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Railtracks is a lightweight framework for building agentic systems - modular, intelligent agents that can work together to solve complex tasks more effectively than any single module could.

Railtracks-CLI is a command-line tool designed to visualize your Railtracks runs. It's lightweight and can be run locally with no sign-up required.

# Core library
pip install railtracks
# [Optional] CLI support for development and visualization
pip install railtracks-cli

We welcome contributions of all kinds! Get started by checking out our contributing guide.

Many frameworks for building LLM-powered applications focus on pipelines, chains, or prompt orchestration. While effective for simple use cases, they can become brittle or overly complex when handling asynchronous tasks, multi-step reasoning, and heterogeneous agents.

Railtracks is designed with developers in mind to support real-world agentic systems with an emphasis on:

• Programmatic structure without rigidity – Unlike declarative workflows (e.g., LangChain), Railtracks encourages clean, Pythonic control flow.
• Agent-first abstraction – Inspired by real-world coordination, Railtracks focuses on defining smart agents that collaborate via tools, not just chaining LLM calls.
• Automatic Parallelism – Executions are automatically parallelized when possible, freeing you from managing threading or async manually.
• Transparent Execution – Integrated logging, history tracing, and built-in visualizations show exactly how your system behaves.
• Minimal API – The small, configurable API simplifies your workflow compared to other tools. No magic.
• Visual Insights – Graph-based visualizations help you understand data flow and agent interactions at a glance.
• Pluggable Models – Use any LLM provider: OpenAI, open-weight models, or your own local inference engine.

While frameworks like LangGraph emphasize pipelines, Railtracks aims to strike the perfect balance: powerful enough for complex systems, yet simple enough to understand, extend, and debug.