What AI models does the agent use?

The agent has access to Claude, DeepSeek, Gemini, and other top models. It automatically routes to the best model for each task — you never have to think about API keys or token limits.

Yes. Each agent runs in an isolated environment. We don't train on your data, and you can export or delete everything at any time. The underlying engine (GoGogot) is open-source, so you can audit exactly how it works.

What can the agent actually do?

Browse the web, process files (CSV, PDF, images), run scheduled tasks, remember context across sessions, and communicate via Telegram or Slack. Think of it as a capable junior employee, not a chatbot.

How does memory work?

The agent maintains persistent memory across sessions. It remembers your preferences, past conversations, and task context. You can also explicitly tell it to remember or forget things.

Can I self-host the agent?

Absolutely. GoGogot is 100% open-source. Run it on your own hardware for free. cowork.ink is the managed version — we handle servers, model costs, updates, and uptime so you don't have to.

Is there a free trial?

We offer a 7-day money-back guarantee. Spin up an agent, give it real tasks, and if it doesn't save you time, we'll refund you — no questions asked.

What is multi-agent collaboration in AI?

Multi-agent collaboration is a system design where multiple specialized AI agents work together to solve complex tasks. Each agent handles a specific subtask — research, coding, review — and they coordinate through shared context or an orchestrator agent. Learn more in our [agent swarm guide](/blog/agent-swarm-explained/).

How do AI agents communicate with each other?

AI agents communicate through message passing, shared memory stores, or standardized protocols like Anthropic's MCP and Google's A2A. An orchestrator agent often routes messages and manages context between specialized worker agents.

When should you use multi-agent instead of a single agent?

Use multi-agent systems when tasks are parallelizable, require different expertise, or when single-agent accuracy degrades under workload. Research shows multi-agent setups improve success rates by up to 70% on complex goals compared to single agents.

What are the best frameworks for building multi-agent systems?

The most popular frameworks in 2026 are LangGraph, CrewAI, OpenAI Agents SDK, and Google ADK. Each suits different patterns — see our [framework comparison](/blog/ag2-vs-crewai-vs-langgraph-openai-agents-sdk/) for a detailed breakdown.

What are the main challenges of multi-agent collaboration?

The biggest challenges are coordination overhead (multi-agent systems use roughly 15x more tokens than single agents), debugging complexity, and performance degradation on strictly sequential tasks. Proper [orchestration patterns](/blog/ai-agent-orchestration/) help mitigate these.

Multi-Agent Collaboration: How AI Teams Solve Complex Tasks

Learn how multi-agent collaboration works — patterns, frameworks, and real performance data. Build AI teams that SOLVE complex tasks. Guide inside.

Frequently Asked Questions

What is multi-agent collaboration in AI?: Multi-agent collaboration is a system design where multiple specialized AI agents work together to solve complex tasks. Each agent handles a specific subtask — research, coding, review — and they coordinate through shared context or an orchestrator agent. Learn more in our [agent swarm guide](/blog/agent-swarm-explained/).
How do AI agents communicate with each other?: AI agents communicate through message passing, shared memory stores, or standardized protocols like Anthropic's MCP and Google's A2A. An orchestrator agent often routes messages and manages context between specialized worker agents.
When should you use multi-agent instead of a single agent?: Use multi-agent systems when tasks are parallelizable, require different expertise, or when single-agent accuracy degrades under workload. Research shows multi-agent setups improve success rates by up to 70% on complex goals compared to single agents.
What are the best frameworks for building multi-agent systems?: The most popular frameworks in 2026 are LangGraph, CrewAI, OpenAI Agents SDK, and Google ADK. Each suits different patterns — see our [framework comparison](/blog/ag2-vs-crewai-vs-langgraph-openai-agents-sdk/) for a detailed breakdown.
What are the main challenges of multi-agent collaboration?: The biggest challenges are coordination overhead (multi-agent systems use roughly 15x more tokens than single agents), debugging complexity, and performance degradation on strictly sequential tasks. Proper [orchestration patterns](/blog/ai-agent-orchestration/) help mitigate these.