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Maxim Sakhno @maxim-sakhno.bsky.social · 28/01/2026

A four-panel "Anakin and Padmé" meme. Padmé (representing a developer) asks LiteStar: "With 1 dependency it's 41k RPS. So with 2 it's 40k, right?" In the third panel, Anakin (LiteStar) stares back silently with a knowing smirk. In the final panel, Padmé looks worried and asks again: "It's 40k, right?"

Maxim Sakhno @maxim-sakhno.bsky.social · 28/01/2026

Found the suspect! 🕵️‍♂️ Look at the resolution logic below. LiteStar is a bit too ambitious: it spawns a full TaskGroup for any batch larger than 1. That overhead is exactly what's tanking the RPS when you add that second dependency. 👇

A screenshot of Python source code from LiteStar's dependency resolver with handwritten annotations. The code shows a loop iterating through "dependency_batches." An if-statement checks if a batch size is 1; if so, it simply awaits the dependency. The "else" block reveals that for any batch larger than 1, LiteStar spawns an async TaskGroup to resolve dependencies in parallel. Annotations highlight that this TaskGroup creation is the source of the performance overhead when moving beyond a single dependency.

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Maxim Sakhno @maxim-sakhno.bsky.social · 28/01/2026

The logic behind the batches: Kahn’s algorithm for topological sorting. 🧠 LiteStar's implementation: dub.sh/0DG8van LiteStar uses it to identify which dependencies can run in parallel and groups them into batches. The algorithm visualization 👇

A technical diagram illustrating how a Dependency Graph is transformed into execution batches using Kahn's Algorithm. On the left, a graph shows node A depending on B and C, which in turn depend on D, E, and F. An arrow labeled "Kahn's Algorithm" points to the right, showing these nodes organized into three sequential batches. Batch 1 (D, E, F) and Batch 2 (B, C) are contained within boxes labeled "Task Group," indicating parallel execution. Batch 3 contains only node A and is labeled "Await," indicating simple sequential execution.

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