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 the difference between RAG and fine-tuning?

RAG retrieves external documents at query time and injects them into the prompt, while fine-tuning retrains a model's weights on domain-specific data. RAG adds knowledge dynamically; fine-tuning bakes behavior into the model permanently.

Is RAG cheaper than fine-tuning?

RAG is cheaper to start — no GPU training costs — but incurs ongoing retrieval and token costs per query. Fine-tuning has higher upfront costs but lower per-query inference costs for high-volume, stable-domain applications.

Can you combine RAG and fine-tuning?

Yes, and most production AI systems in 2026 do exactly that. A common pattern is fine-tuning for domain tone, terminology, and reasoning patterns while using RAG for real-time knowledge retrieval. See our guide to [agentic RAG](/blog/agentic-rag/) for details.

When should I use RAG instead of fine-tuning?

Use RAG when your knowledge base changes frequently, you need source attribution, or you lack labeled training data. RAG is the faster path to production and doesn't require ML infrastructure.

Does fine-tuning reduce hallucinations?

Fine-tuning can reduce hallucinations within the trained domain by teaching the model correct patterns, but it cannot prevent hallucinations about topics outside its training data. RAG reduces hallucinations more broadly by grounding answers in retrieved documents.

RAG vs. Fine-Tuning: Which Is Better for Your AI Agent?

RAG vs fine tuning compared for AI agents — cost, latency, accuracy & when to use each. PRACTICAL decision framework inside. Compare now.

Frequently Asked Questions

What is the difference between RAG and fine-tuning?: RAG retrieves external documents at query time and injects them into the prompt, while fine-tuning retrains a model's weights on domain-specific data. RAG adds knowledge dynamically; fine-tuning bakes behavior into the model permanently.
Is RAG cheaper than fine-tuning?: RAG is cheaper to start — no GPU training costs — but incurs ongoing retrieval and token costs per query. Fine-tuning has higher upfront costs but lower per-query inference costs for high-volume, stable-domain applications.
Can you combine RAG and fine-tuning?: Yes, and most production AI systems in 2026 do exactly that. A common pattern is fine-tuning for domain tone, terminology, and reasoning patterns while using RAG for real-time knowledge retrieval. See our guide to [agentic RAG](/blog/agentic-rag/) for details.
When should I use RAG instead of fine-tuning?: Use RAG when your knowledge base changes frequently, you need source attribution, or you lack labeled training data. RAG is the faster path to production and doesn't require ML infrastructure.
Does fine-tuning reduce hallucinations?: Fine-tuning can reduce hallucinations within the trained domain by teaching the model correct patterns, but it cannot prevent hallucinations about topics outside its training data. RAG reduces hallucinations more broadly by grounding answers in retrieved documents.