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// agents

Autonomous agents,
production-grade.

Long-horizon agents with durable memory, typed tool use, and observable traces. Built to run as services — not as scripts someone kicks off from a notebook and watches over.

▸ durable memory
▸ typed tool use
▸ otel traces
▸ cost caps
// what you get

Everything a production agent needs.

Memory that persists across sessions. Tools that validate their own inputs. Traces you can ship to your existing observability stack. All of it, out of the box.

◇

Durable memory

Vector and relational memory scoped per agent and per session. State survives restarts, rollbacks and version upgrades.

◉

Typed tool use

Schema-validated tools with automatic retries on malformed calls. Agents fail gracefully instead of hallucinating past errors.

◈

Structured output

Enforce JSON schemas on any LLM response. Use constrained decoding for structured extraction without prompt hacks.

◆

OpenTelemetry traces

Every step, tool call and LLM invocation emits an OTEL span. Ship to Grafana, Honeycomb, Datadog or your own collector.

◇

Cost caps

Set token budgets and cost limits per invocation. Agents stop gracefully instead of burning quota on runaway loops.

◉

Model-agnostic

Swap the backing LLM without rewriting the agent. Supports OpenAI, Anthropic, local models via Ollama, and any OpenAI-compatible endpoint.

◈

Eval harness

Record production runs as test fixtures. Re-run any agent version against the same inputs to catch regressions before deployment.

◆

Deploy via Hive or API

Deploy agents to a Hive-managed runtime or as dedicated managed endpoints — same codebase, different operational model.

◇

Audit-ready

Every decision, tool call and output is logged with timestamps and attribution. Ready for compliance review without extra instrumentation.

// use cases

Workloads we're targeting

  • ▸Customer support triage, classification and escalation at volume
  • ▸Back-office research, data extraction and report generation
  • ▸Ops agents that reconcile state across systems on a schedule
  • ▸Content and code review pipelines with human-in-the-loop checkpoints
  • ▸Long-running workflows that span multiple LLM calls and tool invocations
  • ▸Internal copilots that need to read and write real systems via typed tools
// the stack

Agents + Hive + Hosting

All three layers of the agent stack compose cleanly together. Build and iterate on agents using the runtime, deploy and manage them via Hive, and run long-lived or scheduled agents on Agent Hosting.

▸
Agents
The runtime: memory, tools, tracing, cost controls. What you're looking at now.
▸
Hive
One-click deployment to your VPS. Manages agent runtime instances, upgrades and rollbacks.
▸
Agent Hosting
Managed execution for long-lived agents — schedules, checkpoints, auto-scale.
// roadmap

Where we're going

  1. now

    Single-agent runtime

    Memory, typed tools, tracing, eval harness. Available on request.

  2. next

    Multi-agent orchestration

    Named roles, message passing, and shared workspace with auditable handoffs between agents.

  3. planned

    Evals + replay

    Capture production runs as test fixtures. Re-run any agent version against them to catch regressions.

Ask about Agents.

This is not self-serve today. Orion is in private beta and invite only. Tell us what you need at [email protected] and we will reply with what is available.

Talk to us →
// next step

Request access to Orion.

Orion is in private beta and invite only. There is no free trial. Request access and we will email you when you are in. Questions: [email protected].