https://docs.rs/open-agent-sdk

Open Agent SDK (Rust)

Build production-ready AI agents in Rust using your own hardware

What you can build:

• Copy editors that analyze manuscripts and track writing patterns
• Git commit generators that write meaningful commit messages
• Market analyzers that research competitors and summarize findings
• Code reviewers, data analysts, research assistants, and more

Why local?

• No API costs - use your hardware, not OpenAI's
• Privacy - your data never leaves your machine
• Control - pick your model (Qwen, Llama, Mistral, etc.)

How fast? From zero to working agent in under 5 minutes. Rust-native performance (zero-cost abstractions, no GC), fearless concurrency, and production-ready quality with 85+ tests.

---

Overview

Open Agent SDK (Rust) provides a clean, streaming API for working with OpenAI-compatible local model servers. 100% feature parity with the Python SDK—complete with streaming, tool call aggregation, hooks, and automatic tool execution—built on Tokio for high-performance async I/O.

Supported Providers

Supported (OpenAI-Compatible Endpoints)

• LM Studio - http://localhost:1234/v1
• Ollama - http://localhost:11434/v1
• llama.cpp server - OpenAI-compatible mode
• vLLM - OpenAI-compatible API
• Text Generation WebUI - OpenAI extension
• Any OpenAI-compatible local endpoint
• Local gateways proxying cloud models - e.g., Ollama or custom gateways that route to cloud providers

Note on LM Studio: LM Studio is particularly well-tested with this SDK and provides reliable OpenAI-compatible API support. If you're looking for a user-friendly local model server with excellent compatibility, LM Studio is highly recommended.

Not Supported (Use Official SDKs)

• Claude/OpenAI direct - Use their official SDKs, unless you proxy through a local OpenAI-compatible gateway
• Cloud provider SDKs - Bedrock, Vertex, Azure, etc. (proxied via local gateway is fine)

Quick Start

Installation

[dependencies]
open-agent-sdk = "0.1.0"
tokio = { version = "1", features = ["full"] }
futures = "0.3"
serde_json = "1.0"

For development:

git clone https://github.com/slb350/open-agent-sdk-rust.git
cd open-agent-sdk-rust
cargo build

Simple Query (LM Studio)

use open_agent::{query, AgentOptions, ContentBlock};
use futures::StreamExt;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let options = AgentOptions::builder()
        .system_prompt("You are a professional copy editor")
        .model("qwen2.5-32b-instruct")
        .base_url("http://localhost:1234/v1")
        .temperature(0.1)
        .build()?;

    let mut stream = query("Analyze this text...", &options).await?;

    while let Some(block) = stream.next().await {
        match block? {
            ContentBlock::Text(text) => print!("{}", text.text),
            _ => {}
        }
    }

    Ok(())
}

Multi-Turn Conversation (Ollama)

use open_agent::{Client, AgentOptions, ContentBlock};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let options = AgentOptions::builder()
        .system_prompt("You are a helpful assistant")
        .model("qwen3:8b")
        .base_url("http://localhost:11434/v1")
        .build()?;

    let mut client = Client::new(options)?;

    client.send("What's the capital of France?").await?;

    while let Some(block) = client.receive().await {
        match block? {
            ContentBlock::Text(text) => {
                println!("Assistant: {}", text.text);
            }
            ContentBlock::ToolUse(tool_use) => {
                println!("Tool used: {}", tool_use.name);
                // Execute tool and add result
                // client.add_tool_result(&tool_use.id, result, Some(&tool_use.name));
            }
            _ => {}
        }
    }

    Ok(())
}

Function Calling with Tools

Define tools using the builder pattern for clean, type-safe function calling:

use open_agent::{tool, Client, AgentOptions, ContentBlock};
use serde_json::json;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Define tools
    let add_tool = tool("add", "Add two numbers")
        .param("a", "number")
        .param("b", "number")
        .build(|args| async move {
            let a = args["a"].as_f64().unwrap_or(0.0);
            let b = args["b"].as_f64().unwrap_or(0.0);
            Ok(json!({"result": a + b}))
        });

