RAGFlow vs Velozity: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of RAGFlow and Velozity — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
RAGFlow
InfiniFlow
Open-source Retrieval-Augmented Generation engine combining RAG and agent capabilities to provide a richer context layer for LLMs.
Key features
- Retrieval-Augmented Pipeline: Implements end-to-end RAG flows that retrieve relevant document segments and augment LLM prompts with high-quality contextual information to improve response accuracy.
- Agent Integration: Provides mechanisms to orchestrate agent workflows that consume retrieved context for multi-step reasoning, tool invocation, and dynamic decision-making.
- Deep Document Understanding: Parses and encodes documents into semantic chunks to enable precise retrieval and reduce hallucination by supplying targeted context to models.
- Dockerized Deployment & Dev Tools: Includes Dockerfiles, docker-compose configurations, and helper scripts (e.g., download_deps.py) to simplify local setup, testing, and production deployment.
- Open-Source and Extensible: Released under Apache-2.0, with source code and docs available on GitHub for contribution, customization, and on-premise hosting.
- Documentation Sync & Website: Maintains a separate docs repository (ragflow-docs) and a synced documentation site (ragflow.io) for user guides and reference material.
- Retrieval-Augmented Generation engine combining retrieval with generation to ground LLM outputs
- Agent-style capabilities to enable multi-step or tool-augmented workflows
- Deep document understanding and processing for improved retrieval relevance
- Docker-based build and deployment (Dockerfiles and docker-compose examples, including macOS compose file)
- Repository-provided scripts for dependency/download automation (e.g., download_deps.py)
- Documentation site repository (ragflow-docs) synced with main project for usage and deployment guidance
- Apache-2.0 open-source licensing for self-hosting and modification
Best for
- Contextual Customer Support: Powering knowledge-base Q&A systems by retrieving relevant product docs and augmenting LLM responses with exact excerpts.
- LLM-Powered Assistants: Enhancing virtual assistants with up-to-date enterprise documentation and multi-step agent workflows to perform actions and fetch evidence.
- Document-Centric Automation: Automating processes that require reading, summarizing, and acting on large collections of documents using agents that leverage retrieved context.
- Research & Local Evaluation: Running self-hosted RAG experiments and evaluations with Docker-based setups for reproducible research and debugging.
- Safe Upgrades & Maintenance: Managing upgrades and deployments (via repo workflows and docker setups) while preserving indexed data and configuration during updates.
- Building LLM-powered chatbots and assistants with grounded knowledge from document stores
- Document question-answering and knowledge retrieval pipelines
- Enterprise knowledge management and searchable knowledge bases
- Augmenting LLM prompts with relevant context for improved accuracy
- Research and prototyping of RAG and agent-based LLM workflows
Velozity
Akash Mandal
Multiplayer AI workspace where teams and AI agents chat, call, run routines and get work done together.
Key features
- Spaces with Agents: Teams and AI agents share one conversation, with @mentions, threads, reactions and voice notes.
- Custom Agents: Create agents with a name, face and instructions, and manage a cloud computer for them.
- Agent Oversight: Watch an agent step by step, answer its questions, approve plans and grant permissions from a phone.
- Routines: Create, edit and schedule routines and review what each run produced.
- Meeting Transcripts: Ask questions about meeting transcripts and manage tasks and projects in the AI Space.
- Drop-In Collaboration: See who teammates are working with and join conversations without scheduling.
- Event Space: A calendar tab for creating events and managing booking links.
- Built-In Viewer: Open documents, PDFs, CSV tables and images that agents produce.
Best for
- Team Chat with AI: Bring an agent into a team conversation to answer questions or do tasks.
- Meeting Follow-Up: Summarize meetings and automate follow-up actions from transcripts.
- Recurring Work: Schedule routines such as periodic reports run by agents.
- Remote Approvals: Approve an agent's plan or permissions from a phone.
- Private Team Areas: Keep sensitive work in private floors with clear indicators of who can drop in.
- Scheduling: Share booking links and manage events in one workspace.