pip install railtracks railtracks-cli

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())

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

# 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."
)

# 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