VoltAgent/awesome-codex-subagentsPublic

A collection of 130+ specialized Codex subagents covering a wide range of development use cases.

AI summary: A comprehensive directory of specialized Codex subagents organized by development lifecycle categories.

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MITCreated Mar 17, 2026Last push 13d ago+36 stars this week+186 this month

Quick answers

What is awesome-codex-subagents?
A comprehensive directory of specialized Codex subagents organized by development lifecycle categories.
What does awesome-codex-subagents do?
This repository serves as a centralized registry for over 170 custom subagents designed to work with OpenAI's Codex. The agents are defined in standard TOML configuration files, making them immediately compatible with the official Codex CLI agent directory structure. It organizes these specialized AI assistants into 13 distinct categories, ranging from core backend development to quality assurance and security reviews. The collection standardizes how developers can delegate specific, scoped tasks to specialized instances of the Codex model. The repository effectively decentralizes AI workflows by breaking down monolithic tasks into specialized, domain-specific utilities. This curated approach significantly reduces prompt engineering overhead for common software development activities.
Who is awesome-codex-subagents for?
Software engineers and technical leads using OpenAI Codex who want to leverage specialized, role-specific AI agents within their development workflows.
How do I get started with awesome-codex-subagents?
mkdir -p ~/.codex/agents
How popular is awesome-codex-subagents on GitHub?
VoltAgent/awesome-codex-subagents has 6,272 stars and 690 forks on GitHub, and gained 36 stars in the last 7 days.
What license does awesome-codex-subagents use?
VoltAgent/awesome-codex-subagents is released under the MIT license.

Star history

since Jul 28, 2026
02K4K6KJul 2026Aug 2026Sep 2026Oct 2026
6.3K stars as of Oct 4, 2026. Measured daily since Jul 28, 2026; GitHub no longer exposes earlier star timestamps.

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32 commits in the last yearLessMore

Signals and awards

derived from tracked data
  • Permissive license

    MIT

What awesome-codex-subagents does

This repository serves as a centralized registry for over 170 custom subagents designed to work with OpenAI's Codex. The agents are defined in standard TOML configuration files, making them immediately compatible with the official Codex CLI agent directory structure. It organizes these specialized AI assistants into 13 distinct categories, ranging from core backend development to quality assurance and security reviews. The collection standardizes how developers can delegate specific, scoped tasks to specialized instances of the Codex model. The repository effectively decentralizes AI workflows by breaking down monolithic tasks into specialized, domain-specific utilities. This curated approach significantly reduces prompt engineering overhead for common software development activities.

Software engineers and technical leads using OpenAI Codex who want to leverage specialized, role-specific AI agents within their development workflows.

  • Extensive collection: Provides 171+ ready-to-use Codex subagents covering a wide array of software engineering roles.
  • TOML configuration: Uses the native, documented Codex agent configuration format for seamless integration.
  • Categorized structure: Organizes agents logically across 13 domains including development, security, and testing.
  • Global or Local scope: Supports dropping agent files into global system directories or project-specific local folders.
  • Explicit delegation: Requires developers to explicitly call upon these subagents in their prompts, ensuring predictable behavior.
  • Domain Specific Intelligence: Pre-configures prompts and contexts for specialized frameworks and languages
  • Workflow Decentralization: Enables chaining multiple specialized agents for complex engineering pipelines

Where teams use it

Specialized Code Review

Invoking a security-focused subagent to analyze a specific pull request for vulnerabilities.

Project Scaffolding

Using a backend-developer subagent to generate boilerplate code according to specific architectural patterns.

Agent Configuration

Serving as a reference library for developers learning how to write effective TOML configurations for custom Codex agents.

Team Standardization

Sharing a specific set of categorized subagents across an engineering team to enforce coding standards.

Automated Code Review

Deploying a specific subagent to strictly enforce formatting rules on pull requests.

Boilerplate Generation

Using targeted agents to rapidly scaffold new project architectures based on best practices.

Getting started: mkdir -p ~/.codex/agents

README

main branch
codex

The awesome collection of 175+ Codex subagents across 13 categories.

Awesome Subagent Count Last Update Discord


Awesome Codex Subagents

This repository serves as the definitive collection of Codex Subagents, specialized AI assistants designed for specific development tasks. Written specifically for Codex and aligned with the official docs.

Installation

Use Codex custom agent directories exactly as documented:

  • ~/.codex/agents/ for global agents (available in all projects)
  • .codex/agents/ for project-specific agents (higher precedence in that repo)
  1. Clone this repository.
  2. Copy the .toml agent files you want into one of the directories above.
  3. Restart or refresh your Codex session if needed.
  4. Delegate explicitly in prompts (Codex does not auto-spawn custom subagents).

Examples:

mkdir -p ~/.codex/agents
cp categories/01-core-development/backend-developer.toml ~/.codex/agents/
mkdir -p .codex/agents
cp categories/04-quality-security/reviewer.toml .codex/agents/

If you use agent configuration in Codex, keep it in .codex/config.toml under [agents] as described in the official docs.

