Documentation Index
Fetch the complete documentation index at: https://documentation.uponai.com/llms.txt
Use this file to discover all available pages before exploring further.
UponAI’s built-in agent frameworks (single prompt, conversation flow) provide more capabilities and built-in toolsets. Use Custom LLM integration only when required by specific compliance or use case needs.
Interaction Overview Diagram
The upper part of the diagram shows the interaction between your backend response-generating server and the UponAI server. The interaction flow:- A phone or web call is made with the AI agent. Our server establishes the audio WebSocket.
- Our server connects to the
llm_websocket_urlyou provided in the agent. - Your LLM server must send the first message when the WebSocket connection is ready. If you want the agent to speak first, set the content; otherwise set content to an empty string.
- User says, “My name is Mike.”
- Our model detects a high chance of turn-taking, or the user pauses. We request a response from your LLM.
- Your server checks
interaction_typein our JSON. If it isresponse_required, send the response. After receiving your response, our model checks if the AI should speak. - User continues and says “My name is Mike Trump.”
- Same as step 5.
- Our server receives the response from your LLM and decides to speak.
- We send the AI voice in the audio WebSocket. Meanwhile, we send you JSON with
interaction_typeasupdate_only. You don’t need to respond, but you can get the transcript from the JSON body.
Example Custom LLM Demo Repositories
Fork the complete code used in the following guides to integrate your custom LLM solutions. These repos show how to build an LLM solution with OpenAI / Azure OpenAI, how to start an LLM WebSocket server, and how to use Twilio to make phone calls with UponAI agents programmatically. Backend Server: If you encounter issues, open an issue in the respective GitHub repo.YouTube Guide
This video may be outdated. Watch the YouTube guide to set up your backend server.