Letta Configuration logo

Letta Configuration

Organization
letta-ai
letta-configuration

Configure LLM models and providers for Letta agents and servers. Use when setting model handles, adjusting temperature/tokens, configuring provider-specific settings, setting up BYOK providers, or configuring self-hosted deployments with environment variables.

Overview

Publisherletta-ai
Repositoryskills
Skill nameletta-configuration
Stars
144
Forks
25
Bundled files
13
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 13 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by letta-ai on GitHub. Read the source before you install it.

Installation

Install the Letta Configuration AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/letta-ai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/letta/letta-configuration .claude/skills/letta-configuration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Letta Configuration in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Letta Configuration on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Letta Configuration is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Letta Configuration

Complete guide for configuring models on agents and providers on servers.

When to Use This Skill

Agent-level (model configuration):

  • Creating agents with specific model configurations
  • Adjusting model settings (temperature, max tokens, context window)
  • Configuring provider-specific features (OpenAI reasoning, Anthropic thinking)
  • Changing models on existing agents

Server-level (provider configuration):

  • Setting up BYOK (bring your own key) providers
  • Configuring self-hosted deployments with environment variables
  • Validating provider credentials
  • Setting up custom OpenAI-compatible endpoints

Not covered here: Model selection advice (which model to choose) - see agent-development skill.


Part 1: Model Configuration (Agent-Level)

Model Handles

Models use a provider/model-name format:

ProviderHandle PrefixExample
OpenAIopenai/openai/gpt-4o, openai/gpt-4o-mini
Anthropicanthropic/anthropic/claude-sonnet-4-5-20250929
Google AIgoogle_ai/google_ai/gemini-2.0-flash
Azure OpenAIazure/azure/gpt-4o
AWS Bedrockbedrock/bedrock/anthropic.claude-3-5-sonnet
Groqgroq/groq/llama-3.3-70b-versatile
Togethertogether/together/meta-llama/Llama-3-70b
OpenRouteropenrouter/openrouter/anthropic/claude-3.5-sonnet
Ollama (local)ollama/ollama/llama3.2

Basic Model Configuration

python
from letta_client import Letta

client = Letta(api_key="your-api-key")

agent = client.agents.create(
    model="openai/gpt-4o",
    model_settings={
        "provider_type": "openai",  # Required - must match model provider
        "temperature": 0.7,
        "max_output_tokens": 4096,
    },
    context_window_limit=128000
)

Common Settings

SettingTypeDescription
provider_typestringRequired. Must match model provider (openai, anthropic, google_ai, etc.)
temperaturefloatControls randomness (0.0-2.0). Lower = more deterministic.
max_output_tokensintMaximum tokens in the response.

Changing an Agent's Model

python
client.agents.update(
    agent_id=agent.id,
    model="anthropic/claude-sonnet-4-5-20250929",
    model_settings={"provider_type": "anthropic", "temperature": 0.5},
    context_window_limit=64000
)

Note: Agents retain memory and tools when changing models.

Provider-Specific Settings

For OpenAI reasoning models and Anthropic extended thinking, see references/provider-settings.md.


Part 2: Provider Configuration (Server-Level)

Quick Start

bash
# Add provider via API
python scripts/setup_provider.py --type openai --api-key sk-...

# Generate .env for Docker
python scripts/generate_env.py --providers openai,anthropic,ollama

# Validate credentials
python scripts/validate_provider.py --provider-id provider-xxx

Add BYOK Provider

python
# Via REST API
curl -X POST http://localhost:8283/v1/providers \
  -H "Content-Type: application/json" \
  -d '{
    "name": "My OpenAI",
    "provider_type": "openai",
    "api_key": "sk-your-key-here"
  }'

Supported Provider Types

openai, anthropic, azure, google_ai, google_vertex, ollama, groq, deepseek, xai, together, mistral, cerebras, bedrock, vllm, sglang, hugging_face, lmstudio_openai

For detailed configuration of each provider, see:

  • references/common_providers.md - OpenAI, Anthropic, Azure, Google
  • references/self_hosted_providers.md - Ollama, vLLM, LM Studio
  • references/all_providers.md - Complete reference
  • references/environment_variables.md - Docker/self-hosted setup

Anti-Hallucination Checklist

Before configuring:

  • Model handle uses correct provider/model-name format
  • model_settings includes required provider_type field
  • context_window_limit is set at agent level, not in model_settings
  • Provider-specific settings use correct nested structure
  • For self-hosted: embedding model is specified
  • Temperature is within valid range (0.0-2.0)

Scripts

Model configuration:

  • scripts/basic_config.py - Basic model configuration
  • scripts/basic_config.ts - TypeScript equivalent
  • scripts/change_model.py - Changing models on existing agents
  • scripts/provider_specific.py - OpenAI reasoning, Anthropic thinking

Provider configuration:

  • scripts/setup_provider.py - Add providers via REST API
  • scripts/validate_provider.py - Check provider credentials
  • scripts/generate_env.py - Generate .env for Docker

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Letta Configuration AI skill do?

Configure LLM models and providers for Letta agents and servers. Use when setting model handles, adjusting temperature/tokens, configuring provider-specific settings, setting up BYOK providers, or configuring self-hosted deployments with environment variables.

Why use Letta Configuration on TypingMind?

Because you install it once and use it with any model. Letta Configuration is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Letta Configuration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/letta-ai/skills/tree/main/letta/letta-configuration. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Letta Configuration?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Letta Configuration?

As many as you like. As long as a model supports skills, you can use Letta Configuration with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Letta Configuration AI skill free?

Yes. It is published on GitHub by letta-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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