A library of tree search algorithms for AI agents
Hi HN, I built this to address what I see as the fundamental problem with ReAct-style agents: compounding errors. Even a small mistake made early enough in the loop can snowball and ruin the final output. But with search, agents can look multiple steps ahead and backtrack before committing to a particular trajectory. This has already been shown in a few papers to help agents avoid mistakes and boost overall task performance, but there's no easy way to actually build these kinds of agents. So that's why I made this framework. I believe search will eventually become table stakes for building…
A library of tree search algorithms for AI agents launched on November 26, 2024 with 5 votes and more than 50% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
Plug-and-play tree search for agents. Contribute to shobrook/saplings development by creating an account on GitHub.
Most agents call tools on a loop: run a tool, observe the result, then pick the next one. But if they make a mistake early, it can snowball and ruin the final output. Since these agents can't look ahead or backtrack, one bad call can derail the whole loop. Saplings fixes this by adding tree search. Agents can explore and evaluate tool-use trajectories to find the best one. Think of it like Stockfish for your agent. They can look multiple steps ahead, compare outcomes, and choose the optimalfrom github.com
In the maker’s words, at launch
Hi HN, I built this to address what I see as the fundamental problem with ReAct-style agents: compounding errors. Even a small mistake made early enough in the loop can snowball and ruin the final output. But with search, agents can look multiple steps ahead and backtrack before committing to a particular trajectory.…
Does a similar job
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