Vercel AI SDK alternatives

+ Serves many concurrent requests on GPUs far faster than llama.cpp-based tools.
− Linux and NVIDIA-centric; painful on Mac or consumer hardware.

+ Code-as-action agents in about a thousand lines of core code that work with any Hub model.
− Running model-written Python needs sandboxing, which is left mostly to you.

+ Every open model ends up here, with Inference Providers routing to multiple backends.
− Serverless inference rate limits and cold starts make it unreliable for production.

+ One OpenAI-compatible endpoint for Anthropic, OpenAI, Google, and open models with automatic fallbacks.
− Adds a routing layer, so latency and rate limits depend on the upstream provider chosen.

+ MCP-first design means any MCP server becomes a Goose capability with no glue code.
− Less tuned for coding than Claude Code, so results vary widely by model.

+ Self-hosted gateway with spend tracking, virtual keys, and fallbacks across providers.
− Huge dependency surface and frequent releases that occasionally break provider mappings.

+ Explicit state graphs with checkpointing make long-running agents debuggable and resumable.
− Steep learning curve and more boilerplate than simpler agent SDKs.

+ Largest integration catalogue of any framework, so almost any provider is one import away.
− Frequent breaking API changes and heavy abstractions make simple things feel over-engineered.

+ Inherits Claude Code's file, shell, and subagent tooling, so agents are production-grade on day one.
− Tied to Claude models and the Claude Code runtime rather than a provider-agnostic design.

+ Thousands of community models deployable via Cog, not just text models.
− Cold starts on less popular models can take a minute or more.