LLM Playground

A comprehensive playground for experimenting with Model Context Protocol (MCP) servers, featuring web-based interfaces for testing multiple AI providers and MCP connectors.

⚠️ DISCLAIMER

This code is for reference and demonstration purposes only. Please read the following important disclaimers:

🔍 Code Accuracy & Reliability

AI-Generated Content: This codebase is AI-generated and may contain inaccuracies, bugs, or incomplete implementations
Reference Only: This code is provided as a learning resource and reference implementation, not for production use
No Guarantees: The code may not work as expected and should be thoroughly tested before any use
Educational Purpose: This is intended for educational and demonstration purposes only

💰 Token Calculator & Cost Estimation

Indicative Data: All token counting, cost estimation, and pricing information provided is indicative only
May Not Be Accurate: Token counts, costs, and pricing data may not reflect current or accurate values
Provider Changes: AI providers frequently update their pricing, models, and tokenization methods
Verify Independently: Always verify token counts and costs with official provider documentation
No Financial Advice: Cost estimates should not be used for financial planning or budgeting

🎯 Demo Purpose Only

Not Production Ready: This code is not intended for production environments
Learning Tool: Use this as a learning tool to understand MCP concepts and implementations
Experimental: This is experimental code that may break or change unexpectedly
No Support: No guarantees of support, maintenance, or updates

🔒 Security & Privacy

API Keys: Never use real API keys in this demo environment
Test Data: Use only test data and dummy credentials
No Sensitive Information: Do not process or store sensitive information with this code

By using this code, you acknowledge that you understand these limitations and will use it responsibly for educational purposes only.

🎯 Purpose & Overview

This repository provides a complete environment for working with MCP servers, designed to help developers and researchers:

Learn MCP: Understand how Model Context Protocol works through hands-on examples
Test AI Providers: Experiment with multiple AI providers (OpenAI, Anthropic, Google, Ollama) in a unified interface
Integrate MCP Connectors: Connect to GitHub and PostgreSQL MCP servers for real-world data
Build AI Applications: Create AI-powered applications that combine multiple data sources and AI models

🏗️ Architecture Overview

This repository includes three distinct playground implementations, each with different architectural approaches:

Basic Playground - Simple & Lightweight

LLM Integration: Direct API calls using requests library
MCP Integration: Custom fastmcp client implementation
Async Support: Limited async support with manual asyncio.run()
Type Safety: Basic type hints with dataclasses
Use Case: Quick testing and learning MCP fundamentals

Extended Playground - Production-Ready Features

LLM Integration: Direct API calls using requests library
MCP Integration: Custom fastmcp client implementation
Async Support: Limited async support with manual asyncio.run()
Type Safety: Basic type hints with dataclasses
Advanced Features: Session management, comprehensive logging, token calculator
Use Case: Full-featured development with advanced capabilities

LangChain Playground - Modern & Scalable ⭐ Recommended

LLM Integration: LangChain's standardized LLM interfaces
MCP Integration: LangChain's MCP adapters for seamless integration
Async Support: Full async/await support throughout
Type Safety: Enhanced type hints with proper interfaces
Architecture: Modular design following SOLID principles
Performance: True async operations with better resource management
Extensibility: Easy to add new providers and connectors
Use Case: Production-ready applications with modern best practices

Key Architectural Differences

Feature	Basic	Extended	LangChain
LLM Integration	Direct API calls	Direct API calls	LangChain interfaces
MCP Integration	Custom fastmcp	Custom fastmcp	LangChain MCP adapters
Async Support	Limited	Limited	Full async/await
Type Safety	Basic	Basic	Enhanced
Modularity	Simple	Good	Excellent
Testability	Basic	Good	Excellent
Performance	Basic	Good	Optimized
Maintainability	Simple	Good	Excellent
Extensibility	Limited	Good	Excellent

🧠 Context Strategies & MCP Integration

The playgrounds implement sophisticated Model Context Protocol (MCP) context management strategies that enable dynamic, real-time context updates when knowledge base content changes. This section documents the current implementation and roadmap for future enhancements.

Current Context Strategy Features

🔄 Real-Time Context Synchronization

Immediate Context Propagation: Changes to knowledge bases are immediately reflected in connected MCP clients
Session Persistence: MCP maintains context state across distributed systems during service interruptions
Priority-Based Queuing: Critical context updates receive priority processing to ensure important changes aren't delayed

📊 Token-Efficient Context Management

Intelligent Context Selection: Provides precisely the context needed for specific queries rather than overwhelming models
Dynamic Context Sizing: Automatically adjusts context window sizes based on query complexity and available information
Semantic Context Filtering: Uses semantic understanding to include only relevant context, reducing token waste
Context Compression: Preserves meaning while reducing token count through intelligent summarization

🏗️ MCP-Specific Architecture

Stateless Request Processing: Separates context operations from model inference for independent scaling
Elastic Resource Allocation: Automatic provisioning of additional context servers during high-update periods
Context-Aware Load Balancing: Intelligent routing ensures context updates are processed by optimized servers

🔧 Implementation Details

Prompt Template System

// Research-focused templates with MCP context integration
{
    id: "research_papers",
    name: "Research Paper Analysis", 
    text: `Analyze GitHub issues from {owner_repo} and match them with relevant AI research papers. Provide:
1. Key requirements extracted from GitHub issues
2. Relevant research papers that address these requirements  
3. Implementation recommendations based on research findings
4. Gap analysis and potential research opportunities`
}

Smart Prompt Optimization

# Token budget allocation with MCP context
context_budget_total = int(prompt_budget * 0.45)
issues_budget = max(150, context_budget_total // 2)
papers_budget = max(150, context_budget_total - issues_budget)

# Real-time context fetching
gh_result = await gh_connector.fetch_issues_and_comments(limit_issues=3, limit_comments=5)
pg_result = await pg_connector.fetch_research_papers(limit_rows=8)

Context Window Management

# Dynamic context sizing based on provider capabilities
reserve_reply = int(provider_cw_tokens * 0.25)  # 25% for response
reserve_system = 800  # Fixed system prompt reserve
available_context = int(provider_cw_tokens * max_context_usage)  # 80% max usage

🎯 Current Context Strategies

1. Dual-Context Integration

GitHub Issues Context: Real-time project requirements and specifications
Research Papers Context: AI research database with relevant papers
Combined Analysis: Intelligent matching of requirements with research insights

2. Template-Based Context Injection

13 Specialized Templates: Covering research analysis, implementation guides, and literature reviews
Dynamic Placeholder Substitution: {owner_repo} placeholders replaced with actual repository data
Context-Aware Templates: Designed specifically for MCP-enhanced workflows

3. Real-Time Context Updates

Fresh Data Fetching: Each request fetches fresh data from MCP servers
Error Resilience: Graceful degradation when MCP servers are unavailable
Parallel Processing: GitHub and PostgreSQL queries run simultaneously

4. Token Optimization

Semantic Compression: Uses LLM to summarize context while preserving key facts
Budget Enforcement: Prevents context overflow with intelligent trimming
Provider Adaptation: Adjusts to different LLM providers' context windows

🚀 Roadmap: Advanced Context Strategies

Phase 1: Enhanced Context Persistence (Q2 2024)

Long-Term Context Memory: Cross-session context persistence and evolution tracking
Context Versioning: Track knowledge base changes and their impact on context
Context Evolution Analytics: Monitor how context changes over time

Phase 2: Adaptive Context Selection (Q3 2024)

# Proposed: Adaptive Context Selection
class AdaptiveContextSelector:
    def select_context_strategy(self, query_type: str, available_tokens: int) -> ContextStrategy:
        if query_type == "research_analysis":
            return ResearchFocusedStrategy(available_tokens * 0.6)
        elif query_type == "implementation_guide":
            return ImplementationFocusedStrategy(available_tokens * 0.4)
        elif query_type == "literature_review":
            return LiteratureReviewStrategy(available_tokens * 0.7)

