How to use any Python AI agent framework with free GitHub Models
I ❤️ when companies offer free tiers for developer services, since it gives everyone a way to learn new technologies without breaking the bank. Free tiers are especially important for students and people between jobs, when the desire to learn is high but the available cash is low. That's why I'm such a fan of GitHub Models: free, high-quality generative AI models available to anyone with a GitHub account. The available models include the latest OpenAI LLMs (like o3-mini), LLMs from the research community (like Phi and Llama), LLMs from other popular providers (like Mistral and Jamba), multimodal models (like gpt-4o and llama-vision-instruct) and even a few embedding models (from OpenAI and Cohere). With access to such a range of models, you can prototype complex multi-model workflows to improve your productivity or heck, just make something fun for yourself. 🤗 To use GitHub Models, you can start off in no-code mode: open the playground for a model, send a few requests, tweak the parameters, and check out the answers. When you're ready to write code, select "Use this model". A screen will pop up where you can select a programming language (Python/JavaScript/C#/Java/REST) and select an SDK (which varies depending on model). Then you'll get instructions and code for that model, language, and SDK. But here's what's really cool about GitHub Models: you can use them with all the popular Python AI frameworks, even if the framework has no specific integration with GitHub Models. How is that possible? The vast majority of Python AI frameworks support the OpenAI Chat Completions API, since that API became a defacto standard supported by many LLM API providers besides OpenAI itself. GitHub Models also provide OpenAI-compatible endpoints for chat completion models. Therefore, any Python AI framework that supports OpenAI-like models can be used with GitHub Models as well. 🎉 To prove it, I've made a new repository with examples from eight different Python AI agent packages, all working with GitHub Models: python-ai-agent-frameworks-demos. There are examples for AutoGen, LangGraph, Llamaindex, OpenAI Agents SDK, OpenAI standard SDK, PydanticAI, Semantic Kernel, and SmolAgents. You can open that repository in GitHub Codespaces, install the packages, and get the examples running immediately. Now let's walk through the API connection code for GitHub Models for each framework. Even if I missed your favorite framework, I hope my tips here will help you connect any framework to GitHub Models. OpenAI I'll start with openai , the package that started it all! import openai client = openai.OpenAI( api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") The code above demonstrates the two key parameters we'll need to configure for all frameworks: api_key : When using OpenAI.com, you pass your OpenAI API key here. When using GitHub Models, you pass in a Personal Access Token (PAT). If you open the repository (or any repository) in GitHub Codespaces, a PAT is already stored in the GITHUB_TOKEN environment variable. However, if you're working locally with GitHub Models, you'll need to generate a PAT yourself and store it. PATs expire after a while, so you need to generate new PATs every so often. base_url : This parameter tells the OpenAI client to send all requests to "https://models.inference.ai.azure.com" instead of the OpenAI.com API servers. That's the domain that hosts the OpenAI-compatible endpoint for GitHub Models, so you'll always pass that domain as the base URL. If we're working with the new openai-agents SDK, we use very similar code, but we must use the AsyncOpenAI client from openai instead. Lately, Python AI packages are defaulting to async, because it's so much better for performance. import agents import openai client = openai.AsyncOpenAI( base_url="https://models.inference.ai.azure.com", api_key=os.environ["GITHUB_TOKEN"]) model = agents.OpenAIChatCompletionsModel( model="gpt-4o", openai_client=client) spanish_agent = agents.Agent( name="Spanish agent", instructions="You only speak Spanish.", model=model) PydanticAI Now let's look at all of the packages that make it really easy for us, by allowing us to directly bring in an instance of either OpenAI or AsyncOpenAI . For PydanticAI, we configure an AsyncOpenAI client, then construct an OpenAIModel object from PydanticAI, and pass that model to the agent: import openai import pydantic_ai import pydantic_ai.models.openai client = openai.AsyncOpenAI( api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") model = pydantic_ai.models.openai.OpenAIModel( "gpt-4o", provider=OpenAIProvider(openai_client=client)) spanish_agent = pydantic_ai.Agent( model, system_prompt="You only speak Spanish.") Semantic Kernel For Semantic Kernel, the code is very similar. We configure an AsyncOpenAI client, then construct an OpenAIChatCompletion object from Semantic Kernel, and add that object to the kernel. import openai import semantic_kernel.connectors.ai.open_ai import semantic_kernel.agents chat_client = openai.AsyncOpenAI( api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") chat = semantic_kernel.connectors.ai.open_ai.OpenAIChatCompletion( ai_model_id="gpt-4o", async_client=chat_client) kernel.add_service(chat) spanish_agent = semantic_kernel.agents.ChatCompletionAgent( kernel=kernel, name="Spanish agent" instructions="You only speak Spanish") AutoGen Next, we'll check out a few frameworks that have their own wrapper of the OpenAI clients, so we won't be using any classes from openai directly. For AutoGen, we configure both the OpenAI parameters and the model name in the same object, then pass that to each agent: import autogen_ext.models.openai import autogen_agentchat.agents client = autogen_ext.models.openai.OpenAIChatCompletionClient( model="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") spanish_agent = autogen_agentchat.agents.AssistantAgent( "spanish_agent", model_client=client, system_message="You only speak Spanish") LangGraph For LangGraph, we configure a very similar object, which even has the same parameter names: import langchain_openai import langgraph.graph model = langchain_openai.ChatOpenAI( model="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com", ) def call_model(state): messages = state["messages"] response = model.invoke(messages) return {"messages": [response]} workflow = langgraph.graph.StateGraph(MessagesState) workflow.add_node("agent", call_model) SmolAgents Once again, for SmolAgents, we configure a similar object, though with slightly different parameter names: import smolagents model = smolagents.OpenAIServerModel( model_id="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], api_base="https://models.inference.ai.azure.com") agent = smolagents.CodeAgent(model=model) Llamaindex I saved Llamaindex for last, as it is the most different. The llama-index package has a different constructor for OpenAI.com versus OpenAI-like servers, so I opted to use that OpenAILike constructor instead. However, I also needed an embeddings model for my example, and the package doesn't have an OpenAIEmbeddingsLike constructor, so I used the standard OpenAIEmbedding constructor. import llama_index.embeddings.openai import llama_index.llms.openai_like import llama_index.core.agent.workflow Settings.llm = llama_index.llms.openai_like.OpenAILike( model="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], api_base="https://models.inference.ai.azure.com", is_chat_model=True) Settings.embed_model = llama_index.embeddings.openai.OpenAIEmbedding( model="text-embedding-3-small", api_key=os.environ["GITHUB_TOKEN"], api_base="https://models.inference.ai.azure.com") agent = llama_index.core.agent.workflow.ReActAgent( tools=query_engine_tools, llm=Settings.llm) Choose your models wisely! In all of the examples above, I specified the gpt-4o model. The gpt-4o model is a great choice for agents because it supports function calling, and many agent frameworks only work (or work best) with models that natively support function calling. Fortunately, GitHub Models includes multiple models that support function calling, at least in my basic experiments: gpt-4o gpt-4o-mini o3-mini AI21-Jamba-1.5-Large AI21-Jamba-1.5-Mini Codestral-2501 Cohere-command-r Ministral-3B Mistral-Large-2411 Mistral-Nemo Mistral-small You might find that some models work better than others, especially if you're using agents with multiple tools. With GitHub Models, it's very easy to experiment and see for yourself, by simply changing the model name and re-running the code. Join the AI Agents Hackathon We are currently running a free virtual hackathon from April 8th - 30th, to challenge developers to create agentic applications using Microsoft technologies. You could build an agent entirely using GitHub Models and submit it to the hackathon for a chance to win amazing prizes! You can also join our 30+ streams about building AI agents, including a stream all about prototyping with GitHub Models. Learn more and register at https://aka.ms/agentshackUsing Advanced Reasoning Model on EdgeAI Part 1 - Quantization, Conversion, Performance
DeepSeek-R1 is very popular, and it can achieve the same capabilities as OpenAI o1 in advanced reasoning. Microsoft has also added DeepSeek-R1 models to Azure AI Foundry and GitHub Models. We can compare DeepSeek-R1 ith other available models through GitHub Models Playground Note This series revolves around deployment of SLMs to Edge Devices 'Edge AI' we will focus on the deployment advanced reasoning models, with different application scenarios. You can learn more in the following session AI Tour BRK453. In this experiement we want to deploy advanced reasoning models to the edge, so that they can run on edge devices with limited computing power and offline environments. At this time, the recommendation is to use the traditional ONNX model . We can use Microsoft Olive to convert the DeepSeek-R1 Distrill model. Getting started with Microsoft Olive is very straightforward. Install the Microsoft Olive library through the command line and Python 3.10+ (recommended) pip install olive-ai The DeepSeek-R1 Distrill model series has different parameters such as 1.5B, 7B, 8B, 14B, 32B, 70B, etc. This article is mainly based on the 1.5B, 7B, and 14B models (so a Small Language Model). CPU Inference Let's discuss 1.5B and 7B, which are models with lower parameter. We can directly use the CPU as computing for inference to test the effect (hardware environment Azure DevBox, AMD