AI-Forge - Your Coding Assistant

Abstract

The need forquicker development cycles, better teamwork, and shorter technical onboarding times has increased in the modern era of software innovation, especiallyinhugecodebasesandenterprise-gradesoftware systems. When growing modular architectures, maintaining old systems, or integrating new developers, traditional development pipelines frequently run into problems. A recurring barrier to guaranteeing code understanding, consistency, and maintainability is the enormous complexity of these systems, which is exacerbated by poor documentation and the dispersed structure of development teams. The problem is made worse by the lack of context-aware, real-time documentation, which raises deployment mistake rates, reduces productivity, and increases technical debt. In response to this problem, Ai-Forge stands out as a game- changing solution, providing an ecosystem powered byAI that revolutionizes the way developers engage with codebases. Ai-Forge uses sophisticated natural language processing(NLP),retrieval-augmentedgeneration(RAG), andreal-timeembeddingarchitecturestoenableintelligent understanding, querying, and visualization of software systems,incontrasttotraditionaldocumentationtoolsthat function statically or necessitate human intervention. By making sure that every line of code is self-explanatory, current, and dynamically interpretable, this innovation aims to reduce the knowledge imbalance among development teams and transform the software engineering workflow.The innovative multi-agent framework at the heart of Ai- Forge combines cutting-edge large language models (LLMs) for automated documentation production with real-time event-driven triggers. The system uses cloud- based features to identify code contributions and start a series of automated procedures, mostly through GitHubwebhooks and AWS Lambda. Specialized agents are assigned specific tasks by this distributed architecture: documentation synthesis agents create descriptive, readable content that reflects the current state of the codebase, code analysis agents segment and interpret the code\'s semantics, and event detection agents record changes as they happen. Every agent functions within a meticulously crafted communication protocol that draws inspirationfromconversationalparadigmspresentincontemporary multi-agent frameworks. The agents collaboratively improve their outputs through repeated multi-turn dialogues, guaranteeing that the final documentation captures the code\'s underlying functional and contextual subtleties in addition to its syntactic structure. Even with changing project complexity and massive codebases,Ai-Forge is able to continuously adapt anddevelopduetothedynamicinteractionbetweenagents.

Introduction

1. Background & Motivation:
Large Language Models (LLMs) have transformed software engineering by enabling intelligent, language-driven agents to collaborate in design, coding, and testing. Despite advancements, current approaches to software development are fragmented across lifecycle stages, leading to inefficiencies and inconsistencies—particularly in keeping documentation synchronized with evolving codebases.

2. Problem:
Manual and outdated documentation impedes software quality, onboarding, and maintenance. Existing tools suffer from:

• Synchronization delays

• Poor context understanding

• Fragmented workflows

• Scalability limitations

3. Solution – AI-Forge:
AI-Forge is a multi-agent, LLM-powered framework designed to automate real-time software documentation through dynamic collaboration and continuous synchronization with code changes.

4. Key Features of AI-Forge:

• Event Detection: Hooks into version control systems to detect code updates instantly.

• Code Analysis Agents: Parse and extract context from code changes.

• Documentation Synthesis Agents: Generate and refine technical documentation via multi-turn LLM dialogues.

• ChatChain Architecture: Structured dialogue system where agents interact in phases (analysis, synthesis, review), mirroring real team workflows.

• Iterative Refinement: Continuous self-correction and feedback loops improve documentation accuracy.

• Seamless Integration: Embeds directly into existing dev workflows without relying on external cloud services.

5. Operational Workflow:
AI-Forge transforms static documentation into a dynamic, modular process. Agents act as planners, coders, testers, and reviewers, collaborating in structured dialogues, with short- and long-term memory to maintain project context. The system iteratively improves output until convergence is reached.

6. ChatChain & Memory Design:

• ChatChain breaks down documentation into subtasks, each handled via multi-turn dialogues.

• Memory system ensures continuity across short and long phases, maintaining context and reducing errors.

• Communication policies and vectorized memory embeddings guide agent responses.

7. Communicative Dehallucination:
To combat LLM hallucination (factually incorrect outputs), AI-Forge implements a dialogue-based correction protocol, where agents:

1. Extract key claims.

2. Verify them against actual code.

3. Revise any inconsistencies.

8. Contribution:
AI-Forge fills a key research gap by offering a scalable, real-time, multi-agent documentation system that evolves alongside code. It reduces manual work, enhances codebase coherence, and supports maintainability through intelligent automation.

