Function Call Flow Simulator

Samples:
~0 tokens (est.)

Step 1: Define Tools

No tools defined. Add a tool or load a sample flow.

Conversation Timeline

Flow Pattern

UserTool CallTool ResponseAssistant

Tool calls can loop (call multiple tools before the final assistant response).

What This Tool Does

Function Call Flow Simulator is built for deterministic developer and agent workflows.

Simulate AI function calling and tool use flows without API calls — test multi-step agent conversations.

Use How to Use for execution steps and FAQ for constraints, policies, and edge cases.

Last updated:

This tool is provided as-is for convenience. Output should be verified before use in any production or critical context.

Agent Invocation

Best Path For Builders

Browser workflow

Runs instantly in the browser with private local processing and copy/export-ready output.

Browser Workflow

This tool is optimized for instant in-browser execution with local data handling. Run it here and copy/export the output directly.

/function-call-simulator/

For automation planning, fetch the canonical contract at /api/tool/function-call-simulator.json.

How to Use Function Call Flow Simulator

  1. 1

    Simulate an AI agent calling functions step-by-step

    Define tools (e.g., get_weather, search_web) with parameters. Set agent goal ('Get weather for Berlin'). Simulator runs through: agent thinks → calls tool → receives result → repeats until goal met or max steps.

  2. 2

    Test function-calling prompt logic without API calls

    Before deploying real tools, simulate calling them. See if agent correctly formats function calls, handles missing parameters, and interprets results. Catch logic errors early.

  3. 3

    Debug tool parameter issues

    If agent always calls get_user(id='abc') but your API expects get_user(user_id=int), simulator shows the mismatch. Tweak prompt and re-simulate until function calls are correct format.

  4. 4

    Visualize multi-step tool chains

    Simulate complex flows: get_user → fetch_orders → calculate_total → send_invoice. Simulator shows the chain, each tool's input/output, and final result. Validate logic before automation.

  5. 5

    Test error handling in agent loops

    Inject errors into tool responses (e.g., 'user not found'). Watch agent retry, rephrase, or fallback. Confirms agent gracefully handles failures instead of looping infinitely.

Frequently Asked Questions

What is function calling in AI?
Function calling is a capability where LLMs request to call external tools/functions with structured parameters. It enables agents to take actions like searching databases, calling APIs, or running code.
Why simulate function calling instead of using the API?
Simulation is free, instant, and lets you test edge cases (tool errors, wrong tool selection, multi-step flows) before spending API tokens. It's ideal for designing and validating agent tool configurations.
Can I simulate multi-step tool use?
Yes, chain multiple tool calls to simulate agents that need several steps to complete a task — like searching, then reading results, then summarizing. Each step shows token estimates.
What export formats are supported?
Export the simulated conversation as an OpenAI messages array, Anthropic messages array, or generic JSON — ready to use in your application code.
Does this make real API calls?
No. Everything is simulated locally in your browser. No API keys needed, no tokens consumed. You manually specify which tool the model would call and provide mock responses.