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OpenFang is built for performance. All benchmarks are measured from official documentation and public repositories, collected in February 2026.

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Performance Metrics

OpenFang delivers exceptional performance across all critical metrics:

Cold Start Time

180ms - Fast enough for serverless deployments

Memory Footprint

40 MB idle - Efficient resource usage

Install Size

32 MB - Single binary, no dependencies

Security Systems

16 layers - Defense in depth

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Cold Start Time

Lower is better. OpenFang starts in under 200ms. 
ZeroClaw   ██░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░   10 ms
OpenFang   ██████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  180 ms    ★
LangGraph  █████████████████░░░░░░░░░░░░░░░░░░░░░░░░░  2.5 sec
CrewAI     ████████████████████░░░░░░░░░░░░░░░░░░░░░░  3.0 sec
AutoGen    ██████████████████████████░░░░░░░░░░░░░░░░░  4.0 sec
OpenClaw   █████████████████████████████████████████░░  5.98 sec
OpenFang’s Rust architecture enables near-instant startup, making it ideal for serverless and edge deployments.

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Idle Memory Usage

Lower is better. OpenFang uses only 40 MB at idle. 
ZeroClaw   █░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    5 MB
OpenFang   ████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░   40 MB    ★
LangGraph  ██████████████████░░░░░░░░░░░░░░░░░░░░░░░░░  180 MB
CrewAI     ████████████████████░░░░░░░░░░░░░░░░░░░░░░░  200 MB
AutoGen    █████████████████████████░░░░░░░░░░░░░░░░░░  250 MB
OpenClaw   ████████████████████████████████████████░░░░  394 MB
Memory efficiency means you can run more agents simultaneously on the same hardware.

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Install Size

Lower is better. OpenFang ships as a single 32 MB binary. 
ZeroClaw   █░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  8.8 MB
OpenFang   ███░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░   32 MB    ★
CrewAI     ████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  100 MB
LangGraph  ████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  150 MB
AutoGen    ████████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░  200 MB
OpenClaw   ████████████████████████████████████████░░░░  500 MB
One binary means one install, one command, your agents are live. No Python environments, no npm packages, no Docker layers.

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Security Systems

Higher is better. OpenFang implements 16 independent security layers. 
OpenFang   ████████████████████████████████████████████   16      ★
ZeroClaw   ███████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░    6
OpenClaw   ████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    3
AutoGen    █████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    2
LangGraph  █████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    2
CrewAI     ███░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    1
Security isn’t optional for production agents. OpenFang’s defense-in-depth approach ensures every layer is independently testable.

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Channel Adapters

Higher is better. OpenFang supports 40 messaging platforms out of the box. 
OpenFang   ████████████████████████████████████████████   40      ★
ZeroClaw   ███████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░   15
OpenClaw   █████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░   13
CrewAI     ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    0
AutoGen    ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    0
LangGraph  ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    0

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LLM Providers

Higher is better. OpenFang supports 27 LLM providers with 123+ models. 
ZeroClaw   ████████████████████████████████████████████   28
OpenFang   ██████████████████████████████████████████░░   27      ★
LangGraph  ██████████████████████░░░░░░░░░░░░░░░░░░░░░   15
CrewAI     ██████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░   10
OpenClaw   ██████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░   10
AutoGen    ███████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░    8

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Feature Comparison

| Feature | OpenFang | OpenClaw | ZeroClaw | CrewAI | AutoGen | LangGraph | |---------|----------|----------|----------|--------|---------|-----------|| | Language | Rust | TypeScript | Rust | Python | Python | Python | | Autonomous Hands | 7 built-in | None | None | None | None | None | | Security Layers | 16 discrete | 3 basic | 6 layers | 1 basic | Docker | AES enc. | | Agent Sandbox | WASM dual-metered | None | Allowlists | None | Docker | None | | Channel Adapters | 40 | 13 | 15 | 0 | 0 | 0 | | Built-in Tools | 53 + MCP + A2A | 50+ | 12 | Plugins | MCP | LC tools | | Memory | SQLite + vector | File-based | SQLite FTS5 | 4-layer | External | Checkpoints | | Desktop App | Tauri 2.0 | None | None | None | Studio | None | | Audit Trail | Merkle hash-chain | Logs | Logs | Tracing | Logs | Checkpoints | | Cold Start | <200ms | ~6s | ~10ms | ~3s | ~4s | ~2.5s | | Install Size | ~32 MB | ~500 MB | ~8.8 MB | ~100 MB | ~200 MB | ~150 MB | | License | MIT | MIT | MIT | MIT | Apache 2.0 | MIT |

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System Requirements

  • RAM: 128 MB
  • Disk: 50 MB (binary only)
  • CPU: Any x86_64/ARM64
  • OS: Linux, macOS, or Windows

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Production Statistics

Lines of Code

137,728 across 14 crates

Test Coverage

1,767+ tests, all passing

Code Quality

Zero clippy warnings

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Why Rust?

OpenFang is built in Rust for three critical reasons:
  1. Performance: Native compilation, zero-cost abstractions, and minimal runtime overhead
  2. Safety: Memory safety without garbage collection, preventing entire classes of bugs
  3. Reliability: Strong type system and exhaustive pattern matching catch errors at compile time
The performance gap between OpenFang and Python-based frameworks isn’t just about benchmarks - it’s about being able to run production agents on constrained hardware, respond instantly to events, and scale efficiently.

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Methodology

All benchmarks were collected from official documentation and public repositories in February 2026. Performance measurements use:
  • Cold start: Time from process launch to first API response
  • Idle memory: RSS memory usage with daemon running, no active agents
  • Install size: Total disk space after installation
  • Security systems: Count of discrete, independently testable security mechanisms
  • Channel adapters: Number of built-in messaging platform integrations
  • LLM providers: Number of supported LLM API providers
For questions about methodology or to report inaccuracies, please open an issue.