Conclusion

In conclusion, AI-Forge demonstrates a significant advancement in real-time automated documentation by leveraging a multi-agent framework inspired by ChatDev. The system effectively decomposes the complex process of software documentation into interrelated subtasks managed by role-specific agents. Through iterative, multi-turn dialogues and structured communicationprotocols,AI-Forgeachievesconsistent, accurate, and contextually rich documentation that evolves in tandem with its codebase. Ourexperimentalevaluation,encompassingquantitative metrics such as F1-score, BLEU score, DocMatch@K, and latency—as well as qualitative assessments of clarity,accuracy,andconsistency—indicatesthat AI-Forge reduces documentation lag significantly while improvingoverallsoftwaremaintainability.Theablation studiesfurtherconfirmthatkeycomponents,suchasrole specialization and iterative feedback loops, are integral to the system\'s performance, highlighting the critical importance of multi-agent collaboration in overcoming the limitations of single-agent architectures. While the current implementation is robust, future work shouldfocusonexpandingAI-Forge\'scapacitytohandle multiple programming languages and more diverse development environments. Enhancements in agent communication protocols and integration of domain- specific knowledge bases will further improve context awareness and minimize residual semantic drift. Additionally,extensivereal-worlddeploymentanduser- centric studies will be essential to fine-tune the system\'s scalability and operational efficiency under varying project complexities. Ultimately, AI-Forge sets a new benchmark for integrating automated documentation within software development workflows, offering a pathway towards more intelligent, efficient, and autonomous software engineering processes.

