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Twigg
Pricing
- Model
- Usage-Based
Summary
Every time you swap LLM providers mid-conversation, you're back to manually reshaping the transcript, re-fitting the context window, and hoping nothing gets truncated in the handoff — Twigg exists to absorb that plumbing entirely.
Twigg is a hosted context API: you create a chat, send it the next event, and it handles storing the conversation, fitting it to the target model's window, routing it to whichever provider you name, and returning a per-request cost figure. The abstraction is real — switching from Anthropic to OpenAI mid-conversation is a single field change in the request, not a data migration. Where the tool earns its keep is long-running conversations that would otherwise blow past a single model's context limit; Twigg compacts and assembles automatically. The ceiling appears when you need the context layer to do anything beyond storage and routing — conditional logic, retrieval, or branching belong elsewhere.
Bottom line: Reach for Twigg when your application needs persistent, provider-portable conversation state and you'd rather pay per request than maintain that infrastructure yourself — but if your use case requires retrieval-augmented context or custom compaction strategies, you'll be building that layer on top yourself.
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Pros
Sign in to edit- Provider-agnostic routing across Anthropic, OpenAI, Google, xAI, Fireworks, and OpenRouter, so switching providers when rate limits hit or costs spike is a single field change rather than a transcript migration.
- Automatic context window fitting per model, which means conversations that would silently truncate or throw errors when switching between models with different window sizes are handled without application-side logic.
- Per-request cost reporting in USD, so you see what each exchange costs at the moment it happens instead of reconciling aggregate provider invoices at the end of the month.
- Conversation state lives outside the LLM provider, which means a provider outage does not orphan your conversation history — you can re-route to a fallback model against the same stored context.
- Flat markdown documentation published to a single file (llms-full.txt) alongside an OpenAPI spec, so integrating or generating client code does not require scraping a documentation site.
Cons
Sign in to edit- Twigg stores and routes conversation context but offers no retrieval layer — once a conversation grows beyond what compaction preserves, older content is gone from the model's view with no mechanism to search or resurface it. Teams building applications where users reference events from sessions weeks ago hit this wall and typically add a vector store alongside Twigg, running two systems where they expected one.
- There is no self-hosted deployment path, which means any team operating under data residency rules — healthcare, finance, regulated government — cannot use Twigg for the workloads that matter most to them and will evaluate a self-hostable context management layer instead.
- Compaction is automatic and internally managed, meaning teams cannot define custom compaction strategies, prioritize certain message types, or inspect what was dropped. Applications where the shape of what gets preserved is business-critical — legal transcripts, compliance workflows — require a provider that exposes compaction controls, which is the condition under which teams switch to building their own context layer or adopt a competitor that offers configurable summarization.
About
- Platforms
- Web API
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- 2026-09-16T16:53:03.959Z
Best For
Who it's for
- Developers building stateful LLM apps
- Teams needing provider-agnostic context management
- Applications requiring automatic compaction and cost tracking
What it does well
- Multi-turn chat applications with persistent context
- Switching LLM providers mid-conversation
- Building agents that run their own tool loops without managing transcripts
- Long-running conversations that exceed single-request context limits
Integrations
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Sign Up to ContributeFrequently Asked Questions
- Is Twigg free?
- Twigg is a paid tool. No permanent free tier is offered.
- Is Twigg open source?
- No — Twigg is a closed-source tool. Source code is not publicly available.
- Does Twigg have an API?
- Yes. Twigg exposes a developer API. See the official documentation at https://twigg.ai for details.
- What platforms does Twigg support?
- Twigg is available on: Web API.
Curated lists that include this category
Swapping LLM providers mid-chat forces manual transcript fixes
Every time you swap LLM providers mid-conversation, you’re back to manually reshaping the transcript, re-fitting the context window, and hoping nothing gets truncated in the handoff. Twigg exists to absorb that plumbing entirely.
How it works
Twigg is a hosted context API: you create a chat, send it the next event, and it handles storing the conversation, fitting it to the target model’s window, routing it to whichever provider you name, and returning a per-request cost figure. The abstraction is real — switching from Anthropic to OpenAI mid-conversation is a single field change in the request, not a data migration. Where the tool earns its keep is long-running conversations that would otherwise blow past a single model’s context limit; Twigg compacts and assembles automatically.
Key capabilities
The vendor states provider-agnostic routing across Anthropic, OpenAI, Google, xAI, Fireworks, and OpenRouter. Automatic context window fitting per model removes the need for application-side logic when window sizes differ. Per-request cost reporting in USD shows the expense of each exchange at the moment it happens. Use cases include multi-turn chat applications with persistent context, switching LLM providers mid-conversation, building agents that run their own tool loops without managing transcripts, and long-running conversations that exceed single-request context limits.
Limitations
Twigg stores and routes conversation context but offers no retrieval layer. Once a conversation grows beyond what compaction preserves, older content is gone from the model’s view with no mechanism to search or resurface it. There is no self-hosted deployment path.
Who it is for / who should skip it
Best for developers building stateful LLM apps and teams needing provider-agnostic context management with automatic compaction and cost tracking. Skip it if you require a retrieval layer for events from sessions weeks ago or must meet data residency rules that demand self-hosting.