Build Local AI Agents with LangGraph & Ollama

In this tutorial, the host from Data Centric demonstrates how to integrate the Ollama inference server into a custom LangGraph web search agent. The video begins with a brief overview of the differences between integrating OpenAI and Ollama into applications. Unlike OpenAI, which involves making post requests to a remote server, Ollama operates locally, requiring adjustments in endpoint, post request format, and response handling. The host explains these differences and provides a step-by-step guide on how to modify the code accordingly. The integration process is broken down into three main components: setting the endpoint, structuring the post request payload, and parsing the response. The host demonstrates how to create a Python class for Ollama, detailing the necessary adjustments for both JSON and non-JSON responses. The video also covers how to adapt the code for other services like Claude by following a similar pattern. The host shows how to navigate the app.py file to configure the front end for the web search agent, ensuring the correct endpoints and models are used. Additionally, the tutorial includes an example of integrating vLLM, an inference server that mimics OpenAI’s server, allowing for similar payload and response handling. The host discusses the challenges of running large models on limited hardware, emphasizing the need for smaller, quantized models for local execution. Despite these limitations, the host highlights the potential of using VM inference servers on platforms like RunPod to host larger models. The video concludes with a call to action for viewers to suggest models for benchmarking on a VM server, and an invitation to subscribe for more content on large language models and AI engineering.