Production-Ready Autonomous Systems

Building the Infrastructure Layer
for Autonomous AI Systems

I architect and implement the foundational infrastructure that powers production AI agents. From memory synthesis engines to event-driven runtimes and agent orchestration frameworks—I build the systems that make autonomous intelligence possible.

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Abiola Adeshina

Architected & Built by Abiola Adeshina

Agentic Infrastructure Engineer | Systems Architect | Open Source Creator

I don't just use agentic tools—I build them from the ground up. OmniRexFlora Labs represents my complete implementation of production-grade agentic infrastructure: custom memory synthesis engines, event-driven runtime systems, and agent orchestration frameworks. Every component is architected, implemented, and production-tested by me.

Memory Engines Event Systems Agent Frameworks Infrastructure

The Omni Ecosystem

Three pillars of autonomous intelligence.

OmniMemory

THE BRAIN

Self-evolving composite memory synthesis (SECMSA). Mimics human cognition with dual-agent processing and infinite context retention.

OmniDaemon

THE NERVOUS SYSTEM

Universal event-driven runtime. A "Service Mesh" for agents that decouples logic from execution using Redis Streams.

OmniCoreAgent

THE MIND

Production-ready framework. Orchestrates multi-step workflows, validates logic, and handles tool execution autonomously.

OmniMemory

Self-Evolving Composite Memory Synthesis Architecture (SECMSA)

I built OmniMemory from scratch as a complete memory infrastructure system. Unlike traditional RAG implementations, this is a self-evolving cognitive architecture that uses dual-agent parallel processing (Episodic + Summarizer agents) to synthesize memories, then autonomously maintains coherence through AI-powered conflict resolution.

1

Dual-Agent Memory Construction

Parallel Episodic + Summarizer agents synthesize canonical memory notes with full async pipeline.

2

Composite Scoring Engine

Multi-dimensional ranking: relevance × (1 + recency_boost + importance_boost) for intelligent retrieval.

3

AI-Powered Conflict Resolution

Autonomous UPDATE/DELETE/SKIP/CREATE operations maintain memory coherence without manual intervention.

4

Multi-Tenant Isolation Architecture

Three-tier isolation: Physical (App), Logical (User), Context (Session) with status-driven lineage tracking.

5

Production Infrastructure

REST API, async background tasks, connection pooling, daemon service, and comprehensive observability.

$ pip install omnimemory
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OmniMemory Architecture
OmniDaemon Architecture

OmniDaemon

Universal Event-Driven Runtime Infrastructure

I architected OmniDaemon as a complete Service Mesh for Agents—a production-grade event-driven runtime that decouples agent logic from execution. Built on Redis Streams with full state management, retry mechanisms, and Dead Letter Queue handling. This isn't a wrapper around existing tools; it's a complete runtime infrastructure I built from scratch.

Event-Driven Architecture

Redis Streams-based pub/sub with consumer groups for scalable agent orchestration.

Built: Stream management, consumer coordination, event routing

Resilience Infrastructure

Dead Letter Queues, exponential backoff, retry policies, and failure isolation.

Built: DLQ system, retry engine, circuit breakers

Fan-Out & Routing

1 event → N parallel agents with priority queues and VIP routing.

Built: Event router, priority scheduler, load balancer

Framework Agnostic

Works with any agent framework—LangChain, Google ADK, custom implementations.

Built: Adapter layer, protocol abstraction

View Repository

OmniCoreAgent

Production Agent Orchestration Framework

I built OmniCoreAgent as a complete agent framework infrastructure—not just workflow orchestration, but the entire system for building production-ready autonomous agents. Includes workflow engines (Sequential, Parallel, Router), tool discovery systems, MCP protocol implementation, and full observability infrastructure.

  • Workflow Orchestration Engine: Built Sequential, Parallel, and Router workflow patterns with state management and error handling.
  • MCP Client Infrastructure: Native Model Context Protocol implementation for file systems, APIs, and external tool integration.
  • Semantic Tool Discovery: Built semantic search engine that auto-discovers and ranks tools based on agent intent.
  • Observability Stack: Integrated Opik tracing for LLM calls, reasoning step tracking, and full agent execution visibility.
  • Model-Agnostic Architecture: Framework works with any LLM provider—OpenAI, Anthropic, local models, with unified interface.
$ pip install omnicoreagent
View Repository
OmniCoreAgent Architecture

What I Build

I specialize in architecting and implementing the infrastructure layer for autonomous AI systems. These aren't wrappers or integrations—they're complete systems built from scratch.

Memory Infrastructure

Custom memory synthesis engines, embedding pipelines, vector databases, and conflict resolution systems.

• Dual-agent memory construction
• Composite scoring algorithms
• Self-evolving conflict resolution

Event-Driven Runtimes

Service mesh architectures, event streaming, consumer coordination, and distributed state management.

• Redis Streams orchestration
• Dead Letter Queue systems
• Priority routing & fan-out

Agent Frameworks

Complete agent orchestration systems with workflow engines, tool discovery, and execution pipelines.

• Workflow orchestration (Seq/Par/Router)
• MCP protocol implementation
• Semantic tool discovery

API & SDK Infrastructure

REST APIs, async SDKs, connection pooling, daemon services, and developer tooling.

• FastAPI REST endpoints
• Async Python SDKs
• Background task systems

Observability & Metrics

Tracing systems, metrics collection, logging infrastructure, and performance monitoring.

• Opik LLM tracing
• Custom metrics collectors
• Structured logging pipelines

System Architecture

Multi-tenant isolation, status-driven lineage, async processing pipelines, and production patterns.

• Three-tier isolation (App/User/Session)
• Status-based memory evolution
• Async background processing