Embench
Embench is a browser-based retrieval lab that lets you index a corpus and compare retrieval stacks (semantic, BM25 keyword, grep, hybrid, and reranked) side-by-side with inline evaluation metrics (precision, recall, MRR). It provides embedded open-source models and a stable JSON REST contract for runs.
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Best for: Research
What it does
Research software for decision-makers comparing workflow fit and alternatives.
Best fit
Research
Pricing snapshot
Free from Free
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Embench
Embench is a retrieval lab for evaluating and comparing retrieval stacks on your own corpus. It allows you to index documents and run semantic, keyword (BM25), grep, hybrid, and reranked search side-by-side, showing inline metrics such as precision, recall, and MRR so you can validate performance before integrating a retrieval stack into an agent. The lab is accessible in the browser with no signup and provides a stable JSON REST contract for runs so the same harness can be handed to an agent; hosted API, saved corpora, batch jobs, and a CLI are on the project's roadmap and waitlist.
Embedding & Reranker Comparison Tool
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Claim this listing for $29Key Features
Side-by-side retrieval
Run semantic, BM25 keyword, grep, hybrid, and reranked search on the same corpus in a single run to compare results.
Built-in evaluation metrics
Mark expected documents and get precision, recall, and MRR shown next to every result list for immediate comparison.
Open-source embedding and reranker models
Includes MiniLM, BGE, Qwen, Stella embeddings and cross-encoder rerankers with no setup required.
Stable REST JSON contract
Every run in the lab is a plain REST call with a stable JSON contract, enabling reuse of the same harness for agents.
Browser-accessible lab
Explore and run evaluations directly in the browser; free to use and no signup required (quota per browser session).
Retrieval and indexing controls
Options shown in the UI include split/chunk methods, chunk size and overlap, Top K, hybrid weight, and similarity metric (e.g., cosine).
Pricing
Free to use in the browser with no signup; quota is limited per browser session.
Free
Free- Free to use in the browser
- No signup required
- Quota per browser session
Use Cases
Selecting a retrieval stack for an agent
Compare semantic, keyword, hybrid, and reranked approaches on your corpus and measure precision/recall/MRR to choose the best strategy before deployment.
Evaluating embedding and reranker combinations
Test multiple embedding models and cross-encoder rerankers side-by-side to see which combination retrieves the most relevant documents.
Handing a validated harness to production agents
Use the lab's stable JSON REST contract to hand the same evaluation harness to an agent or downstream system for consistent behavior.
Integrations
REST / JSON harness
Runs are plain REST calls with a stable JSON contract so the same harness can be used by agents and automation.
Agent integration
The lab is designed so results and harnesses can be handed to agents for production usage.
CLI & hosted features (roadmap)
Hosted API, saved corpora, batch jobs, and a CLI are on the way (join the waitlist).
Benefits
Limitations
Frequently Asked Questions
No verified FAQs are available.
Getting Started
- 1 Step 1: Open the Embench lab in your browser (no signup required).
- 2 Step 2: Index your corpus (documents one per line or in the form `doc-id | text`).
- 3 Step 3: Select embedding and reranker models, configure chunking/splitting, Top K, and retrieval modes, then run the evaluation.
- 4 Step 4: Mark expected relevant document IDs (or mark results after searching) to compute precision, recall, and MRR.
- 5 Step 5: Use the provided REST JSON contract for runs to hand the same harness to your agent or automation.
Support
No verified support channels are available.
API
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