OLW proposes open routing protocol for cold-start AI agent discovery

u/clubsodaz shared OLW (Open Language Wire) on r/LangChain, framing it as a solution to the cold-start discovery gap in today's multi-agent ecosystem. The post notes that while A2A handles agent-to-agent communication and MCP handles tool connectivity, neither covers the problem of dynamically finding an unknown agent that meets specific capability requirements — the A2A spec explicitly states it does not cover discovery registries. The result, the post argues, is that every multi-agent system today hardcodes its agent relationships with no standard mechanism to query for agents by capability.

OLW's core primitive is an 8-axis capability fingerprint that agents self-declare in a `.well-known/olw/agent.json` file.

OLW's core primitive is an 8-axis capability fingerprint that agents self-declare in a `.well-known/olw/agent.json` file. A public resolution index at `olw.gtll.app` crawls and indexes these files, allowing callers to query by capability and receive agent addresses in return. Compared to A2A's `AgentSkill` schema, OLW introduces four axes — including `context_depth`, `latency_class`, and `trust_level` as semantic enums — that have no A2A equivalent; the post also notes that existing A2A axes rely on free text, which the author argues is not queryable at scale. The protocol spec is MIT-licensed and hosted at `github.com/gtllco/olw-protocol`, with a Python package available via `pip install olw-protocol`. The public index currently has 47 registered agents and is open for registration. The post explicitly solicits critical schema feedback from practitioners shipping production multi-agent systems.

Key facts

1. 01OLW (Open Language Wire) is a new routing protocol for cold-start AI agent discovery, released MIT-licensed at github.com/gtllco/olw-protocol
2. 02The A2A spec explicitly does not cover discovery registries, which OLW is designed to address
3. 03Core primitive is an 8-axis capability fingerprint declared in `.well-known/olw/agent.json`
4. 04A public resolution index at olw.gtll.app currently lists 47 registered agents and is open for registration
5. 05OLW adds axes like `context_depth`, `latency_class`, and `trust_level` as semantic enums, which have no equivalent in A2A's AgentSkill schema
6. 06A2A's existing capability axes use free text, which the post argues is not queryable at scale
7. 07Installable via `pip install olw-protocol`

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