In CAMEL, every model refers specifically to a Large Language Model (LLM) the intelligent core powering your agent’s understanding, reasoning, and conversational capabilities.
Play with different models in our interactive Colab Notebook.

Large Language Models (LLMs)

LLMs are sophisticated AI systems trained on vast datasets to understand and generate human-like text. They reason, summarize, create content, and drive conversations effortlessly.

Flexible Model Integration

CAMEL allows quick integration and swapping of leading LLMs from providers like OpenAI, Gemini, Llama, and Anthropic, helping you match the best model to your task.

Optimized for Customization

Customize performance parameters such as temperature, token limits, and response structures easily, balancing creativity, accuracy, and efficiency.

Rapid Experimentation

Experiment freely, CAMEL’s modular design lets you seamlessly compare and benchmark different LLMs, adapting swiftly as your project needs evolve.

​
Supported Model Platforms in CAMEL

CAMEL supports a wide range of models, including OpenAI’s GPT series, Meta’s Llama models, DeepSeek models (R1 and other variants), and more.

​
Direct Integrations

Model PlatformModel Type(s)
OpenAIgpt-4.5-preview
gpt-4o, gpt-4o-mini
o1, o1-preview, o1-mini
o3-mini, o3-pro
gpt-4-turbo, gpt-4, gpt-3.5-turbo
Azure OpenAIgpt-4o, gpt-4-turbo
gpt-4, gpt-3.5-turbo
Mistral AImistral-large-latest, pixtral-12b-2409
ministral-8b-latest, ministral-3b-latest
open-mistral-nemo, codestral-latest
open-mistral-7b, open-mixtral-8x7b
open-mixtral-8x22b, open-codestral-mamba
magistral-medium-2506, mistral-small-2506
Moonshotmoonshot-v1-8k
moonshot-v1-32k
moonshot-v1-128k
Anthropicclaude-2.1, claude-2.0, claude-instant-1.2
claude-3-opus-latest, claude-3-sonnet-20240229, claude-3-haiku-20240307
claude-3-5-sonnet-latest, claude-3-5-haiku-latest
Geminigemini-2.5-pro, gemini-2.5-flash
gemini-2.0-flash, gemini-2.0-flash-thinking
gemini-2.0-flash-lite
Lingyiwanwuyi-lightning, yi-large, yi-medium
yi-large-turbo, yi-vision, yi-medium-200k
yi-spark, yi-large-rag, yi-large-fc
Qwenqwen3-coder-plus,qwq-32b-preview, qwen-max, qwen-plus, qwen-turbo, qwen-long
qwen-vl-max, qwen-vl-plus, qwen-math-plus, qwen-math-turbo, qwen-coder-turbo
qwen2.5-coder-32b-instruct, qwen2.5-72b-instruct, qwen2.5-32b-instruct, qwen2.5-14b-instruct
DeepSeekdeepseek-chat
deepseek-reasoner
ZhipuAIglm-4, glm-4v, glm-4v-flash
glm-4v-plus-0111, glm-4-plus, glm-4-air
glm-4-air-0111, glm-4-airx, glm-4-long
glm-4-flashx, glm-zero-preview, glm-4-flash, glm-3-turbo
InternLMinternlm3-latest, internlm3-8b-instruct
internlm2.5-latest, internlm2-pro-chat
Rekareka-core, reka-flash, reka-edge
COHEREcommand-r-plus, command-r, command-light, command, command-nightly

​
API & Connector Platforms

Model PlatformSupported via API/Connector
GROQsupported models
TOGETHER AIsupported models
SambaNovasupported models
Ollamasupported models
OpenRoutersupported models
PPIOsupported models
LiteLLMsupported models
LMStudiosupported models
vLLMsupported models
SGLANGsupported models
NetMindsupported models
NOVITAsupported models
NVIDIAsupported models
AIMLsupported models
ModelScopesupported models
AWS Bedrocksupported models
IBM WatsonXsupported models
Crynuxsupported models
qianfansupported models

​
How to Use Models via API Calls

Integrate your favorite models into CAMEL-AI with straightforward Python calls. Choose a provider below to see how it’s done:
Here’s how you use OpenAI models such as GPT-4o-mini with CAMEL:
from camel.models import ModelFactory
from camel.types import ModelPlatformType, ModelType
from camel.configs import ChatGPTConfig
from camel.agents import ChatAgent

model = ModelFactory.create(
    model_platform=ModelPlatformType.OPENAI,
    model_type=ModelType.GPT_4O_MINI,
    model_config_dict=ChatGPTConfig(temperature=0.2).as_dict(),
)

agent = ChatAgent(
    system_message="You are a helpful assistant.",
    model=model
)

response = agent.step("Say hi to CAMEL AI community.")
print(response.msg.content)

