adding-models
Guide for adding new LLM models to Letta Code. Use when the user wants to add support for a new model, needs to know valid model handles, or wants to update the model configuration. Covers models.json configuration, CI test matrix, and handle validation.
Best use case
adding-models is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Guide for adding new LLM models to Letta Code. Use when the user wants to add support for a new model, needs to know valid model handles, or wants to update the model configuration. Covers models.json configuration, CI test matrix, and handle validation.
Teams using adding-models should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/adding-models/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How adding-models Compares
| Feature / Agent | adding-models | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Guide for adding new LLM models to Letta Code. Use when the user wants to add support for a new model, needs to know valid model handles, or wants to update the model configuration. Covers models.json configuration, CI test matrix, and handle validation.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Adding Models
This skill guides you through adding a new LLM model to Letta Code.
## Quick Reference
**Key files**:
- `src/models.json` - Model definitions (required)
- `.github/workflows/ci.yml` - CI test matrix (optional)
- `src/tools/manager.ts` - Toolset detection logic (rarely needed)
## Workflow
### Step 1: Find Valid Model Handles
Query the Letta API to see available models:
```bash
curl -s https://api.letta.com/v1/models/ | jq '.[] | .handle'
```
Or filter by provider:
```bash
curl -s https://api.letta.com/v1/models/ | jq '.[] | select(.handle | startswith("google_ai/")) | .handle'
```
Common provider prefixes:
- `anthropic/` - Claude models
- `openai/` - GPT models
- `google_ai/` - Gemini models
- `google_vertex/` - Vertex AI
- `openrouter/` - Various providers
### Step 2: Add to models.json
Add an entry to `src/models.json`:
```json
{
"id": "model-shortname",
"handle": "provider/model-name",
"label": "Human Readable Name",
"description": "Brief description of the model",
"isFeatured": true, // Optional: shows in featured list
"updateArgs": {
"context_window": 180000,
"temperature": 1.0 // Optional: provider-specific settings
}
}
```
**Field reference**:
- `id`: Short identifier used with `--model` flag (e.g., `gemini-3-flash`)
- `handle`: Full provider/model path from the API (e.g., `google_ai/gemini-3-flash-preview`)
- `label`: Display name in model selector
- `description`: Brief description shown in selector
- `isFeatured`: If true, appears in featured models section
- `updateArgs`: Model-specific configuration (context window, temperature, reasoning settings, etc.)
**Provider prefixes**:
- `anthropic/` - Anthropic (Claude models)
- `openai/` - OpenAI (GPT models)
- `google_ai/` - Google AI (Gemini models)
- `google_vertex/` - Google Vertex AI
- `openrouter/` - OpenRouter (various providers)
### Step 3: Test the Model
Test with headless mode:
```bash
bun run src/index.ts --new --model <model-id> -p "hi, what model are you?"
```
Example:
```bash
bun run src/index.ts --new --model gemini-3-flash -p "hi, what model are you?"
```
### Step 4: Add to CI Test Matrix (Optional)
To include the model in automated testing, add it to `.github/workflows/ci.yml`:
```yaml
# Find the headless job matrix around line 122
model: [gpt-5-minimal, gpt-4.1, sonnet-4.5, gemini-pro, your-new-model, glm-4.6, haiku]
```
## Toolset Detection
Models are automatically assigned toolsets based on provider:
- `openai/*` → `codex` toolset
- `google_ai/*` or `google_vertex/*` → `gemini` toolset
- Others → `default` toolset
This is handled by `isGeminiModel()` and `isOpenAIModel()` in `src/tools/manager.ts`. You typically don't need to modify this unless adding a new provider.
## Common Issues
**"Handle not found" error**: The model handle is incorrect. Run the validation script to see valid handles.
**Model works but wrong toolset**: Check `src/tools/manager.ts` to ensure the provider prefix is recognized.Related Skills
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