I'm trying to follow along with this tutorial but the formatting is off and python is picky about formatting. I've read your comment about "full code can be found in my repo" but it's not obvious where in the repo this code is. Care to provide a link to the .py file for this tutorial please?
The State class that uses a TypedDict at the beginning. The formatting is all off. It had me questioning if there was a novel Python syntax I had not heard of.
class State(TypedDict): text:
str classification:
str entities: List[str] summary: str
On a positive note, it's good motivation to read up on Python's TypedDict feature from the typing package.
My understanding of Agent is that it should be able to handle some complexity which may arise at run time. How is the above designed system different from a simple program which uses LLM to answer some questions? but in the overall flow the so called agent is a linear program flow. If you can elaborate that part
Hi Nir, I'm unable to get an answer to this question from the other comments. I understand you have intentionally demo'd a simple flow but this can very well be done with prompts to an LLM. Can you describe a more complex use case for agents?
Further, I notice that the output generated by the classification and entity extraction nodes are not being used in the summarization node. Can you you please explain what is the use of having these if this is the case? If not, can you explain how the output from those is being used in the summarization node?
Love how you framed this as 'simpler' not 'simple.' The strategic move here isn't just building agents—it's democratizing who can build them. Every marketer, PM, and analyst who learns this shifts from AI consumer to AI composer. That's the real disruption. Subscribed
Thanks for sharing this, I managed to successfully run the whole code and get the intended output.
That being said, I am struggling to see how this code (putting aside the usage of the LangGraph framework) is different from the old good hard coded and predictable series of functions calls, when an output of a function A serves as an input of a function B. What specifically is "agentic" in this code? How this agent "can dynamically adjust its focus based on what it discovers"?
How does the summarization considering the information from the previous steps (classification and entities) if it only uses state["text"] and not state["classification"] or state["entities"].
It seems that summarization logic doesn’t use the classification or entity extraction output. Please correct me if I wrong...
The use case in this blog post is intentionally simple, as its goal is to provide an introduction and help readers understand what agents are and how to create one.
As others have said, I'm uncomfortable with inventing a "new" method "ai agents" for what is good old programming that uses an ai based text processesing function. Using a, state machine abstraction is also as old computer science, in the 80 there was an attempt to have a graphical state machine engine (David Harel StateCharts) I don't think it ever became mainstream since state machines are useful for small examples. Once you have many states and many edges, especially when you introduce error handling potentially on every node, good old programming with sequencing loops and conditions is still the most straightforward and readable way to represent what you want the software to do. My main concern with you simplified example is that people not trained in software engineering will think that ai makes everything simple, anyone can vibe program without bothering to learn how to do it reliably and safely.
Like other readers commented, it is hard to see the power of agents from this example, I understand this was intentionally simplified. Might help to link to a blog post that shows the real power, the autonomous ability to decide based on context. If the agent gets non-English input, could it translate and then summarize?
Hello Nir, thanks for the excellent blog post, I would like to have few inputs from you, especially with the way we use the word AI Agents, In the example you have shown as part of blog is more of an AI system which is a workflow, according to the blog from Anthropic "Building Effective Agents", an AI Agent is one which takes decisions autonomously. It would be helpful if you can write a blog on building a true AI agent, and how to distinguish when to use AI workflow and when to use an AI Agent
The use case in this blog post is intentionally simple, as its goal is to provide an introduction and help readers understand what agents are and how to create one.
template="Extract all the entities (Person, Organization, Location) from the following text. Provide the result as a comma-separated list.\n\nText:{text}\n\nEntities:"
OpenAI has announced the GPT-4 model, which is a large multimodal model that exhibits human-level performance on various professional benchmarks. It is developed to improve the alignment and safety of AI systems.
additionally, the model is designed to be more efficient and scalable than its predecessor, GPT-3. The GPT-4 model is expected to be released in the coming months and will be available to the public for research and development purposes.
