Agents & Tool Calling Simulator
Explore multi-agent workflows, tool calling patterns, and error handling
Compare different agent architectures from simple single-agent to complex multi-agent orchestration.
Single Agent Architecture
One LLM handles all tasks: reasoning, planning, and tool execution. Simple but limited for complex tasks.
| Pattern | Complexity | Latency | Best For |
|---|---|---|---|
| Single Agent | Low | Fast | Simple tasks, Q&A |
| Planner-Executor | Medium | Medium | Multi-step tasks |
| Hierarchical | High | Slow | Complex workflows |
| Collaborative | High | Variable | Specialized domains |
Tools extend LLM capabilities by allowing them to interact with external systems like APIs, databases, and code interpreters.
Available Tools
Tool Schema Example
Execution Flow
"What's the weather in Tokyo?"
Agent chooses: web_search
{"query": "Tokyo weather today"}
Parse and synthesize response
Natural language answer
Multiple specialized agents collaborate to solve complex tasks. Each agent has specific expertise and tools.
Execution Log
Robust agents must handle tool failures, API errors, and unexpected responses gracefully.
Common Error Scenarios
Recovery Strategies
Automatically retry failed calls with exponential delay
Use alternative tool when primary fails
Request clarification when uncertain
Use LLM knowledge when tool unavailable
Click an error scenario to see recovery in action
ReAct interleaves reasoning (thinking) with action (tool use) for better decision-making.
ReAct Loop
Reason about what to do next
Execute tool or action
Observe tool result
Example Trace
ReAct vs Standard Prompting
LLM generates complete response in one shot. May hallucinate or miss steps.
Step-by-step reasoning with tool verification. More reliable and interpretable.
Agent output will appear here