v0.2.0

**Release date:** 2025.08.28  

## Highlights

UltraRAG 2.0 introduces a complete redesign of the system architecture, fully adopting the MCP-based modular structure. This release significantly improves flexibility, extensibility, and developer experience, enabling researchers and engineers to rapidly build, customize, and reproduce complex RAG pipelines with minimal overhead.

The core of UltraRAG 2.0 is the new MCP Server–Client workflow, providing a clean separation between functional components such as Corpus, Retriever, Generation, Evaluation, Router, and Note. The pipeline engine has been rebuilt to support looping, branching, multi-server instances, and fully customizable data flow, enabling Search-o1, RankCoT, WebNote, Iter-RetGen, and other advanced RAG pipelines to run out-of-the-box.

Developer experience is further enhanced with streamlined YAML configuration, Functool-style tool invocation, automatic server building, and improved error handling. UltraRAG 2.0 marks a major step toward a unified, reproducible, and developer-friendly RAG research platform.

## What’s Changed

**MCP Architecture & Server–Client Redesign**

- Unified MCP Server Architecture: All RAG components (Benchmark, Corpus, Retriever, Generation, Evaluation, Router, Note) are now implemented as standalone MCP Servers, enabling modular development and clean extensibility.
by @hm1229 @xhd0728
- Full MCP Client Pipeline Engine: Introduced a new pipeline executor supporting loop, branch, router, multi-server aliases, early stopping, and nested pipelines, making complex iterative workflows natively supported.
by @hm1229 @xhd0728 

**Corpus Server**

- Added unified file-reading support (doc/docx/pdf/md/txt) and multiple chunking strategies (static/semantic/LLM).
by @mssssss123 
- Enhanced Chunk Pipeline: Improved stability and extensibility for large-document processing and JSONL corpus generation.
by @mssssss123 

**Retriever & Reranker Server**

- Retriever Server Rebuilt: Embedding, indexing, retrieval, and reranking fully modularized. by @mssssss123 @Kaguya-19 @gdw439 
- Improved Vector Index Handling: Cleaner index-building API and improved fault tolerance during large-scale indexing.
by @mssssss123 @Kaguya-19 @gdw439 
- Reranker Server Added: Native support for passage reranking in modular form. by @mssssss123  @xhd0728 

**Prompt & Generation Server**

- Unified Generation Server: Supports OpenAI-compatible API, local HF/vLLM models, customizable sampling params, and Prompt Server integration. by @methanechen @hm1229 
- Prompt Server Support: Introduced dedicated Prompt Server for parameterized Jinja templates and reusable prompt logic.
by @hm1229 @xhd0728 

**Benchmark & Evaluation Server**

- Unified Benchmark Loader: Added support for multiple datasets simultaneously with standardized q/gt extraction.
by @lifelsl @xhd0728 
- Expanded Metrics: Added CoverEM, Acc, and improved EM/F1 calculation consistency.
by @xhd0728 @lifelsl 

**Baseline Pipeline Implementations**

- baselines added and tested on UltraRAG 2.0 MCP.
by @xhd0728 @Mignonmy  @lifelsl  
- Multi-Benchmark Leaderboard Support: Standardized evaluation outputs to enable stable leaderboard reproduction.
by @xhd0728 @mssssss123  

**Developer Experience Improvements**

- ultrarag build Command: Automatically compiles servers, generates server.yaml & parameter.yaml, and validates parameters.
by @hm1229 
- Improved Logging & Debugging: Added log-level control, error propagation, and clearer exception tracing.
by @hm1229  
- Multi-Agent/Multi-Retriever Support: Added server aliasing for running multiple retrievers or generators within one pipeline.
by @xhd0728 

**Documents**

- Project Website: The official UltraRAG 2.0 website. by @mssssss123 @xhd0728  
- Comprehensive Tutorial: A full end-to-end guide covering installation, MCP Server definitions, pipeline configuration, loop & branch logic, example baselines, and best practices for developing new servers and tools. by @mssssss123 @xhd0728 
- README: The README has been redesigned to include a clearer introduction to UltraRAG 2.0. by @mssssss123 @xhd0728 

**Contributors**

Thanks to everyone who contributed to the UltraRAG 2.0 beta and release:

- Project Leaders: @yanyk13 @mssssss123 
- Core Contributors: @xhd0728 @hm1229 @mssssss123 
- Contributors: @lifelsl @methanechen @Kaguya-19 @gdw439 @Mignonmy