So we built Omnigent: a meta-harness that sits above the agents you already use (Claude Code, Codex, Pi, or custom agents) and makes them interoperable parts of a richer system. Omnigent targets the problems where a single harness stops: it adds easy ways to compose multiple agents, control them with advanced policies, and collaborate live with teammates.

We believe people will soon work with agents through this new layer, the meta-harness. That’s why today we’re open sourcing Omnigent under Apache 2.0.

At Databricks, we adopted coding agents early across our 5000+ member engineering team and built thousands of agents for customers. That experience convinced us that the frontier of agent engineering is moving up a level. The best results no longer come from a single model in a single harness: Harvey beat a frontier model on quality and cost by giving an open-source worker model a frontier advisor it can call, Anthropic built its research product as a lead agent orchestrating parallel subagents, and our own Genie uses different LLMs for planning, search, and code generation. Engineers are changing how they work, too: instead of prompting one agent at a time, they design loops that drive whole teams of agents.

These patterns span multiple harnesses, models, and people, but each harness only understands its own sessions. To combine agents, govern them, and work on them with other people, you need a layer above the harness. Omnigent is that layer, and it provides:

Omnigent introduces a common interface above command-line agents and agent SDKs to let you easily combine and interchange them, and then focuses on the shared problems where a harness stops. The key insight is that however each agent harness calls into its LLM internally, the interface to users is the same: messages and files in, text streams and tool calls out. Thus we built a common API that wraps both terminal-based coding agents (Claude Code, Codex, Pi, etc) and SDKs (OpenAI Agents, Claude Agents SDK, etc).