oxidizedgraph

A humble attempt at LangGraph in Rust - High-performance agent orchestration framework.

Why oxidizedgraph?

Feature	LangGraph (Python)	oxidizedgraph
Parallelism	Limited by GIL	True multi-core
Memory per session	~50MB	~5MB
Startup time	~200ms	~10ms
Type safety	Runtime	Compile-time
Binary size	Needs Python	~15MB standalone

Quick Start

[dependencies]
oxidizedgraph = "0.1"
tokio = { version = "1", features = ["full"] }
async-trait = "0.1"

use oxidizedgraph::prelude::*;

// Define a simple node
struct ProcessNode;

#[async_trait]
impl NodeExecutor for ProcessNode {
    fn id(&self) -> &str { "process" }

    async fn execute(&self, state: SharedState) -> Result<NodeOutput, NodeError> {
        let mut guard = state.write().unwrap();
        guard.set_context("processed", true);
        Ok(NodeOutput::cont())
    }
}

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    // Build the graph
    let graph = GraphBuilder::new()
        .add_node(ProcessNode)
        .set_entry_point("process")
        .add_edge_to_end("process")
        .compile()?;

    // Execute
    let runner = GraphRunner::with_defaults(graph);
    let result = runner.invoke(AgentState::new()).await?;

    println!("Processed: {:?}", result.get_context::<bool>("processed"));
    Ok(())
}

Core Concepts

State

State flows through the graph between nodes. The built-in AgentState provides common fields:

pub struct AgentState {
    pub messages: Vec<Message>,      // Conversation history
    pub tool_calls: Vec<ToolCall>,   // Pending tool calls
    pub context: HashMap<String, Value>, // Arbitrary key-value storage
    pub iteration: usize,            // Current iteration count
    pub is_complete: bool,           // Completion flag
}

Nodes

Nodes implement NodeExecutor and transform state:

#[async_trait]
impl NodeExecutor for MyNode {
    fn id(&self) -> &str { "my_node" }

    async fn execute(&self, state: SharedState) -> Result<NodeOutput, NodeError> {
        // Access state
        let mut guard = state.write().unwrap();

        // Do work...

        // Return next action
        Ok(NodeOutput::cont())  // Continue to next node via edges
        // or Ok(NodeOutput::finish())  // End execution
        // or Ok(NodeOutput::continue_to("specific_node"))  // Route to specific node
    }
}

Edges

Edges connect nodes. They can be direct or conditional:

GraphBuilder::new()
    // Direct edge
    .add_edge("node_a", "node_b")

    // Edge to END
    .add_edge_to_end("node_b")

    // Conditional edge
    .add_conditional_edge("agent", |state| {
        if state.is_complete {
            transitions::END.to_string()
        } else {
            "continue".to_string()
        }
    })

Built-in Nodes

• EchoNode - Stores a message in context
• DelayNode - Adds a configurable delay
• StaticTransitionNode - Always routes to a fixed target
• ContextRouterNode - Routes based on context values
• ConditionalNode - Routes based on a predicate
• FunctionNode - Create nodes from closures
• LLMNode - Call LLM providers
• ToolNode - Execute pending tool calls

Runner

Execute your graph with configurable options:

let runner = GraphRunner::new(
    graph,
    RunnerConfig::default()
        .max_iterations(100)
        .verbose(true)
        .tag("my-workflow"),
);

let result = runner.invoke(initial_state).await?;

Examples

Run the included examples:

# Simple linear workflow
cargo run --example simple_workflow

# ReAct agent pattern
cargo run --example react_agent

Feature Roadmap

• Core graph primitives
• State management (AgentState)
• NodeExecutor trait
• Conditional edges
• GraphRunner execution
• Built-in nodes (LLM, Tool, Conditional, Function)
• Checkpointing (SQLite, Postgres)
• LLM integrations (Anthropic, OpenAI)
• Streaming execution
• WASM compilation
• Python bindings (PyO3)

License

Apache-2.0 License - see LICENSE file.

Contributing

Contributions welcome! Please open an issue or PR.