FlashRank
Ultra lite & Super fast SoTA cross-encoder based re-ranking for your search & retrieval pipelines.
Decision gist · record as of 2026-08-14
Yes. FlashRank is actively maintained, has no known vulnerabilities, and offers a practical solution for improving search-to-LLM pipelines with minimal resource cost. Install it if you're building retrieval-augmented generation systems and want to improve result quality without adding significant latency or infrastructure overhead. The permissive Apache 2.0 license poses no restrictions.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.6+.
- For LLM-based listwise rerankers, install with the [listwise] extra.
- Model files download on first use; cache_dir can be specified to control storage location.
License · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 is permissive; you can use, modify, and distribute FlashRank freely in commercial and private projects with minimal restrictions.
last release 2025-01-06 (585 days) · last repo commit 2026-07-11 · 1,001 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 770,097 downloads/mo, #5,106 on PyPI
Alternatives
Verify before relying
pip install flashrank
from flashrank import Ranker, RerankRequest
ranker = Ranker(max_length=128)
query = "How to speedup LLMs?"
passages = [{"id": 1, "text": "Introduce lookahead decoding..."}]
results = ranker.rank(RerankRequest(query=query, passages=passages))- Exact reranking latency and throughput benchmarks for different model sizes and passage counts
- Memory footprint during inference for each supported model variant
- Whether multilingual models work reliably for non-English queries
- Sliding window support status for rank_zephyr_7b_v1_full beyond the documented 20-passage limit
What it is and what it does
FlashRank is a Python library that re-ranks search results by scoring query-passage pairs using pre-trained cross-encoder and LLM models. It sits between your retrieval stage and LLM input, improving result quality without the overhead of full transformer inference. The library emphasizes minimal footprint—the default model is ~4MB and runs on CPU—making it suitable for serverless deployments and cost-sensitive environments.
You provide a query and a list of passages; FlashRank scores and sorts them by relevance. It supports multiple model sizes and types: tiny cross-encoders for speed, larger cross-encoders for precision, T5-based listwise rerankers for out-of-domain robustness, and multilingual variants. Runtime dependencies are lightweight (tokenizers, onnxruntime, numpy, requests, tqdm), and the library handles model downloading and caching automatically.
Use it for
- Improve LLM answer quality by re-ranking retrieval results before feeding them as context to an LLM
- Reduce inference cost in serverless deployments by using a tiny reranker model instead of a large LLM for initial filtering
- Re-rank multilingual search results using the ms-marco-MultiBERT-L-12 model for language support
- Optimize retrieval latency by tuning max_length to match your typical passage+query token count
- Fine-tune ranking for domain-specific queries using the ce-esci-MiniLM-L12-v2 model
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
FlashRank is actively maintained, has no known vulnerabilities, and offers a practical solution for improving search-to-LLM pipelines with minimal resource cost. Install it if you're building retrieval-augmented generation systems and want to improve result quality without adding significant latency or infrastructure overhead. The permissive Apache 2.0 license poses no restrictions.
Install
flashrank on PyPI
Before you install
Low friction: pure Python wheel with five runtime dependencies (tokenizers, onnxruntime, numpy, requests, tqdm). Active maintenance with recent commits and strong community interest. No known vulnerabilities.
Requires Python 3.6+. For LLM-based listwise rerankers, install with the [listwise] extra. Model files download on first use; cache_dir can be specified to control storage location.
License in practice
Apache 2.0 is permissive; you can use, modify, and distribute FlashRank freely in commercial and private projects with minimal restrictions.
Quickstart
pip install flashrank
from flashrank import Ranker, RerankRequest
ranker = Ranker(max_length=128)
query = "How to speedup LLMs?"
passages = [{"id": 1, "text": "Introduce lookahead decoding..."}]
results = ranker.rank(RerankRequest(query=query, passages=passages))Verify before relying
- Exact reranking latency and throughput benchmarks for different model sizes and passage counts
- Memory footprint during inference for each supported model variant
- Whether multilingual models work reliably for non-English queries
- Sliding window support status for rank_zephyr_7b_v1_full beyond the documented 20-passage limit
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagestokenizersonnxruntimenumpyrequeststqdm |
| Maintenance | Actively maintained 585 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 770,097 / month, #5,106 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: FlashRank-0.2.10-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “search result reranking”
- FlashRankFlashRank re-ranks search results using lightweight cross-encoder and…
- awslabs.bedrock-kb-retrieval-mcp-serverAn MCP server that connects AI tools to Amazon Bedrock Knowledge…
- FlagEmbeddingFlagEmbedding provides embedding and reranking models for semantic…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also FlagEmbedding · colbert-ai · sentence-transformers · voyageai · glicko2 · llama-index-retrievers-bm25 · colpali-engine · vllm · llama-index-vector-stores-faiss · pyvespa