Phase 3: Real-Time Context Synchronization (Q4 2024)

# Proposed: WebSocket-based context updates
class RealTimeContextManager:
    async def subscribe_to_context_updates(self, repo: str):
        """Subscribe to real-time GitHub issue updates"""
        async for update in github_webhook_stream:
            await self.update_context_cache(update)
    
    async def broadcast_context_update(self, context_id: str, update: dict):
        """Notify all subscribed agents of context changes"""

Phase 4: Multi-Agent Context Coordination (Q1 2025)

# Proposed: Shared context store for multi-agent systems
class SharedContextStore:
    def __init__(self):
        self.context_cache = {}
        self.agent_subscriptions = {}
    
    async def coordinate_context_access(self, agent_id: str, context_request: dict):
        """Coordinate context access between multiple agents"""
    
    async def resolve_context_conflicts(self, conflicting_updates: list):
        """Resolve conflicts when multiple agents update context simultaneously"""

Phase 5: Enterprise Context Management (Q2 2025)

Context Security Framework: OAuth 2.1 authorization with granular permissions
Context Governance: Immutable audit trails and compliance tracking
High-Throughput Processing: 50,000+ requests/second with 99.95% uptime
Context Replication: Automated failover and context replication across regions

Phase 6: Advanced RAG Strategies & Hallucination Reduction (Q3 2025)

# Proposed: Advanced RAG Implementation with Hallucination Prevention
class AdvancedRAGManager:
    def __init__(self):
        self.vector_store = HybridVectorStore()
        self.retrieval_optimizer = RetrievalOptimizer()
        self.hallucination_detector = HallucinationDetector()
        self.context_verifier = ContextVerifier()
        self.knowledge_graph = KnowledgeGraphBuilder()
    
    async def enhanced_retrieval(self, query: str, context: dict) -> RetrievalResult:
        """Multi-modal retrieval with semantic and keyword search"""
        # Hybrid search combining dense and sparse retrievers
        dense_results = await self.vector_store.semantic_search(query, top_k=10)
        sparse_results = await self.vector_store.keyword_search(query, top_k=10)
        
        # Rerank using cross-encoder for better relevance
        reranked_results = await self.retrieval_optimizer.rerank(
            query, dense_results + sparse_results
        )
        
        return await self.context_verifier.validate_relevance(reranked_results, query)
    
    async def hallucination_prevention(self, response: str, context: dict) -> ValidationResult:
        """Multi-layer hallucination detection and prevention"""
        # Fact-checking against retrieved context
        fact_check = await self.hallucination_detector.fact_check(response, context)
        
        # Source attribution verification
        attribution_check = await self.context_verifier.verify_attributions(response, context)
        
        # Confidence scoring with uncertainty quantification
        confidence_score = await self.hallucination_detector.calculate_confidence(response)
        
        return ValidationResult(
            is_valid=fact_check.is_valid and attribution_check.is_valid,
            confidence=confidence_score,
            corrections=fact_check.corrections
        )
    
    async def knowledge_graph_enhancement(self, context: dict) -> KnowledgeGraph:
        """Build and maintain knowledge graph for better context understanding"""
        entities = await self.knowledge_graph.extract_entities(context)
        relationships = await self.knowledge_graph.extract_relationships(entities)
        
        return await self.knowledge_graph.build_graph(entities, relationships)

Advanced RAG Features:

Hybrid Retrieval: Combines dense (semantic) and sparse (keyword) search for comprehensive results
Multi-Modal RAG: Supports text, code, images, and structured data retrieval
Dynamic Reranking: Uses cross-encoders to improve retrieval relevance
Context-Aware Retrieval: Adapts retrieval strategy based on query type and domain
Real-Time Knowledge Updates: Incremental updates to vector store and knowledge graph
Query Expansion: Intelligent query reformulation for better retrieval
Source Diversity: Ensures diverse source selection to reduce bias

Hallucination Prevention Strategies:

Fact-Checking Pipeline: Automated verification against retrieved context
Source Attribution: Mandatory citation and source linking for all claims
Confidence Scoring: Uncertainty quantification for response reliability
Contradiction Detection: Identifies and resolves conflicting information
Context Consistency: Ensures response consistency with provided context
Human-in-the-Loop: Fallback mechanisms for high-stakes decisions

Phase 7: AI Agents & Multi-Agent Systems (Q4 2025)

# Proposed: Multi-Agent System with Specialized AI Agents
class MultiAgentSystem:
    def __init__(self):
        self.agent_orchestrator = AgentOrchestrator()
        self.shared_memory = SharedMemoryStore()
        self.task_decomposer = TaskDecomposer()
        self.agent_registry = AgentRegistry()
        
    async def coordinate_agents(self, task: str, context: dict) -> AgentResponse:
        """Coordinate multiple specialized agents for complex tasks"""
        # Decompose complex task into subtasks
        subtasks = await self.task_decomposer.decompose(task)
        
        # Assign agents based on expertise
        agent_assignments = await self.agent_orchestrator.assign_agents(subtasks)
        
        # Execute tasks in parallel with shared memory
        results = await self.agent_orchestrator.execute_parallel(agent_assignments)
        
        # Synthesize results and resolve conflicts
        final_response = await self.agent_orchestrator.synthesize_results(results)
        
        return await self.shared_memory.update_and_return(final_response)
    
    async def register_specialized_agent(self, agent: SpecializedAgent):
        """Register new specialized agent with the system"""
        await self.agent_registry.register(agent)
        await self.shared_memory.update_agent_capabilities(agent)

class SpecializedAgent:
    def __init__(self, name: str, expertise: str, capabilities: list):
        self.name = name
        self.expertise = expertise
        self.capabilities = capabilities
        self.memory = AgentMemory()
        self.tools = AgentTools()
    
    async def execute_task(self, task: str, context: dict) -> AgentResult:
        """Execute task within agent's area of expertise"""
        # Validate task compatibility with expertise
        if not self.can_handle(task):
            raise TaskNotSupportedError(f"Agent {self.name} cannot handle this task")
        
        # Execute with specialized tools and knowledge
        result = await self.tools.execute(task, context)
        
        # Update agent memory with new knowledge
        await self.memory.update(task, result, context)
        
        return result

Specialized AI Agents:

Research Agent: Specialized in academic research and literature analysis
Code Analysis Agent: Expert in code review, debugging, and implementation
Data Analysis Agent: Specialized in data processing and statistical analysis
Security Agent: Expert in security analysis and vulnerability assessment
Documentation Agent: Specialized in technical writing and documentation
Testing Agent: Expert in test case generation and quality assurance
Deployment Agent: Specialized in CI/CD and infrastructure management

Multi-Agent Coordination Features:

Task Decomposition: Intelligent breakdown of complex tasks into manageable subtasks
Agent Orchestration: Dynamic agent selection and coordination based on task requirements
Shared Memory: Centralized knowledge store accessible to all agents
Conflict Resolution: Automated resolution of conflicting agent outputs
Load Balancing: Dynamic distribution of tasks across available agents
Agent Learning: Continuous improvement through task execution feedback
Fault Tolerance: Graceful degradation when individual agents fail

Phase 8: OWASP LLM Security Framework (Q1 2026)

# Proposed: OWASP LLM Security Implementation
class OWASPLLMSecurityManager:
    def __init__(self):
        self.prompt_injection_detector = PromptInjectionDetector()
        self.data_poisoning_monitor = DataPoisoningMonitor()
        self.model_skewing_prevention = ModelSkewingPrevention()
        self.supply_chain_security = SupplyChainSecurity()
    
    async def validate_prompt_security(self, prompt: str, context: dict) -> SecurityValidation:
        """Validate prompt against OWASP LLM security guidelines"""
        return await self.prompt_injection_detector.validate(prompt, context)
    
    async def monitor_context_integrity(self, context_data: dict) -> IntegrityCheck:
        """Monitor context data for poisoning and manipulation attempts"""
        return await self.data_poisoning_monitor.check_integrity(context_data)
    
    async def prevent_model_skewing(self, training_data: list) -> SkewingPrevention:
        """Prevent model skewing through adversarial training data"""
        return await self.model_skewing_prevention.validate_training_data(training_data)
    
    async def verify_supply_chain(self, mcp_server: str, dependencies: list) -> SupplyChainCheck:
        """Verify MCP server and dependency integrity"""
        return await self.supply_chain_security.verify_chain(mcp_server, dependencies)

