Rust Deep Agents SDK

High-performance Rust framework for composing reusable "deep" AI agents with custom tools, sub-agents, and prompts. This repository contains the SDK workspace, AWS integration helpers, documentation, and deployment scaffolding.

Workspace Layout

crates/agents-core – Domain traits, message structures, prompt packs, and state models.
crates/agents-runtime – Tokio-powered runtime glue between planners, tools, and state stores.
crates/agents-toolkit – Built-in tools (mock filesystem, todo management) and utilities.
crates/agents-aws – AWS adapters (Secrets Manager, DynamoDB, CloudWatch) behind feature flags.
examples/ – Reference agents; getting-started provides the echo smoke test.
- agents-example-cli provides a local CLI harness using OpenAI.
deploy/ – Terraform modules and IaC assets for AWS environments.
docs/ – Roadmap, ADRs, playbooks, and reference material.

Quick Start

cargo fmt
cargo clippy --all-targets --all-features
cargo test --all
cargo run -p agents-example-getting-started
# or try the CLI harness
cargo run -p agents-example-cli

Features

✅ Core Features (Python Parity Achieved)

Agent Builder API

ConfigurableAgentBuilder with fluent interface matching Python's API
.with_model() method supporting OpenAI, Anthropic, and Gemini models
.get_default_model() function returning pre-configured Claude Sonnet 4
Async and sync agent creation: create_deep_agent() and create_async_deep_agent()

Middleware Stack

Planning Middleware: Todo list management with comprehensive tool descriptions
Filesystem Middleware: Mock filesystem with ls, read_file, write_file, edit_file tools
SubAgent Middleware: Task delegation to specialized sub-agents
HITL (Human-in-the-Loop): Tool interrupts with approval policies
Summarization Middleware: Context window management
AnthropicPromptCaching: Automatic prompt caching for efficiency

State Management

State Reducers: Smart merging functions matching Python's file_reducer behavior
Persistence: Checkpointer trait with InMemoryCheckpointer implementation
Thread Management: Save/load/delete agent conversation threads

Provider Support

Anthropic: Claude models with prompt caching support
OpenAI: GPT models integration
Gemini: Google's Gemini Chat models

Built-in Tools

Todo Management: write_todos with detailed usage examples
File Operations: Full CRUD operations on mock filesystem
Task Delegation: task tool for spawning ephemeral sub-agents

🚧 Future Features (Planned)

Custom SubAgent Support

Enable users to define completely custom execution graphs beyond simple prompt/tool configurations:

// Future API design
let custom_subagent = CustomSubAgent {
    name: "data-processor".to_string(),
    description: "Processes complex data with custom logic".to_string(),
    graph: Box::new(MyCustomGraph::new()), // Custom execution graph
};

let agent = ConfigurableAgentBuilder::new("main instructions")
    .with_custom_subagent(custom_subagent)
    .build()?;

Benefits:

Full control over sub-agent execution flow
Custom state management within sub-agents
Complex branching and conditional logic
Integration with external systems and APIs

Dict-based Model Configuration

Allow models to be configured via dictionary/struct configs in addition to instances:

// Future API design
let agent = ConfigurableAgentBuilder::new("main instructions")
    .with_model_config(ModelConfig {
        provider: "anthropic".to_string(),
        model: "claude-sonnet-4".to_string(),
        max_tokens: 64000,
        temperature: 0.1,
        // ... other provider-specific options
    })
    .build()?;