    // Enable automatic tool execution (recommended)
    let options = AgentOptions::builder()
        .system_prompt("You are a helpful assistant with access to tools.")
        .model("qwen2.5-32b-instruct")
        .base_url("http://localhost:1234/v1")
        .tool(add_tool)
        .auto_execute_tools(true)      // Tools execute automatically
        .max_tool_iterations(10)       // Safety limit for tool loops
        .build()?;

    let mut client = Client::new(options)?;
    client.send("What's 25 + 17?").await?;

    // Simply iterate - tools execute automatically!
    while let Some(block) = client.receive().await {
        match block? {
            ContentBlock::Text(text) => {
                println!("Response: {}", text.text);
            }
            _ => {}
        }
    }

    Ok(())
}

Advanced: Manual Tool Execution

For custom execution logic or result interception:

// Disable auto-execution
let options = AgentOptions::builder()
    .system_prompt("You are a helpful assistant with access to tools.")
    .model("qwen2.5-32b-instruct")
    .base_url("http://localhost:1234/v1")
    .tool(add_tool.clone())
    .auto_execute_tools(false)  // Manual mode
    .build()?;

let mut client = Client::new(options)?;
client.send("What's 25 + 17?").await?;

while let Some(block) = client.receive().await {
    match block? {
        ContentBlock::ToolUse(tool_use) => {
            // You execute the tool manually
            let result = add_tool.execute(tool_use.input).await?;

            // Return result to agent
            client.add_tool_result(&tool_use.id, result, Some(&tool_use.name));

            // Continue conversation
            client.send("").await?;
        }
        ContentBlock::Text(text) => {
            println!("{}", text.text);
        }
        _ => {}
    }
}

Key Features:

• Automatic execution - Tools run automatically with safety limits
• Type-safe schemas - Automatic JSON schema generation from parameters
• OpenAI-compatible - Works with any OpenAI function calling endpoint
• Clean builder API - Fluent API for tool definition
• Hook integration - PreToolUse/PostToolUse hooks work in both modes

See examples/calculator_tools.rs and examples/auto_execution_demo.rs for complete examples.

Multimodal Vision Support

Send images alongside text to vision-capable models like llava, qwen-vl, or minicpm-v. The SDK handles OpenAI Vision API formatting automatically.

Simple Image + Text

use open_agent::{Client, Message, ImageBlock, ImageDetail};

// From URL
let msg = Message::user_with_image(
    "What's in this image?",
    "https://example.com/photo.jpg"
)?;
client.send_message(msg).await?;

// From local file path (NEW!)
let msg = Message::new(
    MessageRole::User,
    vec![
        ContentBlock::Text(TextBlock::new("Describe this photo")),
        ContentBlock::Image(ImageBlock::from_file_path("/path/to/photo.jpg")?),
    ],
);
client.send_message(msg).await?;

// From base64 data
let msg = Message::user_with_base64_image(
    "Describe this diagram",
    base64_data,
    "image/png"
)?;
client.send_message(msg).await?;

// Control detail level for token costs
let msg = Message::user_with_image_detail(
    "Analyze the fine details",
    "https://example.com/diagram.png",
    ImageDetail::High  // Low: ~85 tokens, High: variable, Auto: default
)?;
client.send_message(msg).await?;

Supported Image Sources:

• ImageBlock::from_url(url) - HTTPS/HTTP URLs
• ImageBlock::from_file_path(path) - Local filesystem (automatically encodes as base64)
  • Supports: .jpg, .jpeg, .png, .gif, .webp, .bmp, .svg
  • MIME type inferred from file extension
  • File is read and encoded automatically
• ImageBlock::from_base64(data, mime) - Manual base64 with explicit MIME type

Token Cost Management

Control image processing costs using ImageDetail levels:

• ImageDetail::Low - Lower resolution (typically more cost-effective)
• ImageDetail::High - Higher resolution (typically more detailed analysis)
• ImageDetail::Auto - Model decides (balanced default)

⚠️ Token Costs Vary by Model:

OpenAI's Vision API uses ~85 tokens (Low) and variable tokens based on dimensions (High), but local models may have completely different token costs—or no token costs for images at all. The ImageDetail setting may even be ignored by some models.

Always benchmark your specific model instead of relying on OpenAI's published values for capacity planning.

