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## Roadmap | ||
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### Long-term Objective | ||
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Enable MetaGPT to self-evolve, accomplishing self-training, fine-tuning, optimization, utilization, and updates. | ||
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### Short-term Objective | ||
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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. | ||
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### Tasks | ||
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To reach version v0.5, approximately 70% of the following tasks need to be completed. | ||
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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 | ||
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. 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 | ||
7. 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 | ||
8. LLM | ||
1. Support Claude underlying API | ||
2. Support Azure asynchronous API | ||
3. Support streaming version of all APIs | ||
9. Other | ||
1. Clean up existing unused code | ||
2. Unify all code styles and establish contribution standards | ||
3. Multi-language support |