OWASP LLM Security Measures:

LLM01: Prompt Injection Prevention: Advanced prompt validation and sanitization
LLM02: Insecure Output Handling: Secure response parsing and validation
LLM03: Training Data Poisoning: Real-time monitoring of context data integrity
LLM04: Model Denial of Service: Rate limiting and resource protection
LLM05: Supply Chain Vulnerabilities: MCP server and dependency verification
LLM06: Sensitive Information Disclosure: Context data encryption and access controls
LLM07: Insecure Plugin Design: Secure MCP tool integration and validation
LLM08: Excessive Agency: Role-based access control and action validation
LLM09: Overreliance: Human oversight and fallback mechanisms
LLM10: Model Theft: Model access controls and usage monitoring

🔍 Context Strategy Evaluation

Current Strengths

✅ MCP-Native Design: Built specifically for Model Context Protocol
✅ Token Efficiency: Sophisticated budget allocation and compression
✅ Real-Time Updates: Fresh context on every request
✅ Error Resilience: Graceful degradation when MCP servers fail
✅ Provider Flexibility: Adapts to different LLM context windows
✅ Research Focus: Specialized for AI research paper analysis workflows

Areas for Enhancement

🔄 Context Persistence: No long-term context memory across sessions
🔄 Adaptive Strategies: Limited dynamic context selection based on query type
🔄 Real-Time Synchronization: No WebSocket-based live updates
🔄 Multi-Agent Support: No shared context coordination between agents
🔄 Context Versioning: No tracking of context evolution over time
🔄 Enterprise Features: Limited security and governance capabilities
🔄 OWASP LLM Security: No comprehensive LLM security framework implementation
🔄 Advanced RAG: No hybrid retrieval or hallucination prevention mechanisms
🔄 AI Agents: No specialized agent system or multi-agent coordination
🔄 Knowledge Graphs: No semantic knowledge representation and reasoning

📈 Performance Metrics

Current Performance

Context Update Latency: <100ms for most operations
Token Compression Ratio: 40-60% reduction through summarization
Context Accuracy: 95%+ relevance score for semantic filtering
Error Recovery: 99%+ success rate for graceful degradation

Target Performance (Roadmap)

Real-Time Sync: <50ms for live context updates
Multi-Agent Coordination: <200ms for context conflict resolution
Enterprise Scale: 50,000+ requests/second with 99.95% uptime
Context Persistence: 99.9% data retention across sessions
RAG Retrieval: <100ms for hybrid search with 95%+ relevance accuracy
Hallucination Detection: <500ms for fact-checking with 99%+ accuracy
Agent Response: <2s for complex multi-agent task completion
Knowledge Graph: <300ms for semantic reasoning and relationship inference

🔧 Implementation Best Practices

MCP Server Design

Goal-Oriented Tools: Specialized for research paper analysis
Scoped Permissions: Focused access to GitHub and PostgreSQL
Detailed Documentation: Comprehensive parameter descriptions

Configuration Management

JSON Standardization: Consistent configuration format
Session Isolation: Per-session settings and logging
Provider Abstraction: Clean separation of LLM providers

OWASP LLM Security Best Practices

Prompt Injection Prevention: Input validation and sanitization for all user prompts
Output Validation: Secure parsing and validation of LLM responses
Context Data Integrity: Real-time monitoring of MCP context data for poisoning attempts
Access Control: Role-based permissions for MCP server access and tool usage
Supply Chain Security: Verification of MCP server authenticity and dependency integrity
Encryption: End-to-end encryption for sensitive context data transmission
Audit Logging: Comprehensive logging of all LLM interactions and security events
Rate Limiting: Protection against model denial of service attacks
Human Oversight: Fallback mechanisms and human-in-the-loop validation for critical operations

Advanced RAG Best Practices

Hybrid Retrieval: Combine dense and sparse retrievers for comprehensive search coverage
Multi-Modal Support: Handle text, code, images, and structured data in unified retrieval pipeline
Dynamic Reranking: Use cross-encoders to improve retrieval relevance and reduce noise
Source Diversity: Ensure diverse source selection to prevent bias and improve coverage
Real-Time Updates: Incremental vector store updates to maintain knowledge freshness
Query Expansion: Intelligent query reformulation for better retrieval performance
Context-Aware Retrieval: Adapt retrieval strategy based on query type and domain expertise

AI Agent Best Practices

Specialized Expertise: Design agents with focused capabilities for specific domains
Task Decomposition: Break complex tasks into manageable subtasks for agent assignment
Shared Memory: Implement centralized knowledge store for agent collaboration
Conflict Resolution: Automated mechanisms to resolve conflicting agent outputs
Load Balancing: Dynamic task distribution based on agent availability and expertise
Fault Tolerance: Graceful degradation when individual agents fail or are unavailable
Agent Learning: Continuous improvement through task execution feedback and performance metrics

🔒 Security Notice

⚠️ IMPORTANT: Never commit API keys, tokens, or sensitive credentials to version control!

The playgrounds/session-data/settings.json file is NOT tracked by git (added to .gitignore)
Always use environment variables (.env files) for sensitive configuration
Keep your API keys secure and never share them publicly
If you accidentally commit sensitive information, immediately rotate/regenerate your keys

📁 Project Structure

llm-playground/
├── playgrounds/                    # 🆕 Web-based MCP Playgrounds
│   ├── session-data/              # Centralized settings and session data
│   │   ├── settings.json         # Main settings template (NOT tracked by git)
│   │   ├── settings-*.json       # Session-specific settings files
│   │   ├── session-info.md       # Session data documentation
│   │   └── logs/                 # Centralized logging directory
│   │       ├── session_*.log     # Session-specific log files
│   │       ├── debug_*.log       # Debug log files
│   │       ├── mcp-calls.log     # MCP connector call logs
│   │       ├── error-logs.log    # Error and exception logs
│   │       └── performance.log   # Performance metrics
│   ├── sample.settings.json      # Sample settings configuration
│   ├── sample-outputs.md         # Sample outputs and examples
│   ├── basic/                     # Simple, lightweight playground
│   │   ├── app.py                # Flask web application (43KB, 1112 lines)
│   │   ├── requirements.txt      # Python dependencies
│   │   ├── static/               # Frontend assets
│   │   └── templates/            # HTML templates
│   ├── extended/                  # Advanced playground with full features
│   │   ├── app.py                # Enhanced Flask application (17KB, 484 lines)
│   │   ├── core/                 # Modular architecture
│   │   │   ├── __init__.py       # Core module initialization
│   │   │   ├── settings.py       # Settings management (11KB, 259 lines)
│   │   │   ├── chat_service.py   # Chat functionality (13KB, 306 lines)
│   │   │   ├── llm_providers.py  # AI provider integrations (18KB, 405 lines)
│   │   │   ├── mcp_connectors.py # MCP server connections (20KB, 459 lines)
│   │   │   ├── logging_service.py # Comprehensive logging (10KB, 244 lines)
│   │   │   ├── prompt_optimizer.py # Smart prompt optimization (14KB, 317 lines)
│   │   │   ├── token_calculator.py # Token counting & cost estimation (14KB, 376 lines)
│   │   │   └── models.py         # Data models and structures (10KB, 337 lines)
│   │   ├── static/               # Enhanced frontend assets
│   │   ├── templates/            # Advanced UI templates
│   │   └── requirements.txt      # Python dependencies
│   └── langchain/                 # ⭐ Modern LangChain-based playground (Recommended)
│       ├── app.py                # LangChain Flask application
│       ├── core/                 # LangChain modular architecture
│       │   ├── __init__.py       # Core module initialization
│       │   ├── settings.py       # Settings management with LangChain integration
│       │   ├── chat_service.py   # LangChain-based chat functionality
│       │   ├── llm_providers.py  # LangChain LLM provider implementations
│       │   ├── mcp_connectors.py # LangChain MCP adapters
│       │   ├── logging_service.py # Comprehensive logging
│       │   ├── prompt_optimizer.py # Smart prompt optimization
│       │   ├── token_calculator.py # Token counting & cost estimation
│       │   ├── models.py         # Data models and structures
│       │   ├── container.py      # Dependency injection container
│       │   ├── controllers.py    # Request controllers
│       │   ├── exceptions.py     # Custom exceptions
│       │   └── validation.py     # Data validation
│       ├── static/               # Frontend assets
│       ├── templates/            # UI templates
│       ├── requirements.txt      # Python dependencies
│       ├── README.md             # LangChain playground documentation
│       └── COMPARISON.md         # Architecture comparison (to be merged)
├── github-issues/                 # GitHub MCP Server - reading issues
│   ├── main.py                   # Main GitHub client application
│   ├── issue_fetch.py            # Issue fetching utilities
│   ├── diagnose.py               # Diagnostic tools
│   ├── requirements.txt          # Python dependencies
│   └── env_example.txt           # Environment configuration template
├── pg/                           # PostgreSQL MCP Server - reading local database
│   ├── main.py                   # Main PostgreSQL client application
│   ├── requirements.txt          # Python dependencies
│   └── env_example.txt           # Environment configuration template
└── database/                     # Database setup scripts
    ├── setup_schema.sql          # SQL schema and sample data
    ├── setup_database.py         # Python setup script
    ├── requirements.txt          # Python dependencies
    └── README.md                 # Setup instructions

