🤖Coral Agent Solver Pattern

Technical implementation guide for building Agents using LangGraph patterns within the Decentralised AI ecosystem of Coral Protocol

Core Concepts

🤖
Agentic AI
Autonomous agents that can reason, plan, and execute tasks independently. Your solver becomes an intelligent agent capable of understanding problems, selecting appropriate strategies, and providing educational explanations.
🌐
Decentralised AI
Multiple specialized agents collaborate through Coral Protocol's messaging system. No single point of failure - agents communicate peer-to-peer to solve complex, multi-domain problems.
📊
LangGraph Patterns
State-driven agent workflows where each step in your solver represents a node in a graph. Problem classification routes to specialist nodes, creating a robust, extensible problem-solving pipeline.
🔗
Multi-Agent Workflow
Orchestrated collaboration between domain experts. Your solver integrates with other agents - Interface agents handle user interaction, your agent provides domain expertise, and coordination happens through Coral's messaging infrastructure.

Data Flow Architecture

User Input → Interface Agent → Problem Dispatch → Your Agent → Solver Classification → Specialist Method → Response
1
User Input
User poses question through Coral Studio or API
2
🤖 Interface Agent
Receives input, determines which domain agent to involve
3
Problem Dispatch
Routes to your agent via Coral Protocol's messaging system
4
🤖 Your Agent
Receives mention, extracts problem from message
5
Solver Entry Point
solve_problem() processes the raw problem string
6
Classification
Determines problem type (arithmetic, algebra, etc.)
7
Specialist Routing
Calls appropriate domain-specific method
8
Response Generation
Formats solution with explanations
9
Return Path
Sends structured response back through Coral messaging

Core Implementation Pattern

1. Solver Class Structure
Single responsibility principle. Each method handles one specific task in the problem-solving pipeline.
Python Class Structure
class YourSolver:
    def __init__(self):
        # Domain knowledge: formulas, constants, mappings
        
    async def solve_problem(self, problem: str) -> str:
        # Main entry point - handles all incoming problems
        
    async def _identify_problem_type(self, problem: str) -> str:
        # Classification engine - routes to correct specialist
        
    async def _solve_[type](self, problem: str) -> str:
        # Specialist methods for each problem category
2. Problem Classification Strategy
Choose between LLM-based classification for complex domains or pattern matching for well-defined categories.
LLM-Based Classification
async def _identify_problem_type(self, problem: str) -> str:
    classification_prompt = f"""
    Classify this {domain} problem into: {categories}
    Problem: "{problem}"
    Respond with only the category name.
    """
    # Call LLM API, validate response
Pattern Matching Alternative
def _identify_problem_type(self, problem: str) -> str:
    problem_lower = problem.lower()
    if any(word in problem_lower for word in calculus_keywords):
        return "calculus"
    # Continue pattern matching
3. Specialist Method Template
Each specialist follows a consistent structure for handling domain-specific problems.
Specialist Template
async def _solve_[category](self, problem: str) -> str:
    # Extract numerical data
    numbers = re.findall(r'-?\d+\.?\d*', problem)
    
    solution = f"""
**{CATEGORY} ANALYSIS**

**Problem Type:** {specific identification}

**Solution Process:**
1. **Setup:** {what we're given and what we need to find}
2. **Method:** {approach and relevant formulas}
3. **Execution:** {step-by-step calculations}
4. **Verification:** {check reasonableness}

**Key Concepts:**
- {domain-specific principle 1}
- {domain-specific principle 2}

**Applications:**
{when/where this type of problem appears}
"""
    
    # Add numerical calculations if data present
    if numbers:
        # Perform domain-specific computations
        
    return solution
4. Error Handling & Fallbacks
Robust error handling ensures graceful degradation when classification fails or edge cases occur.
Error Handling Pattern
async def solve_problem(self, problem: str) -> str:
    try:
        problem_type = await self._identify_problem_type(problem)
        
        # Route to specialist with fallback
        solver_map = {
            "type_a": self._solve_type_a,
            "type_b": self._solve_type_b,
        }
        
        if problem_type in solver_map:
            return await solver_map[problem_type](problem)
        else:
            return await self._general_guidance(problem)
            
    except Exception as e:
        return self._error_response(problem, str(e))

Working Example

🎓
Coral Economics Agent
A complete, production-ready implementation demonstrating all the patterns described in this guide. The Economics Agent handles high school economics problems including supply/demand analysis, market equilibrium, elasticity calculations, and GDP analysis.
Repository & Integration
# Clone the working example
git clone https://github.com/bivasb/Coral-Economics-Agent.git
cd Coral-Economics-Agent/agents/Coral-Economics-Agent

# Features demonstrated:
- DeepSeek LLM integration for cost-effective AI
- Problem classification and routing
- Educational step-by-step explanations
- Coral Protocol messaging integration
- Docker deployment configuration
- Comprehensive testing framework
Key Implementations:
  • EconomicsSolver class with 7+ problem types
  • LLM-enhanced problem classification
  • Structured educational responses
  • Real-world application examples
  • Error handling and fallback mechanisms
  • Ready-to-deploy Coral agent configuration
🤖 View Economics Agent Repository →

Implementation Checklist

Required Methods
  • __init__() - Initialize domain knowledge
  • async def solve_problem() - Main entry point (required by converter)
  • _identify_problem_type() - Classification logic
  • _solve_[category]() - One method per problem type
  • _general_guidance() - Fallback for unclear problems
Response Requirements
  • Structured format with clear sections
  • Step-by-step process showing methodology
  • Educational content explaining concepts
  • Numerical extraction when applicable
  • Error handling for edge cases
Integration Points
  • Async compatibility for Coral Protocol
  • String input/output interface
  • Graceful degradation when classification fails
  • Consistent formatting across all response types
Technical Considerations
Performance
  • Classification should be fast (<1s keyword-based, <3s LLM-based)
  • Cache domain knowledge in __init__() rather than rebuilding
  • Use async/await properly to avoid blocking
Extensibility
  • Adding new problem types only requires new specialist method
  • Classification logic isolated for easy modification
  • Domain knowledge centralized for maintenance
Robustness
  • Validate LLM classification responses
  • Handle malformed input gracefully
  • Provide meaningful fallbacks for edge cases