Pazi vs Manus

This post compares Pazi and Manus head-to-head and names which one fits which work. Pazi is a platform for building AI agents that live inside your team's workspaces; the agents you ship on Pazi sit in Slack, Discord, Teams, WhatsApp, or whichever 18 other surfaces the team already uses, and they handle recurring work that arrives as messages and ends as messages. Manus is a cloud-sandbox autonomous agent operated by Meta, a single product that takes a one-shot goal, runs it in an isolated virtual computer, and returns the deliverable. For most teams with recurring work to delegate, Pazi is the platform to build on. For one-shot deep research, slide decks, and full-stack prototype builds, Manus is the niche that fits.

Manus is the most-cited "general AI agent" of 2026: a $125M revenue run rate by late 2025, a $2B+ Meta acquisition in December, and a Chinese regulatory order in April that has the deal pending unwind. The press push has operators reaching for the sandbox shape by default, even when their actual work is the recurring channel kind. That mismatch is the call this post is built to help you make.

What kind of product is each one?

Pazi is a platform. You build agents on it, configure what they do, point them at the channels where the work happens, and they live in those channels as a coworker the team can mention by name. The team typically builds operator-shape agents on Pazi: agents that sit inside the channel where the team works, watch a stream of conversations or events, and act on the recurring work. Manus is a single product, a cloud-sandbox autonomous agent that lives inside an isolated virtual Ubuntu environment with file system, terminal, and browser; you hand it a goal, it executes, deliverables come back. Both adopted Anthropic's Agent Skills standard and both ship persistent workspaces, scheduled tasks, and multi-provider LLM support. The split sits one layer deeper: Pazi is the place you build the agents your team needs, Manus is the agent.

Where do the agents actually live and work?

Agents built on Pazi inherit the OpenClaw runtime's channel list: Slack, Discord, WhatsApp, Telegram, iMessage, Signal, Microsoft Teams, Matrix, Google Chat, IRC, Feishu, LINE, Mattermost, Nextcloud Talk, Nostr, Synology Chat, Tlon, Twitch, Zalo, WeChat, QQ, plus Pazi web chat. Every channel is available on every plan tier including free; the agent is a channel participant that answers in the room.

Manus runs in its cloud sandbox primarily, with Slack and Telegram integrations, mobile and desktop apps, and a Browser Operator extension that drives the user's Chromium browser. The Slack pattern is the most concrete illustration of the split. From the Manus integration page:

"When multiple users prompt Manus in a shared thread, only the first user's request will be processed. Manus handles one task at a time per thread."

The task runs in the sandbox; the deliverable lands back in the thread when it finishes.

How does each one handle recurring versus one-shot work?

Recurring work arrives as a stream and never finishes; one-shot work ends when the goal is met. Agents built on Pazi fit the recurring stream: the channel is a long-running surface, the agent carries persistent context, and the work compounds. We ship pre-built operator configurations for customer success, outbound sales, and inbox management as starting points, with more in development; teams take those as templates and extend them, or build their own from scratch.

Manus is built around the one-shot unit. A typical task uses ~150 credits per Manus's own Team-plan page; Wide Research spins up 100+ sub-agents in parallel for one query; 1.5 made tasks ~4x faster than 1.0 and added full-stack web app generation with backend, auth, database, and custom domain; 1.6 added mobile app builds.

Is the runtime open or closed, and does it matter?

The OpenClaw runtime is MIT-licensed on GitHub; Pazi is the managed version that runs on top of it. The same runtime ships from openclaw/openclaw to anyone who wants to self-host at zero software cost. The implication is portability: channels, skills, agents, and workspaces all run on a runtime the customer can move to self-host any time. The agents your team builds on Pazi are not locked to Pazi the managed product; they run on the open-source runtime underneath, and you can take them with you. Manus is closed and proprietary; Wikipedia lists the license as Proprietary, and the sandbox, orchestration layer, and agent framework are not inspectable. Our Pazi vs OpenClaw post from earlier this week covers the runtime trade-off in detail.

What does each one cost in 2026?

Both price by credits; what differs is how the credits get budgeted.

A Pazi pool feeds a stream of conversational turns across the team's agents at a small handful of credits apiece; a Manus pool feeds a queue of one-shot tasks where a single task can run 150 credits or substantially more for Wide Research and full-stack app generation.

What can you build on Pazi for the operator-shape work?

Most teams with recurring work to delegate land here. The pattern is consistent: work that arrives as messages, work that ends as messages, work that repeats on a schedule, and work that needs a report back into the same channel where the team makes the next decision. These are the agent shapes teams typically build on Pazi:

1. Customer-success renewal triage. The CSM and the agent share a Slack or Teams channel; the agent watches the usage stream between meetings, drafts the renewal narrative, and surfaces the at-risk accounts in the same channel where the CSM works.
2. Deal-stage drift. The sales channel pings when a deal has been stalled too long. The agent surfaces the next step in the same thread the rep already lives in, with the deal context attached.
3. Inbox triage. Email or ticket inbox routes into a channel; the agent answers what it can directly, flags the rest with proposed responses for human review.
4. Deploy watch and on-call. The engineering channel sees a pull request; the agent runs the post-merge checklist, posts the status back in the thread, and pings on-call if anything failed.
5. Market and competitor monitoring. A daily ping lists the three signals that moved overnight, in the same channel where the team makes decisions.
6. Content development and distribution. A content channel where the agent drafts blog posts, social posts, and newsletters end-to-end against the team's voice profile, runs a review loop in-thread, and reports back when the asset is ready to publish. We build agents on Pazi this way for the blog you are reading.
7. Reporting on recurring work. Any pipeline that produces a result on a schedule, daily metrics, weekly performance summaries, monthly retrospectives, where the agent assembles the numbers, posts the report in the channel the team checks, and answers follow-ups in the same thread.
8. Cross-channel ops for distributed teams. Teams spanning Slack, Discord, WhatsApp, Telegram, or any combination of the 22+ channels in the runtime list; the same agent meets the team in whichever surface the question lands.

In all eight the agent is a participant in the channel, not a contractor visiting it. The credit pool tied to a plan is a SaaS-shaped line item, not a per-task variable cost. The free tier asks for no credit card; 5,000 credits and one agent are enough to build a small operator and put it in one channel to find out whether the shape fits.

We run Pazi-built agents in our own Slack and Discord every day, alongside the cloud-sandbox tools we use for one-shot research; the conditional fit in this post is what holds up across our work.

When should you choose Manus instead of building on Pazi?

There are real workloads where the cloud-sandbox shape is the right pick. If any of these describes the bulk of your work, Manus is the better surface:

1. Deep research that runs 100+ sub-agents in parallel for one query. Wide Research is Manus's native primitive; there's no equivalent on Pazi at this scale.
2. Large slide decks with sandbox-rendered visuals. Manus Slides produces multi-slide decks with embedded visuals as a single deliverable.
3. Full-stack web or mobile app prototypes from a one-paragraph prompt, deployed on a Manus subdomain in one pass. The 1.5 and 1.6 releases made this first-class.
4. A sandboxed virtual computer with file system, terminal, and Browser Operator. "My Computer" desktop and Browser Operator are the bets on this shape.

For recurring work that lives in your team's channels, Pazi is the platform to build the agents on; for one-shot research, slide, and prototype tasks, Manus is the cloud sandbox that fits. For why operator-shape recurring work needs a coworker in the room rather than a contractor visiting it, see our notes on specialist versus generalist agents and our customer-success operator post.

Try Pazi free at pazi.ai →