| license | Apache-2.0 |
| name | yao-2022-react |
| description | Prompting paradigm combining reasoning traces with actions for LLM agents to interact with external tools |
| category | Research & Academic |
| tags | ["react-prompting","reasoning","acting","llm-agents","tool-use"] |
ReAct: Reasoning-Action Synergy
When to Use This Skill
Load this skill when addressing agent architecture design, debugging agent failures, designing information retrieval strategies, or making agent decision-making interpretable to humans.
Decision Points
1. Knowledge Source Selection
IF agent expresses uncertainty (hedges, "I'm not sure", "might be")
→ THEN trigger external action (search, API call, retrieval)
IF agent states facts with high confidence AND domain is within training
→ THEN allow internal reasoning to continue
IF previous action returned "not found" or empty results
→ THEN reason about query reformulation strategy
IF factual accuracy is mission-critical (medical, legal, financial)
→ THEN default to external grounding regardless of confidence
2. Reasoning Density Control
IF task requires strategic planning or goal decomposition
→ THEN insert sparse, high-level thoughts at decision points
IF simple execution with known action sequence
→ THEN skip reasoning, execute actions directly
IF debugging failure or exception handling needed
→ THEN add reasoning trace to expose decision logic
IF token budget is constrained
→ THEN limit thoughts to: goal decomposition, search reformulation, exception handling
3. Failure Recovery Strategy
IF action returns unexpected result or error
→ THEN add thought analyzing what went wrong + corrective strategy
IF agent enters repetitive loop (same action 3+ times)
→ THEN force reasoning step: "Why am I stuck? What should I try instead?"
IF search returns empty/irrelevant results
→ THEN reason about query reformulation before next search
IF hallucinated information detected
→ THEN immediately insert retrieval action to verify facts
4. Multi-Step Task Orchestration
IF complex task with 4+ subtasks
→ THEN start with thought decomposing into subgoals
IF current subtask complete
→ THEN add thought tracking progress: "Done with X, now need Y"
IF environment state changed unexpectedly
→ THEN reason about how this affects remaining plan
IF stuck on subtask for 3+ actions
→ THEN reason about alternative approaches or skipping
Failure Modes
1. Reasoning Without Grounding (Hallucination Spiral)
Symptoms: Agent confidently states facts, builds elaborate reasoning chains, but information is fabricated
Detection Rule: IF reasoning contains specific facts not verifiable through actions AND no recent external grounding
Fix: Insert retrieval actions at uncertainty points; verify facts before building on them
2. Dense Narration Overhead (Token Waste)
Symptoms: Thought at every step describing obvious actions or restating observations
Detection Rule: IF >60% of tokens are reasoning AND thoughts don't contain strategic decisions
Fix: Limit thoughts to: goal decomposition, search reformulation, exception handling, progress tracking
3. Action Tunneling (No Strategic Planning)
Symptoms: Agent immediately starts acting without decomposing complex tasks, gets stuck or inefficient
Detection Rule: IF complex multi-step task AND first action is attempted execution (not planning)
Fix: Force initial thought decomposing goal into subgoals and strategy
4. Loop Trap (Repetitive Failure)
Symptoms: Same action repeated 3+ times with same failure result, no learning from feedback
Detection Rule: IF identical action-observation pair occurs 3 consecutive times
Fix: Insert reasoning step analyzing why approach isn't working + alternative strategy
5. Search Strategy Blindness (Query Reformulation Failure)
Symptoms: Empty search results followed by identical or minimally different queries
Detection Rule: IF search returns "not found" AND next search query has >80% word overlap
Fix: Add explicit reasoning about why search failed + systematic query reformulation strategy
Worked Examples
Example 1: QA Task with Strategic Information Gathering
Task: "What is the elevation of the birthplace of the director of the movie 'Inception'?"
Expert Trajectory:
Thought 1: This requires finding: (1) director of Inception, (2) director's birthplace, (3) elevation of that place. Let me start with the director.
Action 1: Search[Inception movie director]
Observation 1: Christopher Nolan directed Inception (2010)...
Thought 2: Got it - Christopher Nolan. Now I need his birthplace.
Action 2: Search[Christopher Nolan birthplace]
Observation 2: Christopher Nolan was born in Westminster, London, England
Thought 3: Now I need the elevation of Westminster, London. This might be tricky since it's a district.
Action 3: Search[Westminster London elevation above sea level]
Observation 3: Westminster, London is approximately 24 meters (79 feet) above sea level
Thought 4: Found it. The elevation is 24 meters or 79 feet.
Novice Mistakes: Would search "Inception director birthplace elevation" immediately, get confused results mixing multiple concepts. Expert decomposes systematically and verifies each fact before building on it.
Example 2: Error Recovery with Trade-off Analysis
Task: "Put a clean knife in the countertop" (ALFWorld navigation)
Expert Recovery Trajectory:
Action 1: take knife 1
Observation 1: You pick up the knife 1
Action 2: clean knife 1 with sinkbasin 1
Observation 2: Nothing happens.
Thought 1: Cleaning failed. I'm probably not at the right location. I need to navigate to the sinkbasin first, then clean the knife.
Action 3: go to sinkbasin 1
Observation 3: On the sinkbasin 1, you see a dishsponge 1, and a lettuce 1.
Action 4: clean knife 1 with sinkbasin 1
Observation 4: You clean the knife 1 using the sinkbasin 1.
Thought 2: Great! Now I have a clean knife. I need to find a countertop to place it on.
Key Decision: Expert recognizes failed action immediately, reasons about spatial requirements, adjusts strategy. Shows how external feedback corrects internal assumptions.
Example 3: Multi-hop Reasoning with Confidence Monitoring
Task: "Were Scott Derrickson and Ed Wood of the same nationality?"
Expert Trajectory:
Thought 1: I need to find the nationality of both Scott Derrickson and Ed Wood, then compare them.
Action 1: Search[Scott Derrickson nationality]
Observation 1: Scott Derrickson is an American film director and screenwriter...
Thought 2: Scott Derrickson is American. Now I need Ed Wood's nationality.
Action 2: Search[Ed Wood nationality filmmaker]
Observation 2: Edward Davis Wood Jr. was an American filmmaker...
Thought 3: Ed Wood was also American. Both are American, so yes, they were the same nationality.
Confidence Signal: Expert searches for both facts despite potentially "knowing" Ed Wood is American, ensuring verification rather than risking hallucination.
Quality Gates
NOT-FOR Boundaries
Do NOT use ReAct for:
- Simple, single-step tasks where immediate action is sufficient → Use direct action execution instead
- Creative writing or open-ended generation where grounding constrains desired output → Use pure chain-of-thought instead
- Highly structured API interactions with predetermined workflows → Use programmatic workflows instead
- Real-time systems where reasoning latency is prohibitive → Use learned policies or reactive systems instead
- Tasks requiring extensive mathematical computation → Use specialized math tools with minimal reasoning overhead instead
Delegate to other skills when:
- Task needs mathematical reasoning: Use
mathematical-reasoning-strategies
- Task is pure information synthesis: Use
chain-of-thought-reasoning
- Task needs code generation: Use
code-generation-workflows
- Task requires creative ideation: Use
creative-generation-techniques
