Documentation
Build, route, and replay AI agents you can trust.
ALDO AI is the LLM-agnostic agent orchestrator. Define agents as YAML, route by capability class, enforce privacy at the platform layer, and replay every run. These docs cover the full surface — from your first signup to running fully self-hosted.
Prefer Redoc? Three-column read-only reference. Need the raw spec? openapi.json.
Concepts
Mental models that shape every part of the platform.
- Capability-class routingAgents declare capability classes; the gateway resolves a concrete model. Switching providers is a config change.
- Privacy tierA tier on every agent. `sensitive` is a hard gate: those runs physically cannot reach a cloud endpoint.
- Multi-agent orchestrationPipeline, supervisor, parallel, router, debate, and subscription strategies — declared in YAML, run by the engine.
- Sandbox and guardsPer-agent sandbox policy (network, filesystem) and tool-output guards (spotlighting, output scanner, quarantine).
- Eval harnessSuites, sweeps, evaluators, datasets — the engine for promotion gating.
- ReplayabilityEvery node boundary is a checkpoint. Edit-and-resume any step against a different model.
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Guides
Task-oriented walkthroughs — from "I want to build an agent" to "I want to self-host".
- Building an agentFrom a blank YAML file to a promoted, eval-gated agent in your tenant.
- Writing an eval suiteCases, evaluators, thresholds — the structure of a regression suite.
- Custom evaluatorsShip your own evaluator: signature, lifecycle, sandboxing.
- Dataset uploadsUpload JSONL or CSV; bind to suites; share across agents.
- Dashboards and alertsBuild a dashboard, configure an alert rule, route to integrations.
- IntegrationsSlack, Discord, GitHub, webhooks, Telegram, email — secrets-encrypted at rest. Includes the approval-from-anywhere story (subscribe a Telegram bot to `approval_requested` and approve from your phone).
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SDKs
TypeScript and Python clients for the control plane.
Reference
Changelog, license, and other reference material.