{"signature": "0xb1e0613fdef8807f35dbf42c8ac6c801ec73518ece10d4f5c3665d21f814b0d055b8ca3b5d1a38f9a564f83dc9a5db8380beca3647946fc928f10dbcf92625b81b", "application": {"round": "0x0C0412DDB846096Ea1e37de717921EBF4fEF9A39", "answers": [{"type": "email", "answer": "genielongevity@gmail.com", "hidden": true, "question": "Email Address", "questionId": 0}, {"type": "paragraph", "answer": "15K USD from which 1K from VITA DAO, 10K from HEALES (Healthy Life Extension Society), and the rest from gitcoin", "hidden": false, "question": "Funding Sources and Total prior funding for project in USD", "questionId": 1}, {"type": "number", "answer": "6", "hidden": false, "question": "Team Size", "questionId": 2}, {"type": "paragraph", "answer": "https://github.com/antonkulaga \nhttps://github.com/winternewt https://github.com/marfantyk \n( https://github.com/Alex-Karmazin , https://www.linkedin.com/in/alex-karmazin-ua/), \n( https://github.com/nikhilYadala , https://www.linkedin.com/in/nikhilyadala/ )\nhttps://github.com/Livia-Zaharia ", "hidden": false, "question": "Profiles or socials of other main team members publicly associated with project", "questionId": 3}, {"type": "short-answer", "answer": "antonkulaga", "hidden": true, "question": "Telegram username", "questionId": 4}, {"type": "short-answer", "answer": "24", "hidden": false, "question": "How old is the project? (Months)", "questionId": 5}, {"type": "paragraph", "answer": "Problem\n\nWhile modern AI tools can assist with general literature searches and summarizing existing texts (\"deep research\"), they often fall short when applied to highly specialized and rapidly evolving fields like aging biology and genetics. Accessing and understanding the deep, nuanced concepts requires more than just text processing; it demands familiarity with specific experimental contexts, biological pathways, and the latest validated findings, which generic models typically lack.\n\nThe core limitation arises because standard AI is not designed to effectively interact with or interpret the wealth of structured information stored in specialized biological databases crucial to longevity research (such as GenAge, DrugAge, or genomic repositories). These databases contain vital data on genes, compounds, lifespans, and clinical trials. Without the ability to intelligently query, integrate, and reason over this structured data alongside unstructured text from research papers, AI fails to provide the comprehensive, accurate, and context-aware insights needed by researchers, citizen scientists, and enthusiasts in the field.\n\nSolution\n\nLongevity Genie directly tackles these challenges by developing advanced, open-source AI tools using techniques like Retrieval-Augmented Generation (RAG) and specialized semantic search optimized for scientific content. Our approach prioritizes connecting AI directly to reliable primary sources – both research literature and curated biological databases – ensuring the information is accurate, current, and scientifically grounded.\n\nWe are actively implementing a modular, multi-agent architecture, recognizing its significant advantages. By creating multiple specialized agents – each an expert on a specific database (like GenAge) or analytical task (like gene set analysis) – we achieve greater accuracy and efficiency for targeted inquiries. Furthermore, this modularity allows higher-level orchestrator agents to integrate findings from various specialized agents, enabling complex analyses and comprehensive summaries across diverse data sources. This structure, supported by frameworks like just-agents, offers immense flexibility for building custom workflows and integrating tools easily.\n\nAs a key part of our planned development and future strategy, we are designing and releasing Model Context Protocol (MCP) servers. These specialized servers will act as standardized, efficient interfaces to supply structured, up-to-date context directly from various longevity databases. The importance of MCP lies in its framework-agnostic design, allowing any compatible AI agent framework (not just our own) to readily access this crucial biological data. Implementing MCP will significantly boost the analytical power and domain awareness of interconnected agents as our ecosystem evolves.