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.
This sounds really, really convenient since I spend so many tokens on just having Claude understand all the context for my projects
the truncate/compact-to-fit-schema part is the piece I always end up hand-rolling badly. curious how Twigg decides what to drop when a chat outgrows the target model's context window - is it a simple oldest-messages-first trim, or does it try to preserve things like pinned system instructions and tool call results that a later turn still depends on?
Does Twigg support branching chats, like trying two different tool choices from the same context?
Hi Product Hunt
We've spent the last year building AI workspaces, and before every new launch we'd end up re-engineering the same thing: the context layer. Where the conversation lives, how it gets fitted to a model's context window, how tool calls and files get replayed.
What we really wanted was a hosted, stateful LLM API that didn't only work with one provider. Something like OpenAI's Responses API crossed with OpenRouter. We didn't want to spend time building infrastructure and code that weren't core to our product.
Since we'd already built most of the pieces, we turned them into Twigg: the stateful API for every LLM.
How it works: you create a chat and get back an ID. From then on you only send the next prompt or tool result. You don't send, or even store the context. Twigg manages that for you. Want a different model? Change one field on the next request. Twigg will fetch, assemble and fit the context to the right schema and send back the response. It even auto-compacts conversations, so you never have to worry about a chat getting too long. The idea is to move all the boilerplate logic and infrastructure LLM apps need behind one unified API.
v0.1.0 is live today. It includes namespaces for organising chats per user, a dashboard for system prompts, tool schemas and context budgets. It also track per-run cost and usage so you can bill your own users. Point your agent at twigg.ai/llms.txt for the full rundown!
We'd love to hear what you think, and whether anyone else has had the same context frustrations we have.
Thanks for checking it out!
About Twigg on Product Hunt
“The context layer you never have to build”
Twigg launched on Product Hunt on September 16th, 2026 and earned 121 upvotes and 12 comments, placing #8 on the daily leaderboard. 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.
Twigg was featured in API (98.7k followers), Developer Tools (519.6k followers) and Artificial Intelligence (478.9k followers) on Product Hunt. Together, these topics include over 218k products, making this a competitive space to launch in.
Who hunted Twigg?
Twigg was hunted by Matti De Beer. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Matti De Beer
context compaction as a service, nice. every agent team rebuilds this and its never fun 😅 congrats!!