Sponsors

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👉 You can feature your product here and reach developers using AI coding agents like Claude Code, Codex, Gemini, and more.

Become a Sponsor

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Subagent Storage Locations

Type Path Availability Precedence
Project Subagents .codex/agents/ Current project only Higher
Global Subagents ~/.codex/agents/ All projects Lower

Note: When naming conflicts occur, project-specific subagents override global ones.

Subagent Structure

Each subagent uses a Codex-native .toml format:

name = "subagent-name"
description = "When this agent should be invoked"
model = "gpt-5.6-luna"
model_reasoning_effort = "medium"
sandbox_mode = "read-only"

[instructions]
text = """
You are a [role description and expertise areas]...

[Agent-specific checklists, patterns, and guidelines]...
"""

Smart Model Routing

Each subagent includes a model field that automatically routes it to the right model -- balancing quality and cost:

Model When It's Used Examples
gpt-5.6-sol Deep reasoning -- architecture reviews, security audits, financial logic security-auditor, architect-reviewer, fintech-engineer
gpt-5.6-terra Implementation, diagnostics, evaluation, and multi-step professional analysis docker-expert, test-automator, scientific-literature-researcher
gpt-5.6-luna Bounded search, extraction, routing, drafting, and lightweight synthesis search-specialist, docs-researcher, agent-installer

Sandbox Mode Philosophy

Each subagent's sandbox_mode field controls filesystem access:

  • Read-only agents (reviewers, auditors): sandbox_mode = "read-only" - analyze without modifying
  • Workspace-write agents (developers, engineers): sandbox_mode = "workspace-write" - create and modify files

Categories

Essential development subagents for everyday coding tasks.

Language-specific experts with deep framework knowledge.

DevOps, cloud, and deployment specialists.

04. Quality & Security — Testing, security, and code quality experts (20 agents)
05. Data & AI — Data engineering, ML, and AI specialists (14 agents)
06. Developer Experience — Tooling and developer productivity experts (14 agents)
07. Specialized Domains — Domain-specific technology experts (14 agents)
08. Business & Product — Product management and business analysis (17 agents)
09. Meta & Orchestration — Agent coordination and meta-programming (12 agents)
10. Research & Analysis — Research, search, and analysis specialists (12 agents)
11. AI Governance & Safety - Governance, guardrails, and trustworthy AI specialists (4 agents)
12. Platform Engineering & IDP - Internal developer platform and golden-path specialists (4 agents)
13. LLMOps, Evals & Observability - Production AI quality and runtime visibility specialists (4 agents)

Understanding Subagents

Subagents are specialized AI assistants that enhance Codex's capabilities by providing task-specific expertise. They act as dedicated helpers that Codex can call upon when encountering particular types of work.

What Makes Subagents Special?

Independent Context Windows Every subagent operates within its own isolated context space, preventing cross-contamination between different tasks and maintaining clarity in the primary conversation thread.

Domain-Specific Intelligence Subagents come equipped with carefully crafted instructions tailored to their area of expertise, resulting in superior performance on specialized tasks.

Shared Across Projects After creating a subagent, you can utilize it throughout various projects and distribute it among team members to ensure consistent development practices.

Explicit Delegation Codex does not spawn subagents automatically. Use explicit delegation prompts to specify which agents to spawn, how to divide the work, and what shape the result should take.

Core Advantages

  • Memory Efficiency: Isolated contexts prevent the main conversation from becoming cluttered with task-specific details
  • Enhanced Accuracy: Specialized prompts and configurations lead to better results in specific domains
  • Workflow Consistency: Team-wide subagent sharing ensures uniform approaches to common tasks
  • Codex-Native: Uses .toml agent files aligned with official Codex subagent docs

Example Workflows

PR review workflow:

Review this branch with parallel subagents. Have reviewer look for correctness, security, and missing tests. Have docs_researcher verify the framework APIs this patch depends on. Wait for both and summarize the findings with file references.

Bug investigation workflow:

Investigate the broken settings flow. Have code_mapper trace the owning code paths, browser_debugger reproduce the bug in the browser, and frontend_developer propose the smallest fix after the failure is understood. Wait for the read-heavy agents first, then continue.

Repo exploration and planning workflow:

Use search_specialist to locate the code related to payment retries, knowledge_synthesizer to summarize the current design, and refactoring_specialist to propose a minimal refactor plan. Return a concrete action list.

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

  • Submit new subagents via PR
  • Improve existing definitions
  • Report issues and bugs

License

MIT License - see LICENSE

This repository is a curated collection of subagent definitions contributed by both the maintainers and the community. All subagents are provided "as is" without warranty. We do not audit or guarantee the security or correctness of any subagent. Review before use, the maintainers accept no liability for any issues arising from their use.

If you find an issue with a listed subagent or want your contribution removed, please open an issue in this repository and we'll address it promptly.

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32 commits in the last 52 weeks.

When work happens

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Commit volume by weekday and hour (UTC). Larger dots mean more commits.
DateListRankStars gained
Mar 19, 2026daily#23+109
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