LangSmith Rust

A production-ready Rust crate for manual tracing to LangSmith, providing similar ergonomics to the Python and TypeScript SDKs. Designed for building observable AI agent systems with LangGraph-like architectures.

Features

• Automatic Configuration - Environment-based setup with .env support
• Hierarchical Tracing - Parent-child run relationships with automatic context propagation
• Agent Message Types - Full support for tool calls, tool messages, AI messages, and human messages
• Thread Management - Conversation and session tracking
• Metrics Support - Token counting and cost tracking
• Non-blocking Async - All tracing operations are async and non-blocking
• Decorator Pattern - Automatic node tracing with trace_node helper
• Design Patterns - Strategy, Factory, and Observer patterns for extensibility
• Type Safety - Full Rust type safety with compile-time guarantees

Installation

Add to your Cargo.toml:

[dependencies]
langsmith-rust = "0.1.3"

Or from a git repository:

[dependencies]
langsmith-rust = { git = "https://github.com/your-org/langsmith-rust" }

Quick Start

1. Configuration

Create a .env file in your project root:

LANGSMITH_TRACING=true
LANGSMITH_ENDPOINT=https://api.smith.langchain.com
LANGSMITH_API_KEY=<your-api-key>
LANGSMITH_PROJECT=<your-project-name>
LANGSMITH_TENANT_ID=<workspace-id>  # Optional

2. Initialize

use langsmith_rust;

// Initialize (loads .env automatically)
langsmith_rust::init();

3. Basic Usage

Manual Tracing

use langsmith_rust::{Tracer, RunType};
use serde_json::json;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    langsmith_rust::init();
    
    // Create root tracer
    let mut tracer = Tracer::new(
        "Agent Pipeline",
        RunType::Chain,
        json!({"question": "What is Rust?"})
    ).with_thread_id("thread-123".to_string());
    
    // Post initial run
    tracer.post().await?;
    
    // Create child run for LLM call
    let mut llm_run = tracer.create_child(
        "OpenAI Call",
        RunType::Llm,
        json!({"messages": [{"role": "user", "content": "What is Rust?"}]})
    );
    llm_run.post().await?;
    
    // ... execute LLM call ...
    let completion = "Rust is a systems programming language...";
    
    // Update run with outputs
    llm_run.end(json!({"completion": completion}));
    llm_run.patch().await?;
    
    // End parent run
    tracer.end(json!({"answer": completion}));
    tracer.patch().await?;
    
    Ok(())
}

Automatic Node Tracing (Recommended)

use langsmith_rust::{trace_node, RunType};
use serde_json::json;

async fn llm_node(messages: Vec<String>) -> langsmith_rust::Result<String> {
    // Your node logic here
    let response = call_openai(&messages).await?;
    Ok(response)
}

// Wrap your node function with trace_node
let result = trace_node(
    "llm_node",
    RunType::Llm,
    vec!["Hello".to_string(), "How are you?".to_string()],
    llm_node
).await?;

How it works:

1. Before execution: Serializes function parameters as inputs and posts to LangSmith
2. Executes the function
3. After execution: Serializes return value as outputs and patches the run
4. Error handling: Automatically captures and traces errors

Architecture

This crate follows SOLID principles and uses several design patterns:

• Strategy Pattern - Different tracing strategies (async/sync)
• Factory Pattern - TracerFactory for creating tracers with different configurations
• Observer Pattern - Observable nodes for LangGraph integration

See ARCHITECTURE.md for detailed architecture documentation.

Integration with LangGraph

This crate is designed to integrate seamlessly with LangGraph-style node execution:

use langsmith_rust::{trace_node, RunType, TracerFactory};
use serde_json::json;

// In your graph node implementation
pub struct GraphNode {
    name: String,
    run_type: RunType,
}

impl GraphNode {
    pub async fn execute(&self, state: State) -> Result<State> {
        trace_node(
            &self.name,
            self.run_type.clone(),
            json!(state),
            |state| async move {
                // Your node logic
                process_state(state).await
            }
        ).await
    }
}

See INTEGRATION.md for detailed integration guide.

Integration with Graph DSL (TeachMeWowAgent style)

GraphTrace + RunScope are designed for LangGraph-like executors where each node iteration is a child run:

use langsmith_rust::GraphTrace;
use serde_json::json;

// Start root run
let trace = GraphTrace::start_root(json!({"messages": []}), Some("thread-123".to_string())).await?;

// Each node iteration
let scope = trace.start_node_iteration("chatbot", json!({"messages": []})).await?;
// ... execute node ...
scope.end_ok(json!({"messages": []})).await?;

// Close root run
trace.end_root(json!({"finish_reason": "stop"})).await?;

API Reference

Core Types

Tracer

Main structure for creating and managing runs.

// Create a new tracer
let tracer = Tracer::new("Node Name", RunType::Llm, json!({"input": "..."}));

// Configure tracer
let tracer = tracer
    .with_thread_id("thread-123".to_string())
    .with_client(client);

// Create child run
let child = tracer.create_child("Child Node", RunType::Tool, json!({}));

// Post and patch
tracer.post().await?;
tracer.end(json!({"output": "..."}));
tracer.patch().await?;

TracerFactory

Factory for creating tracers with different configurations:

// Create root tracer
let root = TracerFactory::create_root("Root", RunType::Chain, json!({}));

// Create with thread context
let tracer = TracerFactory::create_with_thread(
    "Node",
    RunType::Llm,
    json!({}),
    "thread-123".to_string()
);

// Create for graph node
let tracer = TracerFactory::create_for_node(
    "Node",
    RunType::Llm,
    json!({}),
    Some(&parent_context)
);

Helper Functions

• trace_node(name, run_type, inputs, f) - Wrap async function with tracing
• trace_node_sync(name, run_type, inputs, f) - Wrap sync function with tracing

Run Types

• RunType::Chain - Chain execution (orchestrator)
• RunType::Llm - LLM call
• RunType::Tool - Tool execution
• RunType::Retriever - Retrieval operation
• RunType::Embedding - Embedding generation
• RunType::Prompt - Prompt execution
• RunType::Runnable - Generic runnable
• RunType::Custom(String) - Custom run type

Examples

See the examples/ directory for complete examples:

• test_llm_tracing.rs - Basic LLM tracing example
• decorator_example.rs - Using trace_node with multiple nodes
• observable_graph.rs - Observable graph nodes with Observer pattern

Run examples with:

cargo run --example test_llm_tracing

Testing

Run all tests:

cargo test

Run specific test suites:

cargo test --test config_test
cargo test --test models_test
cargo test --test tracer_test

Error Handling

All tracing errors are logged to stderr but never break your application. If tracing fails, your code continues to execute normally. This ensures tracing is truly non-intrusive.

// Even if LangSmith is down, your code continues
let result = trace_node("node", RunType::Llm, input, my_function).await?;
// Your application continues normally

Performance

• Non-blocking: All HTTP requests are async and don't block execution
• Lazy initialization: Configuration is loaded only when needed
• Efficient serialization: Uses serde_json for fast serialization
• Minimal overhead: Tracing adds <1ms overhead per node

Contributing

Contributions are welcome! Please read our contributing guidelines before submitting PRs.

License

MIT

Documentation

• ARCHITECTURE.md - Detailed architecture and design patterns
• INTEGRATION.md - Integration guide for LangGraph
• docs.md - Comprehensive documentation for LLMs
• READING_GUIDE.md - Guide to understanding the codebase