Custom Providers

Extensions can register custom model providers via pi.registerProvider(). This enables:

• Proxies - Route requests through corporate proxies or API gateways
• Custom endpoints - Use self-hosted or private model deployments
• OAuth/SSO - Add authentication flows for enterprise providers
• Custom APIs - Implement streaming for non-standard LLM APIs

Example Extensions

See these complete provider examples:

• examples/extensions/custom-provider-anthropic/
• examples/extensions/custom-provider-gitlab-duo/
• examples/extensions/custom-provider-qwen-cli/

Quick Reference

import type { ExtensionAPI } from "@mariozechner/pi-coding-agent";

export default function (pi: ExtensionAPI) {
  // Override baseUrl for existing provider
  pi.registerProvider("anthropic", {
    baseUrl: "https://proxy.example.com"
  });

  // Register new provider with models
  pi.registerProvider("my-provider", {
    baseUrl: "https://api.example.com",
    apiKey: "MY_API_KEY",
    api: "openai-completions",
    models: [
      {
        id: "my-model",
        name: "My Model",
        reasoning: false,
        input: ["text", "image"],
        cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
        contextWindow: 128000,
        maxTokens: 4096
      }
    ]
  });
}

Override Existing Provider

The simplest use case: redirect an existing provider through a proxy.

// All Anthropic requests now go through your proxy
pi.registerProvider("anthropic", {
  baseUrl: "https://proxy.example.com"
});

// Add custom headers to OpenAI requests
pi.registerProvider("openai", {
  headers: {
    "X-Custom-Header": "value"
  }
});

// Both baseUrl and headers
pi.registerProvider("google", {
  baseUrl: "https://ai-gateway.corp.com/google",
  headers: {
    "X-Corp-Auth": "CORP_AUTH_TOKEN"  // env var or literal
  }
});

When only baseUrl and/or headers are provided (no models), all existing models for that provider are preserved with the new endpoint.

Register New Provider

To add a completely new provider, specify models along with the required configuration.

pi.registerProvider("my-llm", {
  baseUrl: "https://api.my-llm.com/v1",
  apiKey: "MY_LLM_API_KEY",  // env var name or literal value
  api: "openai-completions",  // which streaming API to use
  models: [
    {
      id: "my-llm-large",
      name: "My LLM Large",
      reasoning: true,        // supports extended thinking
      input: ["text", "image"],
      cost: {
        input: 3.0,           // $/million tokens
        output: 15.0,
        cacheRead: 0.3,
        cacheWrite: 3.75
      },
      contextWindow: 200000,
      maxTokens: 16384
    }
  ]
});

When models is provided, it replaces all existing models for that provider.

Unregister Provider

Use pi.unregisterProvider(name) to remove a provider that was previously registered via pi.registerProvider(name, ...):

// Register
pi.registerProvider("my-llm", {
  baseUrl: "https://api.my-llm.com/v1",
  apiKey: "MY_LLM_API_KEY",
  api: "openai-completions",
  models: [
    {
      id: "my-llm-large",
      name: "My LLM Large",
      reasoning: true,
      input: ["text", "image"],
      cost: { input: 3.0, output: 15.0, cacheRead: 0.3, cacheWrite: 3.75 },
      contextWindow: 200000,
      maxTokens: 16384
    }
  ]
});

// Later, remove it
pi.unregisterProvider("my-llm");

Unregistering removes that provider's dynamic models, API key fallback, OAuth provider registration, and custom stream handler registrations. Any built-in models or provider behavior that were overridden are restored.

Calls made after the initial extension load phase are applied immediately, so no /reload is required.

