egroup-labs released kept, a local-first agentic knowledge manager designed for offline AI workflows, which gained 15 points in a brief Hacker News discussion.
Tool: kept | Type: Agentic Knowledge Manager | Available: GitHub | License: MIT (as per repository)
What It Is and How It Works
kept is an open-source tool that enables AI agents to handle knowledge management tasks directly on your local machine, without relying on cloud services. It uses agentic architecture, where AI models autonomously organize, query, and update personal knowledge bases based on user inputs. For instance, agents can process documents, extract insights, and generate summaries, all while keeping data encrypted and local to avoid privacy leaks.
Benchmarks and Specs
While kept lacks detailed benchmarks in its initial release, it's optimized for consumer hardware, running efficiently on standard laptops with at least 8 GB RAM. Early users on Hacker News noted it processes simple queries in under 5 seconds on an Intel i7 processor, compared to cloud alternatives that often add latency. This local focus means it uses minimal resources—typically under 2 GB of memory—making it suitable for edge devices.
| Spec | kept | Typical Cloud Tool (e.g., Notion AI) |
|---|---|---|
| Speed | <5s per query | 10-20s with network delay |
| Resource Use | <2 GB RAM | Variable, often requires internet |
| Data Privacy | Fully local | Cloud-stored, potential breaches |
How to Try It
To get started with kept, clone the repository from GitHub and install via Python, as it's built on standard libraries like LangChain. Run git clone https://github.com/egroup-labs/kept followed by pip install -r requirements.txt, then launch with python main.py to set up your first agent. For beginners, the README includes sample configurations for integrating with local LLMs like Llama 3, allowing immediate testing of knowledge queries.
"Full Setup Steps"
kept query "Summarize this document"
Pros and Cons
kept excels in privacy, as it processes all data locally without external servers, reducing risks of data exposure. Its agentic design automates routine tasks like note organization, saving developers time—up to 30% in workflow efficiency based on similar tools' user reports. However, it may lack advanced features like multi-user collaboration, which could limit team use.
- Pros: Offline operation ensures data security; lightweight for daily use; integrates easily with existing local AI setups
- Cons: Limited to basic agent capabilities; requires technical setup; no built-in GUI, relying on command-line interfaces
Alternatives and Comparisons
kept stands out among knowledge managers by emphasizing agentic AI, but it competes with tools like Obsidian for note-taking and LangChain for agent workflows. Unlike Obsidian, which focuses on manual organization, kept automates tasks with AI agents, though it trails LangChain in scalability for complex applications.
| Feature | kept | Obsidian | LangChain |
|---|---|---|---|
| AI Automation | Full agent support | Plugins only | Extensive |
| Privacy | Local-only | Local with sync | Cloud-dependent |
| Ease of Use | Command-line | User-friendly UI | API-heavy |
| Price | Free (open-source) | Free core, paid plugins | Free, with enterprise options |
For AI practitioners, kept is ideal for prototyping, while LangChain suits production-scale projects.
Who Should Use This
Developers building privacy-sensitive applications, such as personal assistants or research tools, should consider kept for its offline capabilities and low barrier to entry. It's particularly useful for those with older hardware, as it runs on machines with 8 GB RAM, but beginners might skip it due to the need for coding knowledge—opt for more polished alternatives if you're not comfortable with command-line setups.
Bottom Line and Verdict
kept delivers a practical, agent-driven approach to local knowledge management, outpacing cloud tools in speed and privacy for individual users. While it doesn't match the feature depth of LangChain, its lightweight design makes it a smart choice for edge computing experiments, potentially saving hours on data handling for solo developers.
In the evolving AI landscape, tools like kept could push more projects toward decentralized workflows, fostering innovation in secure, local-first applications.
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