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open-agent-sdk-rust

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Rust SDK for building AI agents with local OpenAI-compatible servers (LMStudio, Ollama, llama.cpp, vLLM). Features streaming, tools, hooks, retry logic, and comprehensive examples.

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# Open Agent SDK (Rust)

> Build 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, with 353 active tests.

[![Crates.io](https://img.shields.io/crates/v/open-agent-sdk.svg?label=open-agent-sdk%200.6.8)](https://crates.io/crates/open-agent-sdk)
[![Documentation](https://docs.rs/open-agent-sdk/badge.svg)](https://docs.rs/open-agent-sdk)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

---

## 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

```toml
[dependencies]
open-agent-sdk = "0.6.8"
tokio = { version = "1", features = ["full"] }
futures = "0.3"
serde_json = "1.0"
```

For development:

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

### Simple Query (LM Studio)

```rust
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)

```rust
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)?;
            }
            _ => {}
        }
    }

Ok(())
}
```

### Function Calling with Tools

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

```rust
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:

```rust
// 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().clone()).await?;

// Return result to agent
            client.add_tool_result(tool_use.id(), result)?;

// 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

```rust
use open_agent::{Client, Message, MessageRole, ContentBlock, TextBlock, 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 or data URIs (e.g., `data:image/png;base64,...`)
- **`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

```rust
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

```rust
use open_agent::{Client, AgentOptions, estimate_tokens, is_approaching_limit, 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
        let _ = block;
    }
}

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

// Check if approaching limit (margin = 0.8 means warn at 80% of limit)
if is_approaching_limit(client.history(), 32000, 0.8) {
    println!("Warning: approaching context limit");
}

// 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):

```rust
// 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):

```rust
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):

```rust
// 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

```rust
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 the tool completes and before the final result is committed

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

Every hook event exposes `history` as `Vec<serde_json::Value>`, with one structured
JSON object per internal `Message` (`role` plus typed `content` blocks). Prompt and
pre-tool snapshots contain history up to that lifecycle point; post-tool snapshots
also include the completed tool call and its unmodified result.

**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

```rust
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

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

// Note: add_post_tool_use consumes and returns Hooks (builder pattern) — always rebind
let hooks = hooks.add_post_tool_use(move |event| {
    let log = log_clone.clone();
    async move {
        log.lock().unwrap().push(format!(
            "{} -> {:?}",
            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

```rust
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
            let _ = block;
        }
        Ok::<_, Box<dyn std::error::Error>>(())
    }).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

```rust
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

```rust
use std::sync::atomic::Ordering;

let interrupt_handle = client.interrupt_handle();
let cancel_task = tokio::spawn(async move {
    tokio::time::sleep(Duration::from_secs(2)).await;
    interrupt_handle.store(true, Ordering::SeqCst);
});

while let Some(block) = client.receive().await? {
    // Process blocks until cancellation is observed
    let _ = block;
}

cancel_task.await?;
```

### How It Works

When you call `client.interrupt()`:

1. **Atomic signal** - A thread-safe flag tells the receive loop to stop
2. **Stream cleanup** - `receive()` observes the flag, drops the active stream, and returns `Ok(None)`
3. **Clean history** - Partial manual responses are discarded instead of committing incomplete assistant messages
4. **Idempotent** - Safe to call multiple times
5. **Cross-task safe** - `interrupt_handle()` lets another task cancel without locking the `Client`

See `examples/interrupt_demo.rs` for comprehensive patterns.

## Practical Examples

Example agents demonstrating real-world usage:

### Git Commit Agent

**[examples/git_commit_agent.rs](examples/git_commit_agent.rs)**

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

```bash
# 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](examples/log_analyzer_agent.rs)**

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

```bash
# 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):

```rust
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**:

```rust
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