API Types

The api field determines which streaming implementation is used:

API	Use for
anthropic-messages	Anthropic Claude API and compatibles
openai-completions	OpenAI Chat Completions API and compatibles
openai-responses	OpenAI Responses API
azure-openai-responses	Azure OpenAI Responses API
openai-codex-responses	OpenAI Codex Responses API
google-generative-ai	Google Generative AI API
google-gemini-cli	Google Cloud Code Assist API
google-vertex	Google Vertex AI API
bedrock-converse-stream	Amazon Bedrock Converse API

Most OpenAI-compatible providers work with openai-completions. Use compat for quirks:

models: [{
  id: "custom-model",
  // ...
  compat: {
    supportsDeveloperRole: false,      // use "system" instead of "developer"
    supportsReasoningEffort: false,    // disable reasoning_effort param
    maxTokensField: "max_tokens",      // instead of "max_completion_tokens"
    requiresToolResultName: true,      // tool results need name field
    requiresMistralToolIds: true       // tool IDs must be 9 alphanumeric chars
    thinkingFormat: "qwen"             // uses enable_thinking: true
  }
}]

Auth Header

If your provider expects Authorization: Bearer <key> but doesn't use a standard API, set authHeader: true:

pi.registerProvider("custom-api", {
  baseUrl: "https://api.example.com",
  apiKey: "MY_API_KEY",
  authHeader: true,  // adds Authorization: Bearer header
  api: "openai-completions",
  models: [...]
});

OAuth Support

Add OAuth/SSO authentication that integrates with /login:

import type { OAuthCredentials, OAuthLoginCallbacks } from "@mariozechner/pi-ai";

pi.registerProvider("corporate-ai", {
  baseUrl: "https://ai.corp.com/v1",
  api: "openai-responses",
  models: [...],
  oauth: {
    name: "Corporate AI (SSO)",

    async login(callbacks: OAuthLoginCallbacks): Promise<OAuthCredentials> {
      // Option 1: Browser-based OAuth
      callbacks.onAuth({ url: "https://sso.corp.com/authorize?..." });

      // Option 2: Device code flow
      callbacks.onDeviceCode({
        userCode: "ABCD-1234",
        verificationUri: "https://sso.corp.com/device"
      });

      // Option 3: Prompt for token/code
      const code = await callbacks.onPrompt({ message: "Enter SSO code:" });

      // Exchange for tokens (your implementation)
      const tokens = await exchangeCodeForTokens(code);

      return {
        refresh: tokens.refreshToken,
        access: tokens.accessToken,
        expires: Date.now() + tokens.expiresIn * 1000
      };
    },

    async refreshToken(credentials: OAuthCredentials): Promise<OAuthCredentials> {
      const tokens = await refreshAccessToken(credentials.refresh);
      return {
        refresh: tokens.refreshToken ?? credentials.refresh,
        access: tokens.accessToken,
        expires: Date.now() + tokens.expiresIn * 1000
      };
    },

    getApiKey(credentials: OAuthCredentials): string {
      return credentials.access;
    },

    // Optional: modify models based on user's subscription
    modifyModels(models, credentials) {
      const region = decodeRegionFromToken(credentials.access);
      return models.map(m => ({
        ...m,
        baseUrl: `https://${region}.ai.corp.com/v1`
      }));
    }
  }
});

After registration, users can authenticate via /login corporate-ai.

OAuthLoginCallbacks

The callbacks object provides three ways to authenticate:

interface OAuthLoginCallbacks {
  // Open URL in browser (for OAuth redirects)
  onAuth(params: { url: string }): void;

  // Show device code (for device authorization flow)
  onDeviceCode(params: { userCode: string; verificationUri: string }): void;

  // Prompt user for input (for manual token entry)
  onPrompt(params: { message: string }): Promise<string>;
}

OAuthCredentials

Credentials are persisted in ~/.pi/agent/auth.json:

interface OAuthCredentials {
  refresh: string;   // Refresh token (for refreshToken())
  access: string;    // Access token (returned by getApiKey())
  expires: number;   // Expiration timestamp in milliseconds
}

Custom Streaming API

For providers with non-standard APIs, implement streamSimple. Study the existing provider implementations before writing your own:

Reference implementations:

