LLM OrchestrationTools RAG
Core RAG logic for MCP, enabling retrieval-augmented generation.
- Status
- Not yet determined
- Last checked
- Unverified
- License
- Open source
- Pricing
- Free tier · from $0
What it is
This package provides the core Retrieval-Augmented Generation (RAG) functionality for the MCP framework. It is designed to enhance AI applications by integrating retrieval mechanisms with generative models, improving contextual relevance and accuracy in responses.
The only RAG framework specifically designed to integrate seamlessly with MCP, offering a unique blend of retrieval and generation techniques.
When it fits — and when it doesn’t
A good fit for
Teams building RAG applications who need a robust core logic for MCP framework
Developers working on AI projects requiring integration of retrieval with generative models
Projects aiming to improve contextual relevance and accuracy in their AI responses
Look elsewhere if
Teams needing real-time streaming capabilities (batch-only architecture)
Budget-constrained projects that cannot afford the time or resources for self-hosting solutions
Known weaknesses
Each one links to the public issue, changelog or docs page it came from, where we have one.
- 01
Limited language support beyond JavaScript and Python
Primary development focuses on JavaScript, with no official support for other languages like Java or C#
- 02
Small community and limited third-party integrations
GitHub repository has low activity and few contributors compared to more established frameworks
What it does well
Core RAG logic for MCP framework
Integration of retrieval mechanisms with generative models
Enhanced contextual relevance and accuracy in AI applications
Pricing
- Free tier
- YesOpen source — free to use
- Paid from
- $0
- Model
- Flat rate
- Enterprise plan
- No
Pricing not independently confirmed — check the vendor’s page.
Performance benchmarks
How Fast Is It?
Set up Tools RAG
Setup steps, common gotchas and what to check after 30 days.