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 AI agent memory?

AI agent memory is the system that lets an agent store and recall information across interactions. It spans four types: working memory (the active context window), episodic memory (records of past events), semantic memory (structured facts and knowledge), and procedural memory (learned workflows). Without it, agents reset to zero on every session.

What is the difference between short-term and long-term memory in AI agents?

Short-term (working) memory is the agent's in-context scratchpad — it exists only for the current session and disappears when the conversation ends. Long-term memory persists across sessions and is stored externally in vector databases, key-value stores, or knowledge graphs. Learn more in our [AI agent architecture guide](/blog/ai-agent-architecture/).

How do AI agents store long-term memory?

Long-term memory is stored in external systems: vector databases (Pinecone, Weaviate, Chroma) for semantic search over past experiences, key-value stores (Redis) for fast lookup of structured facts, and knowledge graphs for relationship modeling. Frameworks like Mem0 and Zep handle this automatically.

What is episodic memory in AI agents?

Episodic memory stores records of specific past interactions — what happened, when, and what the outcome was. It's the agent's equivalent of autobiographical memory. Agents use it for case-based reasoning: "last time the user asked this, I did X and it worked."

Which is the best memory framework for AI agents in 2026?

Mem0 is the most popular choice for production agents, offering hybrid vector + graph memory with 50,000+ developers using it. Zep is preferred for enterprise applications with sub-100ms retrieval and temporal knowledge graphs. LangMem (by the LangChain team) is the easiest entry point if you're already on LangGraph.

AI Agent Memory: How Agents Remember, Learn & Improve

COMPLETE guide to AI agent memory in 2026. The 4 memory types, hot/cold architecture & top frameworks (Mem0, Zep, LangMem).

Frequently Asked Questions

What is AI agent memory?: AI agent memory is the system that lets an agent store and recall information across interactions. It spans four types: working memory (the active context window), episodic memory (records of past events), semantic memory (structured facts and knowledge), and procedural memory (learned workflows). Without it, agents reset to zero on every session.
What is the difference between short-term and long-term memory in AI agents?: Short-term (working) memory is the agent's in-context scratchpad — it exists only for the current session and disappears when the conversation ends. Long-term memory persists across sessions and is stored externally in vector databases, key-value stores, or knowledge graphs. Learn more in our [AI agent architecture guide](/blog/ai-agent-architecture/).
How do AI agents store long-term memory?: Long-term memory is stored in external systems: vector databases (Pinecone, Weaviate, Chroma) for semantic search over past experiences, key-value stores (Redis) for fast lookup of structured facts, and knowledge graphs for relationship modeling. Frameworks like Mem0 and Zep handle this automatically.
What is episodic memory in AI agents?: Episodic memory stores records of specific past interactions — what happened, when, and what the outcome was. It's the agent's equivalent of autobiographical memory. Agents use it for case-based reasoning: "last time the user asked this, I did X and it worked."
Which is the best memory framework for AI agents in 2026?: Mem0 is the most popular choice for production agents, offering hybrid vector + graph memory with 50,000+ developers using it. Zep is preferred for enterprise applications with sub-100ms retrieval and temporal knowledge graphs. LangMem (by the LangChain team) is the easiest entry point if you're already on LangGraph.