AI

Kronk AI: Building a Basic AI Chat Agent with Message History Pt. 1

Ardan Labs
Ardan Labs•May 7, 2026

Why It Matters

Providing a clear, hands‑on example of streaming chat with persistent context lowers the barrier for developers to prototype AI assistants, and exposes performance bottlenecks that must be addressed for production‑grade deployments.

Key Takeaways

  • •Set up a basic chat server using SSE for model communication.
  • •Implement user input scanner and maintain message history slice.
  • •Stream model responses via server‑side events, handling content and reasoning.
  • •Append assistant replies to conversation to preserve context across turns.
  • •Demonstrates performance limits as cache clears each request.

Summary

The video walks through building a rudimentary chat agent in Go, leveraging Kronk AI’s model server and server‑side events (SSE) to exchange messages with an OpenAI‑compatible backend.

It starts by defining constants for the model endpoint, then creates a simple stdin scanner to capture user input. A factory function builds an agent that wraps an SSE client, writes output to stdout, and maintains a slice of message maps as the conversation history. Each user turn is added with role = "user", packaged into a chat‑completion request, and sent with `stream:true` so the server streams partial tokens back.

During execution the code prints colored chunks—regular content in default color and reasoning in red—demonstrating how to differentiate response parts. The presenter shows a live session: greeting the model, asking it to write a Go “Hello World”, then requesting the same in Rust, confirming that the stored history informs follow‑up answers.

While the prototype successfully preserves context, the host notes that each request clears the KV cache, forcing full re‑decoding and degrading performance as the dialogue grows. This highlights the need for caching optimizations before scaling the agent to more complex tool‑calling scenarios.

Original Description

In this clip from Bill's Ultimate AI Workshop, Bill walks through "step one" of building a custom AI chat agent before diving into more advanced concepts like tool calling. He covers how to establish a basic chat server that connects to an OpenAI-compatible model server. You will learn how to capture terminal input, format the client data, and stream model responses back in real-time using Server-Side Events (SSE).
Bill goes on to explain how to maintain message history locally so your AI remembers the conversation. Because the model server doesn't retain state on its own, he demonstrates how to store user inputs and assistant responses in a conversation slice, passing the full context window back to the model with every new prompt.
What you'll learn in this clip:
• Building a basic AI chat client
• Streaming responses in real-time
• Managing conversation history locally
• Verifying context memory in action
Explore more from Ardan Labs
Connect with Ardan Labs
#AIChatAgent #golang #llm #sse #machinelearning

Comments

Want to join the conversation?

Loading comments...