Twigg

Overview

Large language models have transformed how we approach creative work, research, and software development, yet the interfaces through which we interact with these powerful tools remain surprisingly primitive. Most LLM platforms—from ChatGPT to Claude to Gemini—present conversations as simple linear threads. This linear paradigm creates fundamental challenges for complex, long-term projects: context becomes tangled across dozens or hundreds of messages, exploring alternative approaches requires abandoning existing work, recovering specific earlier conversations means scrolling endlessly through history, and managing what context gets sent to the model remains opaque and uncontrollable. As projects grow in sophistication and duration, these limitations compound, ultimately leading to the familiar experience of conversations “breaking” or becoming unmanageable.

Twigg, launched on Product Hunt on October 22, 2025, and simultaneously debuted on Hacker News as a “Show HN,” reimagines LLM interaction through a fundamental architectural shift: conversations as trees rather than threads. By treating LLM dialogues like version-controlled code repositories—complete with branching, merging, selective context inclusion, and visual navigation—Twigg provides what its creators describe as “Git for LLMs.” This approach targets technical users engaging in extended, multi-faceted projects where traditional linear interfaces prove inadequate: developers building complex features across multiple iterations, researchers exploring hypotheses through branching inquiry paths, writers developing narrative alternatives, and anyone working on sophisticated tasks requiring precise control over conversational context and history.

Key Features

Twigg distinguishes itself through capabilities specifically engineered for managing complex, long-duration LLM projects that outgrow linear chat interfaces.

• Interactive Tree Visualization: Twigg represents your entire conversation history as an interactive, zoomable tree diagram where each node corresponds to a message or response. The tree view displays parent-child relationships, shows branching points clearly, indicates which branches are active, and enables rapid navigation across potentially hundreds of conversational nodes. This spatial representation transforms how users comprehend project structure, identify patterns, locate specific exchanges, and understand decision points—capabilities impossible in linear scroll-based interfaces.
• Conversational Branching: From any point in your conversation, Twigg enables creating new branches to explore alternative directions, test different approaches, or pursue tangents without disrupting the main conversation flow. Each branch maintains full context inheritance from its creation point, preserving continuity while enabling parallel exploration. This branching model mirrors how developers use git branches for feature development—experiment freely knowing you can always return to stable states or compare outcomes across branches.
• Granular Context Control: Traditional LLM interfaces send your entire conversation history or apply proprietary, opaque context windowing. Twigg inverts this model, providing explicit control over exactly which nodes contribute to the context sent with each new message. Users can select specific branches, exclude irrelevant tangents, reorder context elements, and precisely craft the information provided to the model. This granularity enables significant token optimization by excluding redundant or outdated information while maintaining relevant context, directly impacting both cost and response quality.
• Node Manipulation Operations: Twigg supports fundamental editing operations on the conversation tree including moving nodes between branches to reorganize project structure, copying nodes to reuse context or responses in multiple branches, cutting nodes to remove them while preserving for potential restoration, and deleting nodes permanently to clean conversation history. These operations enable ongoing curation of your project, ensuring the tree structure accurately reflects your current understanding rather than accumulating historical clutter.
• Comprehensive Token Usage Tracking: A built-in dashboard provides detailed visibility into token consumption across models, projects, and time periods. Users can see per-model usage (ChatGPT, Claude, Gemini, Grok), understand which branches or conversations consume the most tokens, monitor remaining credits in their subscription tier, and make informed decisions about context management based on actual usage patterns. This transparency supports both cost optimization and strategic decisions about when to prune context versus maintain full history.
• Multi-Provider Model Access: Twigg integrates with major LLM providers including OpenAI (GPT-4, GPT-4o), Anthropic (Claude 3.5 Sonnet, Claude 3 Opus), Google (Gemini Pro, Gemini Ultra), and X.AI (Grok), enabling users to select optimal models for specific tasks or compare outputs across providers within the same project structure. This provider-agnostic approach prevents lock-in and enables strategic model selection based on task requirements.
• Bring Your Own Key (BYOK): For users with existing API relationships or requiring unlimited usage, Twigg’s BYOK tier enables connecting personal API keys from OpenAI, Anthropic, Google, or X.AI directly. In BYOK mode, Twigg charges no markup on token usage, with costs flowing directly to your provider accounts. This model appeals to power users, enterprises with negotiated API rates, and developers wanting complete transparency and control over API relationships.
• Flexible Pricing Models: Twigg offers multiple access tiers including a free plan supporting limited token usage across all models, paid subscription tiers converting payments to credits at provider pricing (no markup), and the BYOK unlimited tier for users providing their own API keys. The transparent credit system shows exactly how tokens convert to costs, eliminating surprises common in opaque subscription models.
• Collaboration Features: Projects can be shared with team members, enabling collaborative exploration of complex problems. Multiple users can work within the same tree structure, create parallel branches, and compare approaches—supporting team-based research, pair programming with AI assistance, or distributed problem-solving.
• Export and Backup: Conversation trees can be exported for backup, archival, or migration purposes, ensuring users maintain ownership and access to their conversational history independent of platform availability.

