SWE-bench vs TruLens
SWE-bench
swebench.comTruLens
trulens.orgSide by side
| Figure | SWE-bench | TruLens |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Web | Yes | — |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 2 |
| Spec score · LLM Evaluation Tools | 7.0 (#25) | 7.9 (#9) |
| Installation | The project requires Python 3.9 or later and Docker for the evaluation environment. | The documented installation uses Conda with Python 3, followed by pip install trulens; source development uses the GitHub repository and editable installation. |
| Integrations | The project documents using Hugging Face datasets to load SWE-bench data and Modal for cloud evaluations. | The documentation lists integrations and providers for LangChain, LangGraph, LlamaIndex, MCP, OpenAI, Anthropic, Amazon Bedrock, HuggingFace, LiteLLM, Google, and Snowflake Cortex. |
| Purpose | SWE-bench evaluates language models on real-world GitHub software issues by asking them to generate patches that resolve the issues. | TruLens evaluates and traces AI agents to help find failures and reduce costs while maintaining quality. |
| Support | The contact page directs general inquiries to the SWE-bench team by email. | The getting-started documentation points users to a community Slack. |
Plans
In their own words
SWE-bench
SWE-bench evaluates language models on software issues collected from GitHub. A model generates a patch, which the evaluation harness applies in a Docker environment before running the repository tests to check whether the issue is resolved. The original benchmark contains 2,294 task instances from 12 Python repositories. Other listed datasets include SWE-bench Lite, Verified, Multimodal and Multilingual. Verified contains 500 instances engineers checked as solvable; Multilingual has 300 instances across nine languages and 42 repositories. Multimodal includes 100 development instances with screenshots and interface elements, while test evaluation uses the SWE-bench API. Its CLI can submit predictions, retrieve reports, manage evaluation runs and track performance in the cloud. Evaluations can run locally or in the cloud using Modal and sb-cli. Installation requires Python 3.9 or later and Docker. Guides cover macOS, Linux and Windows, with WSL 2 required on Windows. The Docker guide recommends at least 120 GB of free disk space and 16 GB of RAM; caching all Docker images can require about 2,000 GB. SWE-bench is free.