Love to hear it! Hop into the agents channel in the Convex Discord if you aren't already - that's where I give updates & folks give feedback to help shape the future of it

Example:
github.com/get-convex/a...
Agent component:
convex.dev/components/a...
Rate Limiter:
www.convex.dev/components/r...

That sounds compelling, and yeah system prompts do feel like a pretty poor bound. Now that I think of it I agree it's pretty aligned.
What's your preferred way to handle routing / dispatching / step-by-step tool calls btw?

but I may be over-indexing on the string-based signature. Definitely on my list of things to dig deeper on. Like most things you end up having more in common than you expect from the outset

Yeah DSPy came on my radar recently. It seems interesting & audacious!
My gut feel is that code is still king though - the second you want more flexibility, composability, or control over context / prompting, you'd be fighting the framework.

A la #4 here stack.convex.dev/ai-agents#go...

Agent component: convex.dev/components/a...
YT talk: youtube.com/watch?v=3Ydg...
Durable workflow article: stack.convex.dev/durable-work...

wdyt?

const messages = useThreadMessages(
api​.foo.listMessages,
{ threadId },
{ initialNumItems: 10, stream: true },
);
const sendMessage = useMutation(
api​.foo.generateText,
).withOptimisticUpdate(
optimisticallySendMessage(api​.foo.listMessages),
);

Agent Component: convex.dev/components/a...
Example code: github.com/get-convex/a...
Changelog: github.com/get-convex/a...

It one-ups persistent-text-streaming by syncing down only the deltas, not the full text, so you don't pay for bandwidth except proportional to the total length

Hosted playground: get-convex.github.io/agent/
Agent component / framework: github.com/get-convex/a...
Playground directory: github.com/get-convex/a...

Fun fact: It can target your @convex.dev backend if you if you expose the API, using API key auth.
Statically hosted on GitHub pages

For an importance of x (0 to 1):
1. Normalize the existing vector to (1-x) and add √.x
2. Search with [...embedding, 0].
e.g.:
Say we have an embedding of 2 numbers [.6, .8]
For 50% importance: [.3, .4, .707]
For [.6, .8] we used to get 1.0.
Now we get .6*.3 + .8+.4+0 = .5🎉

My original thought was to just scale all the values, but vector search normalize the vectors for -1:+1 scores.

The trick is to add an extra number ("feature") to the embedding.
[...1536 numbers, <X>]
Then query with
[...1536 numbers, 0].
How it works: 🧵

Agent component: convex.dev/components/a...
Article: stack.convex.dev/ai-agents
Code:
github.com/get-convex/a...

I pushed a fix this morning, so if you already installed it, upgrade to `@convex-dev/agent@latest`

I launched this today! Adding memory to AI SDK Agents with tools and RAG.
Article on Agentic Workflow: stack.convex.dev/ai-agents
Agent framework: convex.dev/components/a...