Complex Multi-Image Messages

use open_agent::{Message, MessageRole, ContentBlock, TextBlock, ImageBlock, ImageDetail};

let msg = Message::new(
    MessageRole::User,
    vec![
        ContentBlock::Text(TextBlock::new("Compare these images:")),
        ContentBlock::Image(
            ImageBlock::from_url("https://example.com/before.jpg")
                .with_detail(ImageDetail::Low)
        ),
        ContentBlock::Image(
            ImageBlock::from_url("https://example.com/after.jpg")
                .with_detail(ImageDetail::Low)
        ),
    ],
);

Key Features:

• send_message() API - Send pre-built messages with images via client.send_message(msg).await?
• Automatic serialization - Images converted to OpenAI Vision API format
• Multiple sources - URLs, local file paths, or base64 data
• Backward compatible - Text-only messages still work with send("text")
• Data URIs supported - Base64-encoded images transmitted seamlessly
• Token cost control - Choose detail level based on use case

See examples/vision_example.rs for comprehensive working examples including local file paths.

Context Management

Local models have fixed context windows (typically 8k-32k tokens). The SDK provides utilities for manual history management—no silent mutations, you stay in control.

Token Estimation & Truncation

use open_agent::{Client, AgentOptions, estimate_tokens, truncate_messages};

let mut client = Client::new(options)?;

// Long conversation...
for i in 0..50 {
    client.send(&format!("Question {}", i)).await?;
    while let Some(block) = client.receive().await {
        // Process blocks
    }
}

// Check token usage
let tokens = estimate_tokens(client.history());
println!("Context size: ~{} tokens", tokens);

// Manually truncate when needed
if tokens > 28000 {
    let truncated = truncate_messages(client.history(), 10, true);
    *client.history_mut() = truncated;
}

Recommended Patterns

1. Stateless Agents (Best for single-task agents):

// Process each task independently - no history accumulation
for task in tasks {
    let mut client = Client::new(options.clone());
    client.send(&task).await?;
    // Client dropped, fresh context for next task
}

2. Manual Truncation (At natural breakpoints):

use open_agent::truncate_messages;

let mut client = Client::new(options)?;
for task in tasks {
    client.send(&task).await?;
    // Truncate after each major task
    let truncated = truncate_messages(client.history(), 5, false);
    *client.history_mut() = truncated;
}

3. External Memory (RAG-lite for research agents):

// Store important facts in database, keep conversation context small
let mut database = HashMap::new();
let mut client = Client::new(options)?;

client.send("Research topic X").await?;
// Save response to database
database.insert("topic_x", extract_facts(&response));

// Clear history, query database when needed
let truncated = truncate_messages(client.history(), 0, false);
*client.history_mut() = truncated;

Why Manual?

The SDK intentionally does not auto-compact history because:

• Domain-specific needs: Copy editors need different strategies than research agents
• Token accuracy varies: Each model family has different tokenizers
• Risk of breaking context: Silently removing messages could break tool chains
• Natural limits exist: Compaction doesn't bypass model context windows

See examples/context_management.rs for complete patterns and usage.

Lifecycle Hooks

Monitor and control agent behavior at key execution points with zero-cost Rust hooks.

Quick Example

use open_agent::{
    AgentOptions, Client, Hooks,
    PreToolUseEvent, PostToolUseEvent,
    HookDecision,
};

// Security gate - block dangerous operations
let hooks = Hooks::new()
    .add_pre_tool_use(|event| async move {
        if event.tool_name == "delete_file" {
            return Some(HookDecision::block("Delete operations require approval"));
        }
        Some(HookDecision::continue_())
    })
    .add_post_tool_use(|event| async move {
        // Audit logger - track all tool executions
        println!("Tool executed: {} -> {:?}", event.tool_name, event.tool_result);
        None
    });

// Register hooks in AgentOptions
let options = AgentOptions::builder()
    .system_prompt("You are a helpful assistant")
    .model("qwen2.5-32b-instruct")
    .base_url("http://localhost:1234/v1")
    .hooks(hooks)
    .build()?;

let mut client = Client::new(options)?;

Hook Types

PreToolUse - Fires before tool execution

• Block operations: Return Some(HookDecision::block(reason))
• Modify inputs: Return Some(HookDecision::modify_input(json!({}), reason))
• Allow: Return Some(HookDecision::continue_())

PostToolUse - Fires after tool result added to history

• Observational (tool already executed)
• Use for audit logging, metrics, result validation
• Return None or Some(HookDecision::...)

