Juspay Agent Framework (JAF)

A purely functional agent framework built on immutable state, type safety, and composable policies. JAF enables building production-ready AI agent systems with built-in security, observability, and error handling.

📚 Read the Documentation

🎯 Core Philosophy

• Immutability: All core data structures are deeply readonly
• Pure Functions: Core logic expressed as pure, predictable functions
• Effects at the Edge: Side effects isolated in Provider modules
• Composition over Configuration: Build complex behavior by composing simple functions
• Type-Safe by Design: Leverages TypeScript's advanced features for compile-time safety
• Functional Composition: Complex behaviors built through function composition, not inheritance or mutation

🚀 Quick Start

Installation

# Install from npm
npm install @xynehq/jaf

# Or using yarn
yarn add @xynehq/jaf

# Or using pnpm
pnpm add @xynehq/jaf

Development Setup

# Clone the repository
git clone https://github.com/xynehq/jaf.git
cd jaf

# Install dependencies
npm install

# Build the project
npm run build

# Run tests
npm test

📁 Project Structure

src/
├── core/           # Core framework types and engine
│   ├── engine.ts   # Main execution engine
│   ├── errors.ts   # Error handling and types
│   ├── tool-results.ts # Tool execution results
│   ├── tracing.ts  # Event tracing system
│   └── types.ts    # Core type definitions
├── memory/         # Memory providers for conversation persistence
│   ├── factory.ts  # Memory provider factory
│   ├── types.ts    # Memory system types
│   └── providers/
│       ├── in-memory.ts  # In-memory provider
│       ├── postgres.ts   # PostgreSQL provider
│       └── redis.ts      # Redis provider
├── providers/      # External integrations
│   ├── mcp.ts      # Model Context Protocol integration
│   └── model.ts    # LLM provider integrations
├── policies/       # Validation and security policies
│   ├── handoff.ts  # Agent handoff policies
│   └── validation.ts # Input/output validation
├── server/         # HTTP server implementation
│   ├── index.ts    # Server entry point
│   ├── server.ts   # Express server setup
│   └── types.ts    # Server-specific types
├── __tests__/      # Test suite
│   ├── engine.test.ts     # Engine tests
│   └── validation.test.ts # Validation tests
└── index.ts        # Main framework exports
examples/
├── rag-demo/       # Vertex AI RAG integration demo
│   ├── index.ts    # Demo entry point
│   ├── rag-agent.ts # RAG agent implementation
│   └── rag-tool.ts  # RAG tool implementation
└── server-demo/    # Development server demo
    └── index.ts    # Server demo entry point
docs/               # Documentation
├── getting-started.md
├── core-concepts.md
├── api-reference.md
├── tools.md
├── memory-system.md
├── model-providers.md
├── server-api.md
├── examples.md
├── deployment.md
└── troubleshooting.md

🏗️ Key Components

Core Types

import { z } from 'zod';
import { Agent, Tool, RunState, run } from '@xynehq/jaf';

// Define your context type
type MyContext = {
  userId: string;
  permissions: string[];
};

// Create a tool
const calculatorTool: Tool<{ expression: string }, MyContext> = {
  schema: {
    name: "calculate",
    description: "Perform mathematical calculations",
    parameters: z.object({
      expression: z.string().describe("Math expression to evaluate")
    }),
  },
  execute: async (args) => {
    const result = eval(args.expression); // Don't do this in production!
    return `${args.expression} = ${result}`;
  },
};

// Define an agent
const mathAgent: Agent<MyContext, string> = {
  name: 'MathTutor',
  instructions: () => 'You are a helpful math tutor',
  tools: [calculatorTool],
};

Running the Framework

import { run, makeLiteLLMProvider } from '@xynehq/jaf';

const modelProvider = makeLiteLLMProvider('http://localhost:4000');
const agentRegistry = new Map([['MathTutor', mathAgent]]);

const config = {
  agentRegistry,
  modelProvider,
  maxTurns: 10,
  onEvent: (event) => console.log(event), // Real-time tracing
};

const initialState = {
  runId: generateRunId(),
  traceId: generateTraceId(),
  messages: [{ role: 'user', content: 'What is 2 + 2?' }],
  currentAgentName: 'MathTutor',
  context: { userId: 'user123', permissions: ['user'] },
  turnCount: 0,
};

const result = await run(initialState, config);

