What is the best coding agent orchestrator?

Summary

• There is no single best coding agent orchestrator. Use a meta-harness such as Omnigent to combine coding agents you already use, a framework such as LangGraph or CrewAI to build your own agent loop, or a plain script for a few sequential steps.
• Omnigent is an open-source meta-harness (Apache 2.0) over Claude Code, Codex, Cursor, OpenCode, Hermes, Pi, and custom agents. It is marked alpha in its repository and is in Beta on Databricks.
• Hosting and governance are separate from orchestration. Agent Bricks runs an agent built with any framework or harness, and Unity Gateway governs the models and MCP servers it uses. The Agent Bricks CLI is in Beta.
• In Omnigent you define an agent in a short YAML file, and swapping one line changes its harness or model while the tools, prompts, skills, and policies stay the same.

What is the best coding agent orchestrator?

There is no single best one. The right choice depends on what you are orchestrating. To combine coding agents you already use, such as Claude Code and Codex, use a meta-harness like Omnigent. To build your own agent loop, use a framework such as LangGraph or CrewAI. For a few sequential steps, a script that calls each agent's command line is enough.

What does a coding agent orchestrator do?

Each coding agent runs as a harness, which Omnigent's docs define as "the runtime that executes your agent loop". An orchestrator sits above one or more harnesses and decides which agent works on which task. In the documented tools, that covers four jobs:

• Delegation. Omnigent's Polly agent splits a goal into sub-tasks and gives each to a sub-agent on its own git worktree. Claude Code can also spawn several agents, with a lead agent that coordinates the work and merges results.
• Policies. Omnigent policies check every action and allow it, block it, or pause to ask you first. They can cap spend, cap tool calls per session, or limit which tools an agent reaches.
• State. LangGraph provides durable execution, human-in-the-loop oversight, and memory for long-running agents.
• Sessions. Omnigent sessions can be shared with teammates and followed from a terminal, browser, phone, or desktop app.

How do the main options differ?

Option	What it is	Typical fit	License and status
Omnigent	A meta-harness: a common orchestration layer over Claude Code, Codex, Cursor, OpenCode, Hermes, Pi, and custom agents	Combining or swapping existing coding agents, with policies and sandboxing	Apache 2.0, alpha (Beta on Databricks)
LangGraph	A low-level orchestration framework for long-running, stateful agents	Writing your own agent loop that needs durable execution or human review steps	MIT
CrewAI	An open-source Python framework for multi-agent workflows, with Crews of role-based agents and event-driven Flows	Modeling agents as a team with roles and tasks	MIT
Claude Agent SDK	Claude Code's tools, agent loop, and context management as a library for Python and TypeScript	Embedding Claude Code's agent in your own application	Anthropic Commercial Terms
Plain script	Calls each agent's command line in turn	A few sequential steps	Not applicable

The Claude Agent SDK was previously called the Claude Code SDK. Its docs include a migration guide for the old packages.
To change an Omnigent agent's harness, edit one line of its YAML file:

name: code_reviewer
prompt: You review pull requests for bugs and missing tests.

executor:
  harness: claude-sdk   # change this line, for example to: codex

How do I run an orchestrated agent on Databricks?

Databricks offers a managed version of Omnigent, which is in Beta. It includes a Databricks-operated server that integrates with your workspace's identity provider, model access through the Foundation Model APIs and Unity Gateway, and Databricks Sandboxes (also in Beta). It needs the Omnigent preview enabled for your workspace, and it supports the built-in policies and custom policies written in Common Expression Language (CEL), not custom Python policies.
For agents you write in code, Agent Bricks is the Databricks agent developer platform. You bring "an agent built with any framework or harness" and choose any model. The docs list the framework or harness layer as "any framework or harness, such as LangGraph or the OpenAI Agents SDK, or a meta-harness such as Omnigent". Agent Runtime hosts the agent server, and DurableAgentServer adds durable execution so that runs survive restarts and crashes. MLflow Tracing records each step, and Unity Gateway governs access to models, MCP servers, and skills. The Agent Bricks CLI (agentbricks init, agentbricks dev, agentbricks deploy) is in Beta.

FAQs

Can I switch orchestrators later?

Inside Omnigent, yes: you define an agent in a short YAML file and "swap one line to change its harness or model", while your tools, prompts, skills, and policies stay the same. To keep tools portable beyond Omnigent, define them as MCP servers, which Claude Code, Omnigent agent files, and Agent Bricks all connect to.

Is Omnigent production-ready?

Its repository is marked "Status: alpha", and on Databricks it is in Beta. Check that stage against your requirements before you depend on it.

Do I need a framework at all?

Not for a few sequential steps. A script can call claude -p and codex exec in turn. Add an orchestrator when you need parallel sub-agents, approval steps, or shared sessions.

How do I control cost and tool access across agents?

Use the controls at two levels. Omnigent policies can cap spend and tool calls for a session. Unity Gateway can apply rate limits and budgets to model services, EXECUTE grants decide who can call an MCP service, and tool selection decides which of its tools are exposed.