How to Use OmniAgent to Orchestrate Claude and Codex in One Workflow
Learn how to use OmniAgent's Polly orchestrator to delegate implementation to Claude and code review to Codex in a single automated pipeline.
Why Running Two Models Together Changes What’s Possible
Building software with AI assistance has moved well past “ask a model to write a function.” Teams are now chaining multiple models together — one to generate code, another to review it, a third to write tests — so each model does the work it’s genuinely best at.
That’s the core idea behind multi-agent orchestration, and it’s where OmniAgent comes in. With OmniAgent’s Polly orchestrator, you can wire Claude and Codex into a single automated workflow: Claude handles implementation, Codex handles code review, and Polly coordinates the handoff between them without manual intervention.
This guide walks through exactly how to set that up, including the workflow logic, configuration details, and common mistakes to avoid.
What OmniAgent and Polly Actually Do
OmniAgent is a multi-agent orchestration framework designed to coordinate multiple AI models across a shared task. Instead of manually passing outputs between models, you define a pipeline and let OmniAgent manage execution, routing, and data flow.
Polly is OmniAgent’s built-in orchestrator. Think of it as the manager in the workflow — it receives the original task, determines which agent should handle each step, passes context between agents, and aggregates the final output.
How Polly Differs from a Simple Prompt Chain
A basic prompt chain sends output from one model directly to the next. Polly is more sophisticated:
- It maintains shared context across all agents in the workflow
- It can branch conditionally — if Claude’s output meets certain criteria, Polly sends it to Codex; if not, it loops back
- It tracks agent state, so downstream agents know what happened upstream
- It handles error recovery when an agent returns an incomplete or invalid response
Remy doesn't build the plumbing. It inherits it.
Other agents wire up auth, databases, models, and integrations from scratch every time you ask them to build something.
Remy ships with all of it from MindStudio — so every cycle goes into the app you actually want.
This makes Polly suited for workflows where the task isn’t perfectly linear — where a code review might kick something back for revision, for example.
Understanding the Claude + Codex Pairing
Before building the workflow, it helps to understand why these two models complement each other well.
Claude’s Strengths for Implementation
Claude (developed by Anthropic) excels at reasoning through complex requirements and generating readable, well-structured code. Its context window is large enough to hold detailed specs, and it tends to produce code with clear variable naming and inline documentation. It’s also strong at filling in ambiguous requirements — it’ll make reasonable assumptions and flag them explicitly rather than just guessing silently.
For the implementation phase of a pipeline, this matters. You want the model doing the initial build to understand why the code should work, not just what it should look like.
Codex’s Strengths for Code Review
OpenAI’s Codex is optimized for code-specific tasks: analyzing syntax, spotting logic errors, checking for security vulnerabilities, and validating that code matches stated requirements. Where Claude reasons through intent, Codex tends to be more precise about correctness.
For code review specifically, Codex can:
- Identify edge cases the implementation missed
- Flag deprecated APIs or insecure patterns
- Verify that function signatures match their expected interfaces
- Suggest targeted improvements without rewriting large blocks
Why Using Both Is Better Than Using One Twice
You could ask Claude to review its own code. The problem is that the model that generated the output carries its own blind spots into the review — it tends to confirm its own reasoning rather than challenge it.
Using Codex as a separate reviewer introduces genuine independence. The second model has no attachment to how the code was written, so its feedback is more critical and often more useful.
Setting Up the Workflow: Prerequisites
Before configuring Polly, make sure you have the following in place.
API access:
- An Anthropic API key with access to Claude (Claude 3.5 Sonnet or Claude 3 Opus work well here)
- An OpenAI API key with Codex access
OmniAgent installed:
- Install via npm:
npm install -g omniagent - Or via pip:
pip install omniagent - Verify with
omniagent --version
A workspace directory: Set up a project folder where Polly can write intermediate outputs (Claude’s code, Codex’s review notes, final output). OmniAgent uses this as shared state between agents.
A clear task spec:
Polly works best when the initial task is specific. “Write a REST API endpoint” is too vague. “Write a Python FastAPI POST endpoint that accepts a JSON body with fields user_id and action, validates both fields, and logs to a file” gives both models something concrete to work with.
Building the Pipeline Step by Step
Step 1: Initialize the OmniAgent Project
In your project directory, run:
omniagent init my-workflow
cd my-workflowThis creates a workflow.yaml config file and a /agents directory where you’ll define each agent.
Step 2: Define the Claude Implementation Agent
In /agents, create a file called claude-implementer.yaml:
name: claude-implementer
model: claude-3-5-sonnet
provider: anthropic
role: implementer
system_prompt: |
You are a senior software engineer. Your job is to write clean, well-documented code based on the provided specification. Include inline comments for complex logic. Flag any assumptions you make in a section at the end labeled "Assumptions."
output_format: code_block
The role: implementer tag tells Polly this agent is responsible for producing the initial artifact.
