Gobii Review: The AI Coworkers That Do Real Work

Gobii is an AI agent platform built to give teams "AI coworkers" instead of another chatbot window — always-on virtual agents with their own email address and phone number that browse the real web, fill out forms, and hand back finished reports. Gobii positions itself less as an automation tool and more as a digital hire your team can message directly.

What is Gobii?

Gobii is an AI agent platform designed for teams that need to automate web-based work at scale. Instead of a traditional chatbot that only answers questions, Gobii provides always-on virtual coworkers capable of browser automation, web research, data collection, and workflow execution. Agents can navigate websites, authenticate to accounts, fill in forms, extract structured data, and generate reports on their own.

Each Gobii agent maintains a distinct identity, including its own email address and phone number, so a team member can contact it directly the same way they would message a colleague — by sending it a task over email or SMS rather than opening a separate dashboard.

How Gobii's agents work

Gobii agents run as always-on virtual coworkers with persistent identities. Because each one has its own email address and phone number, a teammate can simply email or text an agent to assign it a task. Agents can run continuously or wake on a schedule or an external trigger, and they coordinate with each other through agent-to-agent messaging.

Under the hood, agents keep a SQLite-based operational memory so long-running workflows do not lose state between sessions — an agent researching leads today can pick the task back up tomorrow with full context of what it already found.

The browsing itself happens in real, headed Chrome browsers rather than a stripped-down headless scraper. Agents log into accounts using stored credentials, click buttons, fill out forms, and extract data from pages exactly as a person at a desktop would. Gobii layers persistent browser profiles (to hold login state), proxy-aware routing for distributed execution, and schema-validated data extraction on top of that browsing layer, with per-agent sandboxing and full audit trails for enterprise security.

Key capabilities of the Gobii platform

Beyond browsing, Gobii's differentiator is what happens after data is collected. Agents can extract and organize structured data into CSV files, generate formatted PDF reports, build charts and visualizations, and email the finished files directly to stakeholders — without a separate reporting tool bolted on afterward. Because everything is stored in each agent's persistent SQLite database, results stay queryable and retrievable by name long after the task finishes.

Integrations: connecting Gobii to your stack

Gobii is built to sit inside workflows your team already runs. Native integrations include Salesforce for CRM data, Greenhouse and other ATS platforms for recruiting pipelines, Google Sheets and Google Drive for lightweight storage, Slack for team communication, Jira for project tracking, Zendesk for support, PagerDuty for incident management, and calendar systems for scheduling. Webhooks are treated as a first-class integration: inbound webhooks can wake an agent to start a task, and outbound webhooks let an agent push data or trigger actions elsewhere the moment it finishes.

For teams with a more custom stack, Gobii also offers direct API connections and custom integrations for enterprise customers, so an agent's output can land wherever the rest of the team's data already lives.

Real-world use cases for Gobii agents

Teams deploy Gobii agents across several workflow categories. In recruiting, "Talent Scout" and "Candidate Researcher" workers source candidates from job boards, LinkedIn, and GitHub, then organize the results into an ATS or Google Sheets. In sales, "Lead Hunter" and "Account Researcher" agents find prospects and enrich profiles with funding data, tech stack, and decision-maker details, keeping CRM records current without manual entry. On the operations side, agents track project milestones, monitor vendor pricing, and generate daily status reports. Other named uses include competitive intelligence monitoring, compliance tracking, and incident communication workflows.

If sales outreach specifically is your bottleneck, it's worth comparing Gobii's Lead Hunter approach against a dedicated outreach tool — see our breakdown of Honeybear AI for personalized sales outreach for a narrower, messaging-focused alternative. For a practical walkthrough of setting up your first agent, see our step-by-step guide to using Gobii.

Gobii pricing plans explained

Gobii publishes five named tiers. The Free tier is the open-source, self-hosted release with unlimited always-on agents at no cost. The Pro plan adds a monthly task allowance, a per-agent contact limit, a higher API rate limit, and email and chat support. The Scale plan raises the monthly task allowance and contact limit further, lowers the marginal cost of extra tasks, and adds a higher API rate limit plus priority support. The Team plan is priced per seat and pools task credits across shared agents and templates. Enterprise is custom-priced and adds dedicated infrastructure, SLAs, and governance controls. Every paid tier includes unlimited always-on agents that never expire — check current pricing on Gobii's site before committing to a tier, since limits and rates can change.

The Team tier is worth a closer look for multi-person groups: it lets several teammates monitor, trigger, and refine the same shared agent instead of standing up separate deployments, gives admins private organization-wide agent templates to launch from, and centralizes credentials and API keys so nobody juggles separate auth tokens. Billing pools by seat, with monthly task credits shared flexibly across the team.

Open-source option: self-hosting Gobii

Gobii also ships an open-source, MIT-licensed version of the platform on GitHub, letting teams deploy on their own infrastructure — from public cloud providers like AWS, GCP, and Azure to fully air-gapped, on-premises data centers. Self-hosted deployments get the same browser automation, agent management, and integration capabilities as the hosted Gobii Cloud service, plus per-agent sandboxing, encrypted secrets management for stored credentials, full audit trails for compliance, and proxy-aware routing for distributed execution.

For regulated industries or teams that can't send data through a third-party cloud, this is the practical route: the same agent capability, run entirely inside infrastructure you control.

What users say about Gobii

Third-party review data puts Gobii at 3.8 out of 5 stars across 16 user reviews as of mid-2026. Reviewers commonly cite that agents handle a wide range of tasks, including web scraping and outreach, and that the platform meaningfully cuts time spent on repetitive monitoring and data collection; several also mention the free credits offered to trial up to five agents before committing. The most frequently cited downside is excessive email notifications generated by active agents — worth configuring early if your team is sensitive to inbox noise.

Gobii vs. other automation platforms

Gobii occupies a specific niche relative to broader automation tools. Make (formerly Integromat) is a mature, visual no-code platform with thousands of app integrations built around orchestrating APIs across software — drag-and-drop scenarios, loops, and error routes. Gobii instead specializes in autonomous, browser-based agents that navigate, click, and extract data inside the browser itself, rather than calling APIs behind the scenes. Claude Code, by comparison, is an AI coding agent built for technical, codebase-level tasks rather than production automation workflows.

In practice, teams often pick Gobii specifically for web-based research and browser automation, pick Make for API-driven multi-step business logic, and sometimes combine both — Make handling process orchestration while Gobii agents handle the messy, browser-only parts of the job.

Getting started with Gobii

Gobii's Free tier is the lowest-friction way to see whether AI coworkers fit your workflow: it runs the same open-source core with unlimited always-on agents, so you can wire up a first agent against a real recruiting, sales, or reporting task before deciding on a paid tier. If you've already tried adjacent AI agent tools, it's also worth reading our review of BotDog for a sense of how a different agent-style product compares in day-to-day use.

Once an agent is live, give it a narrow first task — sourcing ten candidates, monitoring one competitor's pricing page, or compiling a weekly CSV report — before expanding into recruiting, sales, or operations workflows at scale.