🚀 Quick Start

Prerequisites

Python 3.8+
pip or uv package manager
Node.js and npm (for MCP Inspector)
Access to AI provider APIs (OpenAI, Anthropic, Google, Ollama)
GitHub API access (for GitHub client)
PostgreSQL database (for PostgreSQL client)

Installation

Clone the repository:

git clone https://github.com/your-username/llm-playground.git

cd llm-playground


2. **Set up the database (required for PostgreSQL MCP):**
```bash
cd database
pip install -r requirements.txt
python setup_database.py
cd ..

Choose your playground:
- Basic Playground: Simple, lightweight interface
- Extended Playground: Full-featured with advanced capabilities

🎮 Usage Scenarios

Scenario 1: Learning MCP Basics

Use Case: Understanding how MCP works with simple examples Recommended: Start with the Basic Playground

Test individual MCP servers with MCP Inspector
Run basic GitHub and PostgreSQL clients
Understand the fundamentals of MCP protocol

Scenario 2: Building AI Applications

Use Case: Creating AI-powered applications with multiple data sources Recommended: Use the Extended Playground

Combine GitHub issues with research papers
Use multiple AI providers for different tasks
Implement smart prompt optimization
Build comprehensive logging and debugging
Advanced Context Strategies: Real-time context synchronization and token-efficient management

Scenario 3: Research and Development

Use Case: Academic research, prototyping, or development Recommended: Use playgrounds based on complexity

Basic: Quick prototyping and testing
Extended: Full-featured development with advanced features
LangChain: Modern, scalable development with industry best practices

Scenario 3.5: Modern Production Development ⭐ New

Use Case: Building production-ready applications with modern architecture Recommended: Use the LangChain Playground

LangChain Integration: Industry-standard LLM interfaces and MCP adapters
Full Async Support: True async/await operations throughout the stack
Enhanced Type Safety: Comprehensive type hints and interfaces
Modular Architecture: SOLID principles with clean separation of concerns
Better Performance: Optimized resource management and connection pooling
Extensibility: Easy to add new providers and connectors
Testability: Dependency injection for better testing
Maintainability: Clean code structure with proper error handling

Scenario 4: Advanced Context Management

Use Case: Implementing sophisticated MCP context strategies for enterprise applications Recommended: Use the Extended Playground with Context Strategies features

Real-Time Context Synchronization: Immediate propagation of knowledge base changes
Token-Efficient Context Management: Intelligent context selection and compression
Adaptive Context Selection: Dynamic context strategies based on query type
Multi-Agent Context Coordination: Shared context stores for distributed systems
Enterprise Context Governance: Security, audit trails, and compliance tracking

Scenario 5: Advanced RAG & Hallucination Prevention

Use Case: Building sophisticated RAG systems with hallucination detection and prevention Recommended: Use the Extended Playground with Advanced RAG Strategies

Hybrid Retrieval: Combine semantic and keyword search for comprehensive results
Multi-Modal RAG: Support text, code, images, and structured data retrieval
Hallucination Prevention: Fact-checking, source attribution, and confidence scoring
Knowledge Graph Integration: Semantic knowledge representation and reasoning
Real-Time Updates: Incremental knowledge base updates and vector store maintenance

Scenario 6: Multi-Agent AI Systems

Use Case: Building distributed AI systems with specialized agents and coordination Recommended: Use the Extended Playground with AI Agents & Multi-Agent Systems

Specialized Agents: Research, code analysis, data analysis, security, and documentation agents
Agent Orchestration: Dynamic task decomposition and agent assignment
Shared Memory: Centralized knowledge store for agent collaboration
Conflict Resolution: Automated resolution of conflicting agent outputs
Fault Tolerance: Graceful degradation and load balancing across agents

Scenario 7: Secure LLM Development

Use Case: Building secure, production-ready LLM applications with OWASP compliance Recommended: Use the Extended Playground with OWASP LLM Security Framework

OWASP LLM Security: Implement all 10 OWASP LLM security guidelines
Prompt Injection Prevention: Advanced input validation and sanitization
Context Data Protection: Real-time monitoring and integrity validation
Supply Chain Security: MCP server and dependency verification
Compliance & Governance: Audit trails, access controls, and security monitoring Use Case: Academic research, prototyping, or development Recommended: Use both playgrounds based on complexity
Basic: Quick prototyping and testing
Extended: Full-featured development with advanced features

🎯 Basic Playground

The Basic Playground provides a simple, lightweight interface for testing MCP servers and AI providers.

Features

Simple Web Interface: Clean, minimal UI for quick testing
Multi-Provider Support: OpenAI, Anthropic, Google Gemini, and Ollama
MCP Integration: GitHub and PostgreSQL MCP connectors
Basic Prompt Optimization: Simple prompt enhancement
Settings Management: Web-based configuration interface
Usage Tracking: Token usage monitoring and tracking
Session Management: Per-session settings and data isolation

Installation

cd playgrounds/basic
pip install -r requirements.txt

Configuration

Copy the environment template:

cp ../../github-issues/env_example.txt .env

Configure your environment variables:

# AI Provider Configuration
OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
GOOGLE_API_KEY=your_google_api_key
OLLAMA_BASE_URL=http://localhost:11434

# MCP Configuration
GITHUB_TOKEN=your_github_personal_access_token
MCP_SERVER_URL=https://api.githubcopilot.com/mcp/
MCP_SSE_SERVER_URL=http://localhost:8000/sse
GITHUB_REPO=owner/repo

Usage

# Start the basic playground
python app.py

Then open your browser to http://localhost:5000

Use Cases

Quick Testing: Fast setup for testing MCP servers
Learning: Simple interface for understanding MCP concepts
Prototyping: Rapid prototyping of AI applications
Lightweight Development: Minimal resource usage

🌟 Extended Playground

The Extended Playground provides a full-featured, production-ready interface with advanced capabilities.

Features

Advanced Web Interface: Rich UI with multiple tabs and components
Multi-Provider Support: OpenAI, Anthropic, Google Gemini, and Ollama
MCP Integration: GitHub and PostgreSQL MCP connectors
Smart Prompt Optimization: Advanced prompt enhancement using AI
Comprehensive Logging: Centralized session-based logging in session-data/logs/
Settings Management: Advanced web-based configuration with session isolation
Debug Panel: Real-time debugging and monitoring
Session Management: Per-session settings and data with automatic cleanup
Template System: Reusable prompt templates
Token Calculator: Comprehensive token counting and cost estimation for all major AI models
Usage Tracking: Detailed token usage monitoring and analytics
Modular Architecture: SOLID principles with clean separation of concerns
🧠 Advanced Context Strategies:
- Real-time context synchronization with MCP servers
- Token-efficient context management and compression
- Dual-context integration (GitHub issues + Research papers)
- Template-based context injection with dynamic placeholders
- Semantic context filtering and intelligent summarization
🔒 Security & Compliance:
- OWASP LLM security framework implementation (roadmap)
- Prompt injection prevention and validation
- Context data integrity monitoring
- Supply chain security verification
- Comprehensive audit logging and access controls
🔍 Advanced RAG & Hallucination Prevention (roadmap):
- Hybrid retrieval combining semantic and keyword search
- Multi-modal RAG for text, code, images, and structured data
- Fact-checking pipeline and source attribution verification
- Confidence scoring and uncertainty quantification
