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Configuration

Models

Configure models in the Letta app or CLI

Letta agents are model-agnostic. Agents can use Claude, GPT, Gemini, or other supported models. Users can change an agent’s underlying model at any time, even mid-conversation.

We recommend first-time users use a large frontier model (for example from OpenAI or Anthropic) to learn how Letta agents work, since weaker models can cause the agent to behave in unexpected ways. See leaderboard.letta.com for benchmarks comparing model performance.

Use the /model command to switch models during a session. This opens a model selector that allows you to swap models and choose their reasoning settings (if applicable).

Different models are trained to work best with different tools. For example, GPT models from OpenAI are trained to use a patch-based editing tool, while Claude models work better with string-based edit tools.

The Letta agent harness handles this automatically with dynamic toolsets - tool configurations optimized for each model family. When you switch models with /model, the toolset switches automatically. If you want to force a specific toolset, use the /toolset command.

The Letta app and CLI support a wide variety of LLM providers, as well as coding plans such as ChatGPT and zAI. To connect additional providers, use /connect in the CLI. To see your available models, use /model.

Local providers are configured on your machine. When you sign in with Letta, /connect also lets you configure providers for agents backed up to the cloud.

Cloud providers are managed through Letta’s LLM gateway. The gateway supports a smaller provider set than local setup; direct integrations with local inference providers such as LM Studio, Ollama, and llama.cpp are available only for local agents. You can pay for LLM gateway usage with pay-as-you-go credits from the usage page.

There are two ways to use your own locally hosted models with Letta agents:

  1. Run the agent and model locally. Use local setup, open /connect, and choose the built-in Ollama or LM Studio provider. Both the agent state and model endpoint remain on your machine.
  2. Connect a cloud-hosted agent to your model. Open /connect, add an OpenAI-compatible API custom provider, and enter the base URL and API key for your inference server. Because the agent runs in the cloud, the endpoint must be reachable from the public internet rather than only through localhost. Use a stable public URL or static IP, secure it with authentication and HTTPS, and restrict access where possible.

The OpenAI-compatible endpoint must support Chat Completions and tool calling. After connecting either kind of provider, use /model to select the model.

ProviderLocal supportSigned in with Letta
Amazon BedrockAWS credentialsAWS credentials
Anthropic APIAPI keyAPI key
Claude Pro/MaxSubscription
Azure OpenAI ResponsesAPI key
CerebrasAPI key
ChatGPT Plus/Pro CodexSubscriptionSubscription
Cloudflare AI GatewayAPI key
Cloudflare Workers AIAPI key
DeepSeekAPI key
FireworksAPI key
GitHub CopilotSubscription
Google GeminiAPI keyAPI key
Google Vertex AIAPI key
GroqAPI key
Hugging FaceAPI key
Kimi CodeAPI keyAPI key
llama.cppLocal endpoint
LM StudioLocal endpoint
MiniMaxAPI key or coding planAPI key or coding plan
MiniMax (China)API key
MistralAPI key
Moonshot AIAPI keyAPI key
Moonshot AI (China)API key
OllamaLocal endpoint
Ollama CloudAPI key
OpenAI APIAPI keyAPI key
OpenAI-compatible APIAPI key + base URLAPI key + base URL
OpenCode GoAPI key
OpenCode ZenAPI key
OpenRouterAPI keyAPI key
Together AIAPI key
Vercel AI GatewayAPI key
xAIAPI key
Xiaomi MiMoAPI key
Xiaomi MiMo Token Plan (Amsterdam)Token plan
Xiaomi MiMo Token Plan (China)Token plan
Xiaomi MiMo Token Plan (Singapore)Token plan
zAI APIAPI keyAPI key
zAI Coding PlanCoding planCoding plan