Mosec serves machine learning models with dynamic batching and CPU or GPU pipelines for inference workloads.
autobatcher
autobatcher batches asynchronous OpenAI-compatible API requests through a drop-in replacement library.

About autobatcher
autobatcher is an open-source library for Python and TypeScript that provides AsyncOpenAI and BatchOpenAI clients with the same interface as the OpenAI SDK. By default, chat-completion and Responses calls are submitted to /v1/responses with flex and background options, then polled until results return as standard ChatCompletion or Response objects. It handles response IDs, polling, JSONL files, uploads, and result matching internally.
Explicit 24-hour batch mode is available by setting the completion window to "24h"; embeddings always use 24-hour batches because they have no flex tier. The library is designed for the Doubleword Inference API, with support for OpenAI's batch API or other compatible APIs described as best effort.
Key features
- Drop-in AsyncOpenAI and BatchOpenAI clients for Python and TypeScript
- Default flex async polling for chat completions and Responses
- Explicit 24-hour batch mode via completion window setting
- Internal handling of polling, JSONL, uploads, result matching
- Configurable batch size, window, poll interval and concurrency
- Embeddings processed through 24-hour batch lifecycle
- Retains OpenAI SDK interface while managing polling and batch lifecycles internally
- Supports both Python and TypeScript clients
- Returns standard ChatCompletion and Response objects for async calls
- Designed for Doubleword Inference API; other compatible APIs are best effort
- Embeddings always use 24-hour batches with no flex tier option
- Default flex HTTP concurrency is limited to 20 operations per client
autobatcher at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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autobatcher FAQ
How do I select 24-hour batch inference instead of flex?
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