Top RAG Frameworks in 2025

We evaluated each llm orchestration solution against these criteria: RAG-specific capabilities — a critical factor for production deployments. Data connector ecosystem — a critical factor for production deployments. Production readiness — a critical factor for production deployments. Community support — a critical factor for production deployments. Documentation quality — a critical factor for production deployments. Each criterion was weighted based on its importance to teams building RAG applications at scale. Our evaluation methodology is transparent and reproducible. Each solution was tested with identical datasets across multiple use cases including document search, question answering, and multi-modal retrieval. We measured query latency at various percentiles (p50, p95, p99), recall at different k values, and indexing throughput for datasets ranging from 10K to 10M vectors. The results reflect real-world performance rather than synthetic benchmarks that may not translate to production conditions.

Best framework specifically designed for building RAG applications. Pros: Purpose-built for RAG, 300+ data connectors, Multiple index types. Cons: Less mature agent framework, Rapid API changes, Documentation gaps. LlamaIndex is a strong choice for teams that prioritize purpose-built for rag and can work around less mature agent framework. We also considered the broader ecosystem around each solution. Documentation quality, community activity, third-party integrations, and the vendor's responsiveness to issues all factor into the overall developer experience. A technically superior solution with poor documentation or an inactive community can be harder to work with than a slightly less performant option with excellent support resources. Our rankings balance technical capabilities with practical usability.

Best for complex LLM applications that go beyond simple RAG. Pros: Mature ecosystem, Strong agent framework, Large community. Cons: Over-abstracted for simple use cases, Frequent breaking changes, Performance overhead. LangChain is a strong choice for teams that prioritize mature ecosystem and can work around over-abstracted for simple use cases.

Best for teams prioritizing production readiness and evaluation. Pros: Pipeline-first design, Strong evaluation tools, Production-focused. Cons: Smaller community, Fewer integrations, Steeper initial setup. Haystack is a strong choice for teams that prioritize pipeline-first design and can work around smaller community.

At a glance: LlamaIndex (ranked #1) excels at purpose-built for rag. LangChain (ranked #2) excels at mature ecosystem. Haystack (ranked #3) excels at pipeline-first design. The best choice depends on your specific requirements, team expertise, and infrastructure constraints.

IngestIQ integrates with all the llm orchestration solutions listed above. Use IngestIQ as your data ingestion and processing layer, then route vectors to whichever llm orchestration solution fits your needs. This decoupled architecture means you can switch between options without rebuilding your pipeline.