UserPromptSubmit - Fires before sending prompt to API

• Block prompts: Return Some(HookDecision::block(reason))
• Modify prompts: Return Some(HookDecision::modify_prompt(text, reason))
• Allow: Return Some(HookDecision::continue_())

Common Patterns

Pattern 1: Redirect to Sandbox

hooks.add_pre_tool_use(|event| async move {
    if event.tool_name == "file_writer" {
        let path = event.tool_input.get("path")
            .and_then(|v| v.as_str())
            .unwrap_or("");

        if !path.starts_with("/tmp/") {
            let safe_path = format!("/tmp/sandbox/{}", path.trim_start_matches('/'));
            let mut modified = event.tool_input.clone();
            modified["path"] = json!(safe_path);
            return Some(HookDecision::modify_input(modified, "Redirected to sandbox"));
        }
    }
    Some(HookDecision::continue_())
})

Pattern 2: Compliance Audit Log

let audit_log = Arc::new(Mutex::new(Vec::new()));
let log_clone = audit_log.clone();

hooks.add_post_tool_use(move |event| {
    let log = log_clone.clone();
    async move {
        log.lock().unwrap().push(format!(
            "[{}] {} -> {:?}",
            chrono::Utc::now(),
            event.tool_name,
            event.tool_result
        ));
        None
    }
})

Hook Execution Flow

• Hooks run sequentially in the order registered
• First non-None decision wins (short-circuit behavior)
• Hooks run inline on async runtime (spawn tasks for heavy work)
• Works with both Client and query() function

See examples/hooks_example.rs and examples/multi_tool_agent.rs for comprehensive patterns.

Interrupt Capability

Cancel long-running operations cleanly without corrupting client state. Perfect for timeouts, user cancellations, or conditional interruptions.

Interrupt Quick Example

use open_agent::{Client, AgentOptions};
use tokio::time::{timeout, Duration};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let options = AgentOptions::builder()
        .system_prompt("You are a helpful assistant.")
        .model("qwen2.5-32b-instruct")
        .base_url("http://localhost:1234/v1")
        .build()?;

    let mut client = Client::new(options)?;
    client.send("Write a detailed 1000-word essay...").await?;

    // Timeout after 5 seconds
    match timeout(Duration::from_secs(5), async {
        while let Some(block) = client.receive().await {
            // Process blocks
        }
    }).await {
        Ok(_) => println!("Completed"),
        Err(_) => {
            client.interrupt();  // Clean cancellation
            println!("Operation timed out!");
        }
    }

    // Client is still usable after interrupt
    client.send("Short question?").await?;
    // Continue using client...

    Ok(())
}

Common Interrupt Patterns

1. Conditional Interruption

let mut full_text = String::new();
while let Some(block) = client.receive().await {
    if let ContentBlock::Text(text) = block? {
        full_text.push_str(&text.text);
        if full_text.contains("error") {
            client.interrupt();
            break;
        }
    }
}

2. Concurrent Cancellation

use tokio::select;

let stream_task = async {
    while let Some(block) = client.receive().await {
        // Process blocks
    }
};

let cancel_task = async {
    tokio::time::sleep(Duration::from_secs(2)).await;
    client.interrupt();
};

tokio::select! {
    _ = stream_task => println!("Completed"),
    _ = cancel_task => println!("Cancelled"),
}

How It Works

When you call client.interrupt():

1. Active stream closure - HTTP stream closed immediately (not just a flag)
2. Clean state - Client remains in valid state for reuse
3. Partial output - Text blocks flushed to history, incomplete tools skipped
4. Idempotent - Safe to call multiple times
5. Thread-safe - Can be called from separate async tasks

See examples/interrupt_demo.rs for comprehensive patterns.

