Alloomi

Alloomi is Meland Labs' early-access workspace for turning a practice's methods, reviews, and project history into a digital employee that keeps working after the chat ends. The live site (alloomi.ai, footer v0.6.1 · early access) sells legal full-stack employees first. Insurance and patent employees are marked coming soon. X account @AlloomiAI is the official handle. GitHub org is melandlabs.

This page exists because the X For You feed surfaced Alloomi's context-runtime pitch (OpenContext / Holistic Context): keep long-term working state outside a 1M window, then retrieve what the current task needs. The product name on the site is Alloomi, not a separate OpenContext download.

Key Features

• Expertise as the OS: the employee is supposed to apply your standards, not a generic assistant voice.
• Holistic / wholistic context: people, projects, files, decisions, and history in one current view. Public report: alloomi.ai/reports/hc.pdf. The landing page quotes 97.6% LongMemEval-S, 97.4% LoCoMo-V2, 67.0% BEAM at 10M. Those are vendor numbers. We did not rerun them.
• Self-evolving loop: learn from delivery and expert correction. Public report: alloomi.ai/reports/sea.pdf. Landing page quotes CL-bench 24.5% to 47.6% on the same backbone (+23.1 pp).
• OKR-driven work: objectives become prioritized tasks; the agent asks for approval when needed.
• Connectors listed on the homepage: Gmail, Calendar, HubSpot, Notion, Slack, Outlook, LinkedIn, X, Drive, Docs, WhatsApp, Telegram, Discord, iMessage, Feishu, DingTalk, QQ, WeChat.

Use Cases

• Law practices that want a revenue partner or a paralegal that keeps TB-scale matter context.
• Founders who already live in Slack / Feishu / mail and want something that watches approved signals instead of waiting for a prompt.
• People comparing agent memory products after a 1M-context tweet. Alloomi's claim is retrieval plus a learning loop, not a bigger window.

Limitation: this is early access. The homepage still lists insurance and patent employees as coming soon. Treat the benchmark PDFs as first-party reports, not third-party audits. If you needed an MIT runtime you can clone tonight, use DeepSeek Harness or Hermes Agent instead.

Pricing

From alloomi.ai/pricing on 2026-08-19. The three plan cards are the source. The comparison table on the same page disagrees on history retention, so we do not quote that table.

Credits: a simple summary is about 10-50 credits; a complex multi-turn analysis is about 100-300, per the pricing FAQ.

Getting Started

1. Open alloomi.ai and the docs.
2. Start on Free if you only need 30k credits and 3-day history.
3. Connect the few sources that actually hold the practice, not every logo on the homepage.
4. Read hc.pdf before you repeat the 97% figures in a deck.

Frequently Asked Questions

Is OpenContext a separate product?

The For You tweet called it OpenContext. The shipping product and pricing page are Alloomi. We catalog the site you can sign up for.

Is it open source?

The marketing site is a hosted workspace. There is a melandlabs org. Do not assume the employee runtime is MIT unless a specific repo says so.

Who makes it?

Footer: Meland Labs, © 2026.

Alternatives

• Cumora: team chat where agents are roster mates. Better if you wanted rooms, not a legal employee.
• Hermes Agent: MIT self-hosted agent with its own learning loop.
• DeepSeek Harness: clone-and-run runtime. You pay the model API only.

Tips

1. Quote the plan cards, not the comparison grid, until Meland fixes the retention mismatch.
2. Keep the learning loop on a leash: Alloomi's own X replies talk about checkpoints and rollback. Use that, or you will bake a bad review into the next file.
3. If the job is "make the 1M window cheaper," this is a memory product, not a context-length toggle.

Conclusion

Alloomi is the For You-feed answer to "the next bottleneck is context runtime." Use it if you will actually connect a practice and pay $15 or $39. If you wanted weights or a CLI, this is the wrong aisle.