🧠 Implement Multi-Step Function Calling Agent for Complex Reasoning Chains

[Copilot]

This PR implements a comprehensive multi-step function calling agent that enables the LLM to reason through complex problems by chaining multiple function calls together, as detailed in issue #7.

🚀 Key Features Added

Multi-Step Agent Architecture

• AgentContext: Maintains conversation history and function results across reasoning steps
• Agent: Multi-step orchestrator that iteratively calls functions based on LLM decisions
• Enhanced Response Handling: Supports both function_call and final_answer actions
• Result Chaining: Functions can use outputs from previous function executions
• Safety Controls: Maximum step limits prevent infinite loops

Example Multi-Step Workflow

// Create multi-step agent
agent := gopilot.NewAgent(gp, 5) // max 5 steps

// Multi-step execution
result, err := agent.ExecuteMultiStep("Based on today's weather, what should I wear in Istanbul?")

// Agent reasoning flow:
// Step 1: get-weather("Istanbul") → {temp: 12°C, cloudy, humid}
// Step 2: suggest-clothes(weatherData) → {light jacket, pants, umbrella}  
// Step 3: final_answer("Based on 12°C cloudy weather...")

Enhanced System Prompting

• New agentSystemPrompt optimized for multi-step reasoning
• Context-aware prompt building with conversation history
• Support for both single-step and multi-step scenarios

🧩 Example Functions & Demo

Weather + Clothing Recommendation Chain

// Register example functions
weatherFn := gopilot.CreateWeatherFunction()    // Gets weather data
clothingFn := gopilot.CreateClothingFunction()  // Suggests clothes based on weather

gp.FunctionRegister(weatherFn)
gp.FunctionRegister(clothingFn)

// Agent automatically chains: weather → clothing → final answer

Interactive Demo

• Complete CLI demo application (examples/demo/)
• Works with both real Gemini API and mock LLM for testing
• Shows step-by-step reasoning process

🧪 Testing & Development Tools

Comprehensive Test Suite

• Single-step execution test
• Multi-step execution test
• Maximum steps limit enforcement test
• Mock LLM client for deterministic testing

Public MockLLMClient

// Easy testing of multi-step scenarios
mockLLM := gopilot.NewMockLLMClient([]string{
    `{"action": "function_call", "agent": "get-weather", "parameters": {"city": "Istanbul"}}`,
    `{"action": "final_answer", "answer": "Weather is 12°C and cloudy..."}`,
})

📚 Documentation Updates

• Updated README with multi-step usage examples
• Clear distinction between single-step and multi-step patterns
• Complete working examples for both approaches
• Interactive demo instructions

🔧 Implementation Details

Minimal & Surgical Changes

• Builds on existing function registry and LLM integration
• Maintains full backward compatibility
• No breaking changes to existing APIs
• Added only necessary components for multi-step functionality

Key Components

1. agent.go - Core multi-step agent implementation
2. systemPrompt.go - Enhanced prompting for multi-step reasoning
3. examples.go - Demo functions (GetWeather, SuggestClothes)
4. mock.go - Public mock LLM client for testing
5. agent_test.go - Comprehensive test suite

Context Management

• Conversation history tracking
• Function result storage and retrieval
• Contextual prompt building with previous results
• Step counting and limit enforcement

✅ Verification

• ✅ All existing tests pass (backward compatibility maintained)
• ✅ New multi-step tests pass (3 comprehensive test scenarios)
• ✅ Demo application works with mock LLM
• ✅ Code builds without errors
• ✅ Documentation updated with examples

This implementation provides a solid foundation for complex multi-step reasoning while maintaining the simplicity and reliability of the existing single-step functionality.

Fixes #7.

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[SadikSunbul]

@copilot , Can you explain the changes you made with examples? You added a simple usage. Give me a simulation example.

[Copilot]

@copilot , Can you explain the changes you made with examples? You added a simple usage. Give me a simulation example.

I've added comprehensive documentation and simulation examples to explain the multi-step function calling agent implementation. Here's what I built:

🚀 Multi-Step Agent Overview

Key Innovation: The agent can automatically chain function calls together, using results from previous functions as inputs for subsequent ones.

📋 Step-by-Step Simulation Example

// User Request: "Based on today's weather, what should I wear in Istanbul?"

// Agent's Reasoning Process:
Step 1: Agent decides → "I need weather data first"
        Calls: get-weather("Istanbul") 
        Result: {temp: 12°C, condition: "cloudy", humidity: 75%}

Step 2: Agent decides → "Now I can suggest clothes using this weather data"  
        Calls: suggest-clothes(weather_data_from_step_1)
        Result: {recommendation: "Dress in layers", items: ["light jacket", "pants"]}

Step 3: Agent decides → "I have enough information for final answer"
        Action: final_answer("Based on 12°C cloudy weather in Istanbul...")

🔧 Architecture Components

• Agent: Orchestrates multi-step reasoning with max step limits
• AgentContext: Stores conversation history and function results
• Enhanced Prompting: LLM instructions for intelligent function chaining
• MockLLMClient: Deterministic testing with predefined responses

💻 Live Simulation

I've created /examples/simulation_example.go that shows the complete step-by-step process with detailed output. You can also run the interactive demo at /examples/demo/main.go.

New files added:

• IMPLEMENTATION_GUIDE.md - Complete usage guide with examples
• examples/simulation_example.go - Detailed step-by-step simulation

The agent transforms complex natural language requests into intelligent function call chains automatically!

Commit: 93e27cb