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:

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.

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:

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