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omniroute multi-provider AI proxy router.

---

proxy router that sits between AI clients (Claude CLI, Codex, Cursor IDE, etc.) and AI providers (Anthropic, Google, OpenAI, AWS, GitHub, etc.). It solves one big problem:

Different AI clients speak different "languages" (API formats), and different AI providers expect different "languages" too. omniroute translates between them automatically.

---

graph LR
    subgraph Clients
        A[Claude CLI]
        B[Codex]
        C[Cursor IDE]
        D[OpenAI-compatible]
    end

    subgraph omniroute
        E[Handler Layer]
        F[Translator Layer]
        G[Executor Layer]
        H[Services Layer]
    end

    subgraph Providers
        I[Anthropic Claude]
        J[Google Gemini]
        K[OpenAI / Codex]
        L[GitHub Copilot]
        M[AWS Kiro]
        N[Antigravity]
        O[Cursor API]
    end

    A --> E
    B --> E
    C --> E
    D --> E
    E --> F
    F --> G
    G --> I
    G --> J
    G --> K
    G --> L
    G --> M
    G --> N
    G --> O
    H -.-> E
    H -.-> G

OpenAI format as the hub:

N translators (one per format) instead of N² (every pair).

---

---

)

single source of truth for all provider configuration.

	object with base URLs, OAuth credentials (defaults), headers, and default system prompts for every provider. Also defines , , , , and .
	and merges them over the hardcoded defaults in . Keeps secrets out of source control while maintaining backwards compatibility.
	, .

flowchart TD
    A["App starts"] --> B["constants.ts defines PROVIDERS\nwith hardcoded defaults"]
    B --> C{"data/provider-credentials.json\nexists?"}
    C -->|Yes| D["credentialLoader reads JSON"]
    C -->|No| E["Use hardcoded defaults"]
    D --> F{"For each provider in JSON"}
    F --> G{"Provider exists\nin PROVIDERS?"}
    G -->|No| H["Log warning, skip"]
    G -->|Yes| I{"Value is object?"}
    I -->|No| J["Log warning, skip"]
    I -->|Yes| K["Merge clientId, clientSecret,\ntokenUrl, authUrl, refreshUrl"]
    K --> F
    H --> F
    J --> F
    F -->|Done| L["PROVIDERS ready with\nmerged credentials"]
    E --> L

---

)

provider-specific logic using the Strategy Pattern. Each executor overrides base methods as needed.

classDiagram
    class BaseExecutor {
        +buildUrl(model, stream, options)
        +buildHeaders(credentials, stream, body)
        +transformRequest(body, model, stream, credentials)
        +execute(url, options)
        +shouldRetry(status, error)
        +refreshCredentials(credentials, log)
    }

    class DefaultExecutor {
        +refreshCredentials()
    }

    class AntigravityExecutor {
        +buildUrl()
        +buildHeaders()
        +transformRequest()
        +shouldRetry()
        +refreshCredentials()
    }

    class CursorExecutor {
        +buildUrl()
        +buildHeaders()
        +transformRequest()
        +parseResponse()
        +generateChecksum()
    }

    class KiroExecutor {
        +buildUrl()
        +buildHeaders()
        +transformRequest()
        +parseEventStream()
        +refreshCredentials()
    }

    BaseExecutor <|-- DefaultExecutor
    BaseExecutor <|-- AntigravityExecutor
    BaseExecutor <|-- CursorExecutor
    BaseExecutor <|-- KiroExecutor
    BaseExecutor <|-- CodexExecutor
    BaseExecutor <|-- GeminiCLIExecutor
    BaseExecutor <|-- GithubExecutor

		Most complex: SHA-256 checksum auth, Protobuf request encoding, binary EventStream → SSE response parsing
		), Google OAuth token refresh

---

)

orchestration layer — coordinates translation, execution, streaming, and error handling.

