OpenAgent Eval
OpenAgent Eval is a free, open-source framework for evaluating retrieval-augmented generation systems and AI agents.
What OpenAgent Eval is
OpenAgent Eval is a free, open-source framework for evaluating retrieval-augmented generation systems and AI agents. It runs locally on Linux, macOS, Windows, or a self-hosted setup, without telemetry or required network calls. Built-in mock providers allow a complete offline evaluation without credentials. You can use the `oaeval` command-line interface or typed Python SDK to place evaluations in test suites. The framework works with LangChain, LlamaIndex, and custom RAG pipelines. Its metrics cover retrieval, generation, performance, and cost, including faithfulness, latency, and token count. Reports can be shown in the terminal or exported as Markdown, HTML, or JSON with failure analysis. Documented integrations include several LLM providers and retrievers, while Sentence Transformers is the listed built-in embedder. Plugins can add metrics, providers, report generators, and custom embedders. Python 3.11 or later is required, and some integrations need extra dependencies. It uses Apache License 2.0.
Good
- Free plan and open-source license
- Runs locally without telemetry or required network calls
- Metrics cover retrieval, generation, performance, and cost
- Reports support terminal, Markdown, HTML, and JSON
- Plugins allow custom metrics and providers
Watch out
- Requires Python 3.11 or later
- Some retrievers and embedders need extra dependencies
- No listed tool-call checks
Who it suits
It suits developers evaluating RAG systems or AI agents in Python-based test suites. Local and offline operation fits teams that want data to remain on their machines.
Verdict
OpenAgent Eval combines local execution, broad integrations, and multiple report formats for RAG and agent evaluation. Check dependency requirements before selecting optional retrievers or embedders.
The facts
Each one read from OpenAgent Eval's own pages, with the date it was checked.
What it does
- Data handling
- The project says it runs on the user's machine without dashboards or accounts and that data never leaves the laptop.checked 8 Oct 2026
- Embedders
- Sentence Transformers is the listed built-in embedder, and custom embedders can be added through provider base classes.checked 7 Oct 2026
- Extensibility
- A plugin architecture supports custom metrics, providers, and report generators.checked 7 Oct 2026
- Framework support
- The framework describes itself as compatible with LangChain, LlamaIndex, and custom RAG pipelines.checked 8 Oct 2026
- Integrations
- Documented LLM providers include OpenAI, Anthropic, Gemini, Groq, OpenRouter, Ollama, and a mock provider.checked 8 Oct 2026
- License
- The project is licensed under Apache License 2.0.checked 7 Oct 2026
- LLM integrations
- Documented LLM providers include OpenAI, Anthropic, Gemini, Groq, OpenRouter, Ollama, and a mock provider.checked 7 Oct 2026
- Local operation
- The project says it runs on the user's machine without telemetry or required network calls.checked 7 Oct 2026
- Metrics
- Built-in metrics cover retrieval, generation, latency, and token cost.checked 8 Oct 2026
- Notable dependency limit
- Some retrievers and embedders require extra dependencies, with an `[all]` package extra offered for the full set.checked 8 Oct 2026
- Notable limitation
- Some retrievers and embedders require extra dependencies, such as chromadb, sentence-transformers, faiss-cpu, or qdrant-client.checked 7 Oct 2026
- Offline use
- Built-in mock providers can run a complete evaluation locally without network calls or credentials.checked 8 Oct 2026
- Purpose
- OpenAgent Eval is an open-source, local-first framework for evaluating RAG systems and AI agents.checked 8 Oct 2026
- Reports
- Reports can be output in terminal, Markdown, HTML, or JSON formats, with built-in failure analysis.checked 8 Oct 2026
- Requirements
- The installation guide lists Python 3.11 or later as a requirement.checked 8 Oct 2026
- Retriever integrations
- Documented retrievers include Chroma, Qdrant, Pinecone, Weaviate, FAISS, PGVector, Elasticsearch, BM25, Memory, HTTP, and Mock.checked 7 Oct 2026
- Retrievers
- Documented retriever providers include Chroma, Qdrant, Pinecone, Weaviate, FAISS, PGVector, Elasticsearch, BM25, Memory, HTTP, and Mock.checked 8 Oct 2026
- Support
- The project directs users to GitHub Issues for bugs and GitHub Discussions for questions and ideas.checked 8 Oct 2026
- Usage
- It provides the `oaeval` command-line interface and a typed Python SDK for embedding evaluations in test suites.checked 8 Oct 2026
- Ways to use
- It provides the `oaeval` command-line interface and a Python SDK for embedding evaluations in test suites.checked 7 Oct 2026
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