Railtracks

Railtracks


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What is Railtracks?

Railtracks is a Python framework for building agentic systems. Agent behavior, tools, and multi-step flows are defined entirely in standard Python using the control flow and abstractions you already know.

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
flow = rt.Flow(name="Weather Flow", entry_point=agent)
result = flow.invoke("What's the weather in Paris?")
# or `await flow.ainvoke("What's the weather in Paris?)` in an async context
print(result.text)  # "Based on the current data, it's sunny in Paris!"

Execution order, branching, and looping are expressed using standard Python control flow.

Why Railtracks?

Pure Python

# Write agents like regular functions
@rt.function_node
def my_tool(text: str) -> str:
    return process(text)
  • No YAML, no DSLs, no magic strings
  • Compatible with standard debuggers
  • Full IDE autocomplete and type checking

Tool-First Architecture

# Any function becomes a tool
agent = rt.agent_node(
    "Assistant",
    tool_nodes=[my_tool, api_call]
)
  • Automatic function-to-tool conversion
  • Seamless API and database integration
  • MCP protocol support

Familiar Interface

# Native Async support
result = await rt.call(agent, query)
  • Standardized call interface, consistent with asyncio patterns
  • Built-in validation, error handling, and retries
  • Automatic parallelization management

Built-in Observability

Railtracks includes a visualizer for inspecting agent runs and evaluations in real-time, run completely locally with no signups required.

See the Observability documentation for setup and usage.

Quick Start

Installation 
pip install railtracks 'railtracks[visual]'
Your First Agent 
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
text_flow = rt.Flow(
  name="Text Analysis Flow",
  entry_point=text_analyzer
)

text_flow.invoke("How many 'r's are in 'strawberry'?")

LLM Support

Railtracks integrates with major model providers through a unified interface:

# OpenAI
rt.llm.OpenAILLM("gpt-5")

# Anthropic
rt.llm.AnthropicLLM("claude-4-6-sonnet")

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

Works with OpenAI, Anthropic, Google, Azure, and more. See the full provider list.

Contributing

Railtracks is developed in the open. Contributions, bug reports, and feature requests are welcome via GitHub Issues.

Quick Start Documentation Examples Join Discord

Licensed under MIT · Made by the Railtracks team

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (1.4.7)

3 maintainers

PyPI data
Data sourced directly from PyPI's database.
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Credits

Author: Logan Underwood

License

MIT License

Requires

Python >=3.10

Provides Extra

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