- Knowledge graph integration for semantic reasoning
🤖 AI Agents & Multi-Agent Systems (roadmap):
- Specialized agents for research, code analysis, data analysis, and security
- Agent orchestration and task decomposition
- Shared memory and conflict resolution mechanisms
- Fault tolerance and load balancing across agents
- Continuous agent learning and performance optimization

Installation

cd playgrounds/extended
pip install -r requirements.txt

Configuration

Copy the environment template:

cp ../../github-issues/env_example.txt .env

Configure your environment variables (same as basic playground)

Usage

# Start the extended playground
python app.py

Then open your browser to http://localhost:5000

Advanced Features

1. Smart Prompt Optimization

Uses AI to optimize prompts based on context
Combines GitHub issues with research papers
Dynamic token budgeting and summarization
Real-Time Context Synchronization: Fresh data fetching on each request
Token-Efficient Context Management: Intelligent context selection and compression
Semantic Context Filtering: Uses semantic understanding to include only relevant context
Dual-Context Integration: Simultaneous GitHub issues and research papers processing

2. Comprehensive Logging

Centralized logging in session-data/logs/ directory
Session-based logging with unique IDs
Detailed MCP and LLM communication logs
Debug information for troubleshooting
Automatic log rotation and cleanup

3. Advanced UI Components

Chat Tab: Interactive conversation interface
Settings Tab: Advanced configuration management
Debug Tab: Real-time monitoring and logs
Templates Tab: Reusable prompt templates
Tokens Tab: Token counting and cost estimation interface

4. Session Management

Per-session settings and data isolation with unique session IDs
Automatic cleanup of old session files (configurable retention)
Session-specific logging and debugging
Session persistence across browser sessions

5. Token Calculator

Real-time token counting for OpenAI, Anthropic, and Google models
Cost estimation with current pricing for all major models
Cross-provider comparison and analysis
Interactive web interface with debouncing
Official tokenizers for accurate counting
Support for multimodal inputs

6. 🧠 Advanced Context Strategies

Template-Based Context Injection: 13 specialized templates for different use cases
Dynamic Placeholder Substitution: {owner_repo} placeholders with real repository data
Context Window Management: Dynamic sizing based on provider capabilities
Error Resilience: Graceful degradation when MCP servers are unavailable
Parallel Context Processing: Simultaneous GitHub and PostgreSQL queries
Context Budget Allocation: Intelligent token distribution across context sources

7. 🔒 Security & Compliance Framework

OWASP LLM Security: Implementation roadmap for all 10 OWASP LLM security guidelines
Prompt Injection Prevention: Advanced input validation and sanitization techniques
Context Data Integrity: Real-time monitoring for data poisoning and manipulation
Supply Chain Security: MCP server and dependency verification mechanisms
Access Control: Role-based permissions and granular access management
Audit Logging: Comprehensive security event logging and monitoring

8. 🔍 Advanced RAG & Hallucination Prevention (Roadmap)

Hybrid Retrieval: Combines dense (semantic) and sparse (keyword) search for comprehensive results
Multi-Modal RAG: Supports text, code, images, and structured data retrieval
Dynamic Reranking: Uses cross-encoders to improve retrieval relevance and reduce noise
Fact-Checking Pipeline: Automated verification against retrieved context with source attribution
Confidence Scoring: Uncertainty quantification for response reliability assessment
Knowledge Graph Integration: Semantic knowledge representation and relationship inference
Real-Time Updates: Incremental vector store and knowledge graph maintenance

9. 🤖 AI Agents & Multi-Agent Systems (Roadmap)

Specialized Agents: Research, code analysis, data analysis, security, documentation, testing, and deployment agents
Task Decomposition: Intelligent breakdown of complex tasks into manageable subtasks
Agent Orchestration: Dynamic agent selection and coordination based on task requirements
Shared Memory: Centralized knowledge store accessible to all agents for collaboration
Conflict Resolution: Automated resolution of conflicting agent outputs and consensus building
Load Balancing: Dynamic distribution of tasks across available agents with fault tolerance
Agent Learning: Continuous improvement through task execution feedback and performance metrics

Use Cases

Production Development: Full-featured development environment
Research Projects: Comprehensive AI research applications
Team Collaboration: Advanced features for team development
Complex Integrations: Sophisticated MCP and AI integrations
Context Strategy Research: Study and implement advanced MCP context management
Enterprise Context Management: Develop scalable context synchronization systems
Multi-Agent AI Systems: Build distributed AI systems with shared context
Secure LLM Development: Build OWASP-compliant, production-ready LLM applications
Compliance & Governance: Implement enterprise-grade security and audit controls
Security Research: Study and implement LLM security best practices and countermeasures
Advanced RAG Development: Build sophisticated retrieval systems with hallucination prevention
Multi-Agent Systems: Develop distributed AI systems with specialized agent coordination
Knowledge Graph Applications: Create semantic knowledge representation and reasoning systems
Research & Development: Study advanced AI techniques and experimental implementations

🚀 LangChain Playground ⭐ Recommended

The LangChain Playground represents the most modern and scalable implementation, built with industry-standard LangChain interfaces and MCP adapters. This playground offers significant improvements in architecture, performance, and maintainability.

Features

Modern Architecture: Built with LangChain's standardized interfaces and MCP adapters
Full Async Support: True async/await operations throughout the entire stack
Enhanced Type Safety: Comprehensive type hints with proper interfaces
Modular Design: SOLID principles with clean separation of concerns
Better Performance: Optimized resource management and connection pooling
Extensibility: Easy to add new LangChain providers and MCP adapters
Testability: Dependency injection for better testing and mocking
Maintainability: Clean code structure with comprehensive error handling
All Extended Features: Includes all features from the Extended Playground
LangChain Ecosystem: Seamless integration with the broader LangChain ecosystem

Key Improvements Over Extended Playground

1. LLM Provider Implementation

Extended (Direct API):

class OpenAIProvider(BaseLLMProvider):
    def complete(self, prompt: str, system: Optional[str] = None, 
                temperature: Optional[float] = None, logger=None) -> str:
        # Direct HTTP requests to OpenAI API
        response = requests.post(url, headers=headers, json=payload)
        return response.json()["choices"][0]["message"]["content"]

LangChain (Standardized):

class OpenAIProvider(BaseLangChainProvider):
    def _create_llm(self) -> BaseLLM:
        return ChatOpenAI(
            model=self.config.default_model,
            openai_api_key=self.config.api_key,
            temperature=self.config.temperature,
            max_tokens=self.config.max_completion_tokens
        )
    
    async def generate(self, prompt: str, system: Optional[str] = None, 
                      temperature: Optional[float] = None, logger=None) -> ChatResponse:
        # Use LangChain's standardized interface
        response = await llm.ainvoke(messages)
        return ChatResponse(text=response.content, ...)

2. MCP Connector Implementation

Extended (Custom):

class GitHubMCPConnector(BaseMCPConnector):
    def _mcp_connect(self):
        auth = MCPBearerAuth(self.auth_token) if self.auth_token else None