Practical Examples

We've included production-ready agents that demonstrate real-world usage:

Git Commit Agent

examples/git_commit_agent.rs

Analyzes your staged git changes and writes professional commit messages following conventional commit format.

# Stage your changes
git add .

# Run the agent
cargo run --example git_commit_agent

# Output:
# Found staged changes in 3 file(s)
# Analyzing changes and generating commit message...
#
# Suggested commit message:
# feat(auth): Add OAuth2 integration with refresh tokens
#
# - Implement token refresh mechanism
# - Add secure cookie storage for tokens
# - Update login flow to support OAuth2 providers

Features:

• Analyzes diff to determine commit type (feat/fix/docs/etc)
• Writes clear, descriptive commit messages
• Follows conventional commit standards

Log Analyzer Agent

examples/log_analyzer_agent.rs

Intelligently analyzes application logs to identify patterns, errors, and provide actionable insights.

# Analyze a log file
cargo run --example log_analyzer_agent -- /var/log/app.log

Features:

• Automatic error pattern detection
• Time-based analysis (peak error times)
• Root cause suggestions
• Supports multiple log formats

Why These Examples?

These agents demonstrate:

• Practical Value: Solve real problems developers face daily
• Tool Integration: Show how to integrate with system commands (git, file I/O)
• Structured Output: Parse and format LLM responses for actionable results
• Privacy-First: Keep your code and logs local while getting AI assistance

Why Not Just Use OpenAI Client?

Without open-agent-sdk (raw reqwest):

use reqwest::Client;

let client = Client::new();
let response = client
    .post("http://localhost:1234/v1/chat/completions")
    .json(&json!({
        "model": "qwen2.5-32b-instruct",
        "messages": [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt}
        ],
        "stream": true
    }))
    .send()
    .await?;

// Complex parsing of SSE chunks
// Extract delta content
// Handle tool calls manually
// Track conversation state yourself

With open-agent-sdk:

use open_agent::{query, AgentOptions};

let options = AgentOptions::builder()
    .system_prompt(system_prompt)
    .model("qwen2.5-32b-instruct")
    .base_url("http://localhost:1234/v1")
    .build()?;

let mut stream = query(user_prompt, &options).await?;
// Clean message types (TextBlock, ToolUseBlock)
// Automatic streaming and tool call handling

Value: Familiar patterns + Less boilerplate + Rust performance

Why Rust?

Performance: Zero-cost abstractions mean no runtime overhead. Streaming responses with Tokio delivers throughput comparable to C/C++ while maintaining memory safety.

Safety: Compile-time guarantees prevent data races, null pointer dereferences, and buffer overflows. Your agents won't crash from memory issues.

Concurrency: Fearless concurrency with async/await lets you run multiple agents or handle hundreds of concurrent requests without fear of race conditions.

Production Ready: Strong type system catches bugs at compile time. Comprehensive error handling with Result types. No surprises in production.

Small Binaries: Standalone executables under 10MB. Deploy anywhere without runtime dependencies.

API Reference

AgentOptions

AgentOptions::builder()
    .system_prompt(str)                  // System prompt
    .model(str)                          // Model name (required)
    .base_url(str)                       // OpenAI-compatible endpoint (required)
    .tool(Tool)                          // Add tools for function calling
    .hooks(Hooks)                        // Lifecycle hooks for monitoring/control
    .auto_execute_tools(bool)            // Enable automatic tool execution
    .max_tool_iterations(usize)          // Max tool calls per query in auto mode
    .max_tokens(Option<u32>)             // Tokens to generate (None = provider default)
    .temperature(f32)                    // Sampling temperature
    .timeout(u64)                        // Request timeout in seconds
    .api_key(str)                        // API key (default: "not-needed")
    .build()?

query()

Simple single-turn query function.

pub async fn query(prompt: &str, options: &AgentOptions)
    -> Result<ContentStream>

Returns a stream yielding ContentBlock items.

