Exploring Gitagent: A Git-Native Standard for Defining AI Agents

Have you ever found yourself building an AI agent, only to realize that switching frameworks means starting over with a completely different structure? It’s a common frustration in the AI world. That’s where Gitagent comes in—a framework-agnostic, git-native standard that lets you define AI agents in a portable way. Simply clone a repo, and you’ve got an agent ready to go.

Gitagent leverages the power of Git for version control, branching, diffing, and collaboration right out of the box. It doesn’t tie you to any specific AI framework; instead, you can export your agent definitions to tools like Claude Code, OpenAI, LangChain, CrewAI, or AutoGen using adapters. On top of that, it builds in support for regulatory compliance, making it ideal for industries like finance where rules from FINRA, the Federal Reserve, and the SEC matter. And it’s all about composability—agents can extend others, depend on them, or delegate tasks seamlessly.

If you’re a developer or AI enthusiast wondering how this fits into your workflow, stick around. We’ll break it down step by step, from the basics to advanced features, answering common questions along the way.

Why Gitagent Matters in AI Development

What makes Gitagent stand out? In the AI landscape, every framework has its own way of structuring agents. There’s no universal standard that lets you move an agent from one tool to another without rework. Gitagent changes that by making everything git-native. This means you get built-in version control, easy branching for experiments, and collaboration features that developers already know and love.

It’s completely framework-agnostic, so you can export your agent to whatever setup you’re using. Plus, it’s designed with compliance in mind from the start—perfect for meeting requirements from bodies like FINRA, the Federal Reserve, and the SEC. Agents become composable too, allowing you to build on existing ones without reinventing the wheel.

You might be thinking, “How does this play out in real scenarios?” Well, treating agents as git repos unlocks four key architectural patterns that make development more intuitive and efficient.

Key Architectural Patterns in Gitagent

When you view AI agents through the lens of git repositories, certain patterns naturally emerge. These help manage everything from learning to deployment.

First, human-in-the-loop for reinforcement learning agents. If an agent updates its memory or picks up a new skill, it creates a branch and pull request for human review before merging to the main branch. Git’s built-in review process turns into your supervision mechanism.

Second, shared context. Files at the root level—like context.md, the skills/ folder, tools/, and knowledge/—are automatically inherited by sub-agents. This keeps a single source of truth and eliminates duplication across your setup.

Third, branch-based deployment. Just like in software engineering, use branches such as dev, staging, and main to promote changes through environments safely.

Fourth, the knowledge tree. The knowledge/ folder organizes entity relationships in a hierarchical structure with embeddings, enabling agents to reason over structured data during runtime.

These patterns shift agent development toward familiar software practices. Curious about applying them? Let’s look at getting started.

Quick Start Guide to Gitagent

Ready to dive in? Here’s how to get up and running with Gitagent. You’ll need Node.js version 18 or higher.

1. Install the Tool: Run npm install -g gitagent in your terminal to install the CLI globally.

2. Create a New Agent: Use gitagent init --template standard to scaffold a new agent repo. Templates include minimal for basics, standard for a balanced setup, or full for everything including compliance features.

3. Validate Your Setup: Execute gitagent validate to check if everything aligns with the spec. For regulatory checks, add --compliance.

4. Get Agent Details: Run gitagent info to see a summary of your agent’s name, version, and more.

5. Export to a System Prompt: Try gitagent export --format system-prompt to generate a prompt usable with any large language model.

These steps are straightforward and get you operational quickly. If you’re unsure about templates, the standard one is great for most users—it includes skills, tools, and rules without overwhelming you.

Understanding Gitagent’s Directory Structure

Gitagent’s repo layout is organized and logical, making it easy to navigate. Here’s a breakdown:

• agent.yaml: Required manifest file covering name, version, model, skills, tools, and compliance details.
• SOUL.md: Required file defining the agent’s identity, personality, communication style, and values.
• RULES.md: Outlines hard constraints, like must-always or must-never rules for safety.
• AGENTS.md: Provides framework-agnostic fallback instructions.
• skills/: Houses reusable modules, each with a SKILL.md file and supporting scripts.
• tools/: Contains MCP-compatible tool definitions in YAML schemas.
• knowledge/: Stores reference documents for the agent to consult.
• memory/: Manages persistent memory across sessions.
• workflows/: Defines multi-step procedures or playbooks.
• hooks/: Handles lifecycle events, such as audit logging or compliance checks.
• examples/: Includes calibration interactions like few-shot examples.
• agents/: Recursive definitions for sub-agents.
• compliance/: Holds regulatory artifacts.
• config/: Environment-specific overrides.
• .gitagent/: Runtime state, ignored by Git.

This structure acts like a well-stocked toolbox. You can expand as needed, but start with the essentials like agent.yaml and SOUL.md.

Diving into the agent.yaml File

The agent.yaml is the backbone with a strict schema. A minimal example looks like this:

spec_version: "0.1.0"
name: my-agent
version: 0.1.0
description: A helpful assistant agent

For something more robust, especially with compliance:

spec_version: "0.1.0"
name: compliance-analyst
version: 1.0.0
description: Financial compliance analysis agent
model:
  preferred: claude-opus-4-6
compliance:
  risk_tier: high
  frameworks: [finra, federal_reserve, sec]
  supervision:
    human_in_the_loop: always
    kill_switch: true
  recordkeeping:
    audit_logging: true
    retention_period: 7y
    immutable: true
  model_risk:
    validation_cadence: quarterly
    ongoing_monitoring: true

This specifies the preferred model, risk level, compliance frameworks, supervision features like constant human oversight and a kill switch, recordkeeping with audit logs and a 7-year retention, and model risk management through quarterly validations and ongoing monitoring.

