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AI Models

Overview

AgentPool supports a wide range of model types thanks to Pydantic-AI. In the simplest form, models are defined by their "identifier", which is defined as PROVIDER_NAME:MODEL_NAME (example: "openai:gpt-5-nano").

For more advanced scenarios, it is also possible to assign a more detailed model config including model settings like temperature etc.

In addition, some more experimental (meta-)Models are supported using LLMling-models.

These include models which let the user get into the role of an Agent, as well as fallback models and lot more.

agents:
  my_agent:
    model: openai:gpt-5-nano  # simple model identifier
  my_agent2:
    model:  # extended model config
      provider: openai
      model: gpt-5-nano
      temperature: 0.5

Supported Models

AgentPool supports the following model providers through Pydantic-AI:

  • openai: OpenAI models (GPT-4, GPT-3.5, etc.)
  • anthropic: Anthropic Claude models
  • google-vertex: Google Vertex AI models
  • groq: Groq models
  • mistral: Mistral AI models
  • cohere: Cohere models
  • gemini: Google Gemini models
  • ollama: Local models via Ollama

Model Configuration Options

Models can be configured with:

Setting Description Default
provider Model provider name Required
model Model identifier Required
temperature Sampling temperature 0.7
max_tokens Maximum tokens per response Varies
top_p Top-p sampling 1.0
timeout Request timeout 60s

For the full schema documentation, see the LLMling-models package.