Llm Provider

LLM Provider

Critical

1. All providers MUST implement the LLMProvider interface from src/llm/types.ts with three methods:

• call(options: LLMCallOptions): Promise — single non-streaming call returning text
• stream(options: LLMStreamOptions, callbacks: LLMStreamCallbacks): Promise — streaming call invoking callbacks
• listModels?(): Promise — optional; list available models from the API

1. Initialize client in constructor and store defaultModel from config. Example: this.client = new YourSDK({ apiKey: config.apiKey }). Never lazy-initialize on first call — providers are instantiated once and cached in src/llm/index.ts.

1. For EVERY response in call() and stream(), invoke trackUsage(model, usage) from src/llm/usage.js before returning/ending. This is mandatory — it captures token metrics for CLI telemetry and cost analysis. If the API doesn't return usage data, estimate via estimateTokens(text), which assumes ~4 chars per token.

1. Both call() and stream() must respect the model parameter using pattern: options.model || this.defaultModel. Never hardcode model names. Callers supply model overrides via LLMCallOptions.model.

1. Error handling: catch all errors, preserve error messages unchanged. The retry logic in src/llm/index.ts handles transient errors (ECONNRESET, socket hang up, 529 overload). For seat-based providers (Cursor, Claude CLI), wrap stderr via parseSeatBasedError() for user-friendly messages.

1. Always update ProviderType union (Step 2), DEFAULT_MODELS (Step 4), and createProvider() switch case (Step 5) in lock-step. Missing any one breaks the build or causes runtime Unknown provider error.

Instructions

Step 1: Create provider class file

Verify directory exists: ls -la src/llm/. Create src/llm/your-provider.ts. Match existing provider patterns (src/llm/anthropic.ts, src/llm/openai-compat.ts).

Minimal structure:

import type { LLMProvider, LLMCallOptions, LLMStreamOptions, LLMStreamCallbacks, LLMConfig, TokenUsage } from './types.js';
import { trackUsage } from './usage.js';
import { estimateTokens } from './utils.js';

export class YourProviderProvider implements LLMProvider {
  private client: YourSDKType;
  private defaultModel: string;

  constructor(config: LLMConfig) {
    if (!config.apiKey) throw new Error('API key required');
    this.client = new YourSDK({ apiKey: config.apiKey, ...(config.baseUrl && { baseURL: config.baseUrl }) });
    this.defaultModel = config.model;
  }

  async call(options: LLMCallOptions): Promise {
    const model = options.model || this.defaultModel;
    const response = await this.client.messages.create({ model, max_tokens: options.maxTokens || 4096, system: options.system, messages: [{ role: 'user', content: options.prompt }] });
    trackUsage(model, { inputTokens: response.usage?.input_tokens || 0, outputTokens: response.usage?.output_tokens || 0 });
    return response.content?.[0]?.text || '';
  }

  async stream(options: LLMStreamOptions, callbacks: LLMStreamCallbacks): Promise {
    const model = options.model || this.defaultModel;
    const messages = [...(options.messages || []), { role: 'user' as const, content: options.prompt }];
    try {
      const stream = await this.client.stream({ model, max_tokens: options.maxTokens || 10240, system: options.system, messages });
      let stopReason: string | undefined, usage: TokenUsage | undefined;
      for await (const chunk of stream) {
        if (chunk.delta?.text) callbacks.onText(chunk.delta.text);
        if (chunk.delta?.stop_reason) stopReason = chunk.delta.stop_reason;
        if (chunk.usage) usage = { inputTokens: chunk.usage.input_tokens, outputTokens: chunk.usage.output_tokens };
      }
      if (usage) trackUsage(model, usage);
      callbacks.onEnd({ stopReason, usage });
    } catch (error) { callbacks.onError(error instanceof Error ? error : new Error(String(error))); }
  }
}

Verify: File exports the class; imports match existing providers.

Step 2: Add to ProviderType union

Edit src/llm/types.ts line 1. Add your provider in kebab-case:

export type ProviderType = 'anthropic' | 'vertex' | 'openai' | 'cursor' | 'claude-cli' | 'your-provider';

Verify: npx tsc --noEmit shows no ProviderType errors.

Step 3: Add config fields

If your provider needs fields beyond apiKey, model, baseUrl, extend LLMConfig in src/llm/types.ts:

export interface LLMConfig {
  provider: ProviderType;
  model: string;
  fastModel?: string;
  apiKey?: string;
  baseUrl?: string;
  yourProviderSecret?: string;
}

Step 4: Update config.ts

Edit src/llm/config.ts:

Line 9: Add to DEFAULT_MODELS:

export const DEFAULT_MODELS: Record = {
  anthropic: 'claude-sonnet-4-6',
  vertex: 'claude-sonnet-4-6',
  openai: 'gpt-5.4-mini',
  cursor: 'sonnet-4.6',
  'claude-cli': 'default',
  'your-provider': 'your-provider/default-model',
};

Line 17: Add to MODELCONTEXTWINDOWS if known:

export const MODEL_CONTEXT_WINDOWS: Record = {
  'your-provider/model-name': 128_000,
};

Line 59: In resolveFromEnv(), add env detection before final return null:

if (process.env.YOUR_PROVIDER_API_KEY) {
  return {
    provider: 'your-provider',
    apiKey: process.env.YOUR_PROVIDER_API_KEY,
    model: process.env.CALIBER_MODEL || DEFAULT_MODELS['your-provider'],
    baseUrl: process.env.YOUR_PROVIDER_BASE_URL,
  };
}

Line 115: In readConfigFile() validation, add 'your-provider' to includes list.

