Build Production-Ready AI Agents with IBM's wxflows Toolkit

Stop reinventing the wheel every time you build an AI agent. IBM's wxflows repository gives you battle-tested examples, tools, and integrations that work across LangGraph, LangChain, OpenAI, and watsonx.ai - all in one place.

Why Another AI Agent Repository?

You've probably built a few AI agents by now. You know the drill: spend hours wiring up tool integrations, debugging framework quirks, and figuring out deployment patterns. wxflows cuts through that repetitive work with production-ready examples that actually work.

This isn't theoretical code - it's practical implementations from IBM's AI team, covering real-world scenarios like chat applications, text-to-SQL agents, and content processing pipelines.

What You Get

Ready-to-Use Tools Five essential integrations already built and tested:

Currency exchange rates for financial applications
Wikipedia search for knowledge-based agents
Google Books API for content research
Mathematical computation engine
Weather data for location-aware applications

Framework Integration Examples Working implementations across the major AI frameworks:

LangGraph: Complex multi-step agent workflows
LangChain: Traditional chain-based approaches
OpenAI: Direct API integration patterns
watsonx.ai: IBM's enterprise AI platform integration

Complete Application Examples Full-stack implementations you can deploy:

End-to-end chat application with agent capabilities
Text-to-SQL agent that converts natural language to database queries
YouTube transcription agent for content processing
Mathematical problem-solving agent with step-by-step reasoning

Real-World Implementation

Here's what sets wxflows apart from typical example repositories - the code handles the messy details you encounter in production.

Tool Calling Architecture

# Clean abstraction that works across frameworks
from wxflows.tools import WeatherTool, WikipediaSearch

# Automatically handles API rate limits, error recovery, and response formatting
weather = WeatherTool()
wiki = WikipediaSearch()

# Works identically in LangGraph, LangChain, or direct OpenAI calls
agent.add_tools([weather, wiki])

Multi-Model Support Switch between Granite, Llama-3, GPT-4, and Mistral models without changing your agent logic. The examples show you how to handle the quirks of each model's tool calling format.

GraphQL Gateway Deploy your agents behind a unified GraphQL interface. Each flow becomes an endpoint you can call from any client application - web, mobile, or other services.

MCP Integration Ready

The repository includes Model Context Protocol examples that demonstrate how to expose your agents through MCP-compliant interfaces. This matters if you're building agents that need to work within MCP ecosystems or want standardized agent communication.

Production Considerations

The examples address real deployment challenges:

Environment variable management for API keys across multiple services
Error handling and retry logic for external API dependencies
Logging and debugging patterns that work in development and production
Token usage optimization for cost-effective operations

Getting Started

git clone https://github.com/IBM/wxflows.git
cd wxflows

# Set up your environment
export WX_API_KEY="your-watsonx-key"
export WX_PROJECT_ID="your-project-id"  
export OPENAI_API_KEY="your-openai-key"

# Run the chat app example
cd examples/chat-app
python run.py

The examples work immediately - no complex setup or infrastructure requirements.

Who Should Use This

You'll find wxflows valuable if you're:

Building production AI applications that need reliable tool integrations
Working across multiple AI frameworks and want consistent patterns
Looking for complete examples rather than minimal tutorials
Deploying agents that need to handle real-world edge cases and API limitations

The 92 GitHub stars and active development from IBM's team indicate this is production-grade code being used in real applications.

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