🧠 Multi-Step Function Calling Agent — Detailed Design

[SadikSunbul]

Absolutely! Here's a detailed explanation of how you can implement an agent-like system in your gopilot library that supports multi-step function calling (a reasoning chain), written in clear and structured English:

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🧠 Multi-Step Function Calling Agent — Detailed Design

✅ Goal

Allow the LLM (e.g., Gemini or others) to reason through multiple steps and dynamically call multiple Go functions in sequence, based on natural language input.

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💬 Example Scenario

User prompt:

"Based on today's weather, what should I wear in Istanbul?"

Desired Function Call Chain:

1. GetWeather(city: "Istanbul") → returns weather data
2. SuggestClothes(weatherData) → returns clothing recommendation based on the weather

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🧩 Architecture Overview

1. Function Registry

A central registry that stores all available functions along with:

• Name
• Description
• Input schema
• Output schema (if needed)

This is already partially in your project. Extend it to support:

type Function interface {
    Name() string
    Description() string
    ParametersSchema() map[string]interface{}
    Execute(params map[string]interface{}) (interface{}, error)
}

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2. Agent Loop (Recursive / Iterative Reasoner)

After the LLM generates the first function call, your agent should:

• Run the function
• Append the result to the conversation context
• Ask the LLM: “What should I do next?”
• Repeat until the LLM replies with a final answer or no more functions to call

🔁 This is similar to how OpenAI function-calling agents or LangChain agents work.

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3. Conversation Context

To maintain reasoning, you'll need a memory of previous steps:

type AgentContext struct {
    Messages []LLMMessage        // chat history
    Results  map[string]any      // named function results
}

At each step:

• Include latest result in the context (e.g., "weather": {"temp": 15, "condition": "rainy"})
• Let LLM decide what to do next

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4. Prompt Template for LLM

Use a dynamic system prompt like this:

You are an intelligent agent that can call Go functions to solve problems.
Available functions:
- GetWeather(city: string): returns weather data
- SuggestClothes(weatherData: Weather): returns clothing suggestions

Your job is to analyze the user's request and use the functions step-by-step.
You must output structured function_call JSON in each step.

You dynamically add previous results like this:

Previous result:
GetWeather("Istanbul") → { "temp": 12, "condition": "cloudy" }

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5. LLM Output Handling

Allow the LLM to respond with structured JSON like:

{
  "function_call": {
    "name": "SuggestClothes",
    "arguments": {
      "weatherData": {
        "temp": 12,
        "condition": "cloudy"
      }
    }
  }
}

Parse, route to the actual Go function, and repeat the loop.

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🔄 Example Loop Flow

1. User: “What should I wear in Istanbul today?”
2. LLM → Suggests calling GetWeather(city: "Istanbul")
3. Agent → Calls function → gets {temp: 12, condition: "cloudy"}
4. LLM → Uses result → suggests calling SuggestClothes(weatherData)
5. Agent → Calls function → gets "Wear a light jacket and waterproof shoes."
6. LLM → Replies with final answer

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📚 Optional Enhancements

• Dependency Graph: Auto-detect which functions depend on others via parameter types.
• Chain Execution Mode: Let the agent plan all steps at once.
• Error Recovery: Let the LLM try a fallback if a function call fails.
• Memory: Save context across sessions.

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Would you like a real Go code example that shows this in practice? I can write a prototype agent loop for you.