Agent definitions are scattered, unvalidated, and locked to frameworks. Automagent gives you one open spec that works everywhere.
Version control stores files. It has no idea what an agent is. Here is what you gain when your agent definitions speak a real spec.
Stores any YAML blindly. Typos, leaked API keys, and invalid model names ship to production.
automagent validate checks your definition against the schema, catches secrets, verifies model configs, and confirms file references exist.
A git repo does not know what an agent is. A missing field is just a missing field.
agent.yaml has a JSON Schema. Editors autocomplete fields, CI enforces structure, misconfigurations fail fast before deploy.
Switching from CrewAI to LangChain means rewriting everything by hand.
automagent import converts CrewAI, OpenAI Agents SDK, and LangChain definitions into a universal format. One file, any framework.
Git does not run your code. Testing means wiring up a framework, writing boilerplate, and hoping the config is right.
automagent run starts an interactive chat session with your agent definition. Test locally, iterate fast, no framework required.
Git has no concept of agent teams. Orchestrating multiple agents means custom glue code for every project.
agent-compose.yaml defines roles, handoffs, and shared context for multi-agent teams. Declare the workflow, not the wiring.
Git does not enforce behavioral rules. Guardrails live in tribal knowledge and code review comments.
The spec includes guardrails, compliance tagging, PII policies, and approval workflows. Governance is declarative, not aspirational.
Every agent.yaml is validated against JSON Schema v7 using Ajv at runtime. No more shipping broken configs to production and finding out from a stack trace. The CLI runs a 4-step pipeline: schema structure, model identifier checks, secret detection, and context file verification. Errors are specific and actionable.
Already have agents defined in CrewAI, OpenAI Agents SDK, or LangChain? Import them. automagent import auto-detects the source format and converts it to a standard agent.yaml. No manual rewriting required. Your agent definitions are portable from day one — no vendor lock-in, no migration dread.
Agents need tools, and MCP is how they get them. Automagent has first-class support for the Model Context Protocol — declare servers by name, transport type, and connection details directly in your agent definition. Both stdio and streamable-http transports are supported. Your agent discovers capabilities at runtime without any custom wiring.
Real workflows need more than one agent. agent-compose.yaml lets you define teams of specialized agents with declared roles, dependency ordering, and shared context. Each agent handles what it's good at; the compose file handles how they hand off work. Think docker-compose, but for agent orchestration.
Declare your primary model, a fallback for when it's unavailable, and a list of compatible alternatives for cost optimization or latency tuning. Switch providers without touching your agent logic. Pin exact model versions to avoid surprise regressions. Temperature, max tokens, and other settings live alongside the model declaration.
model.fallback + model.compatibleBehavioral rules, prohibited actions, and compliance requirements are first-class fields in the spec — not afterthoughts bolted on later. Define what your agent must never do, tag data classification levels, and declare compliance frameworks like SOC2, GDPR, or HIPAA. PII handling policies are explicit: redact, mask, or block. Governance is declarative, auditable, and version-controlled.
guardrails + governanceThe agent.yaml you write on your laptop is the same file that governs thousands in production. No rewrites, no migration, no vendor lock-in.
Start with a single agent.yaml. Validate, run, and iterate locally.
Share agents across your team with private hubs and composable workflows.
Centralized governance for your entire AI fleet.
Install the CLI. Define your first agent. Ship with confidence.
npm install -g automagent