Open Envelope – an open schema for defining AI agent teams
Built an open JSON Schema for defining AI agent teams. Multi-agent systems are becoming a real deployment pattern — not single assistants, but teams with roles, handoffs, and human checkpoints. But there's no shared way to define one that travels across frameworks. Every implementation is scattered, locked to whichever tool you picked first. Built the schema to fix that. The schema lives at schema.openenvelope.org and is registered in SchemaStore, so if you drop a .envelope.json file in VS Code you get autocomplete and validation without installing anything. It's also on npm as…
Open Envelope – an open schema for defining AI agent teams launched on May 28, 2026 with 52 votes, #766 of 20,538 launches that month and more than 95% of that year's launches.
In plain words
Open Envelope is an open JSON Schema for defining AI agent teams that work across different frameworks. It standardizes how multi-agent systems with roles, handoffs, and human checkpoints are configured, solving the problem of scattered, framework-locked implementations. The schema includes agent definitions, supervisor hierarchies, human-in-the-loop gates, pipelines, and access policies. It integrates with VS Code for autocomplete and validation and is available as an npm package for programmatic use.
written from the facts on this page · September 2026
From the sources
The full technical specification for the Envelope team definition format — agent structure, workspace documents, adapters, variables, secrets, and access policies.
Listed on SchemaStore — VS Code, JetBrains, and any SchemaStore-aware editor will validate *.envelope.json files automatically, no configuration needed. The Envelope team definition — the structure that describes agents, roles, hierarchy, escalation paths, required secrets, and adapters — is published as a formal, versioned open source specification. Anyone can read it, validate against it, and build tooling around it. The marketplace, billing, deployment infrastructure, and matching remain proprietary. This mirrors the Elastic model: open source the engine and specification (Elasticsearch), keep the managed distribution proprietary (Elastic Cloud). The open schema drives adoption and…from openenvelope.org
In the maker’s words, at launch
Built an open JSON Schema for defining AI agent teams. Multi-agent systems are becoming a real deployment pattern — not single assistants, but teams with roles, handoffs, and human checkpoints. But there's no shared way to define one that travels across frameworks. Every implementation is scattered, locked to…
Does a similar job
all alternatives →
Agents.json – OpenAPI Specification for LLMs2025 · github.com · ▲212Hey HN, we’re building an open specification that lets agents discover and invoke APIs with natural language, built on the OpenAPI standard. agents.json clearly defines the contract between LLMs and API as a standard that's open, observable, and replicable. Here’s a walkthrough of how it works: https://youtu.be/kby2Wdt2Dtk?si=59xGCDy48Zzwr7ND. There’s 2 parts to this: 1. An agents.json file describes how to link API calls together into outcome-based tools for LLMs. This file sits alongside an OpenAPI file. 2. The agents.json SDK loads agents.json files as tools for an LLM that…


Open AgentsApr 2026 · open-agents.dev · ▲161OpenAgents - The collaboration OS for AI agents. Contribute to openagents-org/openagents development by creating an account on GitHub.


More ai this month
the category →



Paintings by AI models: programs run through a simulation of oil paint.
AI · 21d ago · stillwet.art
PixVerse R2▲381A real-time world model you can explore and change
AI · 30d ago · world.pixverse.video

Source: Show HN post ↗ · launch text quoted in part
Launched alongside, May 2026
the whole month →

Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai



Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com

🌱 Private, quiet space for thinking. Simple app for .md files. - zakirullin/files.md
Dev tools · May 2026 · github.com