adding-models by letta-ai

---
name: adding-models
description: 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 model-specific compatibility behavior. Covers runtime catalog sources, CI test matrices, and handle validation.
---

# Adding Models

This skill guides you through adding a new LLM model to Letta Code.

## Quick Reference

**Key files**:
- `src/agent/remote-model-catalog.ts` - Runtime catalog loading and projection
- `src/agent/model-catalog.ts` - Model lookup and compatibility aliases
- `.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 hosted catalog to see preset IDs and handles:

```bash
curl -s https://api.letta.com/v1/models/catalog | jq '.models[] | [.id, .handle]'
```

To inspect the models currently available from an API backend, query its model inventory:

```bash
curl -s https://api.letta.com/v1/models/ | jq '.[] | .handle'
```

Or filter the inventory 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: Update the Owning Catalog

Letta Code does not bundle a model catalog:

- API and hosted presets come from the server's `GET /v1/models/catalog` response.
- Local model inventory comes from pi-ai and the active provider runtimes.

Add the model at the source that owns it. A hosted preset belongs in the server catalog. A local provider model belongs in pi-ai or that provider's discovery runtime.

Only change this repository when the model needs Letta Code-specific compatibility behavior, such as preserving an established CLI alias or recognizing a new provider for toolset selection. Keep that logic narrow and derive the handle and metadata from the runtime catalog rather than copying model definitions here.

### 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.