railtracks 1.2.2
The Railtracks Framework for building resilient agentic systems in simple python
Requires Python: >=3.10

# Railtracks

<!--Happy Coding ◊ -->

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  Agents in minutes • Zero config • Local visualization • Pure Python
</h3>

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---

## ✨ What is **Railtracks**?

Easy agent building, for no one but **YOU**: Create deployable complex agents using simple, Pythonic style interface with natural control flow.

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```python
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.

---

## 🎯 Why Railtracks?

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<td width="50%" valign="top">

#### 🐍 **Pure Python Experience**
```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
- ✅ Use your existing debugging tools
- ✅ IDE autocomplete & type checking

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<td width="50%" valign="top">

#### 🔧 **Tool-First Architecture**
```python
# 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

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#### ⚡ **Look Familiar?**
```python
# 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

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#### 👁️ **Transparent by Design**
```bash
railtracks viz  # See everything
```
- ✅ Real-time execution visualization
- ✅ Complete execution history
- ✅ Debug like regular Python code

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---

## 🚀 Quick Start

<details open>
<summary><b>📦 Installation</b></summary>

```bash
pip install railtracks railtracks-cli
```
</details>


<details open>
<summary><b>⚡ Your First Agent in 5 Min</b></summary>


```python
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())
```

</details>

<details open>
<summary><b>📊 Visualize Agent in 5 second</b></summary>


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

<p align="center">
  <img src="docs/assets/visualizer_photo.png" alt="Railtracks Visualizer" width="90%">
  <br>
  <em>🔍 See every step of your agent's execution in real-time</em>
</p>

</details>

---

## 💡 Real-World Examples

<details open>
<summary><b>🔍 Multi-Agent Research System</b></summary>

```python
# 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."
)
```

</details>

<details open>
<summary><b>🔄 Complex Workflows Made Simple</b></summary>

```python
# 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
```

</details>

---

## 🌟 What Makes Railtracks Special?

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

<div align="center">

| Feature | Railtracks
|:--------|:----------:|
| **🐍 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:

```python
# 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](https://railtownai.github.io/railtracks/llm/).

## 🛠️ Powerful Features

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<td width="50%" valign="top">

### 📦 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

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### 🔍 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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</table>

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---

## 📚 Learn More

<div align="center">

<table>
<tr>
<td align="center" width="20%">
<a href="https://railtownai.github.io/railtracks/">
<img src="https://img.icons8.com/fluency/96/000000/book.png" width="64" height="64" alt="Documentation"/>
<br><b>Documentation</b>
</a>
<br>
<sub>Complete guides & API reference</sub>
</td>
<td align="center" width="20%">
<a href="https://railtownai.github.io/railtracks/quickstart/quickstart/">
<img src="https://img.icons8.com/fluency/96/000000/rocket.png" width="64" height="64" alt="Quickstart"/>
<br><b>Quickstart</b>
</a>
<br>
<sub>Up and running in 5 minutes</sub>
</td>
<td align="center" width="20%">
<a href="https://github.com/RailtownAI/railtracks/tree/main/examples">
<img src="https://img.icons8.com/fluency/96/000000/code.png" width="64" height="64" alt="Examples"/>
<br><b>Examples</b>
</a>
<br>
<sub>Real-world implementations</sub>
</td>
<td align="center" width="20%">
<a href="https://discord.gg/h5ZcahDc">
<img src="https://img.icons8.com/fluency/96/000000/discord-logo.png" width="64" height="64" alt="Discord"/>
<br><b>Discord</b>
</a>
<br>
<sub>Get help & share creations</sub>
</td>
<td align="center" width="20%">
<a href="./CONTRIBUTING.md">
<img src="https://img.icons8.com/fluency/96/000000/handshake.png" width="64" height="64" alt="Contributing"/>
<br><b>Contributing</b>
</a>
<br>
<sub>Help make us better</sub>
</td>
</tr>
</table>

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---



## 🚀 Ready to Build?

```bash
pip install railtracks railtracks-cli
```
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<br>

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

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

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---

<sub>Made with lots of ❤️ and ☕ by the ◊Railtracks◊ team • Licensed under MIT • [Report Bug](https://github.com/RailtownAI/railtracks/issues) • [Request Feature](https://github.com/RailtownAI/railtracks/issues)</sub>

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