EPYC 7763 64-Core + 64GB Memory + 2T SSD) Quantization conversion olive auto-opt --model_name_or_path <Your DeepSeek-R1-Distill-Qwen-1.5B/7B local location> --output_path <Your Convert ONNX INT4 Model local location> --device cpu --provider CPUExecutionProvider --precision int4 --use_model_builder --log_level 1 You can download it directly from my Hugging face Repo (Note: This model is for testing and has not been fully tested by AI Content Safety or provided as an Offical Model) DeepSeek-R1-Distill-Qwen-1.5B-ONNX-INT4-CPU DeepSeek-R1-Distill-Qwen-7B-ONNX-INT4-CPU Running with ONNX Runtime GenAI Install ONNX Runtime GenAI and ONNX Runtime CPU support libraries pip install onnxruntime-genai pip install onnxruntime Sample Code https://github.com/kinfey/EdgeAIForAdvancedReasoning/blob/main/notebook/demo-1.5b.ipynb https://github.com/kinfey/EdgeAIForAdvancedReasoning/blob/main/notebook/demo-7b.ipynb Performance comparison 1.5B vs 7B We compare two different inference scenarios explain 1+1=2 1.5B quantized ONNX model memory occupied, time consumption and number of tokens generated: 7B quantized ONNX model memory occupied, time consumption and number of tokens generated 2. Find all pairwise different isomorphism groups with order 147 and no elements with order 49 1.5B quantized ONNX model memory occupied, time consumption and number of tokens generated: 7B quantized ONNX model memory occupied, time consumption and number of tokens generated Results of the numbers Through the test, we can see that the 1.5B model of DeepSeek is more suitable for use on CPU inference and can be deployed on traditional PCs or IoT devices. As for 7B, although it has better inference, it is not very effective on CPU operation. GPU Inference It is ideal if we have a GPU on the edge device. We can quantize and convert it to an ONNX model for CPU inference through Microsoft Olive. Of course, it can also be converted to a model for GPU inference. Here I take the 14B DeepSeek-R1-Distill-Qwen-14B as an example and make an inference comparison with Microsoft's Phi-4-14B Quantization conversion olive auto-opt --model_name_or_path <Your Phi-4-14B or DeepSeek-R1-Distill-Qwen-14B local path > --output_path <Your converted Phi-4-14B or DeepSeek-R1-Distill-Qwen-14B local path > --device gpu --provider CUDAExecutionProvider --precision int4 --use_model_builder --log_level 1 You can download it directly from my Hugging face Repo (Note: This model is for testing and has not been fully tested by AI Content Safety and not an Official Model) DeepSeek-R1-Distill-Qwen-14B-ONNX-INT4-GPU Phi-4-14B-ONNX-INT4-GPU Running with ONNX Runtime GenAI CUDA Install ONNX Runtime GenAI and ONNX Runtime GPU support libraries pip install onnxruntime-genai-cuda pip install onnxruntime-gpu Compare the results in the GPU environment with Gradio It is recommended to use a GPU with more than 8G memory To increase the comparison of the results, we compare it with Phi-4-14B-ONNX-INT4-GPU and DeepSeek-R1-Distill-Qwen-14B-ONNX-INT4-GPU to see the different results. We also show we use OpenAI o1-mini (it is recommended to use o1-mini through GitHub Models), Sample Code https://github.com/kinfey/EdgeAIForAdvancedReasoning/blob/main/notebook/Performance_AdvancedReasoning_ONNX_CPU.ipynb You can test any prompt on Gradio to compare the results of Phi-4-14B-ONNX-INT4-GPU, DeepSeek-R1-Distill-Qwen-14B-ONNX-INT4-GPU and OpenAI o1 mini. DeepSeek-R1 reduces the cost of inference models and produces more instructive results on professional problems, but Phi-4-14B also has advantages in reasoning and uses lower computing power to complete inference. As for OpenAI o1 mini, it is more comprehensive and can touch all problems. If you want to deploy to Edge Device, Phi-4-14B and quantized DeepSeek-R1 are good choices for you. This blog is just a simple test and the first in this series. Please share your feedback and continue the discussion in the Microsoft AI Discord Channel. Feel free to me a message or comment. We look forward to sharing more around the opportunity of EdgeAI and more content in this series. Resource DeepSeek-R1 in GitHub Models https://github.com/marketplace/models/azureml-deepseek/DeepSeek-R1 DeepSeek-R1 in Azure AI Foundry https://ai.azure.com/explore/models/DeepSeek-R1/version/1/registry/azureml-deepseek Phi-4-14B in Hugging face https://huggingface.co/microsoft/phi-4 Learn about Microsoft Olive https://github.com/microsoft/olive Learn about ONNX Runtime GenAI https://github.com/microsoft/onnxruntime-genai Microsoft AI Discord Channel BRK453 Exploring cutting-edge models: LLMs, SLMs, local development and more https://aka.ms/aitour/brk453Unlock the Power of AI with GitHub Models: A Hands-On Guide
Ready to elevate your coding game? Imagine having the power of advanced AI at your fingertips, ready to integrate into your projects with just a few clicks. Whether you're building a smart assistant, automating workflows, or creating the next big thing, GitHub Models are here to make it happen. Dive into our guide and discover how to get started, customize responses, and even build your own AI-powered applications—all from within the familiar GitHub interface. Your journey into the world of AI starts now. Click to explore and let your creativity take flight!
