adding-models
Guidance on integrating new LLM models into Letta Code, including model configuration and validation.
Install
mkdir -p .claude/skills/adding-models && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4248" && unzip -o skill.zip -d .claude/skills/adding-models && rm skill.zipInstalls to .claude/skills/adding-models
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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.Key capabilities
- →Query the Letta API for available model handles
- →Add new model entries to src/models.json
- →Configure model-specific settings like context window and temperature
- →Test new models using a headless command
- →Include models in the CI test matrix for automated testing
- →Understand automatic toolset assignment based on provider prefixes
How it works
The skill guides the user to query the Letta API for valid model handles, then add these details to the models.json configuration file, and optionally update the CI test matrix.
Inputs & outputs
When to use adding-models
- →Add new LLM provider
- →Update model configuration
- →Validate model handle support
- →Add model to CI test matrix
About this skill
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:
curl -s https://api.letta.com/v1/models/ | jq '.[] | .handle'
Or filter by provider:
curl -s https://api.letta.com/v1/models/ | jq '.[] | select(.handle | startswith("google_ai/")) | .handle'
Common provider prefixes:
anthropic/- Claude modelsopenai/- GPT modelsgoogle_ai/- Gemini modelsgoogle_vertex/- Vertex AIopenrouter/- Various providers
Step 2: Add to models.json
Add an entry to src/models.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--modelflag (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 selectordescription: Brief description shown in selectorisFeatured: If true, appears in featured models sectionupdateArgs: 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 AIopenrouter/- OpenRouter (various providers)
Step 3: Test the Model
Test with headless mode:
bun run src/index.ts --new --model <model-id> -p "hi, what model are you?"
Example:
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:
# 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/*→codextoolsetgoogle_ai/*orgoogle_vertex/*→geminitoolset- Others →
defaulttoolset
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.
When not to use it
- →When the task involves modifying toolset detection logic for existing providers
Limitations
- →Toolset detection logic modification is rarely needed
- →The skill does not cover adding new providers for toolset detection
How it compares
This skill provides a structured workflow for integrating new LLM models into Letta Code, unlike manually editing configuration files without validation or testing guidance.
Compared to similar skills
adding-models side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| adding-models (this skill) | 1 | 7mo | Review | Beginner |
| skill-creator | 128 | 3mo | Review | Advanced |
| skill-development | 17 | 8mo | Review | Intermediate |
| agent-identifier | 15 | 8mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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