dust-llm

name	dust-llm
description	Step-by-step guide for adding support for a new LLM in Dust. Use when adding a new model, or updating a previous one. Use when this capability is needed.
metadata	{"author":"dust-tt"}

Adding Support for a New LLM Model

This skill guides you through adding support for a newly released LLM.

Quick Reference

Files to Modify

File	Purpose
front/types/assistant/models/{provider}.ts	Model ID + configuration
front/lib/api/assistant/token_pricing.ts	Pricing per million tokens
front/types/assistant/models/models.ts	Central registry
front/lib/api/llm/clients/{provider}/types.ts	Router whitelist
sdks/js/src/types.ts	SDK types
front/components/providers/types.ts	UI availability (optional)
front/lib/api/llm/tests/llm.test.ts	Integration tests

Prerequisites

Before adding, gather:

• Model ID: Exact provider identifier (e.g., gpt-4-turbo-2024-04-09)
• Context size: Total context window in tokens
• Pricing: Input/output cost per million tokens
• Capabilities: Vision, structured output, reasoning effort levels
• Tokenizer: Compatible tokenizer for token counting

Step-by-Step: Adding an OpenAI Model

Step 1: Add Model Configuration

Edit front/types/assistant/models/openai.ts:

export const GPT_4_TURBO_2024_04_09_MODEL_ID = "gpt-4-turbo-2024-04-09" as const;

export const GPT_4_TURBO_2024_04_09_MODEL_CONFIG: ModelConfigurationType = {
  providerId: "openai",
  modelId: GPT_4_TURBO_2024_04_09_MODEL_ID,
  displayName: "GPT 4 turbo",
  contextSize: 128_000,
  recommendedTopK: 32,
  recommendedExhaustiveTopK: 64,
  largeModel: true,
  description: "OpenAI's GPT 4 Turbo model for complex tasks (128k context).",
  shortDescription: "OpenAI's second best model.",
  isLegacy: false,
  isLatest: false,
  generationTokensCount: 2048,
  supportsVision: true,
  minimumReasoningEffort: "none",
  maximumReasoningEffort: "none",
  defaultReasoningEffort: "none",
  supportsResponseFormat: false,
  tokenizer: { type: "tiktoken", base: "cl100k_base" },
};

Step 2: Add Pricing

Edit front/lib/api/assistant/token_pricing.ts:

const CURRENT_MODEL_PRICING: Record<BaseModelIdType, PricingEntry> = {
  // ... existing
  "gpt-4-turbo-2024-04-09": {
    input: 10.0,  // USD per million input tokens
    output: 30.0, // USD per million output tokens
    cache_read_input_tokens: 1.0,      // Optional: cached reads
    cache_creation_input_tokens: 12.5, // Optional: cache creation
  },
};

Step 3: Register in Central Registry

Edit front/types/assistant/models/models.ts:

export const MODEL_IDS = [
  // ... existing
  GPT_4_TURBO_2024_04_09_MODEL_ID,
] as const;

export const SUPPORTED_MODEL_CONFIGS: ModelConfigurationType[] = [
  // ... existing
  GPT_4_TURBO_2024_04_09_MODEL_CONFIG,
];

Step 4: Update Router Whitelist

Edit front/lib/api/llm/clients/openai/types.ts:

export const OPENAI_WHITELISTED_MODEL_IDS = [
  // ... existing
  GPT_4_TURBO_2024_04_09_MODEL_ID,
] as const;

Step 5: Update SDK Types

Edit sdks/js/src/types.ts:

const ModelLLMIdSchema = FlexibleEnumSchema<
  // ... existing
  | "gpt-4-turbo-2024-04-09"
>();

Step 6: Add to UI (Optional)

Edit front/components/providers/types.ts:

export const USED_MODEL_CONFIGS: readonly ModelConfig[] = [
  // ... existing
  GPT_4_TURBO_2024_04_09_MODEL_CONFIG,
] as const;

Step 7: Test (Mandatory)

Edit front/lib/api/llm/tests/llm.test.ts:

const MODELS = {
  // ... existing
  [GPT_4_TURBO_2024_04_09_MODEL_ID]: {
    runTest: true,  // Enable for testing
    providerId: "openai",
  },
};

Run test:

RUN_LLM_TEST=true npx vitest --config lib/api/llm/tests/vite.config.js lib/api/llm/tests/llm.test.ts --run

After test passes, set runTest: false to avoid expensive CI runs.

Adding Anthropic Models

Same pattern with Anthropic-specific files:

1. front/types/assistant/models/anthropic.ts - Add CLAUDE_X_MODEL_ID and config
2. front/lib/api/llm/clients/anthropic/types.ts - Add to ANTHROPIC_WHITELISTED_MODEL_IDS
3. front/types/assistant/models/models.ts - Register in central registry
4. front/lib/api/assistant/token_pricing.ts - Add pricing
5. sdks/js/src/types.ts - Update SDK types
6. Test and validate

Model Configuration Properties

Property	Description
supportsVision	Can process images
supportsResponseFormat	Supports structured output (JSON)
minimumReasoningEffort	Min reasoning level ("none", "low", "medium", "high")
maximumReasoningEffort	Max reasoning level
defaultReasoningEffort	Default reasoning level
tokenizer	Tokenizer config for token counting

Validation Checklist

• Model config added to provider file
• Pricing updated (input, output, cache if applicable)
• Registered in central registry (MODEL_IDS + SUPPORTED_MODEL_CONFIGS)
• Router whitelist updated
• SDK types updated
• UI config added (if needed)
• Integration test passes
• Test disabled after validation

Troubleshooting

Model not in UI: Check USED_MODEL_CONFIGS in front/components/providers/types.ts

API calls failing: Verify model ID matches provider's exact identifier, check router whitelist

Token counting errors: Validate context size and tokenizer configuration

Pricing issues: Ensure prices are per million tokens in USD

Reference

• See front/types/assistant/models/openai.ts and anthropic.ts for examples
• Provider docs: OpenAI, Anthropic, Google, Mistral

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