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Products that do what Remylar AI does

Context Infrastructure for Enterprise AI

  1. 1

    Reusable AI Memory for Smarter Prompts Anywhere

    Nov 2025

  2. 2

    Connect AI agents to governed metadata via MCP

    Jan 2026

  3. 3
    Context324

    The AI office suite

    2025

  4. 4
    Weavable234

    Give every AI agent persistent work context

    May 2026 · weavable.ai

  5. 5

    Shared Context for your AI Agents & Automations

    Feb 2026 · boost.space

  6. 6
    N71141

    Give all your AI agents one shared context

    Jul 2026 · n71.ai

  7. 7IB
  8. 8

    A unified control plane for Magento with Claude Code web

    Jun 2026 · storeframe.io

  9. 9

    Data processing infra & ETL for generative AI applications

    2024

  10. 10

    Operate AI coworkers on a single enterprise platform

    Feb 2026

  11. 11UF

    Hi HN! I want to share our latest project at NEXA AI. We developed AI agent foundation models designed to transform how developers create AI agent powered apps. One major challenge we've observed with current human-computer interactions is that many simple, one-step tasks become unnecessarily complex, multi-step workflows due to limitations of current GUIs. AI agents can solve this, but existing AI agent models are slow and costly. To tackle these issues, we built lightweight AI agent models based on our Octopus V2, small language models for function calling (You can learn more about our…

    2024 · nexa4ai.com

  12. 12

    Build grounded, governed, trustworthy data agents

    Jun 2026 · upsolve.ai

  13. 13

    Knowledge Sharing for AI Agents

    Mar 2026 · ctxoverflow.dev

  14. 14IB

    Excited to share a project I’ve been building for months! Would love to receive honest feedback :) My motivation: AI is clearly going to be the interface for data. But earlier attempts (text-to-SQL, etc.) fell short — they treated it like magic. The space has matured: teams now realize that AI + data needs structure, context, and rules. So I built a product to help teams deliver “chat with data” solutions fast with full control and observability (agent tracing, quality scores, etc) — am I wrong? The product allows you to connect any LLM to any data source with centralized context…

    Oct 2025 · github.com

  15. 15

    Open Source Context Infrastructure for AI Agents

    May 2026 · ravbyte-ai.github.io

  16. 16

    A collaborative AI workspace, built on your company context. Build and orchestrate agents right alongside your team

    5d ago · gem-manatee-cd0.notion.site

  17. 17RA

    Hi, founder of Okteto here! We’ve been experimenting with AI agents in our workflows at Okteto. Running them locally worked at first, but quickly became painful. git worktrees, multiple terminals, and messy context switches slowed us down. So we built Agent Fleets: ephemeral, fully managed environments for AI agents, built on top of Okteto’s development platform. Each agent runs in its own containerized environment on your infrastructure, with the services, tools, and policies it needs. You can spin up agents with a single click or API call. No local setup. No git worktrees. The beta…

    2025 · okteto.com

  18. 18PR
  19. 19

    Make context-aware AI agents, effortlessly

    Feb 2026 · getalchemystai.com

  20. 20AK
  21. 21

    Deploy AI agents that run your business workflows easily

    Jun 2026

  22. 22UA

    Hey HN! I'm Fabio and I built UltraContext, a simple context API for AI agents with automatic versioning. After two years building AI agents in production, I experienced firsthand how frustrating it is to manage context at scale. Storing messages, iterating system prompts, debugging behavior and multi-agent patterns—all while keeping track of everything without breaking anything. It was driving me insane. So I built UltraContext. The mental model is git for context: - Updates and deletes automatically create versions (history is never lost) - Replay state at any point The API is 5 methods:…

    Jan 2026 · ultracontext.ai

  23. 23OS

    We implemented Stanford's Agentic Context Engineering paper which shows agents can improve their performance just by evolving their own context. How it works: Agents execute tasks, reflect on what worked/failed, and curate a "playbook" of strategies. All from execution feedback - no training data needed. Happy to answer questions about the implementation or the research!

    Oct 2025 · github.com

  24. 24BM

    Today we released an open, Valkey-native context layer for AI agents as part of our packages at BetterDB (agent memory, semantic + multi-tier caching, typed retrieval) that run on a Valkey instance no matter where it is - no vendor lock-in. We even started provisioning Valkey instances starting today. Packages are shipped on npm and PyPi. Why we made it: BetterDB originally started as a monitoring and observability platform for Valkey, Redis and any RESP compatible db. This is still the core of the product, but in the process of building this, we kept seeing that one of the fastest-growing…

    Jun 2026 · github.com

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