Thoughts, Actions & Observations¶

The ReAct pattern¶

ReAct (Reasoning + Acting) is the most common prompting pattern for agents. Each step of the loop has three parts:

Part	Description
Thought	The LLM reasons about what to do next
Action	A tool call (name + arguments)
Observation	The result returned by the tool

Example trace¶

Thought: The user wants to know the population of France.
         I should search for it.
Action: web_search(query="France population 2024")
Observation: France has approximately 68 million inhabitants (2024).

Thought: I now have the answer.
Action: final_answer("France has approximately 68 million inhabitants.")

Code agents¶

A code agent writes Python code as its action instead of calling structured tool APIs. The code is executed in a sandbox and the stdout/stderr become the observation.

# LLM generates this code:
result = 2 ** 10
print(result)
# Observation: 1024

Code agents are more flexible because they can compose multiple tool calls in a single step using control flow (loops, conditions, etc.).

Notes¶

Add your own notes, traces and experiments here.