Twigg

Twigg addresses the inherent limitations of traditional linear AI chat interfaces, which often lead to lost ideas, context clutter, and difficulties in managing long-term projects. By reimagining conversation not as a flat timeline but as a dynamic, living tree of ideas, Twigg provides a structured, visual, and highly efficient way for individuals and teams to interact with large language models (LLMs).

The platform's core innovation is its interactive tree diagram, which allows users to visualize their entire project's conversation history. This visual approach enables seamless navigation, context control, and the ability to branch off new ideas without disrupting the main line of thought. Twigg is designed to reduce redundant work, optimize token costs, and ultimately empower users to build better things with AI by fostering a more natural and productive thought process.

How to use Twigg

Using Twigg involves interacting with a dual-panel chat interface and its central 'Tree' visualization. To begin, you can submit prompts in the primary chat panel. The 'Tree' (accessible via the Twigg logo in the chat bar or fullscreen via an expand icon) displays your conversation as a series of nodes (prompt-response pairs) and links. To explore new ideas, you can 'branch' a conversation either by continuing from an existing parent node in the primary chat or by using the dedicated 'branch' button in the secondary chat panel. Context management is crucial: Twigg allows you to manipulate the tree structure by moving or deleting nodes and branches to precisely control what information is fed to the LLM. You can also copy and paste nodes, either inline or as new branches, to reuse useful content. Different LLM models can be selected from a dropdown menu at any time to compare responses.

Core Features of Twigg

• Interactive Tree Visualization: Transforms linear chats into a dynamic, navigable tree diagram for project overview and idea tracking.
• Context Control: Allows precise manipulation of conversation nodes and branches to manage the context fed to the LLM.
• Branching Conversations: Create new branches from any point in the conversation to explore tangents without cluttering the main context.
• Dual-Panel Chat Interface: Enables parallel queries and exploration of ideas in a secondary panel while maintaining the primary conversation.
• Version Control for LLMs: Easily navigate to any point in your conversation history, similar to version control systems.
• Long-Term Project Support: Designed to keep entire projects within one evolving tree, eliminating the need to restart chats or copy context.
• Multi-Model Access: Provides access to various LLMs (e.g., Gemini, GPT, Claude, Grok) with the ability to switch models on the fly.
• Node Management: Copy, paste (inline or as new branch), move, and delete individual nodes or entire branches.
• File Upload and Web Search: Supports uploading and searching files, and integrates web search tools (available in Free tier and above).
• BYOK (Bring Your Own Key) Integration: Connect your own API keys for supported models to pay for usage directly.

Use Cases for Twigg

Twigg is ideal for anyone engaged in complex, iterative, or long-term projects requiring deep interaction with AI. Researchers can use it to explore multiple hypotheses and track their thought process. Content creators can brainstorm different angles for articles or scripts, branching out ideas without losing the core narrative. Software developers can manage complex coding problems, exploring various solutions and debugging paths. Project managers can use it for strategic planning, visualizing different scenarios and decision trees. Academics can organize research notes and arguments, ensuring consistent context for their AI interactions. Essentially, any professional needing to manage intricate information flows and explore diverse ideas with AI will find Twigg invaluable.

Advantages of Twigg

Twigg's primary advantage lies in its ability to overcome the limitations of linear AI chats, fostering a more natural and productive thinking process. It significantly reduces context clutter, ensuring AI responses are always relevant and accurate. The visual tree structure acts as a powerful knowledge management tool, allowing users to easily revisit, refine, and build upon previous ideas, akin to version control for conversations. This structured approach saves time by eliminating repetitive context copying and reduces token costs by allowing precise context feeding. For long-term projects, Twigg ensures continuity and prevents idea fragmentation, making it an indispensable tool for complex problem-solving and creative exploration with AI.

Pricing and Plans

Twigg offers a freemium model with several paid tiers, operating on a credit-based system where a portion of the fee covers platform costs, and the rest converts to usage credits at no markup on token prices. Pricing is available in GBP, USD, and EUR.

• Free: Access to advanced context management, full branching chat features, file upload and search, web search tools, and access to Gemini 2.5 Flash, GPT-5-mini, and other models, with limited monthly token limits.
• BYOK (Bring Your Own Key): £5.00/month (or $5.00/month, €5.00/month). Includes all Free tier features, plus the ability to connect your own API keys for all supported models, paying for usage directly.
• Plus: £15.00/month (or $15.00/month, €15.00/month). Offers access to ChatGPT-5, Gemini 2.5 Pro, and more powerful models, increased token limits, and includes all BYOK features.
• Pro: £80.00/month (or $80.00/month, €80.00/month). Provides access to ChatGPT5-pro and Claude Opus 4.1, the largest token limits, and includes all features from Plus and BYOK plans.

Token costs vary by model and are charged at the same rate as the model providers. For example, Google 2.5 Flash (Free Tier) costs $0.30/1M input tokens and $2.50/1M output tokens, while OpenAI GPT-5-pro (Pro Tier) costs $15.00/1M input tokens and $120.00/1M output tokens. Token usage is estimated, with 1,000 tokens roughly equivalent to 750 English words.