• anthropic.ts - Anthropic Messages API
• openai-completions.ts - OpenAI Chat Completions
• openai-responses.ts - OpenAI Responses API
• google.ts - Google Generative AI
• amazon-bedrock.ts - AWS Bedrock

Stream Pattern

All providers follow the same pattern:

import {
  type AssistantMessage,
  type AssistantMessageEventStream,
  type Context,
  type Model,
  type SimpleStreamOptions,
  calculateCost,
  createAssistantMessageEventStream,
} from "@mariozechner/pi-ai";

function streamMyProvider(
  model: Model<any>,
  context: Context,
  options?: SimpleStreamOptions
): AssistantMessageEventStream {
  const stream = createAssistantMessageEventStream();

  (async () => {
    // Initialize output message
    const output: AssistantMessage = {
      role: "assistant",
      content: [],
      api: model.api,
      provider: model.provider,
      model: model.id,
      usage: {
        input: 0,
        output: 0,
        cacheRead: 0,
        cacheWrite: 0,
        totalTokens: 0,
        cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 },
      },
      stopReason: "stop",
      timestamp: Date.now(),
    };

    try {
      // Push start event
      stream.push({ type: "start", partial: output });

      // Make API request and process response...
      // Push content events as they arrive...

      // Push done event
      stream.push({
        type: "done",
        reason: output.stopReason as "stop" | "length" | "toolUse",
        message: output
      });
      stream.end();
    } catch (error) {
      output.stopReason = options?.signal?.aborted ? "aborted" : "error";
      output.errorMessage = error instanceof Error ? error.message : String(error);
      stream.push({ type: "error", reason: output.stopReason, error: output });
      stream.end();
    }
  })();

  return stream;
}

Event Types

Push events via stream.push() in this order:

1. { type: "start", partial: output } - Stream started

2. Content events (repeatable, track contentIndex for each block):

   • { type: "text_start", contentIndex, partial } - Text block started
   • { type: "text_delta", contentIndex, delta, partial } - Text chunk
   • { type: "text_end", contentIndex, content, partial } - Text block ended
   • { type: "thinking_start", contentIndex, partial } - Thinking started
   • { type: "thinking_delta", contentIndex, delta, partial } - Thinking chunk
   • { type: "thinking_end", contentIndex, content, partial } - Thinking ended
   • { type: "toolcall_start", contentIndex, partial } - Tool call started
   • { type: "toolcall_delta", contentIndex, delta, partial } - Tool call JSON chunk
   • { type: "toolcall_end", contentIndex, toolCall, partial } - Tool call ended

3. { type: "done", reason, message } or { type: "error", reason, error } - Stream ended

The partial field in each event contains the current AssistantMessage state. Update output.content as you receive data, then include output as the partial.

Content Blocks

Add content blocks to output.content as they arrive:

// Text block
output.content.push({ type: "text", text: "" });
stream.push({ type: "text_start", contentIndex: output.content.length - 1, partial: output });

// As text arrives
const block = output.content[contentIndex];
if (block.type === "text") {
  block.text += delta;
  stream.push({ type: "text_delta", contentIndex, delta, partial: output });
}

// When block completes
stream.push({ type: "text_end", contentIndex, content: block.text, partial: output });

Tool Calls

Tool calls require accumulating JSON and parsing:

// Start tool call
output.content.push({
  type: "toolCall",
  id: toolCallId,
  name: toolName,
  arguments: {}
});
stream.push({ type: "toolcall_start", contentIndex: output.content.length - 1, partial: output });

// Accumulate JSON
let partialJson = "";
partialJson += jsonDelta;
try {
  block.arguments = JSON.parse(partialJson);
} catch {}
stream.push({ type: "toolcall_delta", contentIndex, delta: jsonDelta, partial: output });

// Complete
stream.push({
  type: "toolcall_end",
  contentIndex,
  toolCall: { type: "toolCall", id, name, arguments: block.arguments },
  partial: output
});