References

[1] Acuna,S.T.,Juristo,N.,&Moreno,A.M. (2006).Emphasizinghumancapabilitiesinsoftware development. IEEE Software, 23(2), 94–101. [2] Agnihotri, M., & Chug, A. (2020). Asystematic literature survey of softwaremetrics, code smells and refactoringtechniques. Journal of Information Processing Systems, 16(4), 915–934. [3] Banker,R.D.,Davis,G.B.,&Slaughter,S. A. (1998). Software developmentpractices, software complexity, andsoftwaremaintenanceperformance:Afieldstudy.ManagementScience,44(4),433–450. [4] Basili, V. R. (1989). Softwaredevelopment:Aparadigmforthefuture.In Proceedings of the Annual International Computer Software and Applications Conference (pp. 471–485). IEEE. [5] Brants,T., Popat,A. C., Xu, P., Och, F. J., & Dean, J.(2007). Largelanguage modelsin machine translation. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning(EMNLP-CoNLL)(pp.858–867). [6] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., et al. (2020). Language models are few-shotlearners. In Advances in Neural Information Processing Systems (NeurIPS), 33, 1877–1901. [7] Bubeck,S.,Chandrasekaran,V.,Eldan,R., Gehrke, J., Horvitz, E., Kamar, E., et al. (2023). Sparks of artificial generalintelligence:EarlyexperimentswithGPT-4.arXivpreprintarXiv:2303.12712. [8] Cai, T., Wang, X., Ma, T., Chen, X., & Zhou,D.(2023).Largelanguagemodelsastool makers.arXiv preprint arXiv:2305.17126. [9] Chan,C.M.,Chen,W.,Su,Y.,Yu,J.,Xue,W., Zhang, S., et al. (2023). ChatEval:Towards better LLM-based evaluatorsthroughmulti-agentdebate.arXivpreprint arXiv:2308.07201. [10] Chen, D., Wang, H., Huo, Y., Li, Y., & Zhang, H. (2023). GameGPT: Multi-agentcollaborative framework for gamedevelopment. arXiv preprint arXiv:2310.08067. [11] Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., Kaplan, J., et al. (2021).Evaluatinglargelanguagemodelstrained on code.arXiv preprint arXiv:2107.03374. [12] Chen,W.,Su,Y.,Zuo,J.,Yang,C.,Yuan, C., Qian, C., et al. (2023). AgentVerse:Facilitating multi-agentcollaboration andexploring emergent behaviors in agents. In International Conference on Learning Representations (ICLR). [13] Cohen, R., Hamri, M., Geva, M., & Globerson, A. (2023). LM vs LM:Detecting factual errors via crossexamination. arXiv preprint. [14] Dhuliawala, S., Komeili, M., Xu, J., Raileanu, R., Li, X., Celikyilmaz, A., & Weston, J. (2023). Chain-of-verificationreduces hallucination in large languagemodels.arXivpreprintarXiv:2309.11495. [15] Ding,S.,Chen,X.,Fang,Y.,Liu,W.,Qiu, Y.,&Chai,C.(2023).DesignGPT:Multi-agent collaboration in design.arXiv preprint arXiv:2311.11591. [16] Ernst,M.D.(2017).Naturallanguageisaprogramming language:Applying naturallanguage processing to softwaredevelopment. In Leibniz International Proceedings in Informatics (SNAPL), 71, 4:1–4:14. [17] Ezzini, S., Abualhaija, S., Arora, C., &Sabetzadeh, M. (2022). Automatedhandling of anaphoric ambiguity inrequirements: A multi-solution study. In Proceedings of the International Conference on Software Engineering (ICSE), 187–199. [18] Freeman, P., Bagert, D. J., Saiedian, H., Shaw,M.,Dupuis,R.,&Thompson,J.B. (2001). Software engineering body ofknowledge (SWEBOK). In Proceedings of the International Conference on Software Engineering (ICSE), 693–696. [19] Gao,S.,Chen,C.,Xing,Z.,Ma,Y.,Song, W., & Lin, S.-W. (2019).Aneural modelfor method name generation fromfunctional description. In IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 411–421. [20] Hong,S.,Zhuge,M.,Chen,J.,Zheng,X.,Cheng, Y., Zhang, C., et al. (2023). MetaGPT:Metaprogrammingforamulti-agent collaborative framework. In International Conference on Learning Representations (ICLR). [21] Hua,W.,Fan,L.,Li,L.,Mei,K.,Ji,J.,Ge,Y., et al. (2023). War and Peace(WarAgent):Largelanguagemodel-basedmulti-agentsimulationofworldwars.arXivpreprintarXiv:2311.17227. [22] Ji,Z.,Lee,N.,Frieske,R.,Yu,T.,Su,D.,Xu, Y., et al. (2023). Survey ofhallucination in natural languagegeneration.ACMComputingSurveys, 55(12), 1–38. [23] Kaplan, J., McCandlish, S., Henighan,T., Brown, T. B., Chess, B., Child, R., et al. (2020). Scaling laws for neural languagemodels.arXivpreprintarXiv:2001.08361. [24] Li,G.,Hammoud,H.A.A.K.,Itani,H., Khizbullin, D., & Ghanem, B. (2023).CAMEL:Communicativeagentsfor“mind”explorationoflargescalelanguagemodel society. In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS). [25] Li,Y.,Zhang,Y.,&Sun,L.(2023). MetaAgents:Simulatinginteractionsofhuman behaviors for LLM-based task-oriented coordination via collaborativegenerative agents.arXiv preprint arXiv:2310.06500. [26] Liu, Z., Yao, W., Zhang, J., Xue, L., Heinecke,S.,Murthy,R.,etal.(2023).BOLAA:BenchmarkingandorchestratingLLM-augmented autonomous agents.arXiv preprint arXiv:2308.05960. [27] Ma,K.,Zhang,H.,Wang,H.,Pan,X.,& Yu,D.(2023).LASER:LLMagentwithstate-space exploration for webnavigation.arXiv preprint arXiv:2309.08172. [28] López Martín, C., &Abran, A. (2015). Neural networks for predicting thedurationofnewsoftwareprojects.Journal of Systems and Software, 101, 127–135. [29] Nahar,N.,Zhou,S.,Lewis,G.A.,& Kästner, C. (2022). Collaborationchallenges in building ML-enabledsystems:Communication,documentation,engineering, and process. In Proceedings of the International Conference on Software Engineering (ICSE), 413–425. [30] Nijkamp,E.,Pang,B.,Hayashi,H.,Tu, L.,Wang,H.,Zhou,Y.,etal.(2023).CodeGen:An open large language modelfor code with multi-turn programsynthesis.InTheInternationalConference on Learning Representations (ICLR). [31] Osika,A.(2023).GPT-Engineer.GitHubRepository. [32] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., et al. (2022). Training language models tofollowinstructionswithhumanfeedback.arXiv preprint arXiv:2203.02155. [33] Park, J. S., O’Brien, J., Cai, C. J., Liang, P.,&Bernstein,M.S.(2023).Generativeagents:Interactivesimulacraofhumanbehavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST), 1–15. [34] Pudlitz,F.,DeLine,R.,&Xu,W.(2019). DocumentinguserscenariosatscalewithStoryboard Tools. In IEEE 27th International Requirements Engineering Conference (RE), 80–91. [35] Qin,Y.,Gao,S.,&Peng,B.(2023).ToolLLM:Facilitatingcodegenerationbylarge language models with toolaugmentation.arXiv preprint arXiv:2307.16789. [36] Qin,Z.,Huang,Z.,Jiang,J.,&Zhang,H. (2023). AgentSims: A multi-agentsimulationenvironmentforsocialandscientific discovery.arXiv preprint arXiv:2306.17563. [37] Radford,A.,Narasimhan,K.,Salimans,T., & Sutskever, I. (2018). Improvinglanguageunderstandingbygenerativepre-training.OpenAI Blog. [38] Richards,T.B.(2023).AutoGPT.GitHubRepository. [39] Ruan,J.,Zhang,S.,Lin,X.,Yang,H.,& Zhang, Z. (2023). ChatDev:Revolutionizing software developmentwithAI-generatedagents.arXivpreprint arXiv:2308.03427. [40] Sawyer, S., &Guinan, P. J. (1998). Software development: Processes andperformance.IBMSystemsJournal,37(4), 552–569.

Copyright

Copyright © 2025 Malik Mohammed Ali, Sachin Pramod Mishra, Nikunj Hemraj Bhanushali , Pranav Vitthal Salunkhe, Prof. Mahendra Patil. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.