Customizing it? Begin minimal and layer in sections as your project grows.

Gitagent CLI Commands Explained

The CLI is your command center for Gitagent. Here’s a table of key commands:

These cover the full lifecycle. For instance, to run an agent: gitagent run ./my-agent --adapter lyzr.

Built-in Compliance Features in Gitagent

Compliance is non-negotiable in regulated fields like finance, and Gitagent makes it straightforward.

For FINRA:

• Rule 3110: Supervision with human-in-the-loop, escalation triggers, and a kill switch.
• Rule 4511: Recordkeeping including immutable audit logs, retention periods, and SEC 17a-4 alignment.
• Rule 2210: Communications ensuring fair and balanced content without misleading statements.
• Reg Notice 24-09: Applies existing rules to generative AI and LLMs.

For the Federal Reserve:

• SR 11-7: Model risk management with validation cadences, ongoing monitoring, and outcomes analysis.
• SR 23-4: Third-party risk via vendor due diligence, SOC reports, and subcontractor assessments.

For the SEC and CFPB:

• Reg S-P: Handles customer privacy and PII.
• CFPB Circular 2022-03: Focuses on explainable adverse actions and searching for less discriminatory alternatives.

Use gitagent audit to get a tailored compliance checklist based on your config. This integrates compliance into your development from day one.

Wondering how to incorporate it? Embed it in the agent.yaml’s compliance section during setup.

Adapters for Flexibility in Gitagent

Adapters make Gitagent adaptable to different environments. They’re used for both exporting and running agents. Here’s a list:

Examples: Export with gitagent export --format system-prompt. Run with gitagent run ./my-agent --adapter lyzr.

These ensure your work transfers easily across tools.

Inheritance and Composition with Gitagent

Build complex systems through inheritance and dependencies.

Extend a parent in agent.yaml:

extends: https://github.com/org/base-agent.git

Add dependencies:

dependencies:
  - name: fact-checker
    source: https://github.com/org/fact-checker.git
    version: ^1.0.0
    mount: agents/fact-checker

Run gitagent install to handle them. This lets you inherit shared elements and mount sub-agents, saving time and ensuring consistency.

Examples to Get You Started

Gitagent includes practical examples in the examples/ directory:

• minimal/: A two-file hello world with agent.yaml and SOUL.md.
• standard/: A code review agent featuring skills, tools, and rules.
• full/: A production-ready compliance agent with all directories, hooks, workflows, sub-agents, and regulatory artifacts.
• gitagent-helper/: An assistant agent for creating Gitagent definitions.
• lyzr-agent/: Integration example for Lyzr Studio.

These serve as templates you can clone and modify.

The Gitagent Specification

The full spec is in spec/SPECIFICATION.md, with JSON schemas for validation in spec/schemas/.

It follows spec version v0.1.0 and is licensed under MIT.

If you’ve built something with Gitagent, add a ‘gitagent’ topic to your repo or start a discussion on GitHub to share.

HowTo: Building a Compliant AI Agent with Gitagent

Want to create a financial compliance analyst agent? Follow these steps:

1. Install: npm install -g gitagent.

2. Initialize: gitagent init --template full for a complete structure with compliance.

3. Edit agent.yaml: Add compliance details like risk tier, frameworks, and supervision.

4. Define SOUL.md: Outline the agent’s professional, objective personality.

5. Add RULES.md: Specify boundaries, e.g., never disclose PII.

6. Set up tools/ and skills/: Include YAML tools for analysis.

7. Validate: gitagent validate --compliance.

8. Audit: gitagent audit for a report.

9. Export: gitagent export --format crewai for CrewAI.

10. Run: gitagent run . --adapter lyzr.

This ensures a secure, compliant agent.

FAQ: Common Questions About Gitagent

What is Gitagent?
Gitagent is a framework-agnostic, git-native standard for defining AI agents. Clone a repo, and you have an agent.

How does Gitagent handle version control?
It uses Git’s branches, PRs, and merges to manage agent changes like code.

Can I integrate Gitagent with existing frameworks?
Yes, export via adapters to Claude Code, OpenAI, CrewAI, and more.

What compliance frameworks does Gitagent support?
It covers FINRA (e.g., Rule 3110, 4511), Federal Reserve (e.g., SR 11-7), and SEC (e.g., Reg S-P).

How do I install dependencies in Gitagent?
Use gitagent install to resolve them from agent.yaml.

What are the minimum requirements for Gitagent?
Node >= 18, plus agent.yaml and SOUL.md.

Can I import existing agents into Gitagent?
Yes, with gitagent import --from claude <path> and similar.

How does Gitagent ensure safety?
Through RULES.md constraints and compliance features like kill switches.

How do sub-agents work in Gitagent?
Defined recursively in agents/, inheriting root-level context.

How do I run an audit?
gitagent audit generates a report based on your setup.

What’s the difference between Gitagent templates?
Minimal: Core files. Standard: Adds skills/tools. Full: Includes compliance/workflows.

Can I extend other repositories with Gitagent?
Yes, use extends in agent.yaml with a Git URL.

How does the knowledge tree help?
It structures data in knowledge/ for runtime reasoning on entities.

What CLI subcommands does Gitagent offer?
Things like skills search or lyzr create for managing parts.

What if my agent is high-risk?
Set risk_tier: high in compliance and enable human_in_the_loop.

These cover typical queries. For more, check the spec docs.

Gitagent isn’t just a tool—it’s a way to standardize AI agent development. Whether you’re new or experienced, it streamlines your process. Give it a try; it might transform how you build agents.