🔄 Function Composition

JAF emphasizes function composition to build complex behaviors from simple, reusable functions:

Composing Tools

import { createFunctionTool, composeTool, withRetry, withCache } from '@xynehq/jaf';

// Simple base tools
const fetchWeatherTool = createFunctionTool({
  name: 'fetch_weather',
  description: 'Fetch weather data',
  execute: async ({ location }) => {
    const response = await fetch(`/api/weather?location=${location}`);
    return response.json();
  },
  parameters: [{ name: 'location', type: 'string', required: true }]
});

const formatTemperatureTool = createFunctionTool({
  name: 'format_temp',
  description: 'Format temperature reading',
  execute: ({ temp, unit }) => `${temp}°${unit.toUpperCase()}`,
  parameters: [
    { name: 'temp', type: 'number', required: true },
    { name: 'unit', type: 'string', required: true }
  ]
});

// Compose tools with higher-order functions
const cachedWeatherTool = withCache(fetchWeatherTool, { ttl: 300000 }); // 5 min cache
const reliableWeatherTool = withRetry(cachedWeatherTool, { maxRetries: 3 });

// Chain tools together
const weatherReportTool = composeTool([
  reliableWeatherTool,
  formatTemperatureTool
], 'weather_report', 'Get formatted weather report');

Composing Validators

import { compose, createValidator } from '@xynehq/jaf';

// Base validators
const isPositive = createValidator<number>(
  n => n > 0,
  'Value must be positive'
);

const isInteger = createValidator<number>(
  n => Number.isInteger(n),
  'Value must be an integer'
);

const isInRange = (min: number, max: number) => createValidator<number>(
  n => n >= min && n <= max,
  `Value must be between ${min} and ${max}`
);

// Compose validators
const validateAge = compose(
  isPositive,
  isInteger,
  isInRange(0, 150)
);

// Use in tool parameters
const ageTool = createFunctionTool({
  name: 'process_age',
  description: 'Process age data',
  execute: ({ age }) => `Age ${age} is valid`,
  parameters: [{
    name: 'age',
    type: 'number',
    required: true,
    validate: validateAge
  }]
});

Composing Agent Behaviors

import { createAgent, withMiddleware, withFallback } from '@xynehq/jaf';

// Base agents
const primaryAgent = createAgent({
  name: 'primary',
  model: 'gpt-4',
  instruction: 'Primary processing agent',
  tools: [calculatorTool]
});

const fallbackAgent = createAgent({
  name: 'fallback',
  model: 'gpt-3.5-turbo',
  instruction: 'Fallback processing agent',
  tools: [simpleMathTool]
});

// Compose with middleware
const loggingMiddleware = (agent) => ({
  ...agent,
  execute: async (input) => {
    console.log(`[${agent.name}] Processing:`, input);
    const result = await agent.execute(input);
    console.log(`[${agent.name}] Result:`, result);
    return result;
  }
});

const rateLimitMiddleware = (limit: number) => (agent) => {
  let count = 0;
  const resetTime = Date.now() + 60000;
  
  return {
    ...agent,
    execute: async (input) => {
      if (Date.now() > resetTime) {
        count = 0;
      }
      if (count >= limit) {
        throw new Error('Rate limit exceeded');
      }
      count++;
      return agent.execute(input);
    }
  };
};

// Compose everything
const productionAgent = compose(
  withFallback(fallbackAgent),
  withMiddleware(loggingMiddleware),
  withMiddleware(rateLimitMiddleware(100))
)(primaryAgent);

Composing Memory Providers

import { composeMemoryProviders, createCacheLayer } from '@xynehq/jaf';

