FastRAG
A framework for building efficient retrieval-augmented generation applications
- Vendor
- Intel Labs
- Also known as
- fastrag
What is FastRAG?
An open-source Python toolkit that combines document retrieval with large language models to create question-answering and generation systems. Provides optimized components and supports multiple inference backends including Intel accelerators.
FastRAG pricing
We don't have FastRAG's full plan breakdown yet (its pricing page resisted automated reading). Here's what we could confirm. Always check live pricing for exact numbers.
What FastRAG does
The capabilities that matter for rag tools, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- Hybrid retrieval
- -
- Reranking integration
- -
- Multi hop agentic retrieval
- -
- Embedding provider agnostic
- ✓
- LLM provider agnostic
- ✓
- Built in rag eval metrics
- -
- Citation attribution
- -
- Multimodal rag
- ✓
- Managed ingestion pipeline
- -
- Open source
- ✓
Platform & deployment
Independently observed- CLI
- On-premise
- Self-hosted
Integrations (10)
Independently observed- ONNX Runtime•
- Llama CPP•
- Intel Gaudi•
- Haystack
- HuggingFace
- Elastic
- Qdrant
- OpenVINO
- LlamaCpp
- Elasticsearch•
Security & compliance
Known vulnerabilities: 0 (0 in the last 12 months) sourcea count reflects scale & disclosure, not quality
FastRAG FAQ
Common questions about FastRAG, answered from independent, dated evidence.
What is FastRAG?
A toolkit for researchers and developers to construct and optimize RAG applications. It provides streamlined components for retrieval and generation, with support for multiple LLM backends and hardware accelerators, and maintains compatibility with the Haystack framework. It is indexed under RAG Tools.
Source: https://github.com/pricing
Is FastRAG free to use?
FastRAG is open source, so it can be self-hosted and used at no licence cost. It is released under the Apache-2.0 licence. Pricing changes often, so verify at source before relying on it.
Source: https://github.com/pricing
What platforms does FastRAG support?
FastRAG supports a command-line interface. Platforms we have not confirmed are simply not listed here rather than ruled out.
Source: https://github.com/pricing
Can FastRAG be self-hosted?
Yes. FastRAG can be deployed self-hosted, so it does not have to run on the vendor's infrastructure.
Source: https://github.com/pricing
What does FastRAG integrate with?
We have confirmed 6 integrations for FastRAG, including Haystack, HuggingFace, Elastic, Qdrant, OpenVINO and LlamaCpp. This is what we could verify from public sources, so the vendor may support others we have not indexed.
Source: https://github.com/pricing
Is FastRAG open source?
Yes. FastRAG is published under the Apache-2.0 licence, a permissive licence that generally allows commercial use and modification. Licence terms can change between releases, so verify against the repository for the version you intend to use.
FastRAG alternatives
Other rag tools we track, ranked by the same independent score.
- LightRAGA lightweight framework that combines knowledge graphs and vector embeddings for efficient information retrievalmedium · 53%
- RagieA managed AI context platform for agents, assistants, and applicationsmedium · 73%
- Amazon Bedrock Knowledge BasesA managed service connecting AI applications to enterprise data with automatic indexing and retrievalmedium · 74%
- PathwayA streaming data framework for building real-time AI and ML applicationslow · 35%
- Ragaslow · 32%
- RAGFlowAn open-source platform combining retrieval-augmented generation with AI agent capabilities to provide contextual support for language modelslow · 9%
Compare FastRAG
Side by side against other rag tools, attribute by attribute, with a source on every value.
The vioscaleAI score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for FastRAG, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Release cadence | 88 | 0.08 | 7.1 | ✓ |
| Capabilities | 67 | 0.08 | 5.4 | ✓ |
| Stars | 61 | 0.05 | 2.9 | ✓ |
| Integrations | 26 | 0.05 | 1.4 | ✓ |
| Dependent projects | 0 | 0.05 | 0.0 | ✓ |
| Development activity | 0 | 0.14 | 0.0 | ✓ |
| Security posture | 0 | 0.12 | 0.0 | - |
| Package downloads | 0 | 0.04 | 0.0 | - |
| Security score | 0 | 0.05 | 0.0 | - |
| Developer Q&A activity | 0 | 0.14 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Activity
| Attribute | Value | Evidence |
|---|---|---|
| Commits last 30d | 0 | mediumsource · 2026-10-14 · 65% |
Adoption
Content
| Attribute | Value | Evidence |
|---|---|---|
| Faq | 6 items | mediumsource · 2026-09-10 · 66% |
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | Open source, Multimodal rag, Llm provider agnostic, Embedding provider agnostic | mediumsource · 2026-10-14 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 7 | mediumsource · 2026-10-14 · 60% |
Language
| Attribute | Value | Evidence |
|---|---|---|
| Primary | Python | highsource · 2026-10-14 · 90% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | Apache-2.0 | highsource · 2026-10-14 · 95% |
Pricing
Release
Security
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