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.

Is LangChain free to use?

Yes. LangChain is an open-source MIT-licensed framework and completely free. You only pay for the LLM API calls you make (OpenAI, Anthropic, etc.). LangChain Academy also offers free introductory courses.

How long does it take to learn LangChain?

Developers with Python experience can learn LangChain basics in 2–4 weeks. Reaching production-ready proficiency — including RAG, deployment, and error handling — typically takes 3–6 months of self-study or 6–10 weeks with structured coaching.

What is the difference between LangChain and LangGraph?

LangChain is a high-level framework for building LLM-powered applications including chains, agents, and RAG pipelines. LangGraph is a lower-level orchestration library (also by LangChain Inc.) that uses directed graphs for complex, stateful agent workflows. LangChain's agent API is now built on top of LangGraph. See our [LangGraph tutorial](/blog/langgraph-tutorial/) for a deep dive.

What is the ReAct pattern in LangChain?

ReAct stands for "Reasoning + Acting." The agent alternates between thinking about the problem, calling a tool, and observing the result. This loop repeats until the agent reaches a final answer. It is the default agent pattern in LangChain.

Can I use LangChain with models other than OpenAI?

Absolutely. LangChain supports Anthropic Claude, Google Gemini, open-source models via Ollama, and any provider available through OpenRouter. Swap the model class and API key — the rest of your code stays the same.

LangChain Tutorial: Build Your First AI Agent Step-by-Step

Step-by-step LangChain tutorial to BUILD your first AI agent in Python. Covers tools, memory, and the ReAct pattern. Start building now.

Frequently Asked Questions

Is LangChain free to use?: Yes. LangChain is an open-source MIT-licensed framework and completely free. You only pay for the LLM API calls you make (OpenAI, Anthropic, etc.). LangChain Academy also offers free introductory courses.
How long does it take to learn LangChain?: Developers with Python experience can learn LangChain basics in 2–4 weeks. Reaching production-ready proficiency — including RAG, deployment, and error handling — typically takes 3–6 months of self-study or 6–10 weeks with structured coaching.
What is the difference between LangChain and LangGraph?: LangChain is a high-level framework for building LLM-powered applications including chains, agents, and RAG pipelines. LangGraph is a lower-level orchestration library (also by LangChain Inc.) that uses directed graphs for complex, stateful agent workflows. LangChain's agent API is now built on top of LangGraph. See our [LangGraph tutorial](/blog/langgraph-tutorial/) for a deep dive.
What is the ReAct pattern in LangChain?: ReAct stands for "Reasoning + Acting." The agent alternates between thinking about the problem, calling a tool, and observing the result. This loop repeats until the agent reaches a final answer. It is the default agent pattern in LangChain.
Can I use LangChain with models other than OpenAI?: Absolutely. LangChain supports Anthropic Claude, Google Gemini, open-source models via Ollama, and any provider available through OpenRouter. Swap the model class and API key — the rest of your code stays the same.