```rust
AgentOptions::builder()
    .system_prompt(str)                  // System prompt
    .model(str)                          // Model name (required)
    .base_url(str)                       // OpenAI-compatible endpoint (required)
    .tool(Tool)                          // Add a single tool for function calling
    .tools(Vec<Tool>)                    // Add multiple tools at once
    .hooks(Hooks)                        // Lifecycle hooks for monitoring/control
    .auto_execute_tools(bool)            // Enable automatic tool execution
    .max_tool_iterations(u32)            // Max tool calls per query in auto mode
    .max_tokens(u32)                     // Tokens to generate (default: 4096); getter returns Option<u32>
    .max_turns(u32)                      // Max conversation turns (default: 1)
    .temperature(f32)                    // Sampling temperature (default: 0.7)
    .timeout(u64)                        // Request timeout in seconds (default: 60)
    .api_key(str)                        // API key (default: "not-needed")
    .build()?
```

### query()

Simple single-turn query function.

```rust
pub async fn query(prompt: &str, options: &AgentOptions)
    -> Result<Pin<Box<dyn Stream<Item = Result<ContentBlock>> + Send>>>
```

Returns a stream yielding `ContentBlock` items. Use `futures::StreamExt` to iterate.

### Client

Multi-turn conversation client with tool monitoring.

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

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

**Additional Client methods:**

```rust
// Send a pre-built Message (e.g., with images)
client.send_message(msg).await?;

// Access the AgentOptions this client was created with
let opts = client.options();

// Clear conversation history (resets to system prompt only)
client.clear_history();

// Look up a registered tool by name
if let Some(t) = client.get_tool("my_tool") { /* ... */ }

// Obtain a shareable interrupt handle (Arc<AtomicBool>) for use across tasks
let handle = client.interrupt_handle();
```

### MessageRole

Who sent a message. Used when constructing `Message` values directly.

```rust
use open_agent::MessageRole;

MessageRole::System     // Establishes context and instructions
MessageRole::User       // Input from the human or calling application
MessageRole::Assistant  // Response from the AI model
MessageRole::Tool       // Results from tool/function execution
```

### Message

Pre-built message values (for `client.send_message()`). Convenience constructors:

```rust
use open_agent::{Message, MessageRole, ContentBlock, TextBlock};

// Build a message manually (any role)
Message::new(role: MessageRole, content: Vec<ContentBlock>) -> Self

// Convenience constructors — all return Self (infallible):
Message::user(text: &str) -> Self
Message::assistant(text: &str) -> Self
Message::system(text: &str) -> Self
Message::user_with_blocks(blocks: Vec<ContentBlock>) -> Self

// Vision constructors — return Result<Self>:
Message::user_with_image(text: &str, image_url: &str) -> Result<Self>
Message::user_with_image_detail(text: &str, image_url: &str, detail: ImageDetail) -> Result<Self>
Message::user_with_base64_image(text: &str, base64_data: &str, mime: &str) -> Result<Self>
```

### Message Types

- `ContentBlock::Text(TextBlock)` - Text content from model
- `ContentBlock::Image(ImageBlock)` - Image content (for vision models)
- `ContentBlock::ToolUse(ToolUseBlock)` - Tool calls from model
- `ContentBlock::ToolResult(ToolResultBlock)` - Tool execution results

### Tool System

```rust
use open_agent::tool;

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

For full JSON Schema control, use `.schema()` instead of chaining `.param()` calls:

```rust
let my_tool = tool("name", "description")
    .schema(json!({
        "type": "object",
        "properties": { "x": { "type": "number" } },
        "required": ["x"]
    }))
    .build(|args| async move { Ok(json!({})) });
```

### ToolBuilder

The `tool()` function returns a `ToolBuilder` for fluent construction of tool definitions:

```rust
use open_agent::{tool, ToolBuilder, Tool};

let t: Tool = tool("name", "description")
    .param("arg", "string")
    .build(|args| async move { Ok(json!({})) });
```

