Which Models does CrewAI Support?

🤖 Protocol‑Agnostic Architecture

CrewAI implements open standards (Model Context Protocol – MCP and Agent2Agent – A2A), so you can:

Swap models without rewriting orchestration logic
Bridge between agents built on different LLMs
Extend to custom or fine‑tuned checkpoints

No matter where your model lives—AWS, OpenAI, private data center—CrewAI simply passes inputs/outputs over MCP or A2A.

🧩 Supported Foundation Models

You can target any model by specifying its endpoint or ID:

Amazon Bedrock Models
- Amazon Titan (Text & Embeddings)
- AI21 Labs Jurassic‑2
- Anthropic Claude 2 & 3
- Cohere Command
- Meta Llama 2
- Mistral AI
OpenAI Models
- GPT‑4, GPT‑4 Turbo
- GPT‑3.5, Codex
Other Cloud & Open‑Source Models
- Google Gemini & PaLM
- Stability AI’s Stable Diffusion XL (vision)
- Bloom, RedPajama, MPT series
- Your own fine‑tuned checkpoints (via custom MCP client)

🛠️ Example: Switching Between Models

from crewai.protocols import MCPClient
from crewai import Agent, Crew

# Instantiate two MCP clients for different providers
openai_client = MCPClient(
    endpoint="https://api.openai.com/v1",
    api_key="OPENAI_KEY"
)

anthropic_client = MCPClient(
    endpoint="https://api.anthropic.com/v1",
    api_key="ANTHROPIC_KEY"
)

class DynamicAgent(Agent):
    def __init__(self, client):
        super().__init__()
        self.client = client

    def run(self, prompt):
        # Send prompt to the chosen model
        return self.client.generate(prompt)

# Create a Crew that uses both OpenAI GPT-4 and Anthropic Claude
crew = Crew([
    DynamicAgent(openai_client),
    DynamicAgent(anthropic_client),
])

responses = crew.run("Summarize the benefits of CrewAI.")
print(responses)

In this example, you’re orchestrating a hybrid Crew that leverages GPT‑4 for creativity and Claude for safety checks, without changing any orchestration code.

🚀 Getting Started

Choose your model(s): Pick any foundation model’s API endpoint or Bedrock ID.
Configure MCPClient or A2A transport: Supply credentials and endpoint.
Attach to agents: Pass the client into your Agent class.
Run your Crew or Flow: CrewAI handles the rest—routing inputs, aggregating outputs, and managing sessions.

With CrewAI’s model‑agnostic design, you’re free to innovate, compare, and evolve your AI stack without lock‑in.