RAG Frameworks · xerrors

Yuxi

Yuxi is a multi-tenant AI agent platform that combines a knowledge base, knowledge graph management, and agent orchestration using LangChain, Vue, and FastAPI. It enables organizations to build retrieval-augmented generation (RAG) systems where agents can query enterprise knowledge, reason over graph structures, and deliver cited answers with deliverables.

Talk to DEV.co Discuss Implementation

Source: GitHub — github.com/xerrors/Yuxi

GitHub stars

865

Forks

Python

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	xerrors/Yuxi
Owner	xerrors
Primary language	Python
License	MIT — OSI-approved
Stars	6k
Forks	865
Open issues	74
Latest release	v0.7.0 (2026-06-12)
Last updated	2026-07-08
Source	https://github.com/xerrors/Yuxi

What Yuxi is

Built on FastAPI (backend), Vue 3 (frontend), and LangGraph (agent orchestration), Yuxi integrates Milvus for vector search, Neo4j for knowledge graphs, PostgreSQL/Redis for state management, and MinerU/PaddleX for document parsing. It supports DeepAgents, MCP, Skills mounting, and sandbox execution, deployed via Docker Compose with multi-tenant isolation.

Quickstart

Get the Yuxi source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/xerrors/Yuxi.git2cd Yuxi3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Enterprise Knowledge Portal with LLM Access

Deploy a ChatGPT-like interface where employees query company documentation, policies, and domain knowledge. Yuxi's multi-tenant design and permission management support role-based access across departments.

AI-Driven Customer Support Automation

Build support agents that retrieve solutions from internal KBs and knowledge graphs, cite sources, and escalate when needed. MCP and Skills support custom integrations with ticketing systems.

Knowledge Graph-Enhanced Decision Intelligence

Construct entity/relation graphs from documents via Neo4j, then let multi-agent workflows reason over them for complex analytical queries, risk assessments, or compliance checks.

Implementation considerations

Pre-staging: Requires at least one OpenAI-compatible LLM endpoint (e.g., OpenAI, Azure, Ollama, vLLM). Test connectivity and rate limits before production onboarding.
Data ingestion pipeline: Document parsing via MinerU/PaddleX + chunking strategy must be tuned for your domain (PDFs, scanned docs, structured text). Plan for iterative refinement.
Knowledge graph construction: Neo4j schema design and entity/relation extraction quality directly impact reasoning. Consider domain expert involvement in early iterations.
Multi-tenant isolation: Verify PostgreSQL/Redis segregation by tenant_id across all queries and async jobs (ARQ workers). Audit access patterns regularly.
Cold-start performance: Initial Docker build and model download can take 10+ minutes. Use 'LITE' mode for faster iteration if not requiring Milvus/Neo4j initially.

When to avoid it — and what to weigh

Minimal AI/ML Infrastructure Appetite — Yuxi requires Docker, Milvus, Neo4j, PostgreSQL, and Redis. If your team cannot operate containerized multi-service stacks, deployment overhead will be substantial.
Proprietary Data Sensitivity Mandates Air-Gap Isolation — Yuxi integrates external LLM APIs (OpenAI-compatible). If regulatory policy forbids any cloud model calls, this platform is not suitable without extensive private LLM setup.
Sub-Second Latency Requirements — RAG pipelines and graph traversal incur network and compute overhead. Real-time, ultra-low-latency use cases (e.g., HFT, critical control systems) are poor fits.
Small Team, Low Customization Needs — If you need a simple Q&A chatbot over static docs, Yuxi's rich agent framework and multi-tenant architecture add unnecessary complexity. Consider lighter RAG solutions.

License & commercial use

MIT License. Permissive open-source license allowing commercial use, modification, and distribution with minimal restrictions. See LICENSE file for full terms.

MIT permits commercial deployment and derivative works. However, no warranty or liability guarantees are provided by the licensor. Ensure internal legal review before production use. Any modifications should comply with MIT attribution. Third-party dependencies (LangChain, FastAPI, Neo4j drivers, etc.) may have different licenses—audit the full stack.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	High
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Multi-tenant isolation relies on PostgreSQL/Redis row-level security by tenant_id; audit queries and cache segregation. LLM API keys must be securely stored (env vars, secrets manager—not in code). No explicit mention of audit logging, encryption at rest, or RBAC enforcement depth. Data flowing through external LLM APIs may be subject to provider SLAs; review data residency requirements. Redis and Milvus should run on private networks or with authentication enabled. No penetration test or security audit data provided.

Alternatives to consider

LangChain / LangGraph standalone

Lighter, code-first approach. No pre-built UI or multi-tenant scaffolding, but more control and lower operational overhead for dev teams comfortable with Python/Node orchestration.

RAGFlow (by Infiniflow)

Also open-source, multi-tenant RAG platform. May have lower deployment complexity and different feature prioritization; evaluate feature parity and community maturity.

Mendable / MindsDB (commercial SaaS)

Fully managed, no self-hosting required. Trade operational overhead for subscription cost and vendor lock-in. Useful for teams avoiding infrastructure management.

Software development agency

Build on Yuxi with DEV.co software developers

Yuxi offers a complete open-source foundation for multi-tenant knowledge retrieval and agent orchestration. Let our team assess deployment architecture, multi-tenant isolation strategy, and LLM integration for your use case.

Talk to DEV.co

Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

agentic-rag-for-dummies Controllable-RAG-Agent swiftide

Related on DEV.co

Explore the category and the services that help you build with it.

RAG Frameworks AI Application Development Enterprise Software Custom Software Development Talk to DEV.co

Yuxi FAQ

Can I run Yuxi without Docker?

Unknown. Docs emphasize Docker Compose deployment. Manual setup (virtual env, process managers) is possible but not officially documented. Requires significant manual orchestration.

Does Yuxi support local/offline LLMs?

Partially. Yuxi requires OpenAI-compatible API endpoints. You can run Ollama, vLLM, or other local servers and point Yuxi to them, but built-in UI model switcher and full compatibility are Unknown.

What is the multi-tenant isolation guarantee?

Isolation is database-level by tenant_id in PostgreSQL/Redis. No official security audit or certification provided. Recommended for internal teams; external SaaS deployment requires threat modeling and validation.

How do I migrate from another RAG platform to Yuxi?

Data migration tooling is Unknown. Plan manual ETL: export embeddings/graphs from legacy system, re-ingest via Yuxi's document parser, rebuild Milvus indices. Requires engineering effort.

Software developers & web developers for hire

Need help beyond evaluating Yuxi? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and rag frameworks integrations — and maintain them long-term.

Ready to Deploy an Enterprise AI Agent Platform?

Talk to DEV.co Schedule a Consultation