled: Tools from configured MCP servers are centrally defined and injected into requests (prefixed bmcp_). Allowlist/denylist regex patterns control which tools are available. When the model selects an injected tool, the gateway invokes it in an inner agentic loop, and continues the conversation loop until complete.

Passthrough routes (/v1/models, /v1/messages/count_tokens) are reverse-proxied directly.

Observability

Prometheus Metrics

Create metrics with NewMetrics(prometheus.Registerer):

Metric Type Description
interceptions_total Counter Intercepted request count
interceptions_inflight Gauge Currently processing requests
interceptions_duration_seconds Histogram Request duration
passthrough_total Counter Non-intercepted requests forwarded to the upstream
prompts_total Counter User prompt count
tokens_total Counter Token usage (input, output, cache read/write, provider extras)
injected_tool_invocations_total Counter Injected MCP tool invocations performed by the handler
non_injected_tool_selections_total Counter Client-defined tool selections returned by the model
circuit_breaker_state Gauge Circuit breaker state per provider/endpoint (0=closed, 0.5=half, 1=open)
circuit_breaker_trips_total Counter Times the circuit breaker transitioned to open
circuit_breaker_rejects_total Counter Requests rejected due to an open circuit breaker

Recorder Interface

Implement Recorder to persist usage data to your database:

  • aibridge_interceptions - request metadata (provider, model, initiator, timestamps)
  • aibridge_token_usages - input/output and cache read/write token counts per response
  • aibridge_user_prompts - user prompts
  • aibridge_tool_usages - tool invocations (injected and client-defined)
  • aibridge_model_thoughts - model reasoning content (thinking, reasoning summaries, commentary)
type Recorder interface {
    RecordInterception(ctx context.Context, req *InterceptionRecord) error
    RecordInterceptionEnded(ctx context.Context, req *InterceptionRecordEnded) error
    RecordTokenUsage(ctx context.Context, req *TokenUsageRecord) error
    RecordPromptUsage(ctx context.Context, req *PromptUsageRecord) error
    RecordToolUsage(ctx context.Context, req *ToolUsageRecord) error
    RecordModelThought(ctx context.Context, req *ModelThoughtRecord) error
}

Supported Routes

Each provider instance is mounted under /api/v2/ai-gateway/<name>, where <name> is the provider's configured name. For example, with an Anthropic provider named my-anthropic, its /messages endpoint would be reachable at /api/v2/ai-gateway/my-anthropic/v1/messages.

If a name is not set, the route path defaults to the provider's type: anthropic, openai, or copilot. The table below uses the default names.

(/*) denotes a route that handles both the exact path and any subpaths. A trailing /* denotes subpaths only.

Provider Route Type
Anthropic /anthropic/v1/messages Bridged (intercepted)
Anthropic /anthropic/v1/messages/count_tokens Passthrough
Anthropic /anthropic/v1/models(/*) Passthrough
Anthropic /anthropic/api/event_logging/* Passthrough
OpenAI /openai/v1/chat/completions Bridged (intercepted)
OpenAI /openai/v1/responses Bridged (intercepted)
OpenAI /openai/v1/responses/* Passthrough
OpenAI /openai/v1/conversations(/*) Passthrough
OpenAI /openai/v1/models(/*) Passthrough
Copilot /copilot/chat/completions Bridged (intercepted)
Copilot /copilot/responses Bridged (intercepted)
Copilot /copilot/models(/*) Passthrough
Copilot /copilot/agents/* Passthrough
Copilot /copilot/mcp/* Passthrough
Copilot /copilot/.well-known/* Passthrough