adding-models

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: 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: curl -s https://api.letta.com/v1/models/ | jq '.[] | .handle' Or filter the inventory by provider: 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: 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/* → 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.