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 LangGraph used for?

LangGraph is used for building stateful, multi-agent AI workflows where you need complex branching logic, loops, persistence, and human-in-the-loop controls. It powers production systems at Klarna (85M users), Uber, LinkedIn, and Coinbase. If your agent needs to retry, route dynamically, or pause for human approval, LangGraph is the right tool.

Is LangGraph the same as LangChain?

No. LangChain is a higher-level framework for building LLM pipelines using sequential chains. LangGraph is a separate, lower-level framework for graph-based agent orchestration. They are complementary — LangGraph handles workflow structure and state routing, while LangChain components handle LLM calls and tool integrations. LangGraph 1.0 can also be used completely independently.

Do I need to know LangChain to use LangGraph?

No. LangGraph 1.0 is standalone and works with any LLM provider — OpenAI, Anthropic, Google, or open-source models. Familiarity with Python and basic async programming is sufficient to get started.

What is a StateGraph in LangGraph?

A StateGraph is the central abstraction in LangGraph. It defines the entire workflow as a directed graph — nodes are Python functions that process state, and edges define how execution flows between them. You compile the StateGraph at the end to produce a runnable graph object.

How does LangGraph handle memory between sessions?

LangGraph uses checkpointers to persist the full graph state after every node execution. In-memory checkpointing works with MemorySaver for development. For production, you use database-backed checkpointers (PostgreSQL, SQLite, Redis). Each conversation is isolated by a thread_id, so different users maintain separate, persistent state histories.

LangGraph Tutorial: Build Multi-Agent Workflows Step-by-Step

Build multi-agent AI workflows with LangGraph 1.1. Tutorial covers StateGraph, nodes, edges, checkpointing & human-in-the-loop.

Frequently Asked Questions

What is LangGraph used for?: LangGraph is used for building stateful, multi-agent AI workflows where you need complex branching logic, loops, persistence, and human-in-the-loop controls. It powers production systems at Klarna (85M users), Uber, LinkedIn, and Coinbase. If your agent needs to retry, route dynamically, or pause for human approval, LangGraph is the right tool.
Is LangGraph the same as LangChain?: No. LangChain is a higher-level framework for building LLM pipelines using sequential chains. LangGraph is a separate, lower-level framework for graph-based agent orchestration. They are complementary — LangGraph handles workflow structure and state routing, while LangChain components handle LLM calls and tool integrations. LangGraph 1.0 can also be used completely independently.
Do I need to know LangChain to use LangGraph?: No. LangGraph 1.0 is standalone and works with any LLM provider — OpenAI, Anthropic, Google, or open-source models. Familiarity with Python and basic async programming is sufficient to get started.
What is a StateGraph in LangGraph?: A StateGraph is the central abstraction in LangGraph. It defines the entire workflow as a directed graph — nodes are Python functions that process state, and edges define how execution flows between them. You compile the StateGraph at the end to produce a runnable graph object.
How does LangGraph handle memory between sessions?: LangGraph uses checkpointers to persist the full graph state after every node execution. In-memory checkpointing works with MemorySaver for development. For production, you use database-backed checkpointers (PostgreSQL, SQLite, Redis). Each conversation is isolated by a thread_id, so different users maintain separate, persistent state histories.