Usage and Cost

Update usage from API response and calculate cost:

output.usage.input = response.usage.input_tokens;
output.usage.output = response.usage.output_tokens;
output.usage.cacheRead = response.usage.cache_read_tokens ?? 0;
output.usage.cacheWrite = response.usage.cache_write_tokens ?? 0;
output.usage.totalTokens = output.usage.input + output.usage.output +
                           output.usage.cacheRead + output.usage.cacheWrite;
calculateCost(model, output.usage);

Registration

Register your stream function:

pi.registerProvider("my-provider", {
  baseUrl: "https://api.example.com",
  apiKey: "MY_API_KEY",
  api: "my-custom-api",
  models: [...],
  streamSimple: streamMyProvider
});

Testing Your Implementation

Test your provider against the same test suites used by built-in providers. Copy and adapt these test files from packages/ai/test/:

Test	Purpose
stream.test.ts	Basic streaming, text output
tokens.test.ts	Token counting and usage
abort.test.ts	AbortSignal handling
empty.test.ts	Empty/minimal responses
context-overflow.test.ts	Context window limits
image-limits.test.ts	Image input handling
unicode-surrogate.test.ts	Unicode edge cases
tool-call-without-result.test.ts	Tool call edge cases
image-tool-result.test.ts	Images in tool results
total-tokens.test.ts	Total token calculation
cross-provider-handoff.test.ts	Context handoff between providers

Run tests with your provider/model pairs to verify compatibility.

Config Reference

interface ProviderConfig {
  /** API endpoint URL. Required when defining models. */
  baseUrl?: string;

  /** API key or environment variable name. Required when defining models (unless oauth). */
  apiKey?: string;

  /** API type for streaming. Required at provider or model level when defining models. */
  api?: Api;

  /** Custom streaming implementation for non-standard APIs. */
  streamSimple?: (
    model: Model<Api>,
    context: Context,
    options?: SimpleStreamOptions
  ) => AssistantMessageEventStream;

  /** Custom headers to include in requests. Values can be env var names. */
  headers?: Record<string, string>;

  /** If true, adds Authorization: Bearer header with the resolved API key. */
  authHeader?: boolean;

  /** Models to register. If provided, replaces all existing models for this provider. */
  models?: ProviderModelConfig[];

  /** OAuth provider for /login support. */
  oauth?: {
    name: string;
    login(callbacks: OAuthLoginCallbacks): Promise<OAuthCredentials>;
    refreshToken(credentials: OAuthCredentials): Promise<OAuthCredentials>;
    getApiKey(credentials: OAuthCredentials): string;
    modifyModels?(models: Model<Api>[], credentials: OAuthCredentials): Model<Api>[];
  };
}

Model Definition Reference

interface ProviderModelConfig {
  /** Model ID (e.g., "claude-sonnet-4-20250514"). */
  id: string;

  /** Display name (e.g., "Claude 4 Sonnet"). */
  name: string;

  /** API type override for this specific model. */
  api?: Api;

  /** Whether the model supports extended thinking. */
  reasoning: boolean;

  /** Supported input types. */
  input: ("text" | "image")[];

  /** Cost per million tokens (for usage tracking). */
  cost: {
    input: number;
    output: number;
    cacheRead: number;
    cacheWrite: number;
  };

  /** Maximum context window size in tokens. */
  contextWindow: number;

  /** Maximum output tokens. */
  maxTokens: number;

  /** Custom headers for this specific model. */
  headers?: Record<string, string>;

  /** OpenAI compatibility settings for openai-completions API. */
  compat?: {
    supportsStore?: boolean;
    supportsDeveloperRole?: boolean;
    supportsReasoningEffort?: boolean;
    supportsUsageInStreaming?: boolean;
    maxTokensField?: "max_completion_tokens" | "max_tokens";
    requiresToolResultName?: boolean;
    requiresAssistantAfterToolResult?: boolean;
    requiresThinkingAsText?: boolean;
    requiresMistralToolIds?: boolean;
    thinkingFormat?: "openai" | "zai" | "qwen";
  };
}