MoFA is a production-grade, modular agent framework designed for "write once, run everywhere" multi-language compatibility, extreme performance, and runtime programmability. It targets engineers and researchers seeking a highly extensible and performant platform for building complex agent systems, offering a balance between raw speed and dynamic flexibility. The framework's core innovation lies in its Rust-based microkernel and dual-layer plugin system, enabling native interoperability and hot-swappable logic.

How It Works

MoFA leverages a Rust core with UniFFI for high performance and seamless integration with Python, Java, Go, Kotlin, and Swift. Its architecture features a microkernel for core lifecycle management and a novel dual-layer plugin system: compile-time Rust/WASM plugins for maximum performance and type safety, and runtime Rhai scripts for dynamic loading and hot-reloading business logic. This design allows for zero-cost abstractions and native execution while retaining runtime flexibility, supported by Dora-rs for distributed dataflow and Ractor for actor-model concurrency.

Quick Start & Requirements

• Installation: Add mofa-sdk = "0.1.0" as a dependency in your Rust project's Cargo.toml.
• Prerequisites: Rust toolchain. UniFFI implies support for Python, Java, Go, Kotlin, and Swift environments.
• Resources: No specific hardware or GPU requirements are detailed for the core framework.
• Links: Quick Start, Documentation, GitHub Repository.

Highlighted Details

• Polyglot Performance: Rust core with UniFFI enables native calls from multiple languages, offering orders-of-magnitude speed improvements over Python-based frameworks.
• Dual-Layer Plugin System: Combines high-performance compile-time Rust/WASM plugins with dynamic, hot-loadable Rhai runtime scripts for flexible business logic and tool extensions.
• Microkernel Architecture: Provides a stable, extensible foundation with clean separation of concerns, managing agent lifecycles, metadata, and task scheduling.
• Distributed & Cloud-Native: Built on Dora-rs for distributed dataflow, supporting edge deployments and seamless cross-process/machine communication via an actor-model concurrency framework (Ractor).
• Advanced LLM Integration: Features an LLM abstraction layer, support for ReAct patterns, and sophisticated multi-agent collaboration modes (e.g., consensus, debate, parallel, sequential) managed by a workflow engine.
• Runtime Programmability: Embedded Rhai scripting engine allows for hot-reloading logic, dynamic tool definition, and configurable workflows without recompilation.
• Observability & Gateway: Includes Prometheus metrics, OpenTelemetry tracing, structured logging, a built-in dashboard, and a distributed control plane with load balancing and rate limiting.

Maintenance & Community

• Active Development: MoFA is participating in Google Summer of Code 2026 with several project ideas.
• Community: Engages users via GitHub Discussions and Discord.
• Support: Supported by Upstream Labs.

Licensing & Compatibility

• License: Apache License 2.0.
• Compatibility: Permissive license suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

• Some components within the MoFA ecosystem, such as mofa-studio, are explicitly marked as prototypes.
• The CLI production smoke tests are currently manually run and not integrated into CI pipelines.