| ## Roadmap | |
| ### Long-term Objective | |
| Enable MetaGPT to self-evolve, accomplishing self-training, fine-tuning, optimization, utilization, and updates. | |
| ### Short-term Objective | |
| 1. Become the multi-agent framework with the highest ROI. | |
| 2. Support fully automatic implementation of medium-sized projects (around 2000 lines of code). | |
| 3. Implement most identified tasks, reaching version 0.5. | |
| ### Tasks | |
| To reach version v0.5, approximately 70% of the following tasks need to be completed. | |
| 1. Usability | |
| 1. Release v0.01 pip package to try to solve issues like npm installation (though not necessarily successfully) | |
| 2. Support for overall save and recovery of software companies | |
| 3. Support human confirmation and modification during the process | |
| 4. Support process caching: Consider carefully whether to add server caching mechanism | |
| 5. Resolve occasional failure to follow instruction under current prompts, causing code parsing errors, through stricter system prompts | |
| 6. Write documentation, describing the current features and usage at all levels | |
| 7. ~~Support Docker~~ | |
| 2. Features | |
| 1. Support a more standard and stable parser (need to analyze the format that the current LLM is better at) | |
| 2. ~~Establish a separate output queue, differentiated from the message queue~~ | |
| 3. Attempt to atomize all role work, but this may significantly increase token overhead | |
| 4. Complete the design and implementation of module breakdown | |
| 5. Support various modes of memory: clearly distinguish between long-term and short-term memory | |
| 6. Perfect the test role, and carry out necessary interactions with humans | |
| 7. Provide full mode instead of the current fast mode, allowing natural communication between roles | |
| 8. Implement SkillManager and the process of incremental Skill learning | |
| 9. Automatically get RPM and configure it by calling the corresponding openai page, so that each key does not need to be manually configured | |
| 3. Strategies | |
| 1. Support ReAct strategy | |
| 2. Support CoT strategy | |
| 3. Support ToT strategy | |
| 4. Support Reflection strategy | |
| 4. Actions | |
| 1. Implementation: Search | |
| 2. Implementation: Knowledge search, supporting 10+ data formats | |
| 3. Implementation: Data EDA | |
| 4. Implementation: Review | |
| 5. Implementation: Add Document | |
| 6. Implementation: Delete Document | |
| 7. Implementation: Self-training | |
| 8. Implementation: DebugError | |
| 9. Implementation: Generate reliable unit tests based on YAPI | |
| 10. Implementation: Self-evaluation | |
| 11. Implementation: AI Invocation | |
| 12. Implementation: Learning and using third-party standard libraries | |
| 13. Implementation: Data collection | |
| 14. Implementation: AI training | |
| 15. Implementation: Run code | |
| 16. Implementation: Web access | |
| 5. Plugins: Compatibility with plugin system | |
| 6. Tools | |
| 1. ~~Support SERPER api~~ | |
| 2. ~~Support Selenium apis~~ | |
| 3. ~~Support Playwright apis~~ | |
| 7. Roles | |
| 1. Perfect the action pool/skill pool for each role | |
| 2. Red Book blogger | |
| 3. E-commerce seller | |
| 4. Data analyst | |
| 5. News observer | |
| 6. Institutional researcher | |
| 8. Evaluation | |
| 1. Support an evaluation on a game dataset | |
| 2. Reproduce papers, implement full skill acquisition for a single game role, achieving SOTA results | |
| 3. Support an evaluation on a math dataset | |
| 4. Reproduce papers, achieving SOTA results for current mathematical problem solving process | |
| 9. LLM | |
| 1. Support Claude underlying API | |
| 2. ~~Support Azure asynchronous API~~ | |
| 3. Support streaming version of all APIs | |
| 4. ~~Make gpt-3.5-turbo available (HARD)~~ | |
| 10. Other | |
| 1. Clean up existing unused code | |
| 2. Unify all code styles and establish contribution standards | |
| 3. Multi-language support | |
| 4. Multi-programming-language support | |