If you’ve ever found yourself with four or five different coding-agent windows open — Claude Code in one, Codex in another, maybe Cursor in a third — copy-pasting context between them and your docs, you’re exactly the problem Databricks built Omnigent to solve. The Apache 2.0-licensed “meta-harness” sits above the agent tools you already use and makes them interoperable, and it just shipped v0.9.0 on August 11 with a headline feature: automatic model and harness routing.

It’s worth being precise about what’s actually new here, since the framing matters. Omnigent itself isn’t a brand-new product — it was originally open-sourced back on June 13, 2026, according to the official Databricks blog post announcing it. What shipped this week is a point release, adding meaningful new capability on top of an already-established two-month-old project.

What Omnigent Is, For Anyone Catching Up

Per Databricks’ own announcement, Omnigent was built by a team — including Databricks co-founder Matei Zaharia — that found itself “on a treadmill” trying to combine the latest agent harnesses, SDKs, and models, only to run into the problem that each harness wraps LLM capability in its own incompatible interface. Omnigent’s answer is a layer that sits above those harnesses: a runner wraps any supported agent in a sandboxed session behind a uniform API, and a server layer handles policies, sharing, and exposes every session over terminal, app, and web interfaces.

In practice, that means you can compose multiple agents together, control them with shared policies (spend caps, sandboxing, permission rules), and collaborate live with teammates on a running agent session — all without each underlying tool needing to natively support any of that.

What’s New in v0.9.0

According to the official release notes on omnigent.ai, v0.9.0’s centerpiece feature is Smart Routing:

“Choosing the right model for a task — and for every subagent that task spawns — is a decision you shouldn’t have to make by hand on every run. Smart Routing makes it automatic: each task and its subagents are routed to the best model and harness for the job.”

The implementation differs by deployment context: on Databricks itself, routing is powered by the platform’s AI Gateway smart-routing infrastructure; in the open-source build, it uses an LLM classifier to make the same kind of judgment call. Either way, the pitch is that you enable it once and Omnigent handles matching individual tasks — and any subagents they spawn — to whichever model and harness fits best, rather than you manually picking a tool for every run.

Beyond routing, the release notes list several other additions:

  • A redesigned web UI built around a new “Zinc” color palette, tightened typography, and more consistent session controls — including a session filter for All/My/Shared/Archived sessions, a harness picker organized by support tier, and settled conversation turns that collapse into a compact “Worked for Xs” row to keep long sessions readable.
  • Deeper sandbox and deployment control — including a sandbox.type: auto setting that picks the platform default automatically, configurable Boxlite disk sizing via sandbox.boxlite.disk_size_gb, per-agent Kubernetes runner pod targeting by label for team deployments, and per-project default base branches for new worktree sessions.
  • First-class Grok Build support. xAI’s coding agent, Grok Build, joins as a fully supported ACP harness — meaning you can drive Omnigent using Grok Build the same way you’d drive it with Claude Code, Codex, or any other supported harness.
  • A set of ready-to-run example agents — a deep-research agent, a repro-agent that reproduces a reported bug live and writes an end-to-end test for it, and a resolve-agent that fixes the bug and proves the fix — intended as practical starting points for building your own agents around real workflows.
  • New Nimble web builtins for pulling structured data directly off the web into agent workflows.

For anyone tracking version history, this follows v0.8.0 (released August 3), which focused on live model discovery pulled from your active provider catalog and installed CLIs, a reworked editor-style web workspace, and new guardrail policies for catching agents stuck in unproductive loops.

Installing or Upgrading

Per the official release notes, getting Omnigent (or upgrading to the latest version) is a single command:

uv tool install --python 3.12 omnigent
# or
pip install "omnigent"

Why This Matters

Framework fragmentation is a real, everyday pain point for teams running multiple coding-agent tools side by side — and it’s one that no single harness vendor is naturally incentivized to solve, since each has its own reasons to keep you inside its own walled interface. Omnigent’s bet is that the meta-harness layer, not any individual agent tool, becomes where people actually work going forward.

Smart Routing specifically addresses a decision fatigue problem that’s been growing as the number of viable coding-agent models and harnesses multiplies: with Claude Code, Codex, Cursor, Grok Build, and others all credible options depending on the task, manually picking the right tool for every single run doesn’t scale. Automating that choice — and doing it per-subagent, not just per top-level task — is a meaningful step toward treating “which model and harness” as an infrastructure decision rather than a developer decision made fresh every time.

Coming from Databricks, backed by a team with deep enterprise engineering experience (the company says it has adopted coding agents extensively across its 5,000-plus-member engineering team), Omnigent also carries a degree of credibility and likely enterprise adoption that a scrappier open-source project might take longer to earn.

Sources

  1. Introducing Omnigent: A Meta-Harness to Combine, Control and Share Your Agents — Databricks Blog
  2. Omnigent v0.9.0 Release Notes — omnigent.ai/releases
  3. omnigent-ai/omnigent — GitHub repository

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260812-2000

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