Problem: Every AI app wants you to paste your OpenAI/Anthropic key. Keys spread across dozens of apps with zero visibility, and you can only revoke by rotating the key itself.

Proposal: OKAP (Open Key Access Protocol) - like OAuth, but for API keys.

How it works: 1. Keys stay in YOUR vault (self-host or hosted) 2. Apps request access via token (scoped to provider, models, expiry) 3. Vault proxies requests - apps never see your actual key 4. Revoke any app instantly without touching your master key

Not to be confused with LiteLLM/OpenRouter (those are proxies you pay for). OKAP is a protocol for user-owned key management - your keys, your vault, your control.

Working implementation: - Hosted vault: https://vault.okap.dev - Python SDK: pip install okap - Spec: https://okap.dev

Looking for feedback. Would you use this for your AI tools? What's missing?

Hey folks! I just launched https://mcphost.link/ a web-based MCP host that lets you connect to multiple remote MCP servers and interact with them through a simple chat-style interface.

Key Features

Multi-server support — connect to several MCP servers at once

OAuth 2.0 & Bearer Token auth (with PKCE)

Persistent sessions — servers + credentials saved locally

Full MCP features — tools, resources, prompts

LLM support — bring your own inference backend

The goal is to make exploring and working with the Model Context Protocol much more approachable.

Happy to answer questions, take feedback, or hear feature requests!

SEP-1865 MCP Apps Extension, even though in draft currently, will change how AI agents deliver interactive experiences.

The idea: MCP tools return HTML/CSS/JS directly. The client renders it in a sandboxed iframe. That's it.

Your AI agent calls a tool, gets back a full interactive UI. Dashboard, form, chart - whatever you need.

How it works: - Tool returns text/html+mcp resource - Client renders in iframe with CSP - UI talks back via JSON-RPC 2.0 postMessage - Fully sandboxed, secure by default

Built a sample implementation with vanilla Web Components. This is where MCP is heading.

I got tired of complex agent frameworks with their orchestrators and YAML configs, so I built something simpler.

  AgentU uses two operators for workflows: >> chains steps, & runs parallel. That's it.

``` from agentu import Agent, serve import asyncio

  def search(topic: str) -> str:
      return f"Results for {topic}"

  # Agent auto-detects available model, connects to authenticated MCP server
  agent = Agent("researcher").with_tools([search]).with_mcp([
      {"url": "http://localhost:3000", "headers": {"Authorization": "Bearer token123"}}
  ])

  # Memory
  agent.remember("User wants technical depth", importance=0.9)

  # Parallel then sequential: & runs parallel, >> chains
  workflow = (
      agent("AI") & agent("ML") & agent("LLMs")
      >> agent(lambda prev: f"Compare: {prev}")
  )

  # Execute workflow
  result = asyncio.run(workflow.run())

  # REST API with auto-generated Swagger docs
  serve(agent, port=8000)

```

  Features:
  - Auto-detects Ollama models (also works with OpenAI, vLLM, LM Studio)
  - Memory with importance weights, SQLite backend
  - MCP integration with auth support
  - One-line REST API with Swagger docs
  - Python functions are tools, no decorators needed

  Using it for automated code review, parallel data enrichment, research synthesis.

  pip install agentu

  GitHub: https://github.com/hemanth/agentu

  Open to feedback.

This can be done with WebLLM, no?

One of the most concise APIs to interact with an llm!

Keep going! Happy to see ollama support PR in draft.

Why not detect it by checking the magic number of the buffer?

Not every file has one for starters and many can be incorrect.

Especially in the context of use as a virus scanner, you don’t trust what the file says it is

Trying to openai am I missing something?

    import OpenAI from 'openai'

    const openai = new OpenAI({
      baseURL: 'http://localhost:11434/v1',
      apiKey: 'ollama', // required but unused
    })

    const chatCompletion = await 
      openai.chat.completions.create({
      model: 'llama2',
      messages: [{ role: 'user', content: 'Why is the sky blue?' }],
    })

    console.log(completion.choices[0].message.content)

I am getting the below error:

    return new NotFoundError(status, error, message, headers);
                   ^
    NotFoundError: 404 404 page not found

Remove the v1