Client

Multi-turn conversation client with tool monitoring.

let mut client = Client::new(options)?;
client.send(prompt).await?;

while let Some(block) = client.receive().await {
    // Process ContentBlock items
}

Message Types

• ContentBlock::Text(TextBlock) - Text content from model
• ContentBlock::ToolUse(ToolUseBlock) - Tool calls from model
• ContentBlock::ToolResult(ToolResultBlock) - Tool execution results

Tool System

use open_agent::tool;

let my_tool = tool("name", "description")
    .param("param_name", "type")
    .build(|args| async move {
        // Tool implementation
        Ok(json!({"result": value}))
    });

Recommended Models

Local models (LM Studio, Ollama, llama.cpp):

• GPT-OSS-120B - Best in class for speed and quality
• Qwen 3 30B - Excellent instruction following, good for most tasks
• GPT-OSS-20B - Solid all-around performance
• Mistral 7B - Fast and efficient for simple agents

Cloud-proxied via local gateway:

• kimi-k2:1t-cloud - Tested and working via Ollama gateway
• deepseek-v3.1:671b-cloud - High-quality reasoning model
• qwen3-coder:480b-cloud - Code-focused models

Project Structure

open-agent-sdk-rust/
├── src/
│   ├── client.rs          # query() and Client implementation
│   ├── config.rs          # Configuration builder
│   ├── context.rs         # Token estimation and truncation
│   ├── error.rs           # Error types
│   ├── hooks.rs           # Lifecycle hooks
│   ├── lib.rs             # Public exports
│   ├── retry.rs           # Retry logic with exponential backoff
│   ├── tools.rs           # Tool system
│   ├── types.rs           # Core types (AgentOptions, ContentBlock, etc.)
│   └── utils.rs           # SSE parsing and tool call aggregation
├── examples/
│   ├── simple_query.rs              # Basic streaming query
│   ├── calculator_tools.rs          # Function calling (manual mode)
│   ├── auto_execution_demo.rs       # Automatic tool execution
│   ├── multi_tool_agent.rs          # Production agent with 5 tools and hooks
│   ├── hooks_example.rs             # Lifecycle hooks patterns
│   ├── context_management.rs        # Context management patterns
│   ├── interrupt_demo.rs            # Interrupt capability patterns
│   ├── git_commit_agent.rs          # Production: Git commit generator
│   ├── log_analyzer_agent.rs        # Production: Log analyzer
│   └── advanced_patterns.rs         # Retry logic and concurrent requests
├── tests/
│   ├── integration_tests.rs
│   ├── hooks_integration_test.rs    # Hooks integration tests
│   ├── auto_execution_test.rs       # Auto-execution tests
│   └── advanced_integration_test.rs # Advanced integration tests
├── Cargo.toml
└── README.md

Examples

Production Agents

• git_commit_agent.rs – Analyzes git diffs and writes professional commit messages
• log_analyzer_agent.rs – Parses logs, finds patterns, suggests fixes
• multi_tool_agent.rs – Complete production setup with 5 tools, hooks, and auto-execution

Core SDK Usage

• simple_query.rs – Minimal streaming query (simplest quickstart)
• calculator_tools.rs – Manual tool execution pattern
• auto_execution_demo.rs – Automatic tool execution pattern
• hooks_example.rs – Lifecycle hooks patterns (security gates, audit logging)
• context_management.rs – Manual history management patterns
• interrupt_demo.rs – Interrupt capability patterns (timeout, conditional, concurrent)
• advanced_patterns.rs – Retry logic and concurrent request handling

Documentation

• API Documentation
• Python SDK - Reference implementation
• Examples - Comprehensive usage examples

Testing

# Run all tests
cargo test

# Run with output
cargo test -- --nocapture

# Run specific test
cargo test test_agent_options_builder

Test Coverage:

• 57 unit tests across 10 modules
• 28 integration tests
  • 6 hooks integration tests
  • 13 auto-execution tests
  • 9 advanced integration tests

Requirements

• Rust 1.85+
• Tokio 1.0+ (async runtime)
• serde, serde_json (serialization)
• reqwest (HTTP client)
• futures (async streams)

License

MIT License - see LICENSE for details.

Acknowledgments

• Rust port of open-agent-sdk Python library
• API design inspired by claude-agent-sdk
• Built for local/open-source LLM enthusiasts

---

Status: v0.1.0 Published - 100% feature parity with Python SDK, production-ready

Star this repo if you're building AI agents with local models in Rust!