	Central orchestrator (~600 lines). Handles the complete request lifecycle: format detection → translation → executor dispatch → streaming/non-streaming response → token refresh → error handling → usage logging.
	→ converts SSE back to Responses format.

sequenceDiagram
    participant Client
    participant chatCore
    participant Translator
    participant Executor
    participant Provider

    Client->>chatCore: Request (any format)
    chatCore->>chatCore: Detect source format
    chatCore->>chatCore: Check bypass patterns
    chatCore->>chatCore: Resolve model & provider
    chatCore->>Translator: Translate request (source → OpenAI → target)
    chatCore->>Executor: Get executor for provider
    Executor->>Executor: Build URL, headers, transform request
    Executor->>Executor: Refresh credentials if needed
    Executor->>Provider: HTTP fetch (streaming or non-streaming)

    alt Streaming
        Provider-->>chatCore: SSE stream
        chatCore->>chatCore: Pipe through SSE transform stream
        Note over chatCore: Transform stream translates<br/>each chunk: target → OpenAI → source
        chatCore-->>Client: Translated SSE stream
    else Non-streaming
        Provider-->>chatCore: JSON response
        chatCore->>Translator: Translate response
        chatCore-->>Client: Translated JSON
    end

    alt Error (401, 429, 500...)
        chatCore->>Executor: Retry with credential refresh
        chatCore->>chatCore: Account fallback logic
    end

---

)

	Format detection (): analyzes request body structure to identify Claude/OpenAI/Gemini/Antigravity/Responses formats (includes heuristic for Claude). Also: URL building, header building, thinking config normalization. Supports and dynamic providers.
	→ ), alias resolution with collision detection, input sanitization (rejects path traversal/control chars), and model info resolution with async alias getter support.
	every provider: Google (Gemini, Antigravity), Claude, Codex, Qwen, Qoder, GitHub (OAuth + Copilot dual-token), Kiro (AWS SSO OIDC + Social Auth). Includes in-flight promise deduplication cache and retry with exponential backoff.
	Combo models: chains of fallback models. If model A fails with a fallback-eligible error, try model B, then C, etc. Returns actual upstream status codes.
	) to concrete provider/model pairs based on availability and priority.

sequenceDiagram
    participant R1 as Request 1
    participant R2 as Request 2
    participant Cache as refreshPromiseCache
    participant OAuth as OAuth Provider

    R1->>Cache: getAccessToken("gemini", token)
    Cache->>Cache: No in-flight promise
    Cache->>OAuth: Start refresh
    R2->>Cache: getAccessToken("gemini", token)
    Cache->>Cache: Found in-flight promise
    Cache-->>R2: Return existing promise
    OAuth-->>Cache: New access token
    Cache-->>R1: New access token
    Cache-->>R2: Same access token (shared)
    Cache->>Cache: Delete cache entry

stateDiagram-v2
    [*] --> Active
    Active --> Error: Request fails (401/429/500)
    Error --> Cooldown: Apply backoff
    Cooldown --> Active: Cooldown expires
    Active --> Active: Request succeeds (reset backoff)

    state Error {
        [*] --> ClassifyError
        ClassifyError --> ShouldFallback: Rate limit / Auth / Transient
        ClassifyError --> NoFallback: 400 Bad Request
    }

    state Cooldown {
        [*] --> ExponentialBackoff
        ExponentialBackoff: Level 0 = 1s
        ExponentialBackoff: Level 1 = 2s
        ExponentialBackoff: Level 2 = 4s
        ExponentialBackoff: Max = 2min
    }

flowchart LR
    A["Request with\ncombo model"] --> B["Model A"]
    B -->|"2xx Success"| C["Return response"]
    B -->|"429/401/500"| D{"Fallback\neligible?"}
    D -->|Yes| E["Model B"]
    D -->|No| F["Return error"]
    E -->|"2xx Success"| C
    E -->|"429/401/500"| G{"Fallback\neligible?"}
    G -->|Yes| H["Model C"]
    G -->|No| F
    H -->|"2xx Success"| C
    H -->|"Fail"| I["All failed →\nReturn last status"]

---

)

format translation engine using a self-registering plugin system.

