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 agentic RAG?

Standard RAG retrieves context once and passes it to the LLM. Agentic RAG treats retrieval as a tool the agent can call multiple times, reformulate queries, and combine with web search or other tools. Use standard RAG for simple Q&A; use agentic RAG when queries are multi-hop or require reasoning across multiple sources. See our [agentic RAG explainer](/blog/agentic-rag/) for more.

Which vector database is best for building a RAG agent?

For local development, ChromaDB requires zero setup and runs in-process. For production, Pinecone (fully managed) and Qdrant (self-hostable) are the most popular choices. FAISS is fast and in-memory — great for prototyping, but lacks persistence. Choose based on your scale, budget, and hosting requirements.

How do I prevent prompt injection in a RAG agent?

Malicious content in retrieved documents can hijack your agent. Defend against it with defensive prompt templates that clearly delimit retrieved content, limit agent tool permissions, and validate retrieval output before passing it to the LLM. See our [prompt injection guide](/blog/ai-agent-prompt-injection/) for details.

How do I evaluate a RAG agent for accuracy?

Use the RAGAS framework to measure faithfulness (does the answer match the context?), answer relevance (does it address the question?), and context precision (are chunks actually relevant?). A faithfulness score above 0.8 is a solid production threshold. Automated evaluation catches regressions before they reach users.

Should I use RAG or fine-tune my LLM?

RAG is better when your knowledge base changes frequently, you need citations, or you want to update knowledge without retraining. Fine-tuning is better for teaching new style or domain-specific language. Many production systems use both. See our [RAG vs. fine-tuning comparison](/blog/rag-vs-fine-tuning/) for a full breakdown.

RAG-Powered AI Agent Tutorial: Step-by-Step Guide

Build a RAG-powered AI agent in Python — STEP-BY-STEP. Covers chunking, vector DBs, retrieval, reranking, and evaluation. Start FREE today.

Frequently Asked Questions

What is the difference between RAG and agentic RAG?: Standard RAG retrieves context once and passes it to the LLM. Agentic RAG treats retrieval as a tool the agent can call multiple times, reformulate queries, and combine with web search or other tools. Use standard RAG for simple Q&A; use agentic RAG when queries are multi-hop or require reasoning across multiple sources. See our [agentic RAG explainer](/blog/agentic-rag/) for more.
Which vector database is best for building a RAG agent?: For local development, ChromaDB requires zero setup and runs in-process. For production, Pinecone (fully managed) and Qdrant (self-hostable) are the most popular choices. FAISS is fast and in-memory — great for prototyping, but lacks persistence. Choose based on your scale, budget, and hosting requirements.
How do I prevent prompt injection in a RAG agent?: Malicious content in retrieved documents can hijack your agent. Defend against it with defensive prompt templates that clearly delimit retrieved content, limit agent tool permissions, and validate retrieval output before passing it to the LLM. See our [prompt injection guide](/blog/ai-agent-prompt-injection/) for details.
How do I evaluate a RAG agent for accuracy?: Use the RAGAS framework to measure faithfulness (does the answer match the context?), answer relevance (does it address the question?), and context precision (are chunks actually relevant?). A faithfulness score above 0.8 is a solid production threshold. Automated evaluation catches regressions before they reach users.
Should I use RAG or fine-tune my LLM?: RAG is better when your knowledge base changes frequently, you need citations, or you want to update knowledge without retraining. Fine-tuning is better for teaching new style or domain-specific language. Many production systems use both. See our [RAG vs. fine-tuning comparison](/blog/rag-vs-fine-tuning/) for a full breakdown.