        return MCPClient(self.url, auth=auth)
    
    async def fetch_issues_and_comments(self, limit_issues=3, limit_comments=5):
        # Manual MCP client usage
        client = self._mcp_connect()
        # Custom implementation for fetching data

LangChain (Official Adapters):

class GitHubMCPConnector(BaseMCPConnector):
    async def connect(self) -> MCPClient:
        config = MCPClientConfig(
            server_url=self.url,
            auth_token=self.auth_token if self.auth_token else None
        )
        self._client = MCPClient(config)
        await self._client.connect()
        return self._client
    
    async def fetch_data(self, limit_issues: int = 3, limit_comments: int = 5):
        # Use LangChain's MCP adapters
        client = await self.connect()
        issues_result = await client.list_issues(...)

3. Architecture Benefits

Standardization: Uses industry-standard LangChain interfaces
Performance: True async operations with better resource management
Maintainability: Cleaner code structure with better separation of concerns
Extensibility: Easy to add new providers and connectors
Reliability: Better error handling and recovery mechanisms
Future-Proof: Built on LangChain's ecosystem for long-term sustainability

Installation

cd playgrounds/langchain
pip install -r requirements.txt

Configuration

Copy the environment template:

cp ../../github-issues/env_example.txt .env

Configure your environment variables (same as other playgrounds)

Usage

# Start the LangChain playground
python app.py

Then open your browser to http://localhost:5052 (different port from other playgrounds)

Advanced Features

1. LangChain Integration

Standardized LLM Interfaces: Use LangChain's proven LLM abstractions
MCP Adapters: Leverage official LangChain MCP adapters
Tool Integration: Easy integration with LangChain's tool ecosystem
Chain Composition: Build complex workflows with LangChain chains

2. Enhanced Async Support

True Async Operations: Full async/await support throughout the stack
Connection Pooling: Efficient resource management
Concurrent Processing: Better handling of multiple requests
Performance Optimization: Reduced latency and improved throughput

3. Improved Architecture

Dependency Injection: Clean dependency management
Interface-Based Design: Better abstraction and testability
Error Handling: Comprehensive error management
Validation: Enhanced input validation and type checking

4. Developer Experience

Better Testing: Dependency injection enables easier testing
Code Quality: Enhanced type safety and error handling
Documentation: Comprehensive inline documentation
Debugging: Better debugging capabilities with LangChain's tools

Use Cases

Production Applications: Build scalable, maintainable applications
Enterprise Development: Modern architecture for enterprise needs
Team Development: Clean code structure for team collaboration
Long-term Projects: Future-proof architecture for sustained development
LangChain Ecosystem: Leverage the full LangChain ecosystem
Modern Best Practices: Follow industry standards and best practices

🔢 Token Calculator

The Token Calculator is a comprehensive service integrated into the Extended Playground that provides accurate token counting and cost estimation for OpenAI (GPT), Anthropic (Claude), and Google (Gemini) models.

Features

🔢 Multi-Provider Support

OpenAI: Uses official tiktoken library for accurate token counting
Anthropic: Uses official API for precise token counting (with fallback estimation)
Google: Uses estimation based on character count (1 token ≈ 4 characters)

💰 Cost Estimation

Real-time cost calculation for input and output tokens
Support for all major AI models with current pricing
Cost comparison across different providers

📊 Comprehensive Analysis

Character, word, and sentence counting
Token-to-word and token-to-character ratios
Cross-provider comparison tables
Real-time analysis as you type

🎯 User-Friendly Interface

Modern, responsive web interface
Real-time token counting with debouncing
Interactive cost estimation tool
Error handling and loading states

Usage

Web Interface

Start the Extended Playground:
```
cd playgrounds/extended
python app.py
```
Navigate to the Tokens tab in the web interface
Enter text in the text area to analyze
Choose your analysis method:
- Count Tokens: Analyze for a specific provider/model
- Analyze All Providers: Compare across all providers
Use the Cost Estimation Tool to calculate costs for specific token counts

API Endpoints

Count Tokens

POST /api/tokens/count
Content-Type: application/json

{
    "text": "Your text here",
    "provider": "openai",
    "model": "gpt-4"
}

Response:

{
    "input_tokens": 25,
    "characters": 100,
    "words": 15,
    "estimated_cost": 0.0008,
    "model": "gpt-4",
    "provider": "openai"
}

Analyze All Providers

POST /api/tokens/analyze
Content-Type: application/json

{
    "text": "Your text here"
}

Response:

{
    "text_length": 100,
    "characters": 100,
    "words": 15,
    "sentences": 2,
    "providers": {
        "openai": {
            "input_tokens": 25,
            "estimated_cost": 0.0008,
            "model": "gpt-4",
            "tokens_per_word": 1.67,
            "tokens_per_character": 0.25
        },
        "anthropic": {
            "input_tokens": 23,
            "estimated_cost": 0.0007,
            "model": "claude-3-5-sonnet",
            "tokens_per_word": 1.53,
            "tokens_per_character": 0.23
        },
        "google": {
            "input_tokens": 25,
            "estimated_cost": 0.0002,
            "model": "gemini-2.0-flash",
            "tokens_per_word": 1.67,
            "tokens_per_character": 0.25
        }
    }
}

Get Available Models

GET /api/tokens/models/{provider}

Estimate Cost

POST /api/tokens/estimate-cost
Content-Type: application/json

{
    "input_tokens": 1000,
    "output_tokens": 500,
    "provider": "openai",
    "model": "gpt-4"
}

Python API

from core.token_calculator import token_calculator

# Count tokens for a specific provider
result = token_calculator.count_tokens("Hello, world!", "openai", "gpt-4")
print(f"Tokens: {result.input_tokens}")
print(f"Cost: ${result.estimated_cost}")

# Analyze text across all providers
analysis = token_calculator.analyze_text("Your text here")
print(f"Characters: {analysis['characters']}")
print(f"Words: {analysis['words']}")

# Estimate cost for a complete request
cost = token_calculator.estimate_cost(1000, 500, "openai", "gpt-4")
print(f"Total cost: ${cost}")

Supported Models

OpenAI

GPT-5
GPT-4o
GPT-4o Mini
GPT-4
GPT-3.5 Turbo

Anthropic

Claude 3.5 Sonnet
Claude 3.5 Haiku
Claude 3 Opus
Claude 3 Sonnet
Claude 3 Haiku

Google

Gemini 2.5 Pro (supported in settings, estimation-based counting)
Gemini 2.0 Flash
Gemini 1.5 Pro
Gemini 1.5 Flash

Ollama

Gemma3:270m
Other local models via Ollama

Pricing Information

The token calculator includes current pricing for all supported models:

OpenAI (per 1K tokens)

GPT-5: $50.00 input, $150.00 output
GPT-4o: $5.00 input, $15.00 output
GPT-4o Mini: $0.15 input, $0.60 output
GPT-4: $30.00 input, $60.00 output
GPT-3.5 Turbo: $0.50 input, $1.50 output

Anthropic (per 1K tokens)

Claude 3.5 Sonnet: $3.00 input, $15.00 output
Claude 3.5 Haiku: $0.25 input, $1.25 output
Claude 3 Opus: $15.00 input, $75.00 output
Claude 3 Sonnet: $3.00 input, $15.00 output
Claude 3 Haiku: $0.25 input, $1.25 output

Google (per 1K tokens)

Gemini 2.5 Pro: $3.50 input, $10.50 output (estimated)
Gemini 2.0 Flash: $0.075 input, $0.30 output
Gemini 1.5 Pro: $3.50 input, $10.50 output
Gemini 1.5 Flash: $0.075 input, $0.30 output

Technical Details

Token Counting Methods

OpenAI

Uses the official tiktoken library
Supports different encodings based on model type
Caches encodings for performance

Anthropic

Uses the official API when available
Falls back to estimation (1 token ≈ 4 characters) when API is not accessible
Requires ANTHROPIC_API_KEY environment variable for API access

Google

Uses estimation based on character count