How It Works

Twigg’s operational model centers on the tree-based conversation architecture, fundamentally changing how users interact with LLMs compared to traditional linear interfaces.

Users begin projects by creating a new conversation tree and selecting their preferred model from available providers (or using their own API key via BYOK). The initial message creates the root node of the tree, with the model’s response becoming the first child node. From this foundation, users can continue linearly by responding to the latest message—functionally equivalent to traditional chat—or leverage Twigg’s distinctive capabilities by branching the conversation at any node.

Branching occurs simply by selecting a node and creating a new branch, which inherits all context from the root through that node. This enables exploring “what if” scenarios: test different prompt phrasings, pursue alternative research directions, experiment with different approaches to the same problem, or develop multiple solutions in parallel. Each branch operates independently with its own conversation flow, yet all branches remain accessible through the tree visualization for comparison, context mining, or potential merging.

The context control mechanism provides unprecedented precision over what information reaches the model. Before sending each new message, users can visually select which branches and nodes should contribute to the context window. Want to exclude a tangential exploration that consumed 50 messages but proved irrelevant? Simply deselect that branch, immediately reducing token consumption while maintaining the history for potential future reference. Need to provide context from multiple branches simultaneously? Select nodes from each, and Twigg assembles them into coherent context for the model.

The tree visualization updates in real-time as conversations evolve, with nodes color-coded by branch, sized potentially by importance or recency, and organized spatially to reveal structure. Users can zoom, pan, collapse branches for overview perspectives, or expand specific areas for detailed work. This spatial representation transforms comprehension of complex projects—patterns emerge visually that would remain hidden in linear scrolling interfaces.

Token tracking operates continuously in the background, with the dashboard providing real-time updates on usage. Users can see which conversations or branches consume the most tokens, helping identify areas for optimization. The credit system transparently converts token usage to costs at provider rates, ensuring users understand exactly what they’re paying for without markup or hidden fees.

For teams, collaboration features enable multiple users to work within the same tree, with changes synchronized across participants. This supports real-time collaborative exploration or asynchronous work where team members contribute to different branches that later merge or inform each other.

Use Cases

Twigg’s tree-based context management serves diverse scenarios where traditional linear LLM interfaces prove inadequate for project complexity or duration.