I'm trying to follow along with this tutorial but the formatting is off and python is picky about formatting. I've read your comment about "full code can be found in my repo" but it's not obvious where in the repo this code is. Care to provide a link to the .py file for this tutorial please?
sure.
here is the link to the specific tutorial: https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/langgraph-tutorial.ipynb
Copy all tutorial send to deepseek and ask for the code all togheter :)
Yes, same problem. It looks like the formatting was off.
what is the problem?
The State class that uses a TypedDict at the beginning. The formatting is all off. It had me questioning if there was a novel Python syntax I had not heard of.
class State(TypedDict): text:
str classification:
str entities: List[str] summary: str
On a positive note, it's good motivation to read up on Python's TypedDict feature from the typing package.
Here is the corrected syntax:
class State(TypedDict):
text: str
classification: str
entities: List[str]
summary: str
The syntax
variable: type
is required for a TypedDict object.
My understanding of Agent is that it should be able to handle some complexity which may arise at run time. How is the above designed system different from a simple program which uses LLM to answer some questions? but in the overall flow the so called agent is a linear program flow. If you can elaborate that part
Thank for you comment! Please refer to other comments on this blog post, as I answered this question :)
Hi Nir, I'm unable to get an answer to this question from the other comments. I understand you have intentionally demo'd a simple flow but this can very well be done with prompts to an LLM. Can you describe a more complex use case for agents?
Further, I notice that the output generated by the classification and entity extraction nodes are not being used in the summarization node. Can you you please explain what is the use of having these if this is the case? If not, can you explain how the output from those is being used in the summarization node?
May I recommend reading Gary Marcus’ substack for a different pov.
Love how you framed this as 'simpler' not 'simple.' The strategic move here isn't just building agents—it's democratizing who can build them. Every marketer, PM, and analyst who learns this shifts from AI consumer to AI composer. That's the real disruption. Subscribed
Love your comment. Thanks 😊
As easy as it can be.
Next time will try to make it even easier :)
Thanks for sharing this, I managed to successfully run the whole code and get the intended output.
That being said, I am struggling to see how this code (putting aside the usage of the LangGraph framework) is different from the old good hard coded and predictable series of functions calls, when an output of a function A serves as an input of a function B. What specifically is "agentic" in this code? How this agent "can dynamically adjust its focus based on what it discovers"?
How does the summarization considering the information from the previous steps (classification and entities) if it only uses state["text"] and not state["classification"] or state["entities"].
It seems that summarization logic doesn’t use the classification or entity extraction output. Please correct me if I wrong...
This is not an agent but a workflow.
This is a minimal version of a controllable agent
Me, I feel like this needs a stronger use case. It's not clear how AI chaining did something that a series of well-reasoned prompts couldn't.
Thanks for your input, Dave.
The use case in this blog post is intentionally simple, as its goal is to provide an introduction and help readers understand what agents are and how to create one.
As others have said, I'm uncomfortable with inventing a "new" method "ai agents" for what is good old programming that uses an ai based text processesing function. Using a, state machine abstraction is also as old computer science, in the 80 there was an attempt to have a graphical state machine engine (David Harel StateCharts) I don't think it ever became mainstream since state machines are useful for small examples. Once you have many states and many edges, especially when you introduce error handling potentially on every node, good old programming with sequencing loops and conditions is still the most straightforward and readable way to represent what you want the software to do. My main concern with you simplified example is that people not trained in software engineering will think that ai makes everything simple, anyone can vibe program without bothering to learn how to do it reliably and safely.
Like other readers commented, it is hard to see the power of agents from this example, I understand this was intentionally simplified. Might help to link to a blog post that shows the real power, the autonomous ability to decide based on context. If the agent gets non-English input, could it translate and then summarize?
You can see many more tutorials on my GitHub. both explanations + code:
https://github.com/NirDiamant/GenAI_Agents
The example code looks like it could be a workflow. How do you decide whether to implement it as an agent or a workflow?
An agent can be a workflow where each step uses an LLM for a specific complicated task. This way, we have much more control over the agent.
