LocalAgentBench

Benchmark AI agents on real tasks using local hardware

Problem

Developers using local models for coding agents lack a standardized way to evaluate different models on real agentic tasks (file editing, search, multi-step reasoning), and can only choose based on gut feeling.

Solution

A locally runnable benchmark suite that simulates common coding agent operations (parsing codebases, executing commands, modifying files) and records success rates and resource consumption. Core features: pre-built agent task scenarios; one-click running of local models; comparative reports; custom tasks.

Users

Local LLM users, coding agent developers, researchers evaluating model performance.

MVP

5-10 built-in agent tasks (e.g., find function, fix syntax error); support local models via API (Ollama, LM Studio); output accuracy and latency; CLI interface.

Revenue

Open-source core, paid cloud reports and team collaboration features, or donations.

Why now

Local models are rapidly improving, but agentic task evaluation is missing; indie developers can quickly build a benchmark and establish a community standard.