// Layer memory providers for performance and reliability
const memoryProvider = composeMemoryProviders([
  createCacheLayer({ maxSize: 100 }),      // L1: In-memory cache
  createRedisProvider({ ttl: 3600 }),      // L2: Redis cache
  createPostgresProvider({ table: 'chat' }) // L3: Persistent storage
]);

// The composed provider automatically:
// - Reads from the fastest available layer
// - Writes to all layers
// - Falls back on layer failure

🛡️ Security & Validation

Composable Validation Policies

import { createPathValidator, createPermissionValidator, composeValidations } from '@xynehq/jaf';

// Create individual validators
const pathValidator = createPathValidator(['/shared', '/public']);
const permissionValidator = createPermissionValidator('admin', ctx => ctx);

// Compose them
const combinedValidator = composeValidations(pathValidator, permissionValidator);

// Apply to tools
const secureFileTool = withValidation(baseFileTool, combinedValidator);

Guardrails

import { createContentFilter, createRateLimiter } from '@xynehq/jaf';

const config = {
  // ... other config
  initialInputGuardrails: [
    createContentFilter(),
    createRateLimiter(10, 60000, input => 'global')
  ],
  finalOutputGuardrails: [
    createContentFilter()
  ],
};

🔗 Agent Handoffs

import { handoffTool } from '@xynehq/jaf';

const triageAgent: Agent<Context, { agentName: string }> = {
  name: 'TriageAgent',
  instructions: () => 'Route requests to specialized agents',
  tools: [handoffTool],
  handoffs: ['MathTutor', 'FileManager'], // Allowed handoff targets
  outputCodec: z.object({
    agentName: z.enum(['MathTutor', 'FileManager'])
  }),
};

📊 Observability

Real-time Tracing

import { ConsoleTraceCollector, FileTraceCollector } from '@xynehq/jaf';

// Console logging
const consoleTracer = new ConsoleTraceCollector();

// File logging
const fileTracer = new FileTraceCollector('./traces.log');

// Composite tracing
const tracer = createCompositeTraceCollector(consoleTracer, fileTracer);

const config = {
  // ... other config
  onEvent: tracer.collect.bind(tracer),
};

Error Handling

import { JAFErrorHandler } from '@xynehq/jaf';

if (result.outcome.status === 'error') {
  const formattedError = JAFErrorHandler.format(result.outcome.error);
  const isRetryable = JAFErrorHandler.isRetryable(result.outcome.error);
  const severity = JAFErrorHandler.getSeverity(result.outcome.error);
  
  console.error(`[${severity}] ${formattedError} (retryable: ${isRetryable})`);
}

🔌 Provider Integrations

LiteLLM Provider

import { makeLiteLLMProvider } from '@xynehq/jaf';

// Connect to LiteLLM proxy for 100+ model support
const modelProvider = makeLiteLLMProvider(
  'http://localhost:4000', // LiteLLM proxy URL
  'your-api-key'           // Optional API key
);

MCP (Model Context Protocol) Tools

import { makeMCPClient, mcpToolToJAFTool } from '@xynehq/jaf';

// Connect to MCP server
const mcpClient = await makeMCPClient('python', ['-m', 'mcp_server']);

// Get available tools
const mcpTools = await mcpClient.listTools();

// Convert to JAF tools with validation
const jafTools = mcpTools.map(tool => 
  mcpToolToJAFTool(mcpClient, tool, myValidationPolicy)
);

🚀 Development Server

JAF includes a built-in development server for testing agents locally via HTTP endpoints:

import { runServer, makeLiteLLMProvider, createInMemoryProvider } from '@xynehq/jaf';

const myAgent = {
  name: 'MyAgent',
  instructions: 'You are a helpful assistant',
  tools: [calculatorTool, greetingTool]
};

const modelProvider = makeLiteLLMProvider('http://localhost:4000');
const memoryProvider = createInMemoryProvider();

// Start server on port 3000
const server = await runServer(
  [myAgent], 
  { modelProvider },
  { port: 3000, defaultMemoryProvider: memoryProvider }
);

Server provides RESTful endpoints:

• GET /health - Health check
• GET /agents - List available agents
• POST /chat - General chat endpoint
• POST /agents/{name}/chat - Agent-specific endpoint