\n\nRoadmap:\n\nPhase 1, Q2 2025 - Q3 2025: \nRefine prompts and refactor the code to support newly added models to achieve the level of output quality on par with proprietary models \nAdd Longevity MCP servers and promote their usage by agent systems\nRefresh scientific papers datasets, and index scientific papers with novel advanced embeddings (Jina v3)\nPhase 2 (Q4 2025 - Q1 2026) \nRefine RAG search by using several agents and reranker model working together\nCreate serializable multimodal agents to enhance RAG results with textual transcription of scientific papers figures.\nBring up the telegram bot as an alternative interface.", "hidden": false, "question": "Describe your project in a few paragraphs (ideally including a problem statement, solution, roadmap, and timeline for implementation)", "questionId": 6}, {"type": "paragraph", "answer": "Longevity Genie aligns strongly with several core principles and goals of the Decentralized Science (DeSci) movement:\nOpenness and Accessibility: At its heart, Longevity Genie is committed to being open-source. Our core frameworks (just-agents, just-semantic-search), specialized agents, and associated datasets are publicly available (e.g., via GitHub and Hugging Face). This directly supports the DeSci goal of removing barriers and paywalls, making advanced scientific tools and data freely accessible to anyone, anywhere.\n\n\nDemocratization of Science: A primary objective is to make complex scientific fields like aging biology understandable and usable for a broader audience, including citizen scientists, tech enthusiasts, and amateur researchers. By providing tools that simplify access to and interpretation of sophisticated research and databases, we empower individuals outside traditional academic institutions to participate in learning, exploration, and potentially even discovery, aligning perfectly with DeSci's aim to widen participation.\n\n\nTransparency and Verifiability: Our emphasis on Retrieval-Augmented Generation (RAG) and directly querying high-quality research papers and biological databases enhances transparency. Instead of opaque outputs, our tools aim to provide insights grounded in specific, identifiable sources. This focus on source data aligns with the DeSci principle of making scientific claims more verifiable and processes more transparent.\n\n\nInteroperability and Collaboration: The development of a modular, multi-agent architecture and especially the framework-agnostic Model Context Protocol (MCP) promotes interoperability. By creating reusable components and standardized ways for AI agents to access specialized scientific context, we facilitate collaboration. Other researchers or DeSci projects can potentially integrate our tools or adopt MCP, fostering a more interconnected and collaborative scientific ecosystem, rather than building isolated silos.", "hidden": false, "question": "How does your project support the mission or goals of DeSci?", "questionId": 7}, {"type": "paragraph", "answer": "We applied for computational grants to Nebius and Google. We hope to use 80% of the grant on work compensation, while 20% will go on API credits for the best proprietary LLMs (in cases when open-source LLMs fail).", "hidden": false, "question": "Describe how funds from the grants program will be used for your project.", "questionId": 8}, {"type": "paragraph", "answer": "see https://gap.karmahq.xyz/project/longevity-genie/", "hidden": false, "question": "If you participated in past grant rounds (i.e. DeSci GR15/ Beta/GG 20DeSci round/GG21 DeSci round) using a different project payout wallet address, please share it here.", "questionId": 9}, {"type": "paragraph", "answer": "see https://gap.karmahq.xyz/project/longevity-genie/", "hidden": false, "question": "If this project participated in past DeSci grant rounds, please share any new updates or milestones since the last round.", "questionId": 10}, {"type": "paragraph", "answer": "team members affilations: https://heales.org/ , https://ibima.med.uni-rostock.de/ , https://biochatter.org/ , http://aging-research.group/", "hidden": true, "question": "Is there any other information you would like to share about your project, previous work, other project affiliations, or potential conflicts of interest? (or anything not covered elsewhere in this application that you feel is relevant to