- Teams evaluating RAG and LLM applications
- Developers building repeatable quality checks for retrieval workflows
- Builders comparing evaluation frameworks around agent knowledge systems
Apache-2.0 · Tools
Ragas
Open-source evaluation framework for LLM applications and RAG workflows.
14K stars 1.5K forks Apache-2.0 license 2026-06-02 verified
$
pip install ragas Open source
Overview
What is Ragas?
Ragas is an Apache-2.0 evaluation framework for LLM applications, especially retrieval-augmented generation workflows that need structured quality checks.
Rag
Ragas surfaces rag as a core capability in its published project metadata and source links.
This gives readers a starting point for evaluating whether the project fits their workflow before visiting the source repository or docs. Install
One command to start
$
pip install ragas Use cases
What teams use it for
Ecosystem
Tags & capabilities
Comparison
How it stacks up
When to choose Ragas
Compare it with nearby tools by looking at hosting model, integration surface, license, and whether the official docs show the workflow you need.
FAQ
Questions
What should I check before using Ragas?
Add one regression case to a real prompt or RAG workflow, then verify the result can run again in CI or review.
Is Ragas open source?
Ragas is listed on OpenAgent.bot with Apache-2.0 based on the current resource metadata. Re-check the official repository, docs, and license before production use.
Decision brief
JSON
Should you use Ragas?
- Pure browser automation tests
- Teams that need production tracing more than evaluation datasets
- Verified 2026-06-02
- License: Apache-2.0
- Repo: vibrantlabsai/ragas
- Open-source signal
Deployment
self hosted, cloud
Permission surface
browser, memory
Decision signals
No extra signals recorded
Agent packet
Structured decision data for Ragas
This packet is the compact machine-readable view agents should use before following source links or taking action.
Capabilities
rag
Constraints
open source
Deployment
self hosted, cloud
Permission surface
browser, memory
Recommended workflows
Browser automation, Evaluation and observability, Memory or RAG workflow, Reusable skill workflow
Overview
What Ragas does
What it is
Ragas is listed on OpenAgent.bot as a tools resource for open AI builders.
Why it matters
Many agent products use retrieval or long-context workflows. Ragas gives builders a practical evaluation layer for checking answers, context, retrieval quality, and application behavior.
How to evaluate it
Start from the official source links, then validate the project against your deployment needs, license requirements, and maintenance expectations.
Facts
Known metadata and operating surface
These fields are separated from editorial interpretation so agents can reason over facts and missing checks.
Resource type tool
Category Tools
Maturity active
Difficulty Unknown
License Apache-2.0
Pricing open source
Verified 2026-06-02
Source confidence high
Risk level elevated
Fit matrix
Where Ragas fits in an agent stack
strong
Browser automation
Ragas has multiple signals for browser automation, including matching tags, capabilities, category, or positioning.
- Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
strong
Evaluation and observability
Ragas has multiple signals for evaluation and observability, including matching tags, capabilities, category, or positioning.
- Add one repeatable test case and confirm results can run again in review or CI.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
strong
Memory or RAG workflow
Ragas has multiple signals for memory or rag workflow, including matching tags, capabilities, category, or positioning.
- Create, update, retrieve, correct, and delete memory or retrieval objects with real data.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
strong
Reusable skill workflow
Ragas has multiple signals for reusable skill workflow, including matching tags, capabilities, category, or positioning.
- Run one skill end to end and check whether it produces evidence or structured output.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Coding agent workflow
Ragas has at least one signal for coding agent workflow, but should be checked against a real task before adoption.
- Run a small repository change and inspect the diff, tests, and rollback path.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Local or private AI stack
Ragas has at least one signal for local or private ai stack, but should be checked against a real task before adoption.
- Verify hardware requirements, data path, storage, and whether all calls stay in your environment.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
Inputs and outputs
What an agent should inspect
Likely inputs
- Web pages, DOM state, screenshots, forms, or browser sessions
- Repositories, files, issues, terminal output, and test results
- Documents, user facts, entities, context, or retrieval queries
- Official setup instructions and a small real workflow
Likely outputs
- Action traces, changed pages, extracted data, or completed browser steps
- Diffs, commits, explanations, test results, or review notes
- Retrieved context, memory updates, graph relations, or citations
- Scores, traces, regression results, dashboards, or failure cases
- A decision on whether this resource fits the target workflow
Evidence
Sources, claims, and missing checks
Claims are marked separately from source links so future crawlers and reviewers can update them without rewriting the page.
GitHub github
Repository source for code, license, issues, releases, and implementation details.
Homepage homepageOfficial or project-controlled source for this resource profile.
Ragas is listed as open source.
License metadata: Apache-2.0Ragas has a recorded GitHub repository: vibrantlabsai/ragas.
Resource facts and GitHub source link.Ragas supports these recorded deployment modes: self hosted, cloud.
OpenAgent decision signal metadata.Ragas is tagged with rag capabilities.
OpenAgent capability taxonomy.- Dedicated docs link is missing.
- Repository freshness has not been recorded.
Next action
How to start evaluating Ragas
Inspect repository
Check license, recent activity, issues, examples, and security-sensitive code paths.
Open sourceOpen Homepage
Start from the official source before adopting third-party instructions.
Open sourceInstall or run
Run only after checking the official source and local environment assumptions.
pip install ragas Compare
Alternatives and nearby resources
Use related resources to compare category fit, license, deployment model, and first-workflow behavior.
FAQ
Common questions about Ragas
What is Ragas used for?
Ragas is used as a tool for tools workflows. The most relevant recorded capabilities are rag.
Is Ragas open source?
Ragas is listed as open source with Apache-2.0 license metadata. Re-check the official repository or source link before production use.
Can agents use Ragas directly?
Ragas has recorded interfaces such as repo, docs. Agents should prefer the JSON or Markdown profile first, then follow official docs for real execution.
What should I check before production use?
Check source confidence (high), risk level (elevated), license, maintenance freshness, permission surface, required credentials, and whether the first workflow succeeds in a sandbox.