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Overview

Spring AI Bench is an open-source benchmarking framework focused on evaluating AI agents in enterprise Java development contexts. The project aims to provide transparent, reproducible benchmarks that address the limitations of existing approaches.

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Why This Matters

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The Problem with Current Benchmarks

Existing benchmarks like SWE-bench were groundbreaking for their time, but they have limitations:
Key Limitations:
  • Python-centric: 7-10% performance gap for non-Python languages
  • Static datasets: 2023 patches don’t reflect current development patterns
  • Narrow scope: Only patch-loop agents, missing modern declarative approaches
  • Single architecture: Can’t evaluate Claude, Gemini, Amazon Q, or other production agents
  • Contamination: Studies show 60%+ verified → 19% live performance drops

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Our Approach

1

Full Development Lifecycle

Measure agents on real enterprise tasks: issue triage, PR review, test coverage, compliance, API migration
2

Language Diversity

Java-first focus to address training bias, but extensible to other JVM and non-JVM languages
3

Agent Flexibility

Support any agent via AgentModel abstraction—evaluate the tools YOUR team actually uses
4

Transparency & Reproducibility

One-click Docker execution, open scaffolding, clear documentation of methodology

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Technical Foundation

Spring AI Bench provides:

Sandbox Isolation

Docker/local sandboxes for secure, reproducible execution

Agent Abstraction

AgentModel interface supports any agent implementation

Benchmark Tracks

Modular tracks for different enterprise development scenarios

Reporting

HTML/JSON reports with detailed metrics and analysis

Extensibility

Run on YOUR repos with YOUR scenarios

Open Source

Apache 2.0 licensed, community contributions welcome

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Current Status & Roadmap

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✅ Completed

  • Core benchmarking infrastructure
  • Sandbox isolation (Docker + local)
  • Agent integration framework
  • Multi-agent comparison support
  • HTML/JSON reporting

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🚧 In Progress

  • Developing enterprise-focused benchmark tracks (test coverage, PR review, issue triage)
  • Expanding eval data collection
  • Gathering feedback from enterprise Java teams
  • Improving documentation and examples

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📋 Future Plans

  • Expanded language support (Kotlin, Scala, Groovy)
  • Cloud-based distributed execution
  • Integration with CI/CD pipelines
  • Additional benchmark tracks for common enterprise scenarios

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Get Involved

This is a community-driven initiative. We welcome participation from:

Enterprise Teams

Share your real-world use cases and evaluation needs

AI Providers

Contribute agent implementations and participate in benchmarks

Academic Researchers

Collaborate on methodology and research

Open Source Contributors

Improve the framework, add benchmark tracks, fix bugs

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Resources

Spring AI Bench Documentation

Technical documentation for the benchmarking framework

Spring AI Bench Project

Learn more about the Spring AI Bench project

GitHub Repository

View source code and contribute

Contact Us

Get in touch about collaboration opportunities