> ## Documentation Index
> Fetch the complete documentation index at: https://docs.upsonic.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# UEL (Upsonic Expression Language)

> Build powerful AI chains with intuitive composition patterns

<img src="https://mintcdn.com/upsonic/lEfVQiA3d4Ew-NMK/images/concepts-agent.png?fit=max&auto=format&n=lEfVQiA3d4Ew-NMK&q=85&s=c89102d989b5db9319ba1bdcb3080506" alt="Concepts Agent" width="1728" height="573" data-path="images/concepts-agent.png" />

Build powerful AI chains with intuitive composition patterns.

The UEL (Upsonic Expression Language) provides a declarative way to compose AI components into sophisticated chains. Without UEL, building complex AI workflows requires verbose code with manual data passing and error handling. With UEL, you can:

## Overview

UEL can be used with minimal configuration or with extensive customization to suit your specific needs. The system provides a robust foundation for AI-powered applications with built-in support for various advanced features.

## Key Features

* **Chain Components Elegantly** - Use the pipe operator (`|`) for readable, maintainable code
* **Execute Operations in Parallel** - Maximize performance and reduce latency
* **Build Dynamic Chains** - Adapt based on input conditions
* **Manage Conversation History** - Seamlessly handle context across interactions
* **Integrate Tools and Structured Outputs** - Minimal boilerplate required
* **Visualize Your Chains** - Understand complex workflows at a glance
* **Async Support** - Full async/await support for all operations

## Example

### Basic Example

```python theme={null}
from upsonic.uel import ChatPromptTemplate, StrOutputParser
from upsonic.models import infer_model

# Create a simple chain with output parser
chain = (
    ChatPromptTemplate.from_template("Tell me about {topic}") 
    | infer_model("anthropic/claude-sonnet-4-5")
    | StrOutputParser()
)

# Execute the chain
result = chain.invoke({"topic": "quantum computing"})
print(result)
```

<Warning>
  When using `infer_model()` without specifying a model, it defaults to `"openai/gpt-4o"`. Make sure you have the appropriate API key set in your environment.
</Warning>

### Advanced Example with Memory

```python theme={null}
from upsonic.uel import ChatPromptTemplate, StrOutputParser
from upsonic.models import infer_model

# Create model with memory
model = infer_model("anthropic/claude-sonnet-4-5").add_memory(history=True)

# Create chain with conversation history and output parser
chain = (
    ChatPromptTemplate.from_messages([
        ("system", "You are a helpful assistant."),
        ("placeholder", {"variable_name": "chat_history"}),
        ("human", "{input}")
    ])
    | model
    | StrOutputParser()
)

# First interaction
response1 = chain.invoke({
    "input": "My name is Alice",
    "chat_history": []
})
print(response1)

# Second interaction - model remembers context
response2 = chain.invoke({
    "input": "What's my name?",
    "chat_history": [
        ("human", "My name is Alice"),
        ("ai", response1)
    ]
})
print(response2)  # Output: Your name is Alice
```

## Navigation

* [Attributes](/concepts/uel/attributes) - Comprehensive guide to all UEL configuration options
* [Parallel Execution](/concepts/uel/advanced/parallel-execution) - Execute multiple chains simultaneously
* [Conditional Routing](/concepts/uel/advanced/conditional-routing) - Route execution based on conditions
* [Custom Chains](/concepts/uel/advanced/custom-chains) - Create custom chain functions
* [RAG Patterns](/concepts/uel/advanced/rag-patterns) - Build RAG systems with UEL
* [Visualization](/concepts/uel/advanced/visualization) - Visualize your chains
* [Async Execution](/concepts/uel/advanced/async-execution) - Use async/await with chains