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.

Which vector database is best for AI agents with long-term memory?

Pinecone is the best managed option for production AI agents requiring long-term memory at scale — it delivers sub-50ms p99 latency with zero infrastructure management. For teams that need data sovereignty or hybrid search, Weaviate is the stronger choice. See our [AI agent memory guide](/blog/ai-agent-memory/) for a full breakdown of memory patterns.

Is Chroma good enough for production, or is it just for prototyping?

Chroma is excellent for prototyping and local development but has significant limitations in production — its QPS peaks around 2,200 vs. Weaviate's 5,800 and Pinecone's 4,500. For production AI agents serving concurrent users, migrate to Pinecone or Weaviate before you hit scale.

What is the difference between Pinecone, Weaviate, and Chroma for RAG?

Pinecone is a fully managed SaaS with no self-hosting option — fastest to production, most expensive at scale. Weaviate is open-source with a managed cloud tier — best for hybrid (dense + sparse) search and multi-tenancy. Chroma is open-source and runs in-process — best developer experience, not production-hardened. For [agentic RAG](/blog/agentic-rag/) specifically, Weaviate's hybrid search gives agents the most accurate retrieval.

Does Weaviate support hybrid search for AI agents?

Yes — Weaviate has first-class hybrid search that combines dense vector similarity with sparse BM25 keyword matching in a single query. This is critical for AI agents where queries mix semantic intent with exact keywords, and hybrid approaches have been shown to improve RAG accuracy significantly over dense-only retrieval.

How much does a vector database cost for a production AI agent?

Costs vary dramatically by scale. Chroma is free (self-hosted). Weaviate Cloud starts at ~$75/month. Pinecone scales from minimal cost for small workloads to $5,000–$6,000/month at 100M vectors with 150M queries. Don't forget to include embedding API costs (OpenAI, Cohere) in your TCO — they often exceed the vector DB bill at scale.

Vector Databases for AI Agents: Pinecone vs Weaviate vs Chroma

BEST vector databases for AI agents COMPARED. Pinecone vs Weaviate vs Chroma — latency, cost, hybrid search, and RAG support benchmarked. Pick the right one now.

Frequently Asked Questions

Which vector database is best for AI agents with long-term memory?: Pinecone is the best managed option for production AI agents requiring long-term memory at scale — it delivers sub-50ms p99 latency with zero infrastructure management. For teams that need data sovereignty or hybrid search, Weaviate is the stronger choice. See our [AI agent memory guide](/blog/ai-agent-memory/) for a full breakdown of memory patterns.
Is Chroma good enough for production, or is it just for prototyping?: Chroma is excellent for prototyping and local development but has significant limitations in production — its QPS peaks around 2,200 vs. Weaviate's 5,800 and Pinecone's 4,500. For production AI agents serving concurrent users, migrate to Pinecone or Weaviate before you hit scale.
What is the difference between Pinecone, Weaviate, and Chroma for RAG?: Pinecone is a fully managed SaaS with no self-hosting option — fastest to production, most expensive at scale. Weaviate is open-source with a managed cloud tier — best for hybrid (dense + sparse) search and multi-tenancy. Chroma is open-source and runs in-process — best developer experience, not production-hardened. For [agentic RAG](/blog/agentic-rag/) specifically, Weaviate's hybrid search gives agents the most accurate retrieval.
Does Weaviate support hybrid search for AI agents?: Yes — Weaviate has first-class hybrid search that combines dense vector similarity with sparse BM25 keyword matching in a single query. This is critical for AI agents where queries mix semantic intent with exact keywords, and hybrid approaches have been shown to improve RAG accuracy significantly over dense-only retrieval.
How much does a vector database cost for a production AI agent?: Costs vary dramatically by scale. Chroma is free (self-hosted). Weaviate Cloud starts at ~$75/month. Pinecone scales from minimal cost for small workloads to $5,000–$6,000/month at 100M vectors with 150M queries. Don't forget to include embedding API costs (OpenAI, Cohere) in your TCO — they often exceed the vector DB bill at scale.