Agentic AI Notebook
Phase 17

Graph of Thoughts

~3 min read

Concept & How It Works

  • Key points are in the visual diagram above.

Why Does It Exist?

Real problem-solving combines insights from different approaches. GoT lets an agent explore parallel ideas and then merge compatible partial solutions, outperforming ToT on tasks requiring integration of multiple perspectives.

Real-World Analogy

GoT is a research team where members work on different angles, then merge findings into a unified report — rather than one person following a single outline.
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Visual Workflows

What is Graph of Thoughts?

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Example

Scenario

Write a business plan: branch A researches market, branch B researches competitors, branch C drafts financials. Aggregate node merges all three into a coherent document.

Solution

In Agent Design Patterns, apply Graph of Thoughts to this scenario: Write a business plan: branch A researches market, branch B researches competitors, branch C drafts financials. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Practice Task

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to Graph of Thoughts (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Code Walkthrough

Highlighted lines show where Graph of Thoughts happens in the code.

Graph of Thoughts
1# Graph of Thoughts — minimal example2from openai import OpenAI3
4client = OpenAI()  # create API client5
6# Ask the model to explain this topic7response = client.chat.completions.create(  # core API call for Graph of Thoughts8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain graph of thoughts clearly."},11        {"role": "user", "content": f"What is graph of thoughts?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

Commands to Remember

Commands to Remember

  • pip install langchain langchain-openai # patterns work with any LLM SDK
  • python react_agent.py # run a ReAct-style agent loop
  • pip install tenacity # retry logic for agent steps

Common Mistakes

  • Treating Graph of Thoughts as a black box without evaluation
  • Ignoring cost and latency in production
  • Skipping error handling for graph of thoughts

Cheat Sheet

Quick recap — the most important points from this module.

Cheat Sheet

quick ref
  • Graph of Thoughts
  • DAG
  • Aggregation
  • Thought Node