langgraph/how-tos/react-agent-from-scratch/

[giscus[bot]]

langgraph/how-tos/react-agent-from-scratch/

Build language agents as graphs

https://langchain-ai.github.io/langgraph/how-tos/react-agent-from-scratch/

[austinmw]

Great tutorial, but I'm curious: Where's the explicit reasoning step? Doesn't a true ReAct agent show 'Thought, Action, Observation' cycles? This looks more like a tool-using agent. Shouldn't we see the agent's 'Thought' process before each action? Without explicit thought steps, could this lead to less thorough reasoning and potentially worse performance in complex tasks?

[Linnore]

Same question. Probably the reasoning step is hidden inside the ToolNode.

[hinthornw]

For claude and similar models, the reasoning is done prior to tool calling.
Same for o1.

For OpenAI's gpt-4* models you'd have to explicitly add a field for reasoning to the tool(s).

[davlu93]

Sorry I still did not get it. The react agent imply the use of the react prompt Thought action Observation. Here I do not see any react prompt ...
What does it mean that the reasoning is done prior to tool calling?

[hinthornw]

As in it's prompted to use <> tags to reason before calling a tool.

You all are correct in that this example does not actually use the prompt from the original paper.
Good at naming, we are not. It's meant to credit the react paper as one of the first prototypical language model "agents" operating in a loop but I see how the naming is misleading.

[kentwait]

If there were more than one tool available, then the model in call_model would have to decide which tools to use, if any. You're right that it doesn't explicitly tell us the reason for the action, but the reasoning seems to be there.
The "observation" part always occurs as the output of every tool called is going back to the model, then the model decides whether to call tools again or make a response to end the cycle.

[davlu93]

There is no whatsoever react prompting there. The model does not state it's reasoning, acting and observing steps. SO it's not a react agent for me, it's just an angent

[MarcinRutecki]

Yes - I use this approach as Tool Manager Agent. That is exactly what it does.
They should change the name as it's misleading

[MarcinRutecki]

Why do you import END twice?

from langgraph.graph import StateGraph, END
from langgraph.constants import END

[hinthornw]

We want to be extra certain it's imported, since we use stochastic python

[hinthornw]

(jokes - i think it was a grep gone wrong); fixed here: #2022
Thanks for flagging!

[HasnainKhanNiazi]

This guide is misleading as there is no ReAct prompting and all the reasoning steps are missing. This agent can be called simple "Function calling Agent" or something.

[vbarda]

@HasnainKhanNiazi check out this explanation that we added to the conceptual guides: https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#react-implementation

[sataycat]

Finally, the paper asked the LLM to explicitly generate a "Thought" step before deciding which tools to call. This is the "Reasoning" part of "ReAct". Our implementation does not do this by default, largely because LLMs have gotten much better and that is not as necessary. Of course, if you wish to prompt it do so, you certainly can.

If we prompt it to do a 'Thought' step, would it affect the tool calling since a message is generated beforehand?

Also wondering if we skip the reasoning step we will skip out on CoT level improvements in the quality of the response

[davlu93]

For sure using the function calling implicit capabilities is better then explicitly prompt the llm to write its thoughts, but I realized that using function calling is tremendously expensive in terms of token consume. It's cheaper to use the react prompting without the function calling (meaning without the bind_tools method) as it's done for the first version of the langchain create react agent where you use a react output parser and the react prompting explicitly

[gtnpromtior]

can u explain more this please ?