cogencyai

Just shipped v0.4.1 — major simplification under the hood.

Swapped the custom planner architecture for a leaner ReAct loop (reason → act → observe). It’s not new, but it works. Cleaner logic, better streaming, easier to debug. Less magic, more stability.

The core idea remains: separate cognitive operations from tool orchestration. But now it’s battle-tested, pip-installable, and (more) production-ready.

Thanks for asking. I’ve done some initial benchmarking on memory footprint and response latency under concurrent load, showing stable behavior with low overhead. Nothing exhaustive yet, but results so far are promising.

And agreed. It’s a crowded space, and benchmarking is hard without standard tasks or metrics. We’re focused on real-world dogfooding and incremental validation to complement raw numbers atm.

If you want, I can share the current benchmark results and test scenarios.

100%. Scalability starts with discipline in architecture > feature bloat.

I have been building Cogency with real workloads in mind. That means clear separation of concerns, minimal mental load, and predictable data flow.

Keeping it clean upfront pays off when complexity inevitably grows. Thanks for noticing!

Thanks for the kind words. I actually stripped out most routing recently in favor of a lean, single-loop ReAct core. The last architecture was classic wheel reinvention. It still maintains the light abstractions and modular nodes wired together with simple, explicit logic.

That’s part of why the DX feels tight and fast. Boilerplate was ruthless cut to focus on core reasoning and tool use. Keeps things nimble and maintainable.

Glad the approach resonates! :)

Most AI agent frameworks today are really just big event loops pretending to be distributed systems. True actors are isolated, stateful entities that communicate only via messages.

Cogency’s design moves in that direction with modular nodes and message passing, but it’s not fully distributed or truly actor-based yet. The real challenge is building that kind of system in a way that stays manageable and debuggable.

It’s definitely the natural next step for AI frameworks. Thanks for putting it so clearly.

We need to start manufacturing more drones. An F22 raptor costs what $200m? Compared to a DJI drone outfitted with explosives, maybe $20k at most, thats a 4 orders of magnitude difference. 10,000 drones to one. More factories required.