Like your tutorial and have subscribed to your newsletter! I'm new to medium. Can I get you quick feedback on an even simpler agent https://the-pocket.github.io/PocketFlow/design_pattern/agent.html, built on an 100-line framework
I made https://substack.com/home/post/p-158349453
Hello Nir, thanks for the excellent blog post, I would like to have few inputs from you, especially with the way we use the word AI Agents, In the example you have shown as part of blog is more of an AI system which is a workflow, according to the blog from Anthropic "Building Effective Agents", an AI Agent is one which takes decisions autonomously. It would be helpful if you can write a blog on building a true AI agent, and how to distinguish when to use AI workflow and when to use an AI Agent
Thanks for your input, Ravi.
The use case in this blog post is intentionally simple, as its goal is to provide an introduction and help readers understand what agents are and how to create one.
AttributeError Traceback (most recent call last)
in <cell line: 0>()
5
6 state_input = {"text": sample_text}
----> 7 result = app.invoke(state_input)
8
9 print("Classification:", result["classification"])
6 frames
in classification_node(state)
6 )
7 message = HumanMessage(content=prompt.format(text=state["text"]))
----> 8 classification = llm.invoke([message]).content.strip()
9 return {"classification": classification}
10
AttributeError: 'tuple' object has no attribute 'invoke'
Could anyone help identify why this is happening? Thanks!
I answered the open issue on the repo.
I saw that you opened an issue on the repo. I'll take care of it (or someone else will) soon.
Thanks! 🙂
It seems that the app should be changed to; import os
from typing import TypedDict, List
from langgraph.graph import StateGraph, END
from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from langchain.schema import HumanMessage
class State(TypedDict):
text: str
classification: str
entities: List[str]
summary: str
llm = ChatOpenAI(model="gpt-4-0613", temperature=0)
def classification_node(state: State):
prompt = PromptTemplate(
input_variables=["text"],
template="Classify the following text into one of the categories: News, Blog, Research, or Other.\n\nText:{text}\n\nCategory:"
)
message = HumanMessage(content=prompt.format(text=state["text"]))
classification = llm.invoke([message]).content.strip()
return {"classification": classification, **state}
def entity_extraction_node(state: State):
prompt = PromptTemplate(
input_variables=["text"],
template="Extract all the entities (Person, Organization, Location) from the following text. Provide the result as a comma-separated list.\n\nText:{text}\n\nEntities:"
)
message = HumanMessage(content=prompt.format(text=state["text"]))
entities_str = llm.invoke([message]).content.strip()
entities = [e.strip() for e in entities_str.split(",")] if entities_str else []
return {"entities": entities, **state}
def summarization_node(state: State):
prompt = PromptTemplate(
input_variables=["text"],
template="Summarize the following text in one short sentence.\n\nText:{text}\n\nSummary:"
)
message = HumanMessage(content=prompt.format(text=state["text"]))
summary = llm.invoke([message]).content.strip()
return {"summary": summary, **state}
workflow = StateGraph(State)
# Add nodes to the graph
workflow.add_node("classification_node", classification_node)
workflow.add_node("entity_extraction", entity_extraction_node)
workflow.add_node("summarization", summarization_node)
# Add edges to the graph
workflow.set_entry_point("classification_node")
workflow.add_edge("classification_node", "entity_extraction")
workflow.add_edge("entity_extraction", "summarization")
workflow.add_edge("summarization", END)
# Compile the graph
app = workflow.compile() # This line is crucial
sample_text = """
OpenAI has announced the GPT-4 model, which is a large multimodal model that exhibits human-level performance on various professional benchmarks. It is developed to improve the alignment and safety of AI systems.
additionally, the model is designed to be more efficient and scalable than its predecessor, GPT-3. The GPT-4 model is expected to be released in the coming months and will be available to the public for research and development purposes.
"""
state_input = {"text": sample_text, "classification": "", "entities": [], "summary": ""}
result = app.invoke(state_input)
print("Classification:", result["classification"])
print("\nEntities:", result["entities"])
print("\nSummary:", result["summary"])
But at the end no access to gpt-4-0613
the full code can be found in my repo and it works (I tested it yesterday).
if you choose to work with openAI you'll need to configure a private key on their website first. but you can use any llm that you want.
And where is your repo please?
Found the repo, I'll check, thx