Day 12/30: Streaming Agent Reasoning

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:

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)

Enter fullscreen mode Exit fullscreen mode

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.