📚 Documentation

Comprehensive documentation is available in the /docs folder:

• Getting Started - Installation, basic concepts, and first agent
• Core Concepts - JAF's functional architecture and principles
• API Reference - Complete TypeScript API documentation
• ADK Layer - Agent Development Kit for simplified agent creation
• A2A Protocol - Agent-to-Agent communication and task management
• Tools - Building robust tools with validation and error handling
• Memory System - Conversation persistence (in-memory, Redis, PostgreSQL)
• Model Providers - LLM integration and configuration
• Server & API - HTTP server setup and REST API
• Visualization - Generate Graphviz diagrams of agents and tools
• Examples - Tutorials and integration patterns
• Deployment - Production deployment guide
• Troubleshooting - Common issues and debugging
• New Features - Recent enhancements and capabilities

📖 Documentation Website

Browse the full documentation online at https://xynehq.github.io/jaf/

The documentation site features:

• 🔍 Full-text search
• 🌓 Dark/light mode toggle
• 📱 Mobile-friendly responsive design
• 🔗 Deep linking to sections
• 📋 Code block copy buttons

Running Documentation Locally

# Install documentation dependencies
pip install -r requirements.txt

# Run local documentation server
mkdocs serve
# Visit http://127.0.0.1:8000

# Or use the convenience script
./docs/serve.sh

🎮 Example Applications

Explore the example applications to see the framework in action:

Development Server Demo

cd examples/server-demo
npm install
npm run dev

The server demo showcases:

• ✅ Multiple agent types with different capabilities
• ✅ RESTful API with type-safe validation
• ✅ Tool integration (calculator, greeting)
• ✅ Real-time tracing and error handling
• ✅ CORS support and graceful shutdown

Vertex AI RAG Demo

cd examples/rag-demo
npm install
npm run dev

The RAG demo showcases:

• ✅ Real Vertex AI RAG integration with Google GenAI SDK
• ✅ Permission-based access control
• ✅ Real-time streaming responses with source attribution
• ✅ Performance metrics and comprehensive error handling
• ✅ JAF framework orchestration with type-safe tools
• ✅ Multi-turn conversations with observability

🧪 Testing

npm test        # Run tests
npm run lint    # Lint code
npm run typecheck # Type checking

✨ Recent Enhancements

Multi-Agent Coordination

• Intelligent Agent Selection: Automatic keyword-based routing for conditional delegation
• Parallel Response Merging: Smart merging of responses from multiple agents
• Coordination Rules: Define custom rules for sophisticated orchestration
• Hierarchical Delegation: Multi-level agent architectures with automatic handoffs

Enhanced Schema Validation

• Full JSON Schema Draft 7: Complete support for all validation features
• Format Validators: Built-in email, URL, date, UUID, IPv4/IPv6 validation
• Advanced Constraints: minLength, maxLength, pattern, multipleOf, uniqueItems
• Deep Validation: Object property constraints, array uniqueness with deep equality

Visualization System

• DOT Generation: Direct Graphviz DOT generation for better reliability
• Multiple Color Schemes: Default, modern, and minimal themes
• Architecture Diagrams: Generate agent, tool, and runner visualizations
• Fallback Support: DOT content available even if rendering fails

Model Support

• 300+ Models: Comprehensive enum with all major LLM providers
• Type-Safe: Strongly typed model identifiers
• Provider Detection: Automatic provider identification from model names

🏛️ Architecture Principles

Immutable State Machine

• All state transformations create new state objects
• No mutation of existing data structures
• Predictable, testable state transitions

Type Safety

• Runtime validation with Zod schemas
• Compile-time safety with TypeScript
• Branded types prevent ID mixing

Pure Functions

• Core logic is side-effect free
• Easy to test and reason about
• Deterministic behavior

Effect Isolation

• Side effects only in Provider modules
• Clear boundaries between pure and impure code
• Easier mocking and testing

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Run the test suite
5. Submit a pull request

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JAF - Building the future of functional AI agent systems 🚀