• Software Development with Iterative Refinement: Developers building features through AI assistance can create branches for different implementation approaches, maintain separate contexts for frontend versus backend considerations, test alternative architectural patterns, and compare generated code quality across approaches. As bugs emerge or requirements change, developers branch from earlier stable points rather than polluting the main conversation with correction attempts, maintaining clean context for the model.
• Research Projects with Hypothesis Exploration: Researchers can pursue multiple hypotheses simultaneously through parallel branches, maintain separate literature review contexts for different theoretical frameworks, test alternative analytical approaches, and compare findings across methodological variations. The tree structure mirrors the actual cognitive process of research—nonlinear, exploratory, with frequent backtracking and comparative analysis.
• Long-Form Writing with Narrative Alternatives: Authors developing novels, screenplays, or complex documents can explore different plot directions through branches, maintain alternate character development arcs, test various narrative structures, and selectively incorporate successful elements from exploratory branches into the main manuscript. The ability to experiment without disrupting the primary narrative enables creative freedom while maintaining project coherence.
• Technical Documentation with Multiple Audiences: Documentation writers can branch conversations to explore different explanation styles, maintain separate contexts for novice versus expert audiences, develop alternative structural organizations, and compare clarity across approaches before committing to final documentation.
• Learning and Educational Exploration: Students and self-learners can branch conversations when encountering new concepts requiring deep dives, maintain separate contexts for related but distinct topics, compare explanations from different models or prompting approaches, and build comprehensive knowledge trees reflecting their learning journey rather than linear notes.
• Cost Optimization for Token-Constrained Projects: Organizations with strict budget constraints or individual users on limited plans can use Twigg’s context control to minimize redundant token usage, prune irrelevant historical context, precisely select only essential information for each query, and extend project viability within token budgets that would exhaust quickly in traditional interfaces.
• Multi-Model Comparison and Quality Assessment: AI researchers, developers evaluating models for production use, or users seeking optimal output can create parallel branches using different models (GPT-4 vs Claude vs Gemini), compare response quality across identical contexts, assess which models excel for specific task types, and make evidence-based decisions about model selection.
• Project Recovery and Context Archaeology: When conversations become unwieldy or “break” in traditional interfaces, users typically start fresh, losing valuable context. Twigg enables recovering functional branches from tangled trees, identifying where conversations went off track, selectively preserving valuable portions while pruning problematic tangents, and rebuilding coherent project context without complete restart.

Pros \& Cons

Advantages

• Transforms Complex Project Management: The tree visualization and branching capabilities fundamentally solve the problem of managing sophisticated, long-duration LLM projects. What becomes unwieldy in linear interfaces remains navigable and coherent in Twigg’s spatial representation.
• Unprecedented Context Precision: Granular control over exactly which nodes contribute context enables optimization impossible in traditional interfaces. Users can minimize token waste, ensure models receive only relevant information, and extend project viability on constrained budgets.
• Enables True Exploration Without Penalty: Branching allows risk-free experimentation. Pursue tangents, test alternatives, or explore “what if” scenarios knowing you can always return to stable states or abandon unsuccessful branches without polluting the main conversation.
• Visual Comprehension of Project Structure: The interactive tree diagram provides spatial understanding of complex projects impossible to achieve through linear scrolling. Patterns, relationships, and project evolution become visually apparent rather than cognitively reconstructed.
• Cost Transparency and Flexibility: The credit system with provider-rate pricing (no markup) and BYOK option delivers unusual transparency and flexibility in the LLM interface market. Users know exactly what they’re paying and can optimize costs through context management or bring their own API relationships.
• Provider Independence: Support for multiple models across providers prevents lock-in and enables strategic model selection per task, comparison studies, or seamless switching when providers experience outages or pricing changes.
• Collaborative Problem-Solving: Team features enable shared exploration of complex problems, parallel investigation of alternatives, and comparison of approaches—capabilities missing from most LLM interfaces designed for individual use.

Disadvantages

• Learning Curve for Tree-Based Navigation: Users accustomed to linear chat interfaces require adjustment to spatial tree navigation, branching workflows, and explicit context management. This learning investment pays dividends for complex projects but may feel unnecessary for simple queries.
• Overkill for Simple Tasks: Quick questions, straightforward queries, or single-exchange interactions don’t benefit from Twigg’s sophisticated features. The platform targets sustained, complex projects rather than casual LLM use, meaning simpler interfaces may prove more efficient for basic tasks.
• Early-Stage Platform Maturity: Launched in October 2025, Twigg represents a young product still evolving features, gathering user feedback, and discovering edge cases. Users should expect ongoing refinement, occasional limitations, and feature roadmap updates as the platform matures.
• Requires Active Project Management: Unlike passive linear interfaces that simply accumulate messages, Twigg rewards active project management—pruning unnecessary branches, organizing node structure, and curating context. Users unwilling to invest management effort won’t fully leverage the platform’s capabilities.
• Branch Merging Still Evolving: While branching is mature, advanced features like intelligent branch merging (combining successful elements from multiple exploratory branches) remain under development. Users wanting sophisticated merge operations may encounter limitations.
• Limited Mobile Experience: The tree visualization and node manipulation features are optimized for desktop interfaces with larger screens, mouse or trackpad navigation, and keyboard shortcuts. Mobile usage may prove less fluid until dedicated mobile UX development occurs.

