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 model routing in AI agent systems?

Model routing is the practice of dynamically assigning different tasks to different LLMs based on factors like cost, latency, and required capability. Instead of sending every request to your most powerful (and expensive) model, a router dispatches simple tasks to cheap, fast models and reserves heavy-duty models for complex reasoning. See our [multi-agent systems guide](/blog/multi-agent-systems/) for the broader architecture context.

How much can model routing reduce AI agent costs?

Studies and real-world deployments show model routing can cut LLM API costs by 50–80% with minimal quality loss. The LMSYS RouteLLM paper demonstrated that routing just 20% of queries to a stronger model while handling the rest with smaller models matched the quality of using the strong model exclusively, at a fraction of the price. See our [AI agent cost optimization guide](/blog/ai-agent-cost-optimization/) for additional strategies.

What is the difference between static and dynamic model routing?

Static routing uses fixed rules — for example, "always use GPT-4o-mini for classification, Claude Opus for final synthesis." Dynamic routing uses a lightweight classifier or the task's own metadata to decide which model to call at runtime. Dynamic routing adapts better to varied inputs but adds a small latency overhead for the routing decision itself.

Which LLM should I use for code generation in AI agents?

For code generation tasks, Claude Sonnet, DeepSeek V3, and Qwen Coder consistently rank at the top of coding benchmarks (HumanEval, SWE-bench). For quick, low-stakes code snippets, GPT-4o-mini or Claude Haiku offer excellent speed and cost. Always benchmark your specific codebase — model rankings shift with every major release.

What tools support model routing for AI agents?

LiteLLM is the most popular open-source proxy for unified model routing across 100+ providers. OpenRouter provides a hosted marketplace with automatic fallbacks. RouteLLM (LMSYS) offers ML-based routing trained on human preference data. LangChain and LangGraph both provide routing primitives natively. For teams building on a shared platform, [cowork.ink](https://app.cowork.ink) lets you configure per-agent model assignments with built-in cost monitoring.

Model Routing for AI Agents: Pick the Right LLM

Learn model routing for AI agents — dispatch tasks to the right LLM by cost, latency, and capability. SAVE up to 80% without sacrificing quality. Full how-to guide.

Frequently Asked Questions

What is model routing in AI agent systems?: Model routing is the practice of dynamically assigning different tasks to different LLMs based on factors like cost, latency, and required capability. Instead of sending every request to your most powerful (and expensive) model, a router dispatches simple tasks to cheap, fast models and reserves heavy-duty models for complex reasoning. See our [multi-agent systems guide](/blog/multi-agent-systems/) for the broader architecture context.
How much can model routing reduce AI agent costs?: Studies and real-world deployments show model routing can cut LLM API costs by 50–80% with minimal quality loss. The LMSYS RouteLLM paper demonstrated that routing just 20% of queries to a stronger model while handling the rest with smaller models matched the quality of using the strong model exclusively, at a fraction of the price. See our [AI agent cost optimization guide](/blog/ai-agent-cost-optimization/) for additional strategies.
What is the difference between static and dynamic model routing?: Static routing uses fixed rules — for example, "always use GPT-4o-mini for classification, Claude Opus for final synthesis." Dynamic routing uses a lightweight classifier or the task's own metadata to decide which model to call at runtime. Dynamic routing adapts better to varied inputs but adds a small latency overhead for the routing decision itself.
Which LLM should I use for code generation in AI agents?: For code generation tasks, Claude Sonnet, DeepSeek V3, and Qwen Coder consistently rank at the top of coding benchmarks (HumanEval, SWE-bench). For quick, low-stakes code snippets, GPT-4o-mini or Claude Haiku offer excellent speed and cost. Always benchmark your specific codebase — model rankings shift with every major release.
What tools support model routing for AI agents?: LiteLLM is the most popular open-source proxy for unified model routing across 100+ providers. OpenRouter provides a hosted marketplace with automatic fallbacks. RouteLLM (LMSYS) offers ML-based routing trained on human preference data. LangChain and LangGraph both provide routing primitives natively. For teams building on a shared platform, [cowork.ink](https://app.cowork.ink) lets you configure per-agent model assignments with built-in cost monitoring.