Models
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 Platform | Model Type(s) |
---|---|
OpenAI | gpt-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 OpenAI | gpt-4o, gpt-4-turbo gpt-4, gpt-3.5-turbo |
Mistral AI | mistral-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 |
Moonshot | moonshot-v1-8k moonshot-v1-32k moonshot-v1-128k |
Anthropic | claude-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 |
Gemini | gemini-2.5-pro, gemini-2.5-flash gemini-2.0-flash, gemini-2.0-flash-thinking gemini-2.0-flash-lite |
Lingyiwanwu | yi-lightning, yi-large, yi-medium yi-large-turbo, yi-vision, yi-medium-200k yi-spark, yi-large-rag, yi-large-fc |
Qwen | 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 |
DeepSeek | deepseek-chat deepseek-reasoner |
ZhipuAI | glm-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 |
InternLM | internlm3-latest, internlm3-8b-instruct internlm2.5-latest, internlm2-pro-chat |
Reka | reka-core, reka-flash, reka-edge |
COHERE | command-r-plus, command-r, command-light, command, command-nightly |
API & Connector Platforms
Model Platform | Supported via API/Connector |
---|---|
GROQ | supported models |
TOGETHER AI | supported models |
SambaNova | supported models |
Ollama | supported models |
OpenRouter | supported models |
PPIO | supported models |
LiteLLM | supported models |
LMStudio | supported models |
vLLM | supported models |
SGLANG | supported models |
NetMind | supported models |
NOVITA | supported models |
NVIDIA | supported models |
AIML | supported models |
ModelScope | supported models |
AWS Bedrock | supported models |
IBM WatsonX | supported models |
Crynux | supported models |
qianfan | supported 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:
Here’s how you use OpenAI models such as GPT-4o-mini with CAMEL:
Using Google’s Gemini models in CAMEL:
- Google AI Studio (Quick Start): Try models quickly in a no-code environment.
- API Key Setup (Generate Key): Obtain your Gemini API key to start integration.
- Gemini API Docs (Deep Dive): Explore detailed Gemini API capabilities.
Integrate Mistral AI models like Mistral Medium into CAMEL:
Leveraging Anthropic’s Claude models within CAMEL:
Using Groq’s powerful models (e.g., Llama 3.3-70B):
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:
Using Ollama for Llama 3
Install Ollama
Download Ollama and follow the installation steps for your OS.
Pull the Llama 3 model
(Optional) Create a Custom Model
Create a file named Llama3ModelFile
:
You can also create a shell script setup_llama3.sh
:
Integrate with CAMEL-AI
Using vLLM for Phi-3
Install vLLM
Follow the vLLM installation guide for your environment.
Start the vLLM server
Integrate with CAMEL-AI
Using SGLang for Meta-Llama
Install SGLang
Follow the SGLang install instructions for your platform.
Integrate with CAMEL-AI
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.
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.
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.