# promptrs
This crate provides a framework for building agentic workflows that integrate language models
with tool calling capabilities. It handles conversation management, API interactions, tool
invocation, and response parsing in a streamlined, composable manner. The core design
philosophy is to enable fluent method chaining while maintaining minimal state management.
## Features
- Streamed API communication with incremental message processing
- Multi-tool support with function call validation and error handling
- Configurable delimiters for custom message formatting
- Automated message pruning for context size management
- JSON schema generation for tool documentation
- Pythonic tool calling support
## Usage Overview
1. **Configure API**: Set up model parameters and credentials
2. **Define Tools**: Create functions with `#[tool]` attribute
3. **Build Workflow**: Chain configuration and tool registration
4. **Process Messages**: Handle user input through fluent API
5. **Handle Responses**: Manage tool calls and state updates
## Example: Basic Workflow
```rust
use promptrs::{Delims, ToolCallParadigm, UserConfig, tool};
use std::collections::HashMap;
#[derive(Default)]
struct AppState {
notes: HashMap<String, String>,
}
/// Tool implementation for note-taking
/// id: Unique ID for note
/// title: Note title
#[tool]
fn add_note(state: &mut HashMap, id: String, title: String) -> String {
// Actual implementation would store the note
format!("Note {id} added with title: {title}")
}
fn main() -> Result<(), Box<dyn std::error::Error>> {
// Configure language model
let config = UserConfig::builder()
.api_key(Some("sk-examplekey".to_string()))
.temperature(Some(0.3))
.paradigm(ToolCallParadigm::Pythonic {
reasoning_delims: None,
})
.char_limit(8192)
.build("https://api.openai.com".to_string(), "gpt-4".to_string());
// Initialize conversation state
let mut state = AppState::default();
// Setup conversation with system prompt and tools
let init = config
.system(Some(
"You are an assistant that helps organize notes. Use add_note for new notes.",
))
.tool(add_note);
// Process user interaction
let response = init
.user("Create a note about meeting agenda".to_string())
.process(&mut state)?;
println!("Assistant response: {}", response.text);
// Continue conversation
let next = response
.user("Add a note for 'Creating promptrs'".to_string())
.process(&mut state)?;
println!("Follow-up response: {}", next.text);
Ok(())
}
```