How Does It Compare?

Understanding Twigg’s market position requires examining the LLM interface and context management landscape as it exists in late 2025, where competitors range from mainstream chat interfaces to specialized context tools.

ChatGPT with Memory and Projects represents the baseline comparison point given OpenAI’s market dominance. ChatGPT’s 2025 platform includes persistent memory storing user preferences and context across conversations, Projects feature grouping related chats with shared context and custom instructions, and significantly expanded context windows supporting hundreds of thousands of tokens. ChatGPT excels through widespread adoption, seamless experience requiring no learning curve, deep integration with OpenAI’s ecosystem, and continuous refinement from massive user feedback. However, ChatGPT maintains fundamentally linear conversation threading, provides no branching or version control capabilities, implements context management opaquely through proprietary algorithms, and lacks explicit control over which historical information contributes to each response. Where ChatGPT optimizes for simplicity and broad accessibility, Twigg targets sophisticated users requiring explicit control over complex project structure.

Claude with Projects and Artifacts by Anthropic offers Claude Projects grouping conversations with shared knowledge bases, Artifacts creating persistent objects (documents, code, designs) that persist across conversations, and strong long-context capabilities with 200K+ token windows. Claude serves users prioritizing thoughtful, nuanced responses, complex document analysis, and persistent work products. However, like ChatGPT, Claude maintains linear conversation structure, provides no explicit branching or tree navigation, and implements context management algorithmically rather than through user control. Claude’s Artifacts approach different problem space (creating durable work products) compared to Twigg’s conversational versioning.

Google AI Studio provides Google’s interface to Gemini models, offering structured prompting with saved presets, freeform chat with customizable system instructions, multi-turn conversation management, and experimentation tools for prompt engineering. AI Studio serves developers and power users requiring structured interaction with Gemini models, supporting systematic prompt testing and template management. However, AI Studio focuses on prompt engineering rather than conversation structure management, provides no tree visualization or branching, and emphasizes model configuration over historical context control. AI Studio and Twigg address complementary needs—prompt optimization versus conversation architecture.

Notion AI integrates AI capabilities within Notion’s knowledge management platform, offering AI embedded in documents, databases, and wikis, question-answering across organizational knowledge bases, content generation with workspace context, and collaborative AI assistance within team environments. Notion AI excels for teams using Notion as their central knowledge repository, providing AI that understands organizational context and integrates with established workflows. However, Notion AI lives within document-editing contexts rather than dedicated conversation management, provides no conversation branching or tree visualization, and optimizes for content creation within Notion’s structure rather than standalone LLM project management. For organizations already using Notion extensively, its AI features provide value, but it addresses different use cases than Twigg’s specialized conversation management.

MemGPT represents academic research on hierarchical memory systems for LLMs, implementing virtual context management inspired by operating system memory architectures, moving information between fast and slow memory tiers, and enabling conversations extending far beyond base model context windows. MemGPT demonstrates sophisticated approaches to long-context management through automated tiering rather than user control, operates primarily as research prototype rather than production tool, and focuses on technical memory architecture rather than user-facing conversation organization. MemGPT’s research informs the technical foundation of long-context management, while Twigg provides user-facing tools for explicit project control.

ChatGPT Canvas and Claude Canvas provide specialized interfaces for iterative document and code editing within conversations, supporting inline editing with AI assistance, version comparison, and persistent work products. Canvas tools address specific use cases (document editing, code development) through specialized interfaces rather than general conversation management. They complement rather than compete with Twigg—a user might employ Canvas for editing specific documents while using Twigg to manage the broader conversational project structure around those documents.

Linear Chat with Local Context Tools including various VS Code extensions, command-line interfaces, and custom API wrappers enable developers to interact with LLMs while automatically including codebase context, file contents, or documentation. These tools optimize for specific technical workflows, embedding LLM interaction within development environments, but provide no broader conversation management, branching, or project organization capabilities. They serve complementary niches—focused technical workflows versus general project management.

