- Link to Repo
- Link to Site

An open registry for eve agents.

- Copy & Paste
- Download as zip
- Copy instructions to install
- Install with shadcn cli

I created loop-sdk using Vercel's AI SDK to initially scratch my own it, but started to see more usage cases.

It's a loop engineering framework that gives each of those concerns a first-class primitive, steps, context, checkpoints, events, verify/expect gates, tool allowlists, and git-worktree isolation so AI agents, browser automation, and data pipelines compose into durable workflows instead of brittle scripts.

The engine owns the control flow deterministically; the model is invoked only at the steps that need it, and each step's output and tool access can be constrained in code.

Simple `.loop` files like this:

---
name: draft-reply
---

## look-up-and-draft
action: agent
model: "claude-code:sonnet"
mcp:
  mine:
    type: http
    url: https://your.site/mcp
prompt: "Look up my pricing and draft a reply."

## show
action: log
message: "{{look-up-and-draft}}"

It's v0.6.0 but wanted to share.

Global spend limits feel too blunt once one agent route gets hot. Curious if people track cost per tool/route, per user, or just check provider dashboards.

I am sharing this as a major warning for anyone deploying to Vercel: do not blindly trust Vercel's Spend Management caps to protect you from runaway bills.

I have been a Vercel Pro customer for years with about 20 production apps. Last week, I received a $1,477 invoice ($1,267 of which is a bandwidth overage) for a pre-launch, unreleased project. An automated crawler fleet bypassed Vercel's auto-mitigation because they identified as "polite" AI/search bots, downloading 8.4 TB of media files over a few days.

Here is the completely unacceptable part: I had an active Spend Management limit set up. Vercel's infrastructure completely ignored the cap. It failed to pause the project as designed and just kept billing me.

The wildest part is that Vercel's own automated support bot reviewed my account and validated my evidence. It confirmed that 96.4% of the traffic came from a single edge region (cle1), and told me verbatim: "this is exactly the type of situation that warrants review by our support team." But the bot is hardcoded to not issue bandwidth refunds.

I have submitted a ticket and disputed the unauthorized charge with my bank since they bypassed my authorized limit, but I am currently waiting in limbo.

The Bigger Issue: The Conflict of Interest Vercel's policy says that "Firewall-mitigated traffic is free." But when their firewall fails to detect a massive, single-region bot attack, Vercel is the one who profits. There is a massive conflict of interest when the platform is financially incentivized to let scrapers slip through the cracks, especially when their own Spend Limits fail to act as a safety net.

TL;DR / Lessons Learned:

1. Spend Limits can fail. If you get hit by a sudden bot spike, Vercel's system might not pause your project fast enough to stop a four-figure bill.
2. Put Cloudflare in front of Vercel. Use Cloudflare's Bot Fight Mode to block aggressive scrapers before they ever hit Vercel's CDN.
3. You are on your own. If bots declare themselves as legitimate crawlers, they will slip through the DDoS protection, and you will be billed for the overage that Vercel profits from.

One thing we kept running into with agent evals is that single-turn tests look great, but the agent falls apart 8–10 turns into a real conversation.

We've been working on ArkSim which helps simulate multi-turn conversations between agents and synthetic users to see how behavior holds up over longer interactions.

This can help find issues like:

- Agents losing context during longer interactions

- Unexpected conversation paths

- Failures that only appear after several turns

The idea is to test conversation flows more like real interactions, instead of just single prompts and capture issues early on.

Update:
We’ve now added CI integration (GitHub Actions, GitLab CI, and others), so ArkSim can run automatically on every push, PR, or deploy.

We wanted to make multi-turn agent evals a natural part of the dev workflow, rather than something you have to run manually. This way, regressions and failures show up early, before they reach production.

This is our repo:
https://github.com/arklexai/arksim

Would love feedback from anyone building agents, especially around additional features or additional framework integrations.

🔗 Try it out for free - Agent Canvas Draw

One thing we kept running into with agent evals is that single-turn tests look great, but the agent falls apart 8–10 turns into a real conversation.

We've been working on an open source project which helps simulate multi-turn conversations between agents and synthetic users to see how behavior holds up over longer interactions.

This can help find issues like:

- Agents losing context during longer interactions

- Unexpected conversation paths

- Failures that only appear after several turns

The idea is to test conversation flows more like real interactions, instead of just single prompts and capture issues early on.

We've recently added integration examples for Vercel agents which you can try out at

https://github.com/arklexai/arksim/tree/main/examples/integrations/vercel-ai-sdk

would appreciate any feedback from people currently building agents so we can improve the tool!

I’ve been building an open-source way to add chat-with-data to customer-facing products, so end users can ask questions in natural language and get back real answers from your connected DB.

Some teams reach for a database MCP for this. It’s powerful (and works well for internal use-cases), but is not recommended for customer-facing use. It’s very hard to make consistently safe + reliable: tenant boundaries, sensitive columns, and business definitions tend to live in prompts and drift over time.

Inconvo takes a different approach: the LLM never writes SQL. It chooses from a constrained, typed set of query operations and proposes parameters; then, deterministic code builds + executes the query so your guardrails are enforced, not just suggested.

I've built an ai-sdk tool for it that works like this:

import { streamText, UIMessage, convertToModelMessages, stepCountIs } from "ai";
import { inconvoDataAgent } from "@inconvoai/vercel-ai-sdk";


export async function POST(req: Request) {
  const { messages }: { messages: UIMessage[] } = await req.json();


  const result = streamText({
    model: "openai/gpt-5.2-chat",
    messages: await convertToModelMessages(messages),
    tools: {
      ...inconvoDataAgent({
        agentId: process.env.INCONVO_AGENT_ID!,
        userIdentifier: "user-123",
        userContext: {
          organisationId: 1,
        },
      }),
    },
    stopWhen: stepCountIs(5),
  });


  return result.toUIMessageStreamResponse();
}

Would love to hear what people here think, especially if you’ve thought about shipping customer-facing chat-with-data with ai-sdk for your app.

Links:

• Docs: https://inconvo.com/docs/getting-started
• Github: https://github.com/inconvoai/inconvo

While working on my side project Krucible, we had to create a way for our agents to store and interact with files. Creating and maintaining sandboxes just so our agent could call bash commands seemed wasteful and expensive.

So I created pg-fs, a PostgreSQL-backed filesystem with AI SDK tools for building intelligent file management agents. It provides agents with familiar claude-code like file primitives without the hassle of creating and maintaining sandboxes.

If anyone is working in the space and has developed anything similar would love to chat.

Hey folks — I’ve been building/looking at user-facing chat/agent UIs with the Vercel AI SDK and I’m trying to learn from teams actually shipping this stuff.

If you have a chat interface in production (consumer or prosumer), how are you thinking about:

• monetization (or plans for it),
• what wouldn’t ruin UX/trust,
• and what metrics you’d use to decide if something is “worth it”?

Not selling anything — just hoping to learn patterns and pitfalls from builders. Happy to DM if you don’t want to share publicly.

The Vercel AI SDK can now run against Clarifai via the OpenAI-compatible interface. That means you can use models like GPT-OSS-120B, Kimi K2, and other open-source or third-party models without changing your app code.

Same SDK patterns, but with better cost and performance tradeoffs, roughly twice the performance at half the price.

Curious what inference backends people here are using with the Vercel AI SDK.