Luge

Comparison · no chest-thumping

We like these projects.
Here's where we differ.

OpenClaw proved people want personal agents that run on their own hardware. NeoClaw showed it can be done frugally and safely. Hermes showed a personal agent can improve itself — and became one of the most-adopted agents on the planet. Viktor showed teams want one too. Botpress — a visual studio from Québec — showed how to build them for your customers. Respect, five times over. Luge takes a different angle: team AI agents, installable by anyone, compliant by design. Every fact below is checkable — that's the point.

OpenClaw, NeoClaw and Hermes are excellent personal agents; Viktor lives in the cloud, attached to Slack; Botpress builds chatbots for your customers. Luge plays elsewhere: AI for your team, installed without a terminal, recording meetings with no bot, with attributed team memory and compliance already built in — at a flat per-seat price.

The 20-second verdict — for the busy executive

All five projects are excellent… for personal use, or for building bots. Pick Luge if you want AI for your team: installable without IT, meetings captured with no bot, compliance built in, flat price. The table below is for your technical folks — you can skip straight to pricing.

Dimension OpenClaw NeoClaw Hermes Viktor Botpress Luge
Memory Personal, session-based Personal, automatic context summarization Personal, deepens over time (auto-generated skills) Workspace-wide (cloud) Knowledge bases for your customer-facing bots Group memory, attributed per contributor, searchable
Notes & shared knowledge Personal, not team Personal, not team Personal, not team Team memory in their cloud; notes not documented Knowledge bases for your customer-facing bots, not your team Versioned markdown notes — the history names who wrote: person, AI or access key — canvases, knowledge base, meaning-based search bounded by your access rights
Team / multi-user Built for one person (per-channel sandboxing) Telegram allowlist — not collaboration Personal agent; multi-channel gateway, not multi-tenant Native (Slack/Teams) Collaboration between bot builders — the product targets YOUR customers Native: tenants, channels, shared agent-colleagues
External collaboration Single-user — no notion of a guest Single-user — no notion of a guest Single-user — no notion of a guest Not documented Your customers talk to your bots — not guests in your workspace Guests from outside your organization, with a proven address (Google, Microsoft or a one-time code) — they only reach what is explicitly shared with them (v0.59)
Setup npm, CLI, config files — you are the IT department Single binary + tokens to configure One curl command (deps bundled) — still terminal-first Invite @Viktor to Slack (very easy) Cloud visual studio — easy to start, but building the bot is the project Signed installer + 2-click pairing + guided onboarding
Local models Yes — excellent (llama.cpp) Yes (OpenAI-compatible endpoints) Yes — your own endpoint, or cloud (Nous Portal, OpenRouter) No — cloud APIs only No — cloud LLMs, billed as “AI Spend” Yes — llama.cpp built into the app, an edge node paired in one command (luge-edge pair), code agents installable on your machine from Luge
Live meetings & voice — — — Lives in Slack/Teams chat; meetings not documented — Voice and video rooms inside Luge, a notetaker that joins as a visible participant, true per-speaker attribution, real inbound and outbound phone calls
Bot-less meeting recorder — — — — — Yes: Meet, Teams, Zoom, Webex, Jitsi — local capture
Compliance (audit, PI, isolation) Assemble it yourself Solid isolation, no compliance layer Self-hosted; command approval, no compliance layer Enterprise DPA/SLA (cloud) SOC 2 / GDPR on the Enterprise tier Native: audit trails, local PI detection, tenant isolation
Improves with use Personal session memory Remembered facts, single-user Yes — its hallmark: reusable skills, continuous self-improvement (personal) Workspace context (cloud) Knowledge bases updated by hand Lessons extracted after every task + nightly curation — no retraining
Cost Free + your API key, metered (often US$50–200/mo) + your time Free + your key (~US$3–15/mo, summarized context) Free, open source (MIT) + your key; optional Nous Portal tiers Prepaid credits, US$50 to $5,000/mo depending on usage US$0 → 89 → 495/mo + “AI Spend” metered on top of every plan Flat: Solo free (your key or your models); Team and Enterprise on quote — never per token
Source code Open source (MIT), 386K+ stars (August 2026) Open source (MIT) Open source (MIT), 230K+ stars (August 2026) Proprietary, cloud only Open-source roots (v12); the cloud is proprietary Proprietary, built on RoomKit (MIT, open source)

Facts as of August 2026 — sources: openclaw.ai and its docs, the projects' GitHub repos (including github.com/nousresearch/hermes-agent; star counts pulled from the GitHub API in August 2026), hermes-agent.nousresearch.com, viktor.com/pricing, botpress.com/pricing (May 2026 update), press coverage (Fortune, May 2026). Spot an error? Tell us — we'll fix it.

Analysis 1

Group memory is the difference between a gadget and a colleague

A personal agent remembers your stuff. A team agent remembers the team's stuff: what your colleague's agent learned last week feeds your task today. In Luge, every lesson extracted from a job is attributed to its contributor, vector-searchable, and scoped to the right tenant. OpenClaw and NeoClaw are excellent personal agents — but their memory lives and dies with one person. Hermes goes further: it "grows with you," generating its own skills as it works — the most accomplished personal memory on the market. But that's exactly it: it grows with you, not your team. Viktor has real workspace memory; it lives in their cloud, with your data inside.

Analysis 2

"Free" plus your evening spent configuring isn't free

Setting up an OpenClaw is fun — if you're the kind of person who finds that fun. Node, npm, config, keys, Docker sandboxing if you want to do it right, and the security vigil that comes with it (the OpenClaw community had a rough 2026: 138+ CVEs in five months, more than 135,000 instances exposed on the open internet, 63% with no authentication — public research, July 2026. We're not laughing at that, we learned from it). Luge makes the opposite bet: signed installer, two-click pairing, signed automatic updates. Your evening stays yours, and your attack surface stays small.

Analysis 3

Compliance can't be bolted on afterwards

Encrypted audit trails, personal-information detection that runs locally, strict per-organization isolation: it's been in Luge's architecture since the first commit, because our first vertical customer operates in Canadian financial services — a sector where "we'll add logging eventually" is not an answer. For inference, the managed sovereign modules keep compute on Canadian infrastructure. Small, scoped workflow steps do the rest: every action is verifiable, and a model doing one precise job hallucinates a lot less than a big model left to improvise.

Honestly? Here's when to pick the others.

  • Pick OpenClaw if you're a developer who wants to hack your personal agent down to the source, and running your own deployment is part of the fun. It's the liveliest project in the category.
  • Pick NeoClaw if you want a frugal solo agent on Telegram, in one binary, with a tiny API bill.
  • Pick Hermes if you want the most accomplished self-learning personal agent, self-hosted, and the terminal doesn't scare you — it's the king of its category. Luge aims for the inverse: team AI, installable without a terminal, compliant.
  • Pick Viktor if your team lives in Slack, self-hosting doesn't interest you, and a usage-based credit budget is fine.
  • Pick Botpress if you want to build a chatbot for YOUR customers (website, WhatsApp) with a visual studio — one of the best at that, and from Québec too. Luge is the inverse: AI that works for your team, internally.
  • Pick Luge if you want AI agents the whole team can use, that install without a terminal, whose AI can run on your machines or in Canada — at a price that never moves.

The common ground: open source

We're not on the other side of the fence. Luge's conversation orchestration is built on RoomKit, an open-source (MIT) framework — rooms, channels, voice, multi-agent. The code that makes our agents talk is inspectable by anyone. The distribution repo is public, signatures included.

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