graph TD
    subgraph "Request Translation"
        A["Claude → OpenAI"]
        B["Gemini → OpenAI"]
        C["Antigravity → OpenAI"]
        D["OpenAI Responses → OpenAI"]
        E["OpenAI → Claude"]
        F["OpenAI → Gemini"]
        G["OpenAI → Kiro"]
        H["OpenAI → Cursor"]
    end

    subgraph "Response Translation"
        I["Claude → OpenAI"]
        J["Gemini → OpenAI"]
        K["Kiro → OpenAI"]
        L["Cursor → OpenAI"]
        M["OpenAI → Claude"]
        N["OpenAI → Antigravity"]
        O["OpenAI → Responses"]
    end

		on import.
		(system prompt extraction, thinking config), (parts/contents mapping), (format filtering), (ID generation, missing response injection), , .
		, , state management, registry.
		, , , , , , .

// Each translator file calls register() on import:
import { register } from "../index.js";
register("claude", "openai", translateClaudeToOpenAI);

// The index.js imports all translator files, triggering registration:
import "./request/claude-to-openai.js"; // ← self-registers

---

)

	SSE Transform Stream — the core streaming pipeline. Two modes: (full format translation) and (normalize + extract usage). Handles chunk buffering, usage estimation, content length tracking. Per-stream encoder/decoder instances avoid shared state.
	(whitespace-tolerant), (filters empty chunks for OpenAI/Claude/Gemini), , (format-aware SSE serialization with cleanup).
	for application logs and the call log pipeline for persisted request artifacts.
	//). Supports exclusions. Caches config for 30s.

flowchart TD
    A["Provider SSE stream"] --> B["TextDecoder\n(per-stream instance)"]
    B --> C["Buffer lines\n(split on newline)"]
    C --> D["parseSSELine()\n(trim whitespace, parse JSON)"]
    D --> E{"Mode?"}
    E -->|TRANSLATE| F["translateResponse()\ntarget → OpenAI → source"]
    E -->|PASSTHROUGH| G["fixInvalidId()\nnormalize chunk"]
    F --> H["hasValuableContent()\nfilter empty chunks"]
    G --> H
    H -->|"Has content"| I["extractUsage()\ntrack token counts"]
    H -->|"Empty"| J["Skip chunk"]
    I --> K["formatSSE()\nserialize + clean perf_metrics"]
    K --> L["TextEncoder\n(per-stream instance)"]
    L --> M["Enqueue to\nclient stream"]

    style A fill:#f9f,stroke:#333
    style M fill:#9f9,stroke:#333

---

)

	, ), authentication, shared

		)

---

OpenAI format as the hub. Adding a new provider only requires writing one pair of translators (to/from OpenAI), not N pairs.

. The factory in selects the right one at runtime.

. Adding a new translator is just creating a file and importing it.

strings. If the first fails, fallback to the next automatically.

mechanism.

---

---

			header
			header
			endpoint

---

flowchart LR
    A["Client"] --> B["detectFormat()"]
    B --> C["translateRequest()\nsource → OpenAI → target"]
    C --> D["Executor\nbuildUrl + buildHeaders"]
    D --> E["fetch(providerURL)"]
    E --> F["createSSEStream()\nTRANSLATE mode"]
    F --> G["parseSSELine()"]
    G --> H["translateResponse()\ntarget → OpenAI → source"]
    H --> I["extractUsage()\n+ addBuffer"]
    I --> J["formatSSE()"]
    J --> K["Client receives\ntranslated SSE"]
    K --> L["logUsage()\nsaveRequestUsage()"]

flowchart LR
    A["Client"] --> B["detectFormat()"]
    B --> C["translateRequest()\nsource → OpenAI → target"]
    C --> D["Executor.execute()"]
    D --> E["translateResponse()\ntarget → OpenAI → source"]
    E --> F["Return JSON\nresponse"]

flowchart LR
    A["Claude CLI request"] --> B{"Match bypass\npattern?"}
    B -->|"Title/Warmup/Count"| C["Generate fake\nOpenAI response"]
    B -->|"No match"| D["Normal flow"]
    C --> E["Translate to\nsource format"]
    E --> F["Return without\ncalling provider"]