Why Rust? Performance Benchmarks vs Python Alternatives

We often get asked: why build an AI agent framework in Rust? The answer is performance.

Benchmark Methodology

We tested three common operations across AgentSDK, LangChain, and LlamaIndex:

• Cold start time
• Memory usage at idle
• Throughput (requests per second)

All tests were run on an M3 Max MacBook Pro with 64GB RAM, using Claude Sonnet 4 as the backing model.

Results

Cold Start Latency

• AgentSDK: 2.3ms
• LlamaIndex: 89ms
• LangChain: 108ms

That's a 47x improvement over LangChain.

Memory Usage

• AgentSDK: 12MB
• LlamaIndex: 164MB
• LangChain: 218MB

AgentSDK uses 18x less memory than LangChain.

Throughput

• AgentSDK: 8,400 req/s
• LlamaIndex: 920 req/s
• LangChain: 680 req/s

AgentSDK achieves 12x higher throughput.

Why The Difference?

The performance gap comes down to fundamental architectural differences:

1. No runtime type checking - Rust catches errors at compile time
2. No garbage collection - Predictable memory management
3. Zero-cost abstractions - Traits compile to static dispatch
4. Native async - tokio provides efficient green threads

Conclusion

If you're building AI agents at scale, these performance characteristics matter. Check out our getting started guide to try AgentSDK today.