Open-source infrastructure for production-ready, event-driven autonomous AI systems.

We build the foundational runtime, memory, and orchestration layers required to run AI agents as reliable, distributed systems — not demos or chatbots.

Our focus is on:

• Event-driven execution
• Multi-agent coordination
• Persistent memory
• Horizontal scalability
• Production reliability

All components are modular, framework-agnostic, and designed to operate independently or as a unified stack.

Autonomous AI agent framework for reasoning, tool use, routing, and workflow orchestration.

• Model-agnostic (via LiteLLM)
• Multi-agent patterns (routing, parallel, sequential)
• MCP + local tool support
• Streaming and background execution
• Production-proven in real workloads

→ https://github.com/omnirexflora-labs/omnicoreagent

Event-driven runtime engine that turns agents into scalable, distributed systems.

• Framework-agnostic runtime
• Redis Streams event bus
• Consumer groups and horizontal scaling
• Retries, DLQs, and observability built-in

→ https://github.com/omnirexflora-labs/OmniDaemon

Persistent, framework-agnostic memory layer for AI agents.

• Cross-session memory persistence
• Hybrid retrieval (semantic + keyword + metadata)
• Multi-tenant isolation
• Redis, PostgreSQL, MongoDB, SQLite backends
• Vector DB integrations (Qdrant, ChromaDB, MongoDB Atlas)

→ https://github.com/omnirexflora-labs/omnimemory

uv add omnicoreagent
uv add omnidaemon
uv add omnimemory

• Event-driven by default
• Framework-agnostic
• Production-first
• Modular and composable
• Open-source, MIT licensed

See ARCHITECTURE.md for a high-level system overview.