Over the past several major releases, Apache Dubbo has accumulated a large number of modules, dependencies, and legacy integrations. This results in increased framework size, slower startup time, and higher dependency complexity for users who only need core RPC functionality.

With the increasing adoption of cloud-native and microservice environments, lightweight frameworks with minimal dependencies are becoming increasingly important.

Therefore, this project aims to analyze and refactor Dubbo's dependency structure to make the framework more modular and lightweight.

Goal

The project aims to improve Dubbo’s modularization and reduce unnecessary dependencies.

Expected tasks include:

Analyze the dependency graph of the Dubbo core modules.

Identify redundant or unnecessary dependencies.

Refactor module boundaries to improve modularity.

Reduce the size of the minimal runtime dependency set.

Provide documentation and benchmarks comparing before and after results.

Possible Extensions

Provide a minimal runtime profile for Dubbo.

Introduce optional dependency loading mechanisms.

Optimize startup time and memory footprint.

Relevant Skills

Java

Build tools (Maven / Gradle)

Dependency management

Microservice frameworks

Potential Mentors

Albumen Kevin, Apache Dubbo PMC, albumenj@apache.org
dev@dubbo.apache.org

Difficulty: Major

Project size: ~350 hour (large)

Potential mentors:

Liang He, mail: heliang666s (at) apache.org

Project Devs, mail:

GSoC 2026

– Aligning Dubbo APIs with Modern gRPC APIs
- Further strengthen the multi-language development of Dubbo.
BackGround And Goal
The RPC module of Dubbo Rust And Dubbo Python needs to be aligned with Dubbo Java to achieve feature parity: including but not limited to RPC protocol support (e.g., Dubbo, Triple, gRPC), serialization/deserialization mechanisms, load balancing strategies, and fault tolerance capabilities.
Goal
Expectd Tasks include:
1. Dubbo Rust or Dubbo Python: Re-architecture and Documentation Reorganization
2. Develop the registry model for the RPC module of Dubbo Rust/Dubbo Python to support the Triple protocol
3.Support for a variety of load balancing strategies
Relevant Skills
Java
Python or Rust
gRPC
HTTP
RPC Framworks
Background and Goal
Apache Dubbo aims to provide full interoperability with the gRPC ecosystem through the Triple protocol, which is designed to be fully compatible with gRPC communication and tooling.
However, the gRPC ecosystem has evolved significantly in recent years, including improvements in APIs, streaming interfaces, and client/server abstractions.
Some of these updates have not yet been fully reflected in Dubbo's current API design and SDK capabilities.
This project aims to evaluate the latest gRPC APIs and align Dubbo’s APIs with modern gRPC design patterns where appropriate.
Goal
Expected tasks include:
Investigate the latest gRPC API changes and features.
Identify gaps between Dubbo’s APIs and the gRPC ecosystem.
Improve or extend Dubbo APIs to better align with gRPC usage patterns.
Improve interoperability between Dubbo services and gRPC clients.
Provide examples and documentation for cross-framework usage.
Possible Extensions
Improve gRPC streaming support
Enhance proto-based service definitions
Improve developer experience for cross-language services
Relevant Skills
Java
gRPC
RPC frameworks
Distributed systems
Potential Mentors
Albumen KevinRain Yu,Apache Dubbo PMC,albumenj@apacherainyu@apache.org^{Image Modified}
dev@dubbo.apache.org

Difficulty: Major
Project size: ~350 hour (large)
Potential mentors:
Liang HeYu Yu, mail: heliang666s rainyu (at) apache.org
Project Devs, mail:

Apache Fineract

GSoC 2026 - Convert Dubbo capabilities into AI Skills.

Background and Goal

With the continuous evolution of AI technology and the growing popularity of AI coding, this GSOC project aims to add a series of AI Skills for Dubbo. These AI Skills will clearly describe Dubbo's core capabilities, key modules such as RPC, registry, and distributed system, as well as Dubbo's design principles. The goal is to help developers and relevant staff better understand, develop, and use Dubbo and its affiliated projects, and enable users to more efficiently understand and use Dubbo with the help of AI tools.

Goal

This project is designed to build a complete Dubbo Skills.
Expected tasks include:

Analyze the dependency graph of the Dubbo core modules.
Gain an in-depth understanding of Dubbo's inherent design and the design of each of its modules.

Develop a complete standalone Skills directory that can be correctly identified and utilized by AI

Relevant Skills

Java

Build tools (Maven / Gradle)

AI

Microservice frameworks

Potential Mentors

Albumen Kevin, Apache Dubbo PMC, albumenj@apache.org^{Image Added}
dev@dubbo.apache.org

Difficulty: Major

Project size: ~350 hour (large)

Potential mentors:

Yu Yu, mail: rainyu (at) apache.org

Project Devs, mail:

Apache Fineract

Loan Origination (POC)

No one should work on this specific ticket unless assigned - the GSOC candidate we choose will be assigned this ticket.

For more information, you should be reviewing emails on this subject and following the Wiki pages.
https://lists.apache.org/list.html?dev@fineract.apache.org
https://cwiki.apache.org/confluence/display/FINERACT/GSOC+Program+at+Fineract

LOAN ORIGINATION CONTEXT

Fineract has some loan origination functionality but it is not robust enough for many operations. Several vendors, working with Fineract have created new Loan Origination plug ins.

There is also a major enhancement underway that would build out a full Loan Origination flow by supporting the backend needs of data storage for such LOS. See ticket https://issues.apache.org/jira/browse/FINERACT-2418 .

The GSOC student would be expected to propose something as a POC (proof of concept) that would use and expand upon the developed Fineract backend solution. It should not revisit the design of that, and it should be separated enough as to not collide with ongoing work in the project that may be moving faster.

It may be useful to build a new component outside of Fineract to create the flows that would demonstrate the LOS functionality.

That is, this is a moving target, and we would need different proposals from prospective candidates to explore the area of Loan Origination. This may require expertise in risk assessment, loan origination models and business acumen. There will not be much more explanation that this available. The student would be expected to be a self starter.

The mentor for this would need to be an expert at risk modeling, understand Loan Origination, and support a conceptual basis that may involve some things internal to Fineract and some processing elements outside of Fineract. Please comment below if you are an existing Fineract contributor with this expertise.

To try to illustrate: one possible GSOC Proposal archtype we could accept would be a survey of Loan Origination Models, their strengths and weaknesses and to identify commonalities for the community to focus on. This would thus be a Requirements exercise and may help identify future roadmap concepts. In this case, the code to be developed may just expose a few APIs into different screen flows. Thus, perhaps FIGMA flows (or similar) connecting to a set of APIs on the backend.

If those new LOS APIs are existing in June 2026 (ticket 2418 resolved), then those APIs are to be used. if they are NOT there in Fineract, then the student would be requested to create a fork and to implement the POC outside of the main Dev branch.

I welcome additions to this write up. jdailey

Difficulty: Minor

Project size: ~350