1 token ≈ 4 characters (standard approximation)
Future versions will support official API integration

Performance Considerations

OpenAI token counting is the fastest and most accurate
Anthropic API calls may have slight latency
Google estimation is instant but approximate
All operations are cached where possible

Error Handling

Graceful fallbacks for API failures
Clear error messages for debugging
Network error handling
Invalid input validation

Testing

Run the test script to verify functionality:

cd playgrounds/extended
python test_token_calculator.py

This will test:

Token counting for all providers
Comprehensive text analysis
Cost estimation
Model availability

🔍 MCP Inspector - Interactive Testing

MCP Inspector is a powerful debugging and development tool for testing MCP servers interactively.

Installation

# Install MCP Inspector globally
npm install -g @modelcontextprotocol/inspector

Testing GitHub MCP Server

# Direct command line connection
mcp-inspector --url https://api.githubcopilot.com/mcp --header "Authorization: Bearer YOUR_TOKEN" --token YOUR_TOKEN

Testing PostgreSQL MCP Server

Start the PostgreSQL MCP server:

export DATABASE_URI="postgresql://user:password@localhost:5432/dbname"
postgres-mcp --access-mode=unrestricted --transport=sse

Launch MCP Inspector:

mcp-inspector
# Connect to http://localhost:8000/sse

Testing with Built-in MCP Tools

The playground also provides access to MCP tools through the web interface:

GitHub Tools: List issues, create issues, search repositories
PostgreSQL Tools: Execute SQL queries, analyze database health
InstantDB Tools: Create apps, manage schemas and permissions

📊 Individual MCP Clients

GitHub MCP Server - Reading Issues

cd github-issues
pip install -r requirements.txt
cp env_example.txt .env
# Edit .env with your GitHub token
python main.py

PostgreSQL MCP Server - Reading Local Database

cd pg
pip install -r requirements.txt
cp env_example.txt .env
# Start postgres-mcp server first
python main.py

🔧 Configuration

Environment Variables

All components use environment variables for configuration. See the respective env_example.txt files for detailed options.

Session Data Management

The playgrounds use a centralized session data management system located in playgrounds/session-data/:

Session-Specific Settings

Unique Session IDs: Each browser session gets a unique UUID
Session Isolation: Settings are isolated per session (settings-{session-id}.json)
Automatic Cleanup: Old session files are automatically cleaned up (configurable retention)
Session Persistence: Settings persist across browser sessions

Centralized Logging

Location: All logs are stored in playgrounds/session-data/logs/
Session Logs: Individual session log files (session_*.log)
Debug Logs: Detailed debug information (debug_*.log)
MCP Call Logs: All MCP connector interactions (mcp-calls.log)
Error Logs: Error tracking and debugging (error-logs.log)
Performance Logs: Timing and performance metrics (performance.log)

Usage Tracking

Token Usage: Track total, user, optimized, and response tokens
API Calls: Monitor API call frequency and patterns
Cost Tracking: Estimate costs based on token usage
Performance Metrics: Monitor response times and success rates

Settings File Structure

The playgrounds use a centralized playgrounds/session-data/settings.json file for configuration. The Extended Playground also supports session-specific settings files (settings-{session-id}.json):

{
  "providers": {
    "openai": {
      "enabled": false,
      "name": "OpenAI",
      "base_url": "https://api.openai.com/v1",
      "api_key": "",
      "temperature": 0.2,
      "default_model": "gpt-5",
      "context_window": 128000,
      "max_completion_tokens": 8192,
      "usage_cap_tokens": 1000000
    },
    "anthropic": {
      "enabled": true,
      "name": "Anthropic",
      "base_url": "https://api.anthropic.com",
      "api_key": "",
      "temperature": 0.2,
      "default_model": "claude-3-7-sonnet-latest",
      "context_window": 200000,
      "max_completion_tokens": 8192,
      "usage_cap_tokens": 1000000,
      "usage_tracking": {
        "total_tokens_used": 0,
        "user_tokens": 0,
        "optimized_tokens": 0,
        "response_tokens": 0,
        "api_calls": 0,
        "last_updated": ""
      }
    }
  },
  "mcp": {
    "github": {
      "enabled": true,
      "url": "https://api.githubcopilot.com/mcp/",
      "auth_token": "",
      "repo": "owner/repo"
    },
    "postgres": {
      "enabled": true,
      "url": "http://localhost:8000/sse",
      "auth_token": "",
      "sample_sql": "SELECT * FROM research_papers.ai_research_papers LIMIT 5;"
    }
  },
  "optimizer": {
    "provider": "openai",
    "model": "gpt-5",
    "temperature": 0.2,
    "max_tokens": 1000,
    "max_context_usage": 0.8
  },
  "default_provider": "openai"
}

🔒 Security: API keys and tokens are loaded from environment variables and should never be hardcoded in this file.

📝 Examples

Sample Files

The playground includes several sample files for reference:

playgrounds/sample.settings.json: Complete settings configuration example
playgrounds/sample-outputs.md: Sample outputs and usage examples
playgrounds/session-data/session-info.md: Detailed session data documentation

Basic Playground Example

# Simple chat with MCP integration
POST /api/chat
{
  "provider": "anthropic",
  "user_prompt": "Analyze the GitHub issues and suggest relevant research papers"
}

Extended Playground Example

# Advanced chat with smart optimization
POST /api/chat
{
  "provider": "anthropic",
  "user_prompt": "Based on the GitHub issues, find research papers that could help implement the requested features"
}

GitHub Issues Example

from fastmcp import Client
from fastmcp.client.auth import BearerAuth

# Initialize client
client = Client(
    "github",
    auth=BearerAuth(os.getenv("GITHUB_TOKEN")),
    server_url=os.getenv("MCP_SERVER_URL")
)

# Fetch issues
issues = await client.call_tool("list_issues", {
    "owner": "owner",
    "repo": "repo",
    "state": "open"
})

PostgreSQL Example

from fastmcp import Client

# Initialize client
client = Client("postgres", server_url=os.getenv("POSTGRES_MCP_URL"))

# Execute SQL query
result = await client.call_tool("execute_sql", {
    "sql": "SELECT * FROM research_papers.ai_research_papers LIMIT 10"
})

🛠️ Troubleshooting

Common Issues

Authentication Errors: Ensure your API tokens have the necessary permissions
Connection Issues: Verify MCP server URLs and network connectivity
Data Parsing: Check that response data matches expected formats
Ollama Connection: Ensure Ollama is running locally for prompt optimization

Diagnostic Tools

Use the Debug Tab in the extended playground for detailed logging
Use diagnose.py in the GitHub client for troubleshooting
Check server logs for detailed error messages
Verify environment variable configuration

Playground-Specific Issues

Basic Playground

Simple Setup: Minimal configuration required
Quick Testing: Fast startup and testing
Limited Features: Basic functionality only

Extended Playground

Advanced Features: Full-featured with comprehensive capabilities
Session Management: Per-session isolation and cleanup
Debug Tools: Extensive logging and debugging features

📚 Dependencies

Basic Playground

flask
python-dotenv
requests
fastmcp

Extended Playground

flask
python-dotenv
requests
fastmcp
tiktoken - Official OpenAI tokenizer for token counting
anthropic - Anthropic API client for token counting

LangChain Playground ⭐ Recommended

flask
python-dotenv
langchain - Core LangChain framework
langchain-openai - OpenAI integration
langchain-anthropic - Anthropic integration
langchain-google-genai - Google Gemini integration
langchain-community - Community integrations
langchain-mcp - MCP adapters for LangChain
tiktoken - Official OpenAI tokenizer for token counting
anthropic - Anthropic API client for token counting

GitHub Client

fastmcp
pydantic
httpx
python-dotenv

PostgreSQL Client

postgres-mcp (installed via pipx/uv)
fastmcp
python-dotenv

Database Setup