share)", "questionId": 11}, {"type": "dropdown", "answer": "Yes", "hidden": false, "question": "Does 50% of your team reflect the diversity of the community your project serves with a particular emphasis on including women, Black, Indigenous, and People of Color (BIPoC), individuals with disabilities, and members of the LGBTQ+ community?", "questionId": 12}], "project": {"id": "0xa8accb6ea853b273231c6410d29440c9593329e74b5ffa19b3349a7eb9549056", "title": "Longevity Genie", "logoImg": "bafkreibgf2f3d6lahxa53uzyzmtskuyl35arrzii2znrek7raev4bvqrgq", "metaPtr": {"pointer": "bafkreia42crok6suybl76ag5iag6kpvsprj3giqrlaf2yjpyhvdkg2icfe", "protocol": "undefined"}, "website": "https://longevity-genie.info", "bannerImg": "bafkreidkibavvvbsckh73p72frac62hhilcxxx7wxyc3rs6dj6lsoli3f4", "createdAt": 1692052572842, "userGithub": " antonkulaga", "credentials": {"github": {"type": ["VerifiableCredential"], "proof": {"jws": "eyJhbGciOiJFZERTQSIsImNyaXQiOlsiYjY0Il0sImI2NCI6ZmFsc2V9..8UY0duFZBO6x4b9bvrlRQwI7TvB5smr7WuuewmebV4jegZoHVPHc_cyR9NYKRI64puk6AkJS9dxjstfr3hrJBQ", "type": "Ed25519Signature2018", "created": "2024-08-08T21:39:10.482Z", "proofPurpose": "assertionMethod", "verificationMethod": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC#z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC"}, "issuer": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC", "@context": ["https://www.w3.org/2018/credentials/v1"], "issuanceDate": "2024-08-08T21:39:10.482Z", "expirationDate": "2024-11-06T21:39:10.482Z", "credentialSubject": {"id": "did:pkh:eip155:1:0xBc7BeB2d44Eb0Cddf6c0F2560a21475c2E2aC19C", "hash": "v0.0.0:r0kUiWSMqjJbd+4W0SZZE5bGkFm5Q3cM2AkYzs1Gi0w=", "@context": [{"hash": "https://schema.org/Text", "provider": "https://schema.org/Text"}], "provider": "ClearTextGithubOrg#longevity-genie#842436"}}, "twitter": {"type": ["VerifiableCredential"], "proof": {"jws": "eyJhbGciOiJFZERTQSIsImNyaXQiOlsiYjY0Il0sImI2NCI6ZmFsc2V9..dEwlCRPBAZoCuPstoUlShFIBgdDGOLU_sEdrh0ME1o4VUIzimx26Mlnn_dh-XWC3MMlaq0NalxK6KTSELR5NDQ", "type": "Ed25519Signature2018", "created": "2024-08-08T21:38:33.662Z", "proofPurpose": "assertionMethod", "verificationMethod": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC#z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC"}, "issuer": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC", "@context": ["https://www.w3.org/2018/credentials/v1"], "issuanceDate": "2024-08-08T21:38:33.661Z", "expirationDate": "2024-11-06T21:38:33.661Z", "credentialSubject": {"id": "did:pkh:eip155:1:0xBc7BeB2d44Eb0Cddf6c0F2560a21475c2E2aC19C", "hash": "v0.0.0:zYJ0YKL8PLWq97eeaty6tyZt6OHVkkbkERgI4QB98fk=", "@context": [{"hash": "https://schema.org/Text", "provider": "https://schema.org/Text"}], "provider": "ClearTextTwitter#longevity_genie"}}}, "description": "\n\n## Longevity Genie: Advanced AI Tools for Aging Biology Research\n\nLongevity Genie develops advanced, open-source AI tools that provide reliable access and insights into complex scientific fields like **aging biology, genetics, and longevity research**. Our goal is to make cutting-edge scientific knowledge easier to access and use for citizen scientists, tech enthusiasts, and amateur researchers alike.\n\nTypical AI models often struggle with precise and up-to-date scientific details, especially in fast-moving fields such as geroscience. Longevity Genie addresses this problem by using advanced techniques like Retrieval-Augmented Generation (RAG), Hybrid Search, and specialized AI agents built with our own frameworks and easy to integrate with others. Our tools retrieve data directly from high-quality research papers and biological databases, improving accuracy and trustworthiness for scientific exploration.\n\n**New Strategy: A Modular, Multi-Agent Future (In Progress!)**\n\nWe are currently **transitioning to a more powerful, modular multi-agent architecture**. Rather than relying on a single large system, we are developing multiple specialized AI agents, each designed for specific scientific databases or research tasks (such as analyzing gene sets, summarizing biological pathways, or finding relevant research papers). This strategy focuses on building reusable, flexible AI tools that can easily integrate into diverse workflows.