OmniRAG
🧱 TL;DR
OmniRAG is a retrieval strategy that dynamically selects the best data source for a particular user query. This source could be vector, graph, or a traditional database. The selection is based on the evaluation of intent by an LLM or a rules based system. I suggest a categorization of Trial, given that it is enterprise ready but early in the adoption curve.
🚦 Radar Status
| Field | Value |
|---|---|
| Technology Topic Name | OmniRAG: Intent-Based Multi-Source Retrieval |
| Radar Category | Trial |
| Category Rationale | Enterprise-ready but still in early adoption phases |
| Date Evaluated | 2025-08-05 |
| Version | CosmosAIGraph + AiGraph4pg |
| Research Owner | Steven Frankenfield |
💡 Why It Matters
Traditional RAG apps often struggle with structure or transactional queries. OmniRAG introduces intent-based routing to dynamically select retrieval sources like a knowledge graph for hierarchical queries or a structured database for straightforward lookups. This implementation improves the reliability of LLM responses for less exploratory user questions. This improves user experience and usefulness of AI systems in commercial settings where transactional and hierarchical queries will be more frequent.
📊 Summary Assessment
| Criteria | Status (✅ / ⚠️ / ❌) | Notes / Explanation |
|---|---|---|
| Maturity Level | ✅ - Enterprise Ready | Both CosmosAIGraph and AiGraph4pg are deployable now. |
| Innovation Value | ✅ - High | Strong conceptual upgrade over traditional RAG |
| Integration Readiness | ⚠️ - Medium | Tooling is solid but graph adoption is uneven (unfamiliar) |
| Documentation & Dev UX | ⚠️- Medium | Improving via growing-adoption and our own internal resources (Chris Joakim) |
| Tooling & Ecosystem | ⚠️- Medium | AiGraph4pg helps reduce barrier to entry. |
| Security & Privacy | ✅ - High | Retrieval-based approach avoids privacy issues associated with model retraining on private or proprietary data. |
| Licensing Viability | ✅ - Strong | AiGrah4pg is open-source and familiar (PostgreSQL) |
| Use Case Fit | ✅ - High | HR, Finance, Manufacturing, Support Assistants |
| Performance & Benchmarking | ⚠️ - Unclear | More testing needed in production workloads |
| Community & Adoption | ⚠️ - Growing | Interest rising; AiGraph4pg more approachable |
| Responsible AI | ✅ - High | Supports traceable, grounded completions/responses. |
🛠️ Example Use Cases
- Manufacturing - Bill of materials retrieval
- Finance - Connected parties, fraud detection, transactional relationships
- Social Networks - Relationships, consumer activity
📌 Key Findings
- CosmosAIGraph is fully enterprise-ready, but adoption can be slowed by lack of familiarity with tools like CosmosDB and knowledge graphs.
- AiGraph4pg addresses this barrier by offering a PostgreSQL alternative that is easier to adopt and simpler to implement while still not sacrificing the core innovation of intent-based routing to determine where and how to retrieve information.
- New approaches augment OmniRAG even further using MCP servers to assemble and inject the right information into LLMs or agents at runtime.
🧷 Resources
| Type | Link |
|---|---|
| Official Website | Microsoft OmniRAG learning page |
| GitHub Repo | CosmosAIGraph AIGraph4pg (includes tutorial, recommended) |
🧠 Recommendation
It's my opinion that the value merits active trials. I recommend the categorization Trial simply because the technology is maturing very quickly in parallel with other technologies (MCP, Agentic AI) and there is a technical hurdle: These implementations are not trivial, and the technologies involved are unfamiliar to many.
- Consultants: Assessing the necessity for graph based OmniRAG solutions will be key. The benefits can be great if the use case is supported.
- Engineers: These technologies are engrossing and offer insight into the evolution of data science into practical applications. The future versions of OmniRAG are very exciting but will be moving very quickly.
- Product Teams: Your most savvy, innovative clients will see the value of OmniRAG. They may need guidance to navigate the technical transformation.
🔁 Follow-ups / Watchlist
The addition of MCP Servers and Agentic AI are the future of OmniRAG.
✍️ Author Notes
Thank you to Chris Joakim for sharing his expertise. He was a key contributor to CosmosAiGraph while he was at Microsoft and is the creator of AiGraph4pg. He shared much of what has been simplified and condensed in these articles, and I give much credit to him for his impressive insights and cutting-edge work in this area.