Agno Says It Builds Agents 529× Faster Than LangGraph. I Measured What That Actually Buys You
Last Updated on July 27, 2026 by Editorial Team
Author(s): Praveen Kumar
Originally published on Towards AI.
Agno Says It Builds Agents 529× Faster Than LangGraph. I Measured What That Actually Buys You
Open the Agno performance page and the first thing you see is a number designed to end the argument: an Agno agent instantiates in about 3 microseconds, which the docs report as 529× faster than LangGraph and using 24× less memory. Agno shipped version 2.8.2 to PyPI on July 24, 2026, and that benchmark is still the headline of the pitch.
The author explains that Agno’s headline is a microbenchmark measuring only Python object construction cost (agent instantiation) using tracemalloc and runs a reproducible comparison across Agno, LangGraph, and Pydantic AI. On their machine, Agno is still hundreds of times faster to instantiate (305× vs LangGraph), but the memory ratio is smaller than claimed (5.8× vs the documented 24×) and is sensitive to how allocations are counted. They further show that Pydantic AI’s much slower instantiation is largely due to eagerly building an HTTP client and loading the SSL trust store, meaning the benchmark is not a direct proxy for how fast agents respond to prompts. Finally, they put the “winning” number in context: construction time is amortized over real workloads because each LLM call takes hundreds of milliseconds to seconds, making the instantiation difference negligible for typical request-driven agent usage; the takeaway is to treat such microbenchmarks as framework-construction overhead tests rather than overall agent performance metrics, and to reproduce outlier results before trusting headline ratios.
Read the full blog for free on Medium.
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