Geniusrise, an open source framework and ecosystem for AI agents
Hello! Introducing geniusrise, an agent framework and component ecosystem for building AI agent networks that are as flexible as your team. landing page: https://geniusrise.ai (fancy but useless) docs: https://docs.geniusrise.ai (please check this out) github: https://github.com/geniusrise (for dear devs) ## Thought process Since the ChatGPT disruption, I've been pondering on what the tooling layer is going to look like for building LLM-interfacing agents. Saw a plethora of tools coming out as we witness here every week. I'd broadly categorize them into the…
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Hello! Introducing geniusrise, an agent framework and component ecosystem for building AI agent networks that are as flexible as your team. landing page: https://geniusrise.ai (fancy but useless) docs: https://docs.geniusrise.ai (please check this out) github: https://github.com/geniusrise (for dear devs) ## Thought process Since the ChatGPT disruption, I've been pondering on what the tooling layer is going to look like for building LLM-interfacing agents. Saw a plethora of tools coming out as we witness here every week. I'd broadly categorize them into the GUI drag-and-drop variety, and the airflow and MLflow competitors pivoting. Not counting experiment dumps. My opinions kind of differed from these. The thesis that I chose to work with, is based on Conway’s law - the tooling for building agents must accommodate various team, communication structures and expertise available in an org. This scheme could only work if the tooling itself was more of a thin infrastructure glue layer supporting loosely coupled components such that each component could be built, tested, deployed and re-used independently while leveraging the context and expertise of the ones building them. Also these components need to be "Write once, run anywhere". ## Scheme 1. Developers build components relevant to their infrastructure and services. 2. ML engineers and data scientists get to work on data massaging, prompt engineering and model training without having to build data ingestion pipelines from whatever database engineering teams or whatever service product may be using. 3. Devops could deploy these components in their choice of runner (e.g. k8s) and orchestrate with their choice of orchestrator (e.g. airflow). 4. Product, engineers or whoever could take these components and compose them into workflows, experiment, test, then hand over to devops to deploy. 5. Some of these components end up as open source, and benefit the entire community. Whichever of the above layer is not available, well, that’s where our open source modules kick in For example, no data science team? Use our standard huggingface components. Thus, the builders build, the users use, and open source plugs in the holes. The framework enables EVERYONE, without getting in the way. ## Community and Future Guys, this is for you, and I want to make it useful. Some feedback would be nice, and it would help me create a better roadmap. I plan to make this framework production ready by December '23. Having worked alone, and after countless sleepless nights and social sacrifices, I wanted to share this project before I start working on it full time from next week. Meanwhile I forgot to activate github sponsorships Anyway, I am excited! There is more in the pipeline - integrations (For example, model management, data governance and quality, runners, etc etc), modularization of the core framework, MOAR components, unit tests for components, etc Hell, the batch data modules do not even have partitioning schemes yet As the project grows bigger, I will be open to co-maintainers. The north star goal would be to build something worthwhile that can be donated to Apache / CNCF.
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