Question 1

What is the ReAct pattern and when should I use it?

Accepted Answer

ReAct (Reasoning + Acting) interleaves chain-of-thought reasoning with action execution in a think → act → observe loop. The model reasons about what to do, calls a tool, observes the result, then reasons again. Use ReAct when your agent needs to use external tools (web search, APIs, code execution) and must adapt its plan based on intermediate results. It's the most common architecture for tool-using agents and the basis for LangChain's AgentExecutor.

Question 2

What is the difference between Chain-of-Thought and Tree of Thoughts?

Accepted Answer

Chain-of-Thought (CoT) generates one linear reasoning path: step 1 → step 2 → answer. It works well for problems with a clear sequential solution path. Tree of Thoughts (ToT) generates multiple candidate reasoning paths in parallel, evaluates each branch, and backtracks from dead ends. ToT is better for complex problems with many possible approaches (e.g., math proofs, planning, puzzles), but costs 5–20× more tokens because it explores multiple branches simultaneously.

Question 3

What is the Reflexion pattern?

Accepted Answer

Reflexion adds a self-critique loop to standard generation: the agent produces an output, then a separate 'evaluator' step (which can be the same model) scores the output and generates verbal feedback. The agent then revises its answer using that feedback. This mimics human iterative improvement. Reflexion is effective for coding tasks where you can run tests and feed failures back as feedback. It typically converges in 2–4 revision cycles.

Question 4

When should I use multi-agent debate instead of a single agent?

Accepted Answer

Multi-agent debate is valuable when: (1) You need to catch hallucinations — agents arguing against each other surface errors that self-review misses. (2) The problem benefits from diverse perspectives (strategy planning, risk assessment). (3) You want to simulate adversarial conditions (security review, legal analysis). Downside: debate multiplies your token cost by the number of agents × rounds. Start with 2 agents and 2 rounds; add more only if quality improves measurably.

Question 5

How do I estimate token usage for agent patterns?

Accepted Answer

Rough estimates per task: CoT adds ~200–500 tokens of reasoning overhead. ReAct adds ~500–2000 tokens depending on tool call count (each observe-reason cycle costs ~300–600 tokens). Self-Consistency runs the same prompt N times — multiply base cost by N. Tree of Thoughts explores K branches with depth D: cost ≈ base × K × D. Plan-and-Execute costs base + plan tokens upfront, then each sub-task execution cost. For production systems, instrument actual token counts and set budget limits per agent run.

LLM Agent Workflow Designer

Select Agent Pattern

Visual Flow Builder

Related Products

Developer Productivity Bundle

AI Prompt Engineering Toolkit

Developer Cheat Sheet Bundle

You Might Also Need

JSON Formatter & Validator

Regex Tester

Base64 Encoder & Decoder

More Free Tools

Related Articles

curl 深度使用手冊：API 測試從入門到進階

Best AI Code Review Tools 2026: CodeRabbit, Sourcery, DeepCode & Bito Compared

AI Code Review Tools 2026: GitHub Copilot, CodeRabbit, Qodo Merge Ranked

AI Coding Tools Comparison 2026: GitHub Copilot vs Cursor vs Windsurf vs Cline

AI Pair Programming in 2026: GitHub Copilot vs Cursor vs Claude Code

Enjoying this tool? Get more like it.

Unlock AI-Powered Dev Tools

Get DevPlaybook Pro