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 ReAct pattern in AI agents?

ReAct (Reason + Act) is an agent architecture where the model interleaves internal reasoning traces ("Thought: I need to look up X") with external tool calls ("Action: search[X]") and their results ("Observation: X is Y"). This loop repeats until the agent produces a final answer. Introduced by Yao et al. at ICLR 2023, it is the default agent pattern in LangChain and LangGraph.

How is ReAct different from Chain-of-Thought prompting?

Chain-of-Thought (CoT) keeps reasoning entirely inside the model — no external tools. ReAct extends CoT by adding tool actions between reasoning steps, grounding each thought in real observations. CoT is cheaper but relies on the model's parametric memory; ReAct reduces hallucination by checking facts against external sources at every step.

What are the main failure modes of the ReAct pattern?

The most common failure modes are infinite loops (the agent cycles through the same actions without progress), context window overflow (long trajectories exceed the model's context limit), and thought hallucination (the model reasons correctly but calls the wrong tool). A max_iterations guard and a context-length budget are essential safeguards in production.

Is the ReAct pattern still used in 2026?

Yes, but in evolved form. Most production agents use native function calling (GPT-4o, Claude 3.x, Gemini 2.x) which is functionally equivalent to ReAct but more reliable than text-parsed Thought/Action traces. The ReAct mental model — reason, act, observe, repeat — remains the dominant paradigm. LangGraph's create_react_agent is the standard production implementation.

What is the best framework for ReAct agents in 2026?

LangGraph (v1.0, GA October 2025) is the production standard. It implements ReAct as a stateful graph where nodes handle tool calls and edges route based on the model's next action. For simpler use cases, LangChain's create_react_agent still works. For multi-agent systems, CrewAI and AutoGen wrap ReAct internally.

The ReAct Pattern: How AI Agents Reason and Act in Loops

COMPLETE guide to the ReAct pattern for AI agents. How Thought-Action-Observation loops work, benchmarks, failure modes & LangGraph examples.

Frequently Asked Questions

What is the ReAct pattern in AI agents?: ReAct (Reason + Act) is an agent architecture where the model interleaves internal reasoning traces ("Thought: I need to look up X") with external tool calls ("Action: search[X]") and their results ("Observation: X is Y"). This loop repeats until the agent produces a final answer. Introduced by Yao et al. at ICLR 2023, it is the default agent pattern in LangChain and LangGraph.
How is ReAct different from Chain-of-Thought prompting?: Chain-of-Thought (CoT) keeps reasoning entirely inside the model — no external tools. ReAct extends CoT by adding tool actions between reasoning steps, grounding each thought in real observations. CoT is cheaper but relies on the model's parametric memory; ReAct reduces hallucination by checking facts against external sources at every step.
What are the main failure modes of the ReAct pattern?: The most common failure modes are infinite loops (the agent cycles through the same actions without progress), context window overflow (long trajectories exceed the model's context limit), and thought hallucination (the model reasons correctly but calls the wrong tool). A max_iterations guard and a context-length budget are essential safeguards in production.
Is the ReAct pattern still used in 2026?: Yes, but in evolved form. Most production agents use native function calling (GPT-4o, Claude 3.x, Gemini 2.x) which is functionally equivalent to ReAct but more reliable than text-parsed Thought/Action traces. The ReAct mental model — reason, act, observe, repeat — remains the dominant paradigm. LangGraph's create_react_agent is the standard production implementation.
What is the best framework for ReAct agents in 2026?: LangGraph (v1.0, GA October 2025) is the production standard. It implements ReAct as a stateful graph where nodes handle tool calls and edges route based on the model's next action. For simpler use cases, LangChain's create_react_agent still works. For multi-agent systems, CrewAI and AutoGen wrap ReAct internally.