Twigg
Twigg · Productivity · Other
Twigg is a stateful LLM API that keeps, fits and routes conversation context for you. You create a chat once, send only the next event, and name the model you want it to go to. It stores the thread, trims it to each model's LLM context window, translates it for Anthropic, OpenAI, Google, xAI, Fireworks or OpenRouter, streams the answer back, and reports what every request cost. Context management is the tedious part of building on LLMs. Why rebuild it for every provider?
About Twigg
What Is Twigg
Twigg is a hosted context management API, a context store, assembler and model router aimed at developers building apps on top of large language models. Instead of designing your own message table, writing the code that decides what to drop when a transcript outgrows the window, and maintaining a translation layer for each provider, you send the next turn and let Twigg handle the mechanics. It sits between your application and the model providers, and it stays out of the parts you still own: the agent loop and the tools themselves.
The core promise is portability. With most provider APIs, the conversation lives inside the thing that generated it, so the stored thread is also the reason you can't leave. Twigg stores the conversation beside the models. The model is a per-request choice, not a property of the chat, so a thread started on one model can be continued with another by changing one field. Nothing in the record is written in a single provider's dialect.
The scope is specific, and that matters. Twigg handles conversational turns with tools across every model in its catalogue. It doesn't do embeddings, image generation, batch jobs or fine-tuning, and it never executes a tool for you. If your project needs those things, they live elsewhere.
Getting Started
- Create an API key in the console at twigg.ai/dashboard/api-keys. Keys look like
tw_live_…. - Create a chat with
POST /api/v1/chats, choosing a namespace path such asacme/proj-7. Keep the returned chat id. - Submit a turn to
POST /api/v1/chats/{chat_id}/responseswith the model name and only the new input, such as a prompt or a tool result. - Read the server-sent event stream for the answer, which ends with a
doneevent carrying usage and cost. - Where the model asked for a tool, run it in your own code and submit the result to the same endpoint.
Product Information
A quick look at Twigg's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Backend and AI engineers
- Product teams running multi-model features
- Startups avoiding provider lock-in
Tasks
- Building a multi-turn assistant
- Cost auditing per request
- Tool-calling workflows
- Switching models mid-conversation
Scenarios
- A support bot that escalates from a fast, cheap model to a stronger one on hard tickets.
- Prototyping an agent without designing a database schema for messages and tool results.
- Comparing providers on the same transcript to see which gives better answers for your use case.
Key features
Stateful conversation storage
Twigg holds the conversation for you as a managed context store, so you never replay the whole transcript. You create a chat once and then submit only what just happened, such as a prompt or a tool result. The stored thread becomes the single record your app reads from and writes to.
Provider translation layer
The same chat can be served by Anthropic, OpenAI, Google, xAI, Fireworks or OpenRouter models. Twigg translates the stored context into whatever schema the chosen provider expects, so you don't maintain per-provider formatting. Sending the model's own string isn't needed; you name a catalogue entry instead.
Per-request model routing
The model is a per-request choice, not a property of the chat. You can switch models mid-conversation by changing a single field. Start on one model, continue on another. That makes cost or quality routing a one-line change rather than a migration.
Context budgets and compaction
Each namespace carries its own rules, including context budgets and retention policy. When a transcript outgrows a model's window, Twigg compacts it and warns you where that happened. Compaction runs are billed as their own line at the same rates.
Run-level cost reporting
Every interaction is a run that records what context was assembled, which model served it, and what it cost. The done event returns usage and cost, and the run id is what you use to audit requests. Prices are listed per million tokens and include the platform margin.
Namespaces for organisation
A namespace is a path you choose, like acme/proj-7, set at chat creation and immutable after. It groups chats for listing and is the scope key that decides which instructions, tools, retention policy and context budget apply. Configuration published higher up the path applies beneath it.
Streaming with tool awareness
Responses always stream as server-sent events. Beyond text deltas, the stream reports tool calls, config and translation warnings, and compaction work in progress. Twigg tells you a tool was called; it never runs one for you.
Pros and cons
Pros
- Conversations live outside any single provider, so switching models doesn't strand your history.
- You send only the newest event, not the full transcript, which cuts the code and payload you manage.
- One API key reaches Anthropic, OpenAI, Google, xAI, Fireworks and OpenRouter models.
- Each run reports the model served and the cost, which helps with billing and budgeting.
- No SDK required: any HTTP client that reads server-sent events is enough.
Cons
- No non-streaming mode, so every integration has to handle server-sent events, which is more work than a plain JSON call.
- It never executes tools, meaning you still build and run the tool layer yourself.
- Scope stops at conversational turns with tools; no embeddings, image generation, batch or fine-tuning, so mixed workloads need other services.
- Namespaces are immutable once a chat is created, so re-organising later means starting new chats.
Frequently asked questions
It stores, assembles and routes LLM conversation context behind a single API. You create a chat, send the next event with a model name, and it fits the context to that model and streams the reply.
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