Step 3: Define the Codex Review Agent
Create /agents/codex-reviewer.yaml:
name: codex-reviewer
model: code-davinci-002
provider: openai
role: reviewer
system_prompt: |
You are a code reviewer. You will receive a code implementation and its original specification. Your job is to:
1. Check if the code correctly meets the specification
2. Identify any bugs, edge cases, or security issues
3. Flag any deprecated APIs or anti-patterns
4. Return a structured review with sections: PASS/FAIL, Issues Found, Suggestions
input_context: ["original_spec", "claude-implementer.output"]The input_context field tells Polly what to inject into this agent’s context window — the original spec and whatever Claude produced.
Step 4: Configure the Polly Orchestrator
Back in workflow.yaml, configure Polly:
orchestrator: polly
agents:
- claude-implementer
- codex-reviewer
pipeline:
- step: implement
agent: claude-implementer
input: "{{task_spec}}"
output_to: implementation
- step: review
agent: codex-reviewer
input:
original_spec: "{{task_spec}}"
implementation: "{{implementation}}"
output_to: review_report
- step: evaluate
condition: "{{review_report.verdict}} == PASS"
on_pass: finalize
on_fail:
loop_to: implement
max_retries: 2
inject: "{{review_report.issues}}"
output:
final: "{{implementation}}"
review: "{{review_report}}"This is where the workflow becomes more than a prompt chain. Polly evaluates Codex’s verdict, and if the code fails review, it sends Claude back to fix the flagged issues — up to two retries.
Step 5: Run the Workflow
Execute the pipeline with:
omniagent run --task "Write a Python FastAPI POST endpoint that accepts user_id and action, validates both, and logs to file" --config workflow.yamlPolly handles the rest. You’ll see step-by-step progress in the terminal, and outputs land in your workspace directory.
How Polly Manages the Handoff
The handoff between Claude and Codex is where most DIY multi-agent setups break down. Here’s what Polly does automatically that you’d otherwise have to build yourself.
Context Injection
When Polly passes Claude’s output to Codex, it doesn’t just append raw text. It structures the context so Codex receives:
- The original task specification (so it can check compliance)
- Claude’s code block (clearly delimited)
- Claude’s stated assumptions (so the reviewer can validate them)
Without structured injection, the reviewer model often loses track of what it’s actually reviewing against.
Feedback Loop Handling
If Codex returns a FAIL verdict with a list of issues, Polly does three things:
- Strips the issues from the review report into a structured format
- Prepends them to Claude’s next prompt as explicit revision instructions
- Increments a retry counter to prevent infinite loops
This means Claude’s second attempt is informed by specific feedback, not just “try again.” The output quality improves significantly with this loop in place.
Output Aggregation
At the end of the pipeline, Polly writes two artifacts:
- The final approved code (from Claude’s last implementation pass)
- The review report (from Codex’s final pass)
Both are stored in your workspace, so you have a full audit trail of what was built and why it passed review.
A Real-World Scenario: API Endpoint with Security Review
To make this concrete, here’s how the pipeline runs in practice.
Initial task: Build a user authentication token validation endpoint in Python.
Step 1 — Claude implements: Claude writes a FastAPI endpoint with JWT validation, input checking, and structured error responses. It flags an assumption that the JWT secret is stored in an environment variable.
Step 2 — Codex reviews: Codex returns a FAIL with two issues:
- The endpoint doesn’t check token expiry
- The error response leaks internal exception details
Step 3 — Polly loops back: Polly sends Claude back with Codex’s specific issues injected. Claude revises: adds expiry checking, sanitizes the error response.
Step 4 — Codex re-reviews: Codex returns PASS with a suggestion (not a blocker) to add rate limiting.
Output: The final code includes both fixes. The review report documents what changed and why. Total runtime: around 45–60 seconds depending on model latency.
This is the kind of automated quality loop that used to require a human in the middle.
Where MindStudio Fits Into Multi-Agent Workflows
If the OmniAgent setup above feels like a lot of configuration work — YAML files, API keys, CLI commands — MindStudio offers a no-code alternative for building the same kind of multi-agent pipeline through a visual interface.
MindStudio gives you access to 200+ AI models including Claude and OpenAI’s models out of the box, with no separate API accounts required. You can build a Claude-to-Codex review pipeline by connecting model nodes visually, setting the context injection between them, and defining conditional logic (pass/fail routing) without writing a single line of YAML.
For teams that want to move fast without managing infrastructure, MindStudio handles the orchestration layer — rate limiting, retries, auth — so you can focus on what each agent should actually do.
