Features
Everything AgentField gives you out of the box.
Four primitives. Three SDKs. One control plane.
AgentField is not just an SDK for writing agents. It is the control plane, execution layer, memory fabric, and audit system that turns agent code into production infrastructure.
If you assemble this stack yourself, you end up stitching together HTTP services, service discovery, async queues, workflow tracing, shared state, identity, and observability before you even get to agent logic.
Core features are available across Python, TypeScript, and Go SDKs, but a few APIs differ by language. For example, Go registers deterministic functions with RegisterReasoner, and Go memory reads the session scope by default.
What You'd Otherwise Build
| Traditional stack | AgentField |
|---|---|
| HTTP services for every agent function | Every reasoner and skill becomes an endpoint automatically |
| Service registry and routing | Control plane registration, discovery, and routing built in |
| Queue + retries + webhooks | Durable async execution with polling and webhooks |
| Redis / vector DB / pub-sub glue | Shared memory, vector search, and memory events |
| Tracing + metrics + audit exports | Workflow DAGs, notes, metrics, DIDs, and VCs |
Platform Proof
The visible difference is that you can inspect a workflow like infrastructure, not guess at it like app code:
{
"execution_id": "exec_a1b2c3",
"workflow_id": "wf_d4e5f6",
"target": "support-triage.process_refund",
"status": "completed",
"children": [
{ "target": "orders.get_order", "status": "completed", "duration_ms": 18 },
{ "target": "fraud-detector.assess", "status": "completed", "duration_ms": 941 },
{ "target": "payments.issue_refund", "status": "completed", "duration_ms": 77 }
]
}That is the real product: agent code plus a control plane that tracks, routes, governs, and proves what happened.
What you get
The visible pattern across the platform is that AgentField collapses infrastructure you would normally assemble yourself into one operating model. You write agent code, and the control plane gives you routing, execution tracking, memory, async delivery, and governance around it.
{
"write": "agent code",
"get_back": [
"endpoints",
"discovery",
"workflow_dag",
"shared_memory",
"policy_checks",
"audit_artifacts"
]
}Core Primitives
| Primitive | What it does |
|---|---|
| Agents | Production-ready microservice container. Discoverable, observable, governed. Every function becomes a REST endpoint. |
| Reasoners | LLM-powered decision-making that works with any model provider -- OpenAI, Anthropic, Google, open-source. |
| Skills | Deterministic tools agents use -- API calls, file ops, calculations. Typed inputs and outputs. |
| Harness | Bridge between coding agent frameworks (Claude Code, Codex, Gemini CLI) and the control plane. |
Why This Matters
| Audience | Why it matters |
|---|---|
| Developers | Write normal agent code, then get endpoints, tracing, memory, and async execution automatically. |
| Platform teams | Run a stateless control plane, scale agents independently, and inspect workflows like distributed systems. |
| Security / compliance | Prove what ran, which agent ran it, and how the workflow evolved with DIDs, VCs, notes, and DAGs. |
Next Steps
Ready to build? Start with the Quickstart -- go from zero to a running agent in 60 seconds.