Search and retrieve documents with sub-200ms latency using 4-layer fusion RAG optimized for AI agents. Built for developers needing fast, production-grade retrieval without external dependencies.
io.github.HaseebKhalid1507/velocirag
Local install
STDIO
No auth required
How models use it and what it is built for.
Search and retrieve documents with sub-200ms latency using 4-layer fusion RAG optimized for AI agents. Built for developers needing fast, production-grade retrieval without external dependencies.
Local install — runs as a subprocess.
Where to find authoritative docs and source for Haseebkhalid1507 Velocirag.
Paste any of these into Agent Studio after connecting Haseebkhalid1507 Velocirag.
Common questions about connecting and running Haseebkhalid1507 Velocirag.
What is 4-layer fusion in velocirag?
The registry describes a 4-layer fusion approach, but specific details aren't documented in the available metadata. Consult the project repository or documentation for the exact fusion strategy and how it improves retrieval quality.
How fast is the search latency?
Velocirag achieves sub-200ms search latency by leveraging ONNX Runtime for efficient inference. This makes it suitable for real-time AI agent applications requiring quick document retrieval.
Do I need external services to run velocirag?
Velocirag uses ONNX Runtime for local inference, suggesting minimal external dependencies. Install via `uvx velocirag@0.7.4` to get started with stdio transport for MCP integration.
What are the alternatives to velocirag for RAG?
Other RAG solutions include Pinecone, Weaviate, and Milvus, but they typically require external infrastructure. Velocirag's local ONNX-based approach offers lower latency and no managed service dependency.
Is velocirag suitable for production AI agents?
Yes—sub-200ms latency and optimized ONNX inference make it production-ready for AI agents. However, verify indexing capacity and fusion strategy details in the project docs before deploying at scale.
MCP Playground runs 10,000+ hosted MCP servers — GitHub, Linear, Notion, Stripe, Sentry and more — across Claude, GPT, Gemini, DeepSeek and 30+ AI models. Compare model answers side-by-side, save agent presets, share runs. Zero install.
Open Agent Studio