I've been building LLM systems in production for years. Financial data pipelines, thematic indexing systems, RAG at scale.

And for years I kept running into the same two walls:

Wall 1: The async lie.

Every major Python LLM framework claims async support. Most of them are lying. Underneath the async def wrappers, you find run_in_executor() calls, ThreadPoolExecutor wrappers around blocking IO, and sync internals that kill the throughput of any real concurrent service.

For a FastAPI service handling 50 concurrent RAG requests, that matters enormously. You're paying thread overhead on top of event loop overhead with none of the actual benefits of async IO.

Wall 2: Chains don't model reality.

Production pipelines aren't linear. You have parallel retrievers, conditional routing, stages that need to fail loudly without silently degrading downstream results. Every major framework pushes you toward chains. Chains make you fight the abstraction constantly.

I got tired of patching around both.

So I built SynapseKit —> async from the data layer up, DAG-based pipeline engine, no vendor lock-in.

The response surprised me. ~10k PyPI downloads without a single launch post. Developers found it through search because they were looking for exactly this and not finding it elsewhere.

This subreddit is the community home for everyone building with it.

What I'm working on next:

→ EvalCI: LLM quality gates for every PR (already live: github.com/SynapseKit/evalci)

→ Expanded provider support

→ Better observability and tracing primitives

→ Agent memory patterns

If you've been using SynapseKit or just discovered it: introduce yourself below. What are you building?

Personal and honest: don't sound like a press release

[留言]

为什么值得关注

能改变理解方式，而不只是重复常识；符合当前抓取需求；它提供了新的理解或解释，而不只是表面观点

来源：reddit，领域：tech，保留分：0.76