### Provider Configuration

Helper types and functions for mapping provider names to their default endpoints:

```rust
use open_agent::{Provider, get_base_url, get_model};

// get_base_url(provider: Option<Provider>, fallback: Option<&str>) -> String
let url = get_base_url(Some(Provider::LMStudio), None);   // http://localhost:1234/v1
let url_with_fallback = get_base_url(None, Some("http://localhost:8080/v1"));

// get_model(fallback: Option<&str>, prefer_env: bool) -> Option<String>
let model = get_model(Some("qwen2.5-32b"), false);  // use provided model
let env_model = get_model(None, true);              // prefer OPEN_AGENT_MODEL env var
```

### OpenAI Wire Types

Low-level serialization types matching the OpenAI API request format, exported for callers that need to construct raw payloads:

```rust
use open_agent::{OpenAIContent, OpenAIContentPart};
```

`OpenAIContent` and `OpenAIContentPart` are used internally by the SDK when serializing messages to the OpenAI-compatible format. They are exported for advanced use cases where callers need to inspect or construct raw request content.

### Error and Result Types

```rust
use open_agent::{Error, Result};
```

`Error` is the SDK's unified error type, covering HTTP errors, parse failures, configuration errors, and I/O errors. `Result<T>` is an alias for `std::result::Result<T, Error>`.

### Newtype Wrappers

Strong-typed wrappers used internally by `AgentOptions` and exported for external use:

```rust
use open_agent::{BaseUrl, ModelName, Temperature};
```

### Retry Module

Exponential-backoff retry utilities, exported as a public module:

```rust
use open_agent::retry::{RetryConfig, retry_with_backoff, retry_with_backoff_conditional, is_retryable_error};

// Configure retry behavior (builder pattern)
let config = RetryConfig::default()          // 3 attempts, exponential backoff
    .max_attempts(5)
    .initial_delay_ms(100)
    .max_delay_ms(5000)
    .backoff_multiplier(2.0);

// Retry any async operation
let result = retry_with_backoff(config.clone(), || async {
    some_fallible_operation().await
}).await?;

// Retry with a custom condition (only retry on certain errors)
let result = retry_with_backoff_conditional(config, |err| is_retryable_error(err), || async {
    some_fallible_operation().await
}).await?;

// Check if an SDK error is worth retrying (network errors, 429, 5xx)
let retryable = is_retryable_error(&some_error);
```

### Prelude Import

For convenience, import the most commonly used types at once:

```rust
use open_agent::prelude::*;
```

### Hook Name Constants

String constants for hook event types are exported for use in custom registries:

```rust
use open_agent::{HOOK_PRE_TOOL_USE, HOOK_POST_TOOL_USE, HOOK_USER_PROMPT_SUBMIT};
```

### Context Utilities

```rust
use open_agent::{estimate_tokens, is_approaching_limit, truncate_messages};

// Estimate tokens in message history (character-based approximation)
let tokens = estimate_tokens(client.history());

// Check if approaching a context limit (margin=0.8 means 80% of limit)
let near_limit = is_approaching_limit(client.history(), 32000, 0.8);

// Truncate history, keeping the last N messages (preserve_system=true keeps system prompt)
let truncated = truncate_messages(client.history(), 10, true);
```

