Day 12/30: Streaming Agent Reasoning

A developer built a support bot using LangGraph and MCP, but found that the bot's reasoning process was not streamed to the frontend, causing users to see a loading spinner. They implemented real-time streaming of the agent's thought process using LangGraph's StateGraph and MCP's tools and resources, improving transparency and user experience.

I recently spent hours debugging a support bot built with LangGraph and MCP, only to realize that the issue wasn't with the agent's reasoning itself, but with how we were presenting that reasoning to the user. The bot would often appear to be stuck on a loading spinner, leaving the user wondering if it had frozen or was still actively working on their query. The problem was that our frontend was only updated once the agent had completed its entire thought process, which could take several seconds or even minutes for complex queries. This experience led me to explore how we could stream an agent's reasoning to the frontend in real time, providing a more interactive and engaging experience for the user. By doing so, we could give users a better understanding of what the agent was doing and why, which is especially important for applications where transparency and trust are key. To achieve this, we can leverage LangGraph's StateGraph and add node functionality to build a graph that represents the agent's reasoning process. We can then use MCP's tools and resources to create a stream of updates that can be sent to the frontend as the agent works through its thought process. Here's an example of how we might implement this using Python: python import langgraph as lg from mcp import tools, resources Create a new StateGraph to represent the agent's reasoning process graph = lg.StateGraph Define a node that represents the agent's current thought process def thought process node graph, thought process : node = graph.add node thought process Add conditional edges to represent the possible next steps in the thought process graph.add conditional edges node, thought process + " - considering option A", 0.7 , thought process + " - considering option B", 0.3 return node Create a stream of updates that can be sent to the frontend def stream reasoning graph, initial thought process : current node = thought process node graph, initial thought process while True: Get the next node in the graph based on the current node and the agent's reasoning next node = graph.get next node current node Send an update to the frontend with the current thought process and the next possible steps yield { "thought process": next node.value, "next steps": edge.value for edge in graph.get outgoing edges next node } Update the current node current node = next node Create a checkpoint to save the agent's reasoning process checkpoint = resources.Checkpoint "agent reasoning" Start the agent's reasoning process and stream the updates to the frontend initial thought process = "considering user query" for update in stream reasoning graph, initial thought process : Send the update to the frontend print update Save the current state of the agent's reasoning process checkpoint.save graph This code creates a StateGraph to represent the agent's reasoning process and defines a node that represents the current thought process. It then creates a stream of updates that can be sent to the frontend as the agent works through its thought process. The stream reasoning function yields an update at each step, which includes the current thought process and the next possible steps. One practical gotcha to watch out for when implementing this approach is that the agent's reasoning process can be highly branching, leading to an exponential number of possible next steps. To mitigate this, we can use techniques such as beam search or pruning to limit the number of possible next steps and prevent the graph from becoming too large. As we continue to explore the possibilities of agentic AI, we'll be delving deeper into the complexities of agent reasoning and how we can use LangGraph and MCP to build more transparent and interactive systems. Tomorrow, we'll be looking at how to integrate multiple agents and models to create even more powerful and flexible AI applications.