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Basic Information

Orra is infrastructure for resilient AI agent workflows and a Plan Engine that generates, validates, and executes multi-agent plans. It coordinates tasks across agents, tools, and existing systems to maintain progress through failures such as API outages or failed evaluations. The repository provides a Plan Engine service with a CLI, language SDK examples for Python and JavaScript, Docker deployment and compose instructions, and guidance for self-hosted or cloud model endpoints. It requires OpenAI-compatible reasoning and embedding model endpoints and includes model configuration documentation. Orra persists orchestration state for durable execution, exposes webhooks and audit logs for observability, and is designed to integrate with various agent frameworks or deployment platforms without framework-specific changes.

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Features
Core features include an AI-driven planning agent with automatic agent and service discovery, progressive planning levels from base to production grounding, and semantic validation of execution plans. The engine offers durable execution via persistent state storage and BadgerDB, state transition validation, pre-validated execution plans, automatic retries, revert and rollback mechanisms, and health monitoring. It supports real-time status tracking and webhook notifications for completions and failures. The repo contains SDK examples for registering and running agents in Python and JavaScript, a Dockerfile and docker compose setup for the Plan Engine, model configuration for cloud and self-hosted OpenAI-compatible models, documentation, and example apps demonstrating common patterns.
Use Cases
Orra helps teams move multi-agent applications from fragile prototypes to production by automating planning, capability matching, validation, and error recovery so workflows continue without brittle custom logic. It reduces operational burden through persisted workflow state, audit logs, built-in retries and compensation, health checks, and webhooks for monitoring and integration. Domain grounding and constraint enforcement validate execution against safety and business rules. The model-agnostic design enables on-premises or cloud deployments using OpenAI-compatible endpoints and the CLI and SDKs simplify agent registration and observability. The Plan Engine is especially useful for use cases like incident response, fraud detection pipelines, e-commerce orchestration, and research assistants that require resilience, traceability, and on-premises deployment options.

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