railtracks 1.1.11

The Railtracks Framework for building resilient agentic systems in simple python

Railtracks

Agents in minutes • Zero config • Local visualization • Pure Python

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✨ What is Railtracks?

[!NOTE] Easier, for no one but YOU: While other frameworks force you into rigid workflows or complex APIs, Railtracks lets you create deployable complex agent using pythonic style with natural control flow.

import railtracks as rt

# Define a tool (just a function!)
def get_weather(location: str) -> str:
    return f"It's sunny in {location}!"

# Create an agent with tools
agent = rt.agent_node(
    "Weather Assistant",
    tool_nodes=(rt.function_node(get_weather)),
    llm=rt.llm.OpenAILLM("gpt-4o"),
    system_message="You help users with weather information."
)

# Run it
result = await rt.call(agent, "What's the weather in Paris?")
print(result.text)  # "Based on the current data, it's sunny in Paris!"

That's it. No complex configurations, no learning proprietary syntax. Just Python.

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🎯 Why Railtracks?

🐍 Pure Python Experience # Write agents like regular functions @rt.function_node def my_tool(text: str) -> str: return process(text) ✅ No YAML, no DSLs, no magic strings ✅ Use your existing debugging tools ✅ IDE autocomplete & type checking	🔧 Tool-First Architecture # Any function becomes a tool agent = rt.agent_node( "Assistant", tool_nodes=(my_tool, api_call) ) ✅ Instant function-to-tool conversion ✅ Seamless API/database integration ✅ MCP protocol support
⚡ Look Familiar? # Smart parallelization built-in with interface similar to asyncio result = await rt.call(agent, query) ✅ Easy to learn standardized interface ✅ Built-in validation, error handling & retries ✅ Auto-parallelization management	👁️ Transparent by Design railtracks viz # See everything ✅ Real-time execution visualization ✅ Complete execution history ✅ Debug like regular Python code

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🚀 Quick Start

📦 Installation

pip install railtracks railtracks-cli

⚡ Your First Agent in 5 Min

import railtracks as rt

# 1. Create tools (just functions with decorators!)
@rt.function_node
def count_characters(text: str, character: str) -> int:
    """Count occurrences of a character in text."""
    return text.count(character)

@rt.function_node
def word_count(text: str) -> int:
    """Count words in text."""
    return len(text.split())

# 2. Build an agent with tools
text_analyzer = rt.agent_node(
    "Text Analyzer",
    tool_nodes=(count_characters, word_count),
    llm=rt.llm.OpenAILLM("gpt-4o"),
    system_message="You analyze text using the available tools."
)

# 3. Use it to solve the classic "How many r's in strawberry?" problem
@rt.session
async def main():
    result = await rt.call(text_analyzer, "How many 'r's are in 'strawberry'?")
    print(result.text)  # "There are 3 'r's in 'strawberry'!"

# Run it
import asyncio
asyncio.run(main())

📊 Visualize Agent in 5 second

railtracks init  # Setup visualization (one-time)
railtracks viz   # See your agent in action

🔍 See every step of your agent's execution in real-time

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💡 Real-World Examples

🔍 Multi-Agent Research System

# Research coordinator that uses specialized agents
researcher = rt.agent_node("Researcher", tool_nodes=(web_search, summarize))
analyst = rt.agent_node("Analyst", tool_nodes=(analyze_data, create_charts))
writer = rt.agent_node("Writer", tool_nodes=(draft_report, format_document))

coordinator = rt.agent_node(
    "Research Coordinator",
    tool_nodes=(researcher, analyst, writer),  # Agents as tools!
    system_message="Coordinate research tasks between specialists."
)

🔄 Complex Workflows Made Simple

# Customer service system with context sharing
async def handle_customer_request(query: str):
    with rt.Session() as session:
        # Technical support first
        technical_result = await rt.call(technical_agent, query)
        
        # Share context with billing if needed
        if "billing" in technical_result.text.lower():
            session.context["technical_notes"] = technical_result.text
            billing_result = await rt.call(billing_agent, query)
            return billing_result
        
        return technical_result

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🌟 What Makes Railtracks Special?

[!NOTE] A lightweight agentic LLM framework for building modular, multi-LLM workflows with a focus on simplicity and developer experience.

Feature	Railtracks	LangGraph	Google ADK
🐍 Python-first, no DSL	✅	❌	✅
📊 Built-in visualization	✅	✅	⚠️
⚡ Zero setup overhead	✅	✅	❌
🔄 LLM-agnostic	✅	✅	✅
🎯 Pythonic style	✅	❌	⚠️

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🔗 Universal LLM Support

Switch between providers effortlessly:

# OpenAI
rt.llm.OpenAILLM("gpt-4o")

# Anthropic
rt.llm.AnthropicLLM("claude-3-5-sonnet")

# Local models
rt.llm.OllamaLLM("llama3")

Works with OpenAI, Anthropic, Google, Azure, and more! Check out our neatly crafted docs.

🛠️ Powerful Features

📦 Rich Tool Ecosystem Use existing tools or create your own: ✅ Built in Tools RAG, CoT, etc. ✅ Functions → Tools automatically ✅ MCP Integration as client or as server ✅ Agents as Tools → agent cluster

🔍 Built-in Observability Debug and monitor with ease: ✅ Real-time execution graphs ✅ Performance metrics ✅ Error tracking & debugging ✅ Local visualization ✅ Session management ✅ No signup required!

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📚 Learn More

Documentation Complete guides & API reference

Quickstart Up and running in 5 minutes

Examples Real-world implementations

Discord Get help & share creations

Contributing Help make us better

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🚀 Ready to Build?

pip install railtracks railtracks-cli

✨ Join developers across the world building the future with AI agents

You grow, we grow - Railtracks will expand with your ambitions.

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_{Made with lots of ❤️ and ☕ by the ◊Railtracks◊ team • Licensed under MIT • Report Bug • Request Feature}