## 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

```text
open-agent-sdk-rust/
├── src/
│   ├── client.rs          # Public client module docs/imports and fragment orchestration
│   ├── client/            # Query, send, send_message, setup, streaming, receive, history, state, and tests
│   ├── config.rs          # Provider helpers (Provider, get_base_url, get_model)
│   ├── context.rs         # Token estimation and truncation
│   ├── error.rs           # Error types
│   ├── hooks.rs           # Public lifecycle-hook module orchestration
│   ├── hooks/             # Hook events, decisions, handlers, registry, and tests
│   ├── lib.rs             # Public exports and prelude module
│   ├── retry.rs           # Retry logic with exponential backoff
│   ├── tools.rs           # Public tool module orchestration
│   ├── tools/             # Tool, schema, builder, handler, factory, and tests
│   ├── types.rs           # Public core-type module orchestration
│   ├── types/             # Options, messages, images, wire types, validated newtypes, and tests
│   ├── utils.rs           # SSE parsing and tool call aggregation
│   └── utils/             # Utility unit tests
├── 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
│   ├── vision_example.rs            # Multimodal: URLs, local files, base64
│   ├── vision_api_demo.rs           # Vision API walkthrough
│   └── test_tool_serialization.rs   # Tool call serialization verification
├── benches/
│   └── performance.rs               # Criterion benchmarks (token estimation, history ops)
├── tests/
│   ├── integration_tests.rs         # Core integration tests
│   ├── advanced_integration_test.rs
│   ├── auto_execution_test.rs
│   ├── backward_compatibility_test.rs
│   ├── client_image_serialization_test.rs
│   ├── debug_logging_test.rs
│   ├── defensive_validation_test.rs
│   ├── edge_cases_test.rs
│   ├── hooks_history_snapshot_test.rs
│   ├── hooks_integration_test.rs
│   ├── image_serialization_test.rs
│   ├── package_manifest_test.rs     # Package exclusion coverage (CLAUDE.md, .markdownlint.json)
│   ├── security_bypass_test.rs
│   ├── send_message_test.rs         # Manual-mode history regression (v0.6.2)
│   ├── source_file_size_test.rs      # Repository Rust hard-limit guard
│   └── tool_call_content_test.rs    # Tool call serialization tests
├── .github/
│   ├── dependabot.yml               # Grouped weekly Cargo dependency updates
│   └── workflows/
│       ├── ci.yml                   # CI pipeline (fmt, clippy, test matrix [ubuntu+macos × stable+beta], msrv, security audit, docs build, coverage [Tarpaulin], benchmarks [PR-only Criterion])
│       └── scheduled-audit.yml      # Scheduled dependency audit
├── .markdownlint.json               # Markdown lint rules (disable MD013, allow duplicate sibling headings)
├── Cargo.toml
├── Cargo.lock
├── CHANGELOG.md
└── 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
- `vision_example.rs` – Multimodal image support (URLs, local files, base64)
- `vision_api_demo.rs` – Vision API walkthrough with token cost notes
- `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
- `test_tool_serialization.rs` – Verifies tool call serialization (see `examples/test_tool_serialization.rs`)

## Documentation

- [API Documentation](https://docs.rs/open-agent-sdk)
- [Python SDK](https://github.com/slb350/open-agent-sdk) - Reference implementation
- [Examples](examples/) - Comprehensive usage examples

## Testing

```bash
# Run all tests
cargo test

# Run with output
cargo test -- --nocapture

# Run specific test
cargo test test_agent_options_builder
```

**Test Coverage:**

- 120 unit tests (lib)
- 82 active integration tests across 16 test files (12 additional tests are `#[ignore]`d by default)
  - Hooks integration tests
  - Auto-execution tests
  - Image serialization tests
  - Defensive validation tests
  - Backward compatibility tests
  - Advanced integration tests
  - Edge cases, security bypass, debug logging, send message, tool call content tests
- 151 active doctests (17 additional doctests are `ignore`d)

Total: 353 active unit, integration, and documentation tests

## Requirements

- Rust 1.85+
- Tokio 1.50+ (async runtime)
- serde, serde_json (serialization)
- reqwest (HTTP client)
- futures, tokio-stream (async streams)
- eventsource-stream (SSE parsing)
- async-trait (async trait support)
- rand (retry jitter)

## License

MIT License - see [LICENSE](LICENSE) for details.

## Acknowledgments

- Rust port of [open-agent-sdk](https://github.com/slb350/open-agent-sdk) Python library
- API design inspired by claude-agent-sdk
- Built for local/open-source LLM enthusiasts

---

**Status**: v0.6.8 source - complete structured hook history, source-size architecture guards, Rust 1.85-compatible dependencies, hardened GitHub Actions, non-locking cancellation, manual-mode history fixes, and multimodal image support

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

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