aibridge is an HTTP gateway that sits between AI clients and upstream AI providers (Anthropic, OpenAI). It intercepts requests to record token usage, prompts, and tool invocations per user. Optionally supports centralized MCP tool injection with allowlist/denylist filtering.

┌─────────────────┐     ┌───────────────────────────────────────────┐
│    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)   │   │
                        │  └ ─ ─ ─ ─ ─ ─ ─ ┘      └─────────────┘   │
                        └───────────────────────────────────────────┘

• 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

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()).
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.

Create metrics with NewMetrics(prometheus.Registerer):

Implement Recorder to persist usage data to your database:

• aibridge_interceptions - request metadata (provider, model, initiator, timestamps)
• aibridge_token_usages - input/output token counts per response
• aibridge_user_prompts - user prompts
• aibridge_tool_usages - tool invocations (injected and client-defined)

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
}