\n\nThis modular architecture includes:\n\n* **[just-agents](https://github.com/longevity-genie/just-agents):** A lightweight, flexible framework we created to quickly build and organize specialized AI agents. It emphasizes simplicity and versatility, allowing agents to integrate seamlessly into various research workflows—not limited to chat interfaces.\n* **[just-chat](https://github.com/longevity-genie/just-chat):** \nA versatile tool showcasing how agents built with just-agents can be interacted with directly via a simple chat interface: equally useful for testing, iterating prompts, exploration, and specific applications alike.\n\n* **[just-semantic-search](http://github.com/longevity-genie/just-semantic-search/):** An advanced search library optimized specifically for scientific content, efficiently indexing and retrieving information from research papers and biological databases, supporting a hybrid of conventional and semantic (vector similarity based) search\n\nAs part of this approach, we're also developing and releasing **Model Context Protocol (MCP) servers**. These specialized servers efficiently supply context from various longevity-focused databases (such as GenAge and DrugAge) and research tools directly to AI agents. MCP is designed to be framework-agnostic, compatible with most popular agent frameworks—including but not limited to our just-agents framework, many of which either plan or have already implemented MCP support.\n\nThe ultimate vision is connecting these specialized agents into a sophisticated multi-agent system, providing comprehensive scientific insights via flexible, accessible tools.\n\n**Core Components & Frameworks:**\n\n* **[GitHub Organization](https://github.com/longevity-genie):** Home for our open-source codebase, including core frameworks (just-agents, just-chat, just-semantic-search) and specialized agents built to explore multiple [aging-related databases](https://github.com/longevity-genie/longdata).\n* **[Project Website](https://longevity-genie.info/):** General project information and updates.\n* **[Genetics Genie Chat](https://chat.longevity-genie.info/):** Web interface demonstrating the just-chat functionality with an agent specialized in aging-related genetics research.\n* **Example Agent Applications & Demonstrations:** Real-world examples showcasing our frameworks, usually built around specific biological databases:\n\t* **[Genetics Genie GPT](https://chat.openai.com/g/g-HnvTUyFeq-genetics-genie):** A GPT-based agent using hybrid search across multiple genetics databases relevant to aging research. (This agent alone facilitated over 10,000 conversations, earning 200+ user ratings averaging 4.3/5 stars, organically demonstrating significant community interest.)\n\t* **[Clinical-Trials Genie](https://chat.openai.com/g/g-tpOIOEeWm-clinical-trials-genie):** An agent offering insights from ClinicalTrials.gov, useful for tracking aging-related clinical trials (GPT example).\n\t* **[DrugAge Genie](https://chat.openai.com/g/g-tpOIOEeWm-clinical-trials-genie):** An agent focused on compounds that influence lifespan, utilizing the DrugAge database (GPT example). *(Note: Link appears identical to Clinical-Trials Genie in the original text)*\n\t* **[AnAge Genie](https://chat.openai.com/g/g-I8OGEVL4X-animal-ageing-genie):** An agent providing lifespan information across species from the AnAge database (GPT example).\n* **[Longevity GPT](https://asklongevitygpt.com/):** Our initial project demonstrating Retrieval-Augmented Generation and advanced referencing techniques tailored to aging literature.\n* **[Aging-related papers datasets](https://huggingface.co/longevity-genie):** Aging research datasets hosted on Hugging Face, supporting development and research.\n\n**Our Goal: Accessible and Modular AI Tools for Aging Research**\n\nLongevity Genie is committed to accelerating discoveries in aging biology using powerful AI. Our overarching goal is to create and share robust, specialized AI agents and tools, accessible for integration into diverse applications—empowering citizen scientists, amateur researchers, and enthusiasts. These tools go far beyond typical chatbots, enabling advanced scientific discovery for everyone interested in longevity science.", "lastUpdated": 0, "projectGithub": "longevity-genie", "projectTwitter": "longevity_genie"}, "recipient": "0x410e98D59B2B9596Dc6fE9b801f8d222B606d90c"}}