# Introduction Qolaba's Chatbot is a multi-model AI assistant built for professional and team-based workflows. It goes beyond standard chat by combining access to multiple large language models, executable tools, custom agents, and knowledge-aware responses — all within your organization's workspace structure ### Who It's For The Qolaba Chatbot is built for teams and individuals who need more than a basic AI chat interface: * **Startups & Creators** — Rapid content generation, product documentation, growth experimentation * **Marketing Teams** — Campaign ideation, ad copy, SEO drafts * **Agencies** — Multi-client workspace management with shared credit pools * **Developers** — Prompt engineering, model comparison, AI experimentation * **Enterprises** — Structured AI collaboration with governance and audit trails *** ### Key Capabilities Five capabilities make the Qolaba Chatbot distinct from a standard AI chat tool. #### **1. Multi-LLM Access** Most AI tools lock you into a single model. Qolaba gives you access to six leading large language models from one interface — **GPT, Gemini, Claude, DeepSeek, Grok, and Sonar (Perplexity)** — and lets you switch between them freely. This means you can: * Run the same prompt across multiple models and compare outputs * Choose the best model for a specific task — creative writing, coding, research, summarization * Optimize credit usage by matching task complexity to the right model * Avoid being locked into one provider's strengths or limitations\ [Learn more about Model Selection →](/model-reference/chatbot-models.md) *** #### **2. Agents** Agents are purpose-built AI assistants that go beyond a one-off chat. Each agent has a defined **role, behavioral instructions, tone, and knowledge base** — so it responds consistently, stays on-topic, and understands your context without you re-explaining it every session. Qolaba offers two types: * **Pre-built agents** — Ready-to-use assistants for common roles like Marketing Strategist, Content Creator, and more. Select one and start immediately with no setup required. * **Custom agents** — Build your own from scratch. Define the agent's name, role, model, expertise, and attach knowledge bases so it answers with your specific context in mind. Agents are persistent, reusable across sessions, and can be shared within a workspace — making them especially valuable for teams with recurring workflows. [Learn more about Agents →](/chatbot/agents.md) *** #### **3. Toolkit** The Toolkit turns the Chatbot from a conversational tool into an execution environment. Instead of just generating text, it can actively perform tasks — searching the web, generating media, running code, analyzing files, and more. Tools can run in **Auto mode**, where Qolaba automatically selects the right tool based on your prompt, or you can enable them manually for full control. Toolkit includes tools for web search, media generation, code execution, file and URL analysis, and output safety — covering most tasks a professional workflow demands, without leaving the chat interface. [Learn more about the Toolkit →](/chatbot/toolkit.md) *** #### **4. Knowledge Base & Files** Uploading knowledge base and files lets you extend the chatbot's context with your own material — so it can answer questions, summarize content, and extract insights from documents that are specific to your work. You can: * **Upload files directly** into a chat — supported formats include PDF, CSV, Excel, and plain text * **Create knowledge bases** — organized collections of files that any agent or chat session can reference persistently This transforms the Chatbot from a generic assistant into a domain-aware one that understands your products, processes, and data. [Learn more about Knowledge Bases & Files →](/chatbot/adding-context-and-resources.md) *** #### **5. Chat Branching** When a response isn't quite right, you don't need to start over or manually re-prompt. Chat Branching lets you **regenerate any response using a different model** at any point in a conversation — and compare outputs side-by-side across LLMs. This is particularly useful for: * Testing how different models interpret the same prompt * Selecting the best response before using it in production * Iterating on complex outputs like code, copy, or structured data without losing conversation context [Learn more about Chat Branching →](/chatbot/model-selection/chat-branching.md) *** #### Additional Features The Chatbot also includes several supporting features covered in their own pages: * [**Model Settings**](/chatbot/model-selection/model-settings.md) — Fine-tune Temperature and Thinking Depth to control response style and reasoning * [**Chat Management**](/chatbot/chat-history-management.md) — Pin, rename, share, and delete conversations from your history * [**Prompt Management**](/chatbot/prompt-and-controls.md) — Save, edit, and reuse prompts across chats and agents *** #### What's Next Follow the recommended flow to get started: 1. [Start a New Chat →](/chatbot/starting-a-new-chat.md) 2. [Select a model and configure settings →](/chatbot/model-selection.md) 3. [Set up an agent →](/chatbot/agents.md) 4. [Upload files or create a knowledge base →](/chatbot/adding-context-and-resources.md) 5. [Explore the Toolkit →](/chatbot/toolkit.md) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.qolaba.ai/chatbot/introduction.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.