Dust dust-llm

Step-by-step guide for adding support for a new LLM in Dust. Use when adding a new model, or updating a previous one.

install

source · Clone the upstream repo

git clone https://github.com/dust-tt/dust

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/dust-tt/dust "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/dust-llm" ~/.claude/skills/dust-tt-dust-dust-llm && rm -rf "$T"

manifest: .claude/skills/dust-llm/SKILL.md

source content

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:

front/types/assistant/models/anthropic.ts

- Add

CLAUDE_X_MODEL_ID

and config

front/lib/api/llm/clients/anthropic/types.ts

- Add to

ANTHROPIC_WHITELISTED_MODEL_IDS

```
front/types/assistant/models/models.ts
```
- Register in central registry

front/lib/api/assistant/token_pricing.ts

- Add pricing

```
sdks/js/src/types.ts
```
- Update SDK types
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

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