LLM Agents Are Simply Graph – Tutorial for Dummies
zacharyhuang.substack.com
LLM Agents Are Simply Graph – Tutorial for Dummies
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Re: LLM Agents Are Simply Graph – Tutorial for Dummies
#2Hey folks! I just posted a quick tutorial explaining how LLM agents (like OpenAI Agents, Pydantic AI, Manus AI, AutoGPT or PerplexityAI) are basically small graphs with loops and branches. For example:
OpenAI Agents: for the workflow logic: https://github.com/openai/openai-agents-python/blob/48ff99bb...
Pydantic Agents: organizes steps in a graph: https://github.com/pydantic/pydantic-ai/blob/4c0f384a0626299...
Langchain: demonstrates the loop structure: https://github.com/langchain-ai/langchain/blob/4d1d726e61ed5...
If all the hype has been confusing, this guide shows how they actually work under the hood, with simple examples. Check it out!
https://zacharyhuang.substack.com/p/llm-agent-internal-as-a-...
Re: LLM Agents Are Simply Graph – Tutorial for Dummies
#3Hey folks! I just posted a quick tutorial explaining how LLM agents (like OpenAI Agents, Pydantic AI, Manus AI, AutoGPT or PerplexityAI) are basically small graphs with loops and branches. For example: OpenAI Agents: for the workflow logic: https://github.com/openai/openai-agents-python/blob/48ff99bb... Pydantic Agents: organizes steps in a graph: https://github.com/pydantic/pydantic-ai/blob/4c0f384a0626299... Langch…
Thank you - really interesting looking read, thanks for crafting the deep explanation, with links to actual internal code examples. Also, thanks for not putting it behind the Medium paywall
Re: LLM Agents Are Simply Graph – Tutorial for Dummies
#4Hey folks! I just posted a quick tutorial explaining how LLM agents (like OpenAI Agents, Pydantic AI, Manus AI, AutoGPT or PerplexityAI) are basically small graphs with loops and branches. For example: OpenAI Agents: for the workflow logic: https://github.com/openai/openai-agents-python/blob/48ff99bb... Pydantic Agents: organizes steps in a graph: https://github.com/pydantic/pydantic-ai/blob/4c0f384a0626299... Langch…
Thank you - really interesting looking read, thanks for crafting the deep explanation, with links to actual internal code examples. Also, thanks for not putting it behind the Medium paywall
Thank you!!
Re: LLM Agents Are Simply Graph – Tutorial for Dummies
#5Everything that was previously just called automation or pipeline processing on-top of LLM is now the buzzword "agents". The hype bubble needs constant feeding to keep from imploding.
Re: LLM Agents Are Simply Graph – Tutorial for Dummies
#6[deleted]
Re: LLM Agents Are Simply Graph – Tutorial for Dummies
#7I follow Mr. Huang, read/watch his content and also plan to use PocketFlow in some cases. A preamble, because I don't agree with this assessment. I think agents as nodes in a DAG workflow is _an_ implementation of an agentic system, but is not the systems I most often interact with (e.g. Cursor, Claude + MCP).
Agentic systems can be simply the LLM + prompting + tools[1]. LLMs are more than capable (especially chain-of thought models) to breakdown problems into steps, analyze necessary tools to use and then executing the steps in sequence. All of this is done with the model in the driver seat.
I think the system described in the post need a different name. It's a traditional workflow system with an agent operating on individual tasks. Its more rigid in that the workflow is setup ahead of time. Typical agentic systems are largely undefined or defined via prompting. For some use cases this rigidity is a feature.
[1 https://docs.anthropic.com/en/docs/build-with-claude/tool-us...
Re: LLM Agents Are Simply Graph – Tutorial for Dummies
#8I follow Mr. Huang, read/watch his content and also plan to use PocketFlow in some cases. A preamble, because I don't agree with this assessment. I think agents as nodes in a DAG workflow is _an_ implementation of an agentic system, but is not the systems I most often interact with (e.g. Cursor, Claude + MCP). Agentic systems can be simply the LLM + prompting + tools[1]. LLMs are more than capable (especially chain-o…
Let me clarify: this tutorial focuses on the technical internal implementation of the agent (e.g., OpenAI agent, Pydantic AI, etc.), rather than the UI/UX of the agent-based products that end users interact with.
Re: LLM Agents Are Simply Graph – Tutorial for Dummies
#9It is hard to put a pin on this one because there are so many thing wrong with this definition. There are agent frameworks that are not rebranded workflow tools too. I don't think this article helps explain anything except putting the intended audience in the same box of mind we were stuck since the invention of programming - i.e. it does not help.
Forget about boxes and deterministic control and start thinking of error tolerance and recovery. That is what agents are all about.
Re: LLM Agents Are Simply Graph – Tutorial for Dummies
#10It is hard to put a pin on this one because there are so many thing wrong with this definition. There are agent frameworks that are not rebranded workflow tools too. I don't think this article helps explain anything except putting the intended audience in the same box of mind we were stuck since the invention of programming - i.e. it does not help. Forget about boxes and deterministic control and start thinking of er…
Hey, sorry for the confusion. This tutorial is focusing on the low-level internals of how agents are implemented—much like how intelligent large language models still boil down to matrix multiplications at their core.