​
Using On-Device Open Source Models

Run Open-Source LLMs Locally

Unlock true flexibility: CAMEL-AI supports running popular LLMs right on your own machine. Use Ollama, vLLM, or SGLang to experiment, prototype, or deploy privately (no cloud required).
CAMEL-AI makes it easy to integrate local open-source models as part of your agent workflows. Here’s how you can get started with the most popular runtimes:
1

Using Ollama for Llama 3

1

Install Ollama

Download Ollama and follow the installation steps for your OS.
2

Pull the Llama 3 model

ollama pull llama3
3

(Optional) Create a Custom Model

Create a file named Llama3ModelFile:
FROM llama3

PARAMETER temperature 0.8
PARAMETER stop Result

SYSTEM """ """
You can also create a shell script setup_llama3.sh:
#!/bin/zsh
model_name="llama3"
custom_model_name="camel-llama3"
ollama pull $model_name
ollama create $custom_model_name -f ./Llama3ModelFile
chmod +x setup_llama3.sh
./setup_llama3.sh
4

Integrate with CAMEL-AI

from camel.agents import ChatAgent
from camel.models import ModelFactory
from camel.types import ModelPlatformType

ollama_model = ModelFactory.create(
    model_platform=ModelPlatformType.OLLAMA,
    model_type="llama3",
    url="http://localhost:11434/v1",
    model_config_dict={"temperature": 0.4},
)
agent = ChatAgent("You are a helpful assistant.", model=ollama_model)
response = agent.step("Say hi to CAMEL")
print(response.msg.content)
2

Using vLLM for Phi-3

1

Install vLLM

Follow the vLLM installation guide for your environment.
2

Start the vLLM server

python -m vllm.entrypoints.openai.api_server \
  --model microsoft/Phi-3-mini-4k-instruct \
  --api-key vllm --dtype bfloat16
3

Integrate with CAMEL-AI

from camel.agents import ChatAgent
from camel.models import ModelFactory
from camel.types import ModelPlatformType

vllm_model = ModelFactory.create(
    model_platform=ModelPlatformType.VLLM,
    model_type="microsoft/Phi-3-mini-4k-instruct",
    url="http://localhost:8000/v1",
    model_config_dict={"temperature": 0.0},
)
agent = ChatAgent("You are a helpful assistant.", model=vllm_model)
response = agent.step("Say hi to CAMEL AI")
print(response.msg.content)
3

Using SGLang for Meta-Llama

1

Install SGLang

Follow the SGLang install instructions for your platform.
2

Integrate with CAMEL-AI

from camel.agents import ChatAgent
from camel.models import ModelFactory
from camel.types import ModelPlatformType

sglang_model = ModelFactory.create(
    model_platform=ModelPlatformType.SGLANG,
    model_type="meta-llama/Llama-3.2-1B-Instruct",
    model_config_dict={"temperature": 0.0},
    api_key="sglang",
)
agent = ChatAgent("You are a helpful assistant.", model=sglang_model)
response = agent.step("Say hi to CAMEL AI")
print(response.msg.content)

Looking for more examples?

Explore the full CAMEL-AI Examples library for advanced workflows, tool integrations, and multi-agent demos.

​
Model Speed and Performance

Why Model Speed Matters

For interactive AI applications, response speed can make or break the user experience. CAMEL-AI benchmarks tokens processed per second (TPS) across a range of supported models—helping you choose the right balance of power and performance.

Benchmark Insights

We ran side-by-side tests in this notebook comparing top models from OpenAI (GPT-4o Mini, GPT-4o, O1 Preview) and SambaNova (Llama series), measuring output speed in tokens per second.
Key Findings:
  • Small models = blazing speed: SambaNova’s Llama 8B and OpenAI GPT-4o Mini deliver the fastest responses.
  • Bigger models = higher quality, slower output: Llama 405B (SambaNova) and similar large models trade off speed for more nuanced reasoning.
  • OpenAI models = consistent speed: Most OpenAI models maintain stable throughput across use cases.
  • Llama 8B (SambaNova) = top performer: Outpaces others in raw tokens/sec.
Model Speed Comparison: Tokens per second for various AI models

Local Inference: vLLM vs. SGLang

We compared local inference speeds between vLLM and SGLang on the same hardware. SGLang (meta-llama/Llama-3.2-1B-Instruct) hit 220.98 tokens/sec, while vLLM peaked at 107.2 tokens/sec.

Bottom line: For maximum speed in local environments, SGLang currently leads.

​
Next Steps

You’ve now seen how to connect, configure, and optimize models with CAMEL-AI.

Continue: Working with Messages

Learn how to create, format, and convert BaseMessage objects—the backbone of agent conversations in CAMEL-AI.