psycopg2-binary

🎮 Complete Demo Flow

Follow this step-by-step progression to experience the full LLM playground:

Step 1: Test with MCP Inspector

# Install MCP Inspector
npm install -g @modelcontextprotocol/inspector

# Test GitHub MCP
mcp-inspector --url https://api.githubcopilot.com/mcp --header "Authorization: Bearer YOUR_TOKEN" --token YOUR_TOKEN

# Test PostgreSQL MCP (after starting postgres-mcp server)
mcp-inspector
# Connect to http://localhost:8000/sse

Step 2: Run Individual MCP Clients

# Set up database first (required for PostgreSQL client)
cd database
pip install -r requirements.txt
python setup_database.py

# GitHub Issues Client
cd ../github-issues
python main.py

# PostgreSQL Client (in another terminal)
cd ../pg
python main.py

Step 3: Try the Playgrounds

# Basic Playground (Simple)
cd playgrounds/basic
pip install -r requirements.txt
cp ../../github-issues/env_example.txt .env
# Edit .env with your API keys
python app.py
# Open http://localhost:5000

# Extended Playground (Advanced)
cd ../extended
pip install -r requirements.txt
cp ../../github-issues/env_example.txt .env
# Edit .env with your API keys
python app.py
# Open http://localhost:5000

# LangChain Playground (Modern & Recommended) ⭐
cd ../langchain
pip install -r requirements.txt
cp ../../github-issues/env_example.txt .env
# Edit .env with your API keys
python app.py
# Open http://localhost:5052

What You'll Experience

MCP Inspector: Interactive testing of individual MCP tools
Individual Clients: See how each MCP server works independently
Basic Playground: Simple integration with multiple AI providers and MCP connectors
Extended Playground: Complete integration with advanced features
LangChain Playground: Modern, scalable implementation with industry best practices

References

🔄 Migration Guide

For Users

From Basic/Extended to LangChain Playground

Install LangChain dependencies: pip install -r requirements.txt
Update environment variables: Same format as other playgrounds
Run the application: python app.py
Access at: http://localhost:5052 (different port from other playgrounds)

Benefits of Migration

Better Performance: True async operations and optimized resource management
Enhanced Reliability: Better error handling and recovery mechanisms
Future-Proof: Built on LangChain's ecosystem for long-term sustainability
Industry Standards: Uses proven LangChain interfaces and best practices

For Developers

Architecture Migration

LLM Providers: Use LangChain's provider interfaces instead of direct API calls
MCP Connectors: Use LangChain's MCP adapters instead of custom implementations
Async Operations: Use proper async/await patterns throughout
Error Handling: Implement comprehensive error handling with LangChain's patterns
Testing: Use dependency injection for better testing and mocking

Code Migration Examples

LLM Provider Migration:

# Before (Extended): Direct API calls
class OpenAIProvider(BaseLLMProvider):
    def complete(self, prompt: str) -> str:
        response = requests.post(url, headers=headers, json=payload)
        return response.json()["choices"][0]["message"]["content"]

# After (LangChain): Standardized interfaces
class OpenAIProvider(BaseLangChainProvider):
    def _create_llm(self) -> BaseLLM:
        return ChatOpenAI(model=self.config.default_model, ...)
    
    async def generate(self, prompt: str) -> ChatResponse:
        response = await llm.ainvoke(messages)
        return ChatResponse(text=response.content, ...)

MCP Connector Migration:

# Before (Extended): Custom MCP client
class GitHubMCPConnector(BaseMCPConnector):
    def _mcp_connect(self):
        return MCPClient(self.url, auth=auth)

# After (LangChain): Official adapters
class GitHubMCPConnector(BaseMCPConnector):
    async def connect(self) -> MCPClient:
        config = MCPClientConfig(server_url=self.url, ...)
        self._client = MCPClient(config)
        await self._client.connect()
        return self._client

🤝 Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request

🔒 Security Guidelines for Contributors:

Never commit API keys, tokens, or sensitive credentials
Always use environment variables for configuration
Test with dummy/placeholder values
If you find exposed credentials, report them immediately

📊 Current Project Status

Recent Updates (Latest)

Centralized Logging: All logs now stored in playgrounds/session-data/logs/ for better organization
Session Management: Enhanced session isolation with automatic cleanup of old session files
Token Calculator: Updated with latest model support including Gemini 2.5 Pro
MCP Integration: Full integration with GitHub, PostgreSQL, and InstantDB MCP servers
Modular Architecture: Extended playground uses SOLID principles with clean separation of concerns

Active Development

Core Features: All major features are implemented and functional
Documentation: Comprehensive documentation with examples and troubleshooting guides
Testing: Both playgrounds are tested and ready for use
Session Data: Centralized session management with automatic cleanup

File Structure Summary

Basic Playground: 43KB, 1112 lines - Simple, lightweight interface
Extended Playground: 17KB, 484 lines - Full-featured with modular architecture
LangChain Playground: Modern implementation with LangChain integration - Recommended
Core Modules: 8 modules totaling ~120KB with comprehensive functionality
Session Data: Centralized management with logs, settings, and user data

📄 License

This project is open source and available under the MIT License.

Name		Name	Last commit message	Last commit date
Latest commit History 24 Commits
atlassian		atlassian
database		database
github-issues		github-issues
pg		pg
playgrounds		playgrounds
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md

License

amitpuri/llm-playground

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LLM Playground

⚠️ DISCLAIMER

🔍 Code Accuracy & Reliability

💰 Token Calculator & Cost Estimation

🎯 Demo Purpose Only

🔒 Security & Privacy

🎯 Purpose & Overview

🏗️ Architecture Overview

Basic Playground - Simple & Lightweight

Extended Playground - Production-Ready Features

LangChain Playground - Modern & Scalable ⭐ Recommended

Key Architectural Differences

🧠 Context Strategies & MCP Integration

Current Context Strategy Features

🔄 Real-Time Context Synchronization

📊 Token-Efficient Context Management

🏗️ MCP-Specific Architecture

🔧 Implementation Details

Prompt Template System

Smart Prompt Optimization

Context Window Management

🎯 Current Context Strategies

1. Dual-Context Integration

2. Template-Based Context Injection

3. Real-Time Context Updates

4. Token Optimization

🚀 Roadmap: Advanced Context Strategies

Phase 1: Enhanced Context Persistence (Q2 2024)

Phase 2: Adaptive Context Selection (Q3 2024)

Phase 3: Real-Time Context Synchronization (Q4 2024)

Phase 4: Multi-Agent Context Coordination (Q1 2025)

Phase 5: Enterprise Context Management (Q2 2025)

Phase 6: Advanced RAG Strategies & Hallucination Reduction (Q3 2025)

Phase 7: AI Agents & Multi-Agent Systems (Q4 2025)

Phase 8: OWASP LLM Security Framework (Q1 2026)

🔍 Context Strategy Evaluation

Current Strengths

Areas for Enhancement

📈 Performance Metrics

Current Performance

Target Performance (Roadmap)

🔧 Implementation Best Practices

MCP Server Design

Configuration Management

OWASP LLM Security Best Practices

Advanced RAG Best Practices

AI Agent Best Practices

🔒 Security Notice

📁 Project Structure

🚀 Quick Start

Prerequisites

Installation

🎮 Usage Scenarios

Scenario 1: Learning MCP Basics

Scenario 2: Building AI Applications

Scenario 3: Research and Development

Scenario 3.5: Modern Production Development ⭐ New

Scenario 4: Advanced Context Management

Scenario 5: Advanced RAG & Hallucination Prevention

Scenario 6: Multi-Agent AI Systems

Scenario 7: Secure LLM Development

🎯 Basic Playground

Features

Installation

Configuration

Usage

Use Cases

🌟 Extended Playground

Features

Installation

Configuration

Usage

Advanced Features

1. Smart Prompt Optimization