Awaiting web version...
531 karma · joined March 22, 2010
Bhagavata 5th Skandha is one such example that you can hear to.
I've been tracking the explosion of AI Agent protocols over the last 18 months. The contrast with history is staggering: - TCP/IP: 9 years from paper to "Flag Day." - OAuth 2.1: 5+ years and still counting. - Model Context Protocol (MCP): <1 year from launch to Linux Foundation.
It’s not just MCP. In 2025 alone, we saw: - Google's Agent2Agent (50+ partners), - Universal Commerce Protocol (20+ retailers) - AP2 (Payments) all ship. - Agent Protocol, UTCP and few more.
We are entering an era of "Room Consensus": where a few giants agree on a spec and ship it to billions, bypassing the slow deliberation of the RFC era. Is this efficiency? Or fragility?
I break down the landscape of the new agent protocols and what this means for developers in my latest post.
kb = Ragi(["./docs", "s3://bucket/data/*/*.pdf", "https://api.example.com/docs"])
answer = kb.ask("How do I deploy this?")
that's it! with https://pypi.org/project/piragi/
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?
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!
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.
AgentU uses two operators for workflows: >> chains steps, & runs parallel. That's it.
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.Keep going! Happy to see ollama support PR in draft.
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)
return new NotFoundError(status, error, message, headers);
^
NotFoundError: 404 404 page not found