Orion

The declarative runtime for AI agents, workflows, microservices, and event processing.

Safe enough to let an AI write your services. Fast enough to run them in production.

Orion is a declarative services runtime. A service is one JSON document holding the logic, the connectors it reaches, and the endpoint it answers on. Post it to a running server and it is live a second later. No rebuild, no restart, no downtime.

Everything around that logic is the runtime's job, and it works the same way for every service you put on it: route and protocol matching, ingress guards, rate limiting, circuit breaking, fault tolerance, connection pooling, zero-downtime hot reload, and end-to-end observability. That is the glue you would otherwise write again for every microservice, agent backend, stream processor, and data pipeline.

It ships as a single Rust binary on Tokio and Axum, storing your service definitions in an embedded database. There is nothing to containerize and nothing to provision.

Jump to: Quickstart · What you get · What you can build · Is Orion right for you? · Three primitives · The console · What's built in · Connectors · Functions · Performance · Install · Docs

What You Get

Open a small internal microservice and count the lines. HTTP server setup, connection pools, a Prometheus exporter, OpenTelemetry wiring, retry loops, a circuit breaker, health checks, a Dockerfile, a deploy manifest. Somewhere in the middle sits the logic you actually cared about, and it is maybe fifty lines long. Orion runs that middle part for you and provides everything around it, the same way, for every service.

• No service to build. Post a JSON document and you have a live REST or Kafka endpoint. No Dockerfile, no CI pipeline, no server code.
• Production features included. Rate limiting, circuit breakers, timeouts, caching, and payload validation are things you configure on a channel instead of writing.
• Safe for AI-written logic. Draft-before-activate, dry-run, percentage rollout, and one-command rollback mean AI output cannot quietly break production.
• Services that call services. channel_call runs another workflow in-process, so composition costs no network hop and no serialization.
• One binary, one file. A single Rust binary with an embedded database — with PostgreSQL or MySQL waiting for when you outgrow that.
• Measured, not claimed. 5.1K–5.7K workflow requests/sec per instance with single-digit millisecond latency, on the published v1.0.0 benchmark record — run conditions and all.

Your First Service in 2 Minutes

No code. No Dockerfile. No CI pipeline. Just a running service.

Watch the 60-second demo
Zero to a live service in under a minute: declare the logic, validate and dry-run it, give it an endpoint, then send a request. Tracing and metrics are already on. Prefer a terminal? The same flow is four curl calls, below.

1. Start Orion

brew install GoPlasmatic/tap/orion-server   # or: curl installer, cargo install (see Install)
orion-server

2. Deploy your first service (one command)

curl -fsSL https://raw.githubusercontent.com/GoPlasmatic/Orion/main/examples/quickstart.sh | bash

The script talks to the same admin API you would use in production. It creates a workflow (the logic: flag any order over $10,000 for review) and a channel (the endpoint: POST /orders), activates both, and sends a first test order. Re-running it is safe. Cloned the repo? Run ./examples/quickstart.sh instead.

What the script does: the four API calls, spelled out

Create the workflow, with the business logic as JSON (a parse task, then a conditional flag task):

Terminal

Copy

curl -s -X POST http://localhost:8080/api/v1/admin/workflows \
  -H "Content-Type: application/json" \
  -d '{
    "workflow_id": "quickstart-orders",
    "name": "High-Value Order",
    "condition": true,
    "tasks": [
      { "id": "parse", "name": "Parse payload", "function": {
          "name": "parse_json",
          "input": { "source": "payload", "target": "order" }
      }},
      { "id": "flag", "name": "Flag order",
        "condition": { ">": [{ "var": "data.order.total" }, 10000] },
        "function": {
          "name": "map",
          "input": { "mappings": [
            { "path": "data.order.flagged", "logic": true },
            { "path": "data.order.alert", "logic": { "cat": ["High-value order: $", { "var": "data.order.total" }] } }
          ]}
      }}
    ]
  }'

# Activate it (draft → active; the engine hot-reloads)
curl -s -X PATCH http://localhost:8080/api/v1/admin/workflows/quickstart-orders/status \
  -H "Content-Type: application/json" -d '{"status": "active"}'

Create the channel, the endpoint that routes to the workflow, and activate it:

Terminal

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curl -s -X POST http://localhost:8080/api/v1/admin/channels \
  -H "Content-Type: application/json" \
  -d '{ "channel_id": "orders", "name": "orders", "channel_type": "sync",
        "protocol": "rest", "route_pattern": "/orders",
        "methods": ["POST"], "workflow_id": "quickstart-orders" }'

curl -s -X PATCH http://localhost:8080/api/v1/admin/channels/orders/status \
  -H "Content-Type: application/json" -d '{"status": "active"}'

3. Call it. Your service is live

curl -s -X POST http://localhost:8080/api/v1/data/orders \
  -H "Content-Type: application/json" \
  -d '{ "data": { "order_id": "ORD-9182", "total": 25000 } }'

{
  "status": "ok",
  "data": {
    "order": {
      "order_id": "ORD-9182",
      "total": 25000,
      "flagged": true,
      "alert": "High-value order: $25000"
    }
  }
}

That is it. The business logic is a JSON document, deploying it was an API call, and rate limiting, metrics, health checks, and request tracing were already active when it went live. Change the threshold? One API call. No rebuild, no redeploy, no restart.

Prefer to describe the service instead of writing it? Workflow JSON is easy for LLMs to generate. Tell your AI assistant "flag orders over $10,000 for manual review with an alert message" and deploy what it returns. AI Writes Services, Not Code shows the safe path from prompt to production.

What You Can Build

Orion carries the same infrastructure across five kinds of service:

• Microservices: one channel and one workflow make a service, and Orion answers the request in-process — nothing you built sits in the path.
• AI Agent Tools: an agent calls your channels as tools over HTTP. With the Orion agent skill and orion-cli, an assistant drafts, dry-runs, activates, and rolls back those workflows itself, inside Orion's lifecycle rules.
• Business Rules & Decision APIs: pricing tiers, eligibility checks, routing decisions. Write the rules as JSONLogic conditions over the request, branch between them, and return the result as the response body.
• Kafka Event Consumers: a topic is the ingress: consume records, transform and enrich them as they arrive, publish results onward, and send poison messages to a dead-letter topic instead of letting one stall the partition.
• Webhook & Data Ingestion: normalize payloads from Stripe, GitHub or Shopify, then read and write across PostgreSQL, MySQL, SQLite, MongoDB and Elasticsearch through one portable dialect. Credentials stay on the connector, so the workflow JSON is safe to commit.

Read the full README on GitHub →