Twigg: Context management interface for organizing LLM conversations

What it does

Twigg is an API layer that manages LLM conversations independently of any provider. Instead of sending requests directly to ChatGPT or Claude, developers send events to Twigg, which stores conversation history, reformats context to fit each model's limits, routes requests to providers like OpenAI, Anthropic, Google, or others, and tracks costs. A single conversation can persist across model switches without manual context transfer.

Who it is for

Developers building applications that rely on long-running LLM interactions. Teams that need to switch between LLM providers mid-project without losing conversation state. Engineers who find the linear chat interfaces of existing services inadequate for managing complex, multi-day projects. Anyone frustrated by context limits or the friction of manually copying conversations between services.

Pricing

The site does not show prices.

How it stands out

Twigg separates conversation storage from model execution. This inversion means conversations become first-class resources that exist outside any single provider. Developers create a chat once and send incremental events rather than resending full history each time. The system automatically handles context window management for different models and enables switching providers mid-conversation. Cost reporting per request provides transparency across model usage. The API is stateful by design, eliminating the need for developers to manage prompt assembly themselves.

What a founder should check

First, understand the switching costs incumbents impose. Major LLM providers offer their own conversation persistence and history management. A founder building a competitor should verify whether developers value portability and provider independence enough to adopt a middleman layer versus staying within a single ecosystem.

Second, examine the moat around context window handling. Twigg's core value lies in automatically fitting conversations to each model's limits. Check whether this abstraction is genuinely difficult to replicate or if it becomes commoditized as providers standardize their APIs and context handling.

Third, evaluate pricing pressure. If Twigg charges per API call or per stored conversation, it sits between developers and their LLM spend. Confirm whether customers will accept this margin or demand Twigg be free to justify its existence. Also assess whether LLM providers will directly compete by offering their own cross-model context management.

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