Twigg

The context layer you never have to build

Developer ToolsArtificial IntelligenceAPI

▲ 109 votes6 commentsLaunched Sep 16, 2026

Weekly #27

Twigg is a stateful API for calling LLMs. Instead of rebuilding and resending your whole conversation on every request, you create a chat once and send only the next event. Twigg holds the state: it fits context to the target model's schema, compacts or truncates when it runs long, and routes the call. Control tool schemas, system prompts and context windows from the dashboard, and track usage and billing. Build anything from personal agents to enterprise apps. You never manage context again.

AI Analysis

📝 Summary

Twigg is a stateful API for LLM calls that eliminates manual context management. Users create a chat once and send only new events; Twigg maintains state, fits context to model schemas, compacts/truncates long contexts, and routes requests. Dashboard controls for tool schemas, system prompts, context windows, plus usage/billing tracking. It solves the pain of rebuilding/resending full conversation histories on every request, reducing complexity, token waste, and errors. Value proposition: Build personal agents to enterprise apps without managing context again.

📈 Market Timing

In 2025-2026, LLM adoption is surging with increasing demand for complex AI agents and efficient context handling as models grow more capable. Technology for stateful APIs is mature, user needs for simplified dev tools are rising, and economic conditions favor productivity-enhancing AI infrastructure. Excellent Timing.

✅ Feasibility

High. Technical implementation is achievable leveraging existing LLM APIs and cloud services, though multi-model schema support needs maintenance. Moderate dev and operational costs (API hosting, inference fees). Strong scalability potential via cloud. Low regulatory risks for a developer API tool. Suitable for experienced AI engineering teams.

🎯 Target Market

Primary segments: AI developers, software engineers, and startups building LLM-powered apps/agents; industries include tech, SaaS, enterprise software (global, concentrated in US, Europe, China tech hubs). TAM for AI dev tools ~$15B+, SAM for context/LLM infrastructure ~$2B, SOM growing with adoption. Core pains: tedious context engineering and high token costs. Strong willingness to pay via usage-based or subscription pricing for time and cost savings.

⚔️ Competition

Medium. Direct competitors: 1. OpenAI Assistants API (openai.com), 2. LangChain/LangGraph (langchain.com), 3. Zep (getzep.com), 4. Mem0 (mem0.ai). Advantages: Simpler pure context layer, automatic schema fitting/compaction, unified dashboard for prompts/windows. Disadvantages: Newer player with potentially smaller ecosystem and less brand trust vs OpenAI; may require more custom integrations.

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