Verify: npm run test -- src/llm/__tests__/ -t config confirms env var detection works.

Step 5: Register in factory

Edit src/llm/index.ts. Add import (line ~4):

import { YourProviderProvider } from './your-provider.js';

In createProvider() switch (line ~24), add before default case:

case 'your-provider':
  return new YourProviderProvider(config);

Verify: npx tsc --noEmit passes; no type errors on switch cases.

Step 6: Write tests

Create src/llm/__tests__/your-provider.test.ts:

import { describe, it, expect, beforeEach } from 'vitest';
import { YourProviderProvider } from '../your-provider.js';

describe('YourProviderProvider', () => {
  let provider: YourProviderProvider;
  beforeEach(() => {
    provider = new YourProviderProvider({ provider: 'your-provider', model: 'test', apiKey: 'test' });
  });

  it('implements LLMProvider interface', () => {
    expect(typeof provider.call).toBe('function');
    expect(typeof provider.stream).toBe('function');
  });

  it('call() returns string', async () => {
    const result = await provider.call({ system: 'helpful', prompt: 'hi' });
    expect(typeof result).toBe('string');
  });

  it('stream() invokes callbacks', async () => {
    const texts: string[] = [];
    let ended = false;
    await provider.stream({ system: 'helpful', prompt: 'hi' }, {
      onText: (t) => texts.push(t),
      onEnd: () => { ended = true; },
      onError: () => {},
    });
    expect(ended).toBe(true);
  });
});

Verify: npm run test -- src/llm/__tests__/your-provider.test.ts passes.

Step 7: Integration test

Run factory tests with your provider env var:

YOUR_PROVIDER_API_KEY=test npm run test -- src/llm/__tests__/index.test.ts

Verify: getProvider() instantiates your provider; llmCall() dispatches correctly.

Examples

Example 1: Local LM Studio server

User says: "I need caliber to use my local LM Studio instance."

Actions: Create src/llm/lm-studio.ts extending OpenAICompatProvider. Add 'lm-studio' to ProviderType. In config.ts:

if (process.env.LM_STUDIO_BASE_URL) {
  return { provider: 'lm-studio', apiKey: '', model: 'local', baseUrl: process.env.LM_STUDIO_BASE_URL };
}

Register in createProvider() case. User: export LM_STUDIO_BASE_URL=http://localhost:8000/v1. Result: caliber uses local LM Studio; tokens estimated via estimateTokens().

Example 2: Ollama (seat-based)

User says: "Ollama is auto-detected; no API key needed."

Actions: Create src/llm/ollama.ts extending OpenAICompatProvider. Add 'ollama' to ProviderType and SEATBASEDPROVIDERS. In config.ts:

if (process.env.OLLAMA_HOST) {
  return { provider: 'ollama', model: 'mistral', baseUrl: process.env.OLLAMA_HOST || 'http://localhost:11434/v1' };
}

Result: Offline per-machine LLM without API keys.

Common Issues

Unknown provider: your-provider

• Cause: ProviderType updated but createProvider() case missing.
• Fix: Add case in src/llm/index.ts and import the class.

Cannot find module './your-provider.js'

• Cause: File named your_provider.ts (underscore) not your-provider.ts (kebab).
• Fix: Rename file to use kebab-case.

API key is required for YourProvider

• Cause: Env var YOURPROVIDERAPI_KEY not set; resolveFromEnv() didn't detect it.
• Fix: Verify env var name in config.ts matches. Test: YOUR_PROVIDER_API_KEY=test npm run test -- src/llm/__tests__/index.test.ts.

LLM response did not include usage tokens

• Cause: Provider API doesn't return usage (local models).
• Fix: Estimate tokens: trackUsage(model, { inputTokens: estimateTokens(options.system + options.prompt), outputTokens: estimateTokens(response.text) });

Stream callbacks never fire; onEnd not called

• Cause: Async iterator not fully consumed before method returns.
• Fix: Ensure iteration completes before onEnd(): for await (const chunk of stream) { } callbacks.onEnd({ stopReason, usage });

My model parameter is ignored

• Cause: call()/stream() doesn't use options.model || this.defaultModel.
• Fix: Replace hardcoded model: const model = options.model || this.defaultModel; const response = await this.client.create({ model, ... });

trackUsage() is never called

• Cause: Forgot to call trackUsage() in call() or stream().
• Fix: Add after every response: trackUsage(model, { inputTokens: ..., outputTokens: ... });

Type error: Provider doesn't implement LLMProvider

• Cause: Missing method or wrong signature.
• Fix: Verify all required methods exist with exact signatures from src/llm/types.ts. Copy-paste from anthropic.ts as reference.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: caliber-ai-org
• Source: caliber-ai-org/ai-setup
• License: MIT
• Homepage: https://caliber-ai.dev

Install and usage instructions live in the source repository linked above.