Twigg’s distinctive positioning emerges at the intersection of sophisticated users requiring explicit control, complex projects outgrowing linear interfaces, and cost-conscious users seeking token optimization. Where mainstream platforms (ChatGPT, Claude, Gemini) prioritize simplicity and broad accessibility through linear interfaces with algorithmic context management, workspace-integrated AI (Notion AI, Coda AI) optimizes for content creation within specific platforms, research systems (MemGPT) explore technical memory architectures, and specialized tools (Canvas, coding assistants) target specific workflows, Twigg exclusively addresses conversational project management through version-control paradigms. This focus makes Twigg particularly compelling for developers managing multi-week features through AI assistance, researchers conducting systematic inquiries with hypothesis branching, writers exploring narrative alternatives, technical users comfortable with git-like mental models, and cost-conscious users needing precise token optimization. The platform succeeds in serving a specific, sophisticated audience rather than attempting to satisfy all users—a strategic positioning appropriate for its stage and mission.

Final Thoughts

Twigg represents a thoughtful response to genuine pain points emerging as LLM usage matures from novelty toward serious tooling for complex work. The platform’s creators—who developed Twigg during their master’s programs after repeatedly encountering the limitations of linear interfaces in extended projects—demonstrate understanding that different paradigms serve different needs. Simple questions benefit from simple interfaces; sophisticated projects demand sophisticated structure.

The “Git for LLMs” metaphor resonates because it captures core insight: version control revolutionized software development not by making code easier to write but by making complex change management tractable. Similarly, Twigg doesn’t improve individual LLM responses—it improves project-level manageability across dozens or hundreds of interactions where structure, context control, and navigability determine success.

The tree visualization succeeds by making implicit cognitive structure explicit. Experienced LLM users already think in branching terms—mentally tracking alternative approaches, imagining counterfactuals, organizing conceptual hierarchies—but must maintain this structure purely cognitively while using linear interfaces. Twigg externalizes this structure, reducing cognitive load and enabling more sophisticated project organization than working memory permits.

The context control capabilities address a less obvious but equally important issue: traditional interfaces either send too much context (wasting tokens, introducing confusion) or too little (losing continuity, requiring repetition). Twigg’s granular selection enables optimal middle ground—exactly the right context, neither more nor less, customized per interaction. This precision matters increasingly as projects grow and token budgets tighten.

However, prospective users should honestly assess whether their use cases warrant Twigg’s sophistication. Quick queries, casual exploration, or simple tasks gain little from tree management and explicit context control. The platform targets sustained, complex projects where traditional interfaces demonstrably break down. Users working primarily on such projects will find Twigg transformative; casual users may find it unnecessarily complex.

The learning curve, while real, mirrors git adoption among developers—initial overhead proves worthwhile once projects reach sufficient complexity. Teams should expect initial adjustment periods as users internalize tree-based navigation, develop branching strategies, and discover optimal context management patterns for their work styles.

The October 2025 launch means Twigg remains young with feature development ongoing. Early adopters trade cutting-edge capabilities for occasional rough edges, feature requests potentially unanswered, and evolving best practices as the user community discovers optimal workflows. This tradeoff suits technically sophisticated, feedback-oriented users—Twigg’s target demographic—better than mainstream consumers expecting polished, stable products.

The business model deserves attention: transparent credit pricing with no markup and BYOK options demonstrate unusual customer alignment in an industry often characterized by opaque pricing and aggressive lock-in. This transparency builds trust and positions Twigg for long-term adoption by sophisticated users who notice and reward fair pricing.

As LLM usage continues maturing from experimental to production, from casual to professional, from simple to complex, tools addressing the full lifecycle of sophisticated projects become increasingly essential. Twigg demonstrates that conversational interfaces need not remain primitive threads—structure, control, and management can coexist with natural language flexibility. For the growing population of users tackling genuinely complex work through AI assistance, Twigg offers a pragmatic path toward project manageability that linear interfaces simply cannot match. The platform succeeds not by reimagining what LLMs do but by reimagining how we organize, navigate, and control extended engagement with them—precisely the kind of tooling innovation that defines mature technology ecosystems.