You can also expose the finished pipeline as a webhook or API endpoint, which means it integrates directly into your existing CI/CD process. Trigger the review workflow from a pull request, get structured output back, done.
MindStudio is free to start at mindstudio.ai. If you’re already building multi-agent workflows and want to reduce the overhead, it’s worth a look.
For teams exploring similar orchestration patterns with different tooling, MindStudio’s guide to building multi-agent systems covers the underlying architecture in more detail.
Common Mistakes and How to Fix Them
The Review Agent Has No Access to the Original Spec
If Codex only sees Claude’s code — not the original task — it can only review for code quality, not for requirement compliance. Always inject the original spec into the reviewer’s context.
No Retry Limit on the Loop
Without a max_retries cap, a stubborn loop can run indefinitely and rack up API costs. Set a hard limit (2–3 retries is usually enough) and have Polly surface the last version with a flag if it never passes.
The System Prompt Is Too Generic
“Review this code” produces surface-level feedback. Give Codex a structured output format (PASS/FAIL, specific issue categories) so Polly can parse the verdict programmatically. Unstructured prose reviews are hard to route conditionally.
Claude’s Output Format Is Inconsistent
If Claude sometimes wraps code in markdown blocks and sometimes doesn’t, Polly’s output parser may fail. Set output_format: code_block in the implementer config and include an instruction in the system prompt to always return code in a fenced block.
Models Are Mismatched to the Task
Using a lighter model (like Claude Haiku) for complex implementation to save costs, then a stronger model for review, creates a mismatch — the reviewer finds more issues than necessary and the loop runs longer. Match model capability to task complexity.
FAQ
What is an orchestrator in a multi-agent workflow?
An orchestrator is the coordination layer that manages which agent runs when, what information each agent receives, and how output flows between them. In OmniAgent, Polly is the orchestrator — it reads your pipeline configuration, executes agents in sequence, handles conditional branching, and aggregates final outputs. Without an orchestrator, you’d manually manage every handoff between models.
Can I use Claude and Codex together without OmniAgent?
Yes, but you’d need to build the coordination logic yourself. That means handling API calls to each model, structuring context injection, parsing outputs, writing retry logic, and managing state between steps. OmniAgent’s Polly orchestrator automates all of that. Alternatively, platforms like MindStudio let you build the same pipeline visually without code.
How do I prevent the Claude-Codex loop from running indefinitely?
Set a max_retries value in your Polly pipeline configuration. When the limit is hit, Polly stops the loop and outputs the last version with a flag indicating it didn’t pass final review. This prevents runaway API costs and gives you a clear signal when a task needs human intervention.
What kind of tasks work best with this Claude + Codex pattern?
This pattern works well for any code generation task where correctness matters and requirements are specific: API endpoints, data transformation scripts, validation logic, utility functions. It works less well for exploratory or creative coding tasks where there’s no clear pass/fail criteria for the reviewer.
Is OmniAgent’s Polly different from other orchestrators like LangChain or CrewAI?
LangChain and CrewAI are broader frameworks that handle everything from retrieval-augmented generation to tool use. Polly is more focused — it’s designed specifically for orchestrating model-to-model handoffs with structured output parsing and conditional routing. If your workflow is primarily about chaining model outputs with feedback loops, Polly’s simpler configuration can be easier to reason about than a full LangChain agent setup.
How does context window management work across agents?
Polly passes only what’s specified in each agent’s input_context configuration, not the entire conversation history. This is important for large tasks — if you injected every previous step into every subsequent agent, you’d quickly hit context limits. By being explicit about what each agent needs, you keep prompts lean and latency low.
Key Takeaways
- OmniAgent’s Polly orchestrator handles the coordination logic between Claude and Codex — context injection, conditional routing, retry loops — so you don’t have to build that infrastructure yourself.
- Claude handles implementation because of its strength in reasoning through requirements and producing readable code with clear documentation.
- Codex handles review because it’s optimized for code correctness, security checks, and requirement compliance — and because using a separate model for review avoids confirmation bias.
- The feedback loop is what separates this from a basic prompt chain. Polly can send Claude back with specific revision instructions from Codex’s report, producing higher-quality output without manual intervention.
- Configuration quality matters — structured output formats, specific system prompts, explicit context injection, and retry limits are the difference between a reliable pipeline and a flaky one.
Other agents start typing. Remy starts asking.
Scoping, trade-offs, edge cases — the real work. Before a line of code.
If you want to build this kind of multi-agent workflow without managing YAML configurations and API credentials yourself, MindStudio lets you connect Claude, Codex, and other models visually — with orchestration, retries, and integrations built in. Start free and have a working pipeline running in under an hour.
