OmniCoreAgent is the complete AI development platform that combines two powerful systems into one revolutionary ecosystem.
Documentation
🚀 OmniCoreAgent
The AI Agent Framework Built for Production
Switch memory backends at runtime. Manage context automatically. Deploy with confidence.
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🎬 See It In Action
import asyncio
from omnicoreagent import OmniCoreAgent, MemoryRouter, ToolRegistry
# Create tools in seconds
tools = ToolRegistry()
@tools.register_tool("get_weather")
def get_weather(city: str) -> dict:
"""Get current weather for a city."""
return {"city": city, "temp": "22°C", "condition": "Sunny"}
# Build a production-ready agent
agent = OmniCoreAgent(
name="assistant",
system_instruction="You are a helpful assistant with access to weather data.",
model_config={"provider": "openai", "model": "gpt-4o"},
local_tools=tools,
memory_router=MemoryRouter("redis"), # Start with Redis
agent_config={
"context_management": {"enabled": True}, # Auto-manage long conversations
"guardrail_config": {"strict_mode": True}, # Block prompt injections
}
)
async def main():
# Run the agent
result = await agent.run("What's the weather in Tokyo?")
print(result["response"])
# Switch to MongoDB at runtime — no restart needed
await agent.switch_memory_store("mongodb")
# Keep running with a different backend
result = await agent.run("How about Paris?")
print(result["response"])
asyncio.run(main())What just happened?
- ✅ Registered a custom tool with type hints
- ✅ Built an agent with memory persistence
- ✅ Enabled automatic context management
- ✅ Switched from Redis to MongoDB *while running*
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⚡ Quick Start
pip install omnicoreagentecho "LLM_API_KEY=your_api_key" > .envfrom omnicoreagent import OmniCoreAgent
agent = OmniCoreAgent(
name="my_agent",
system_instruction="You are a helpful assistant.",
model_config={"provider": "openai", "model": "gpt-4o"}
)
result = await agent.run("Hello!")
print(result["response"])That's it. You have an AI agent with session management, memory, and error handling.
📚 Want to learn more? Check out the Cookbook — progressive examples from "Hello World" to production deployments.
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🎯 What Makes OmniCoreAgent Different?
| Feature | What It Means For You |
|---|---|
| Runtime Backend Switching | Switch Redis ↔ MongoDB ↔ PostgreSQL without restarting |
| Cloud Workspace Storage | Agent files persist in AWS S3 or Cloudflare R2 ⚡ NEW |
| Context Engineering | Session memory + agent loop context + tool offloading = no token exhaustion |
| Tool Response Offloading | Large tool outputs saved to files, 98% token savings |
| Built-in Guardrails | Prompt injection protection out of the box |
| MCP Native | Connect to any MCP server (stdio, SSE, HTTP with OAuth) |
| Background Agents | Schedule autonomous tasks that run on intervals |
| Workflow Orchestration | Sequential, Parallel, and Router agents for complex tasks |
| Production Observability | Metrics, tracing, and event streaming built in |
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🎯 Core Features
📖 Full documentation: docs-omnicoreagent.omnirexfloralabs.com/docs
| # | Feature | Description | Docs |
|---|---|---|---|
| 1 | OmniCoreAgent | The heart of the framework — production agent with all features | Overview → |
| 2 | Multi-Tier Memory | 5 backends (Redis, MongoDB, PostgreSQL, SQLite, in-memory) with runtime switching | Memory → |
| 3 | Context Engineering | Dual-layer system: agent loop context management + tool response offloading | Context → |
| 4 | Event System | Real-time event streaming with runtime switching | Events → |
| 5 | MCP Client | Connect to any MCP server (stdio, streamable_http, SSE) with OAuth | MCP → |
| 6 | DeepAgent | Multi-agent orchestration with automatic task decomposition | DeepAgent → |
| 7 | Local Tools | Register any Python function as an AI tool via ToolRegistry | Local Tools → |
| 8 | Community Tools | 100+ pre-built tools (search, AI, comms, databases, DevOps, finance) | Community Tools → |
| 9 | Agent Skills | Polyglot packaged capabilities (Python, Bash, Node.js) | Skills → |
| 10 | Workspace Memory | Persistent file storage with S3/R2/Local backends | Workspace → |
| 11 | Sub-Agents | Delegate tasks to specialized agents | Sub-Agents → |
| 12 | Background Agents | Schedule autonomous tasks on intervals | Background → |
| 13 | Workflows | Sequential, Parallel, and Router agent orchestration | Workflows → |
| 14 | BM25 Tool Retrieval | Auto-discover relevant tools from 1000+ using BM25 search | Advanced Tools → |
| 15 | Guardrails | Prompt injection protection with configurable sensitivity | Guardrails → |
| 16 | Observability | Per-request metrics + Opik distributed tracing | Observability → |
| 17 | Universal Models | 9 providers via LiteLLM (OpenAI, Anthropic, Gemini, Groq, Ollama, etc.) | Models → |
| 18 | OmniServe | Turn any agent into a production REST/SSE API with one command | OmniServe → |
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📚 Examples & Cookbook
All examples are in the **Cookbook** — organized by use case with progressive learning paths.
| Category | What You'll Build | Location |
|---|---|---|
| Getting Started | Your first agent, tools, memory, events | cookbook/getting_started |
| Workflows | Sequential, Parallel, Router agents | cookbook/workflows |
| Background Agents | Scheduled autonomous tasks | cookbook/background_agents |
| Production | Metrics, guardrails, observability | cookbook/production |
| 🏆 Showcase | Full production applications | cookbook/showcase |
🏆 Showcase: Full Production Applications
| Application | Description | Features |
|---|---|---|
| **OmniAudit** | Healthcare Claims Audit System | Multi-agent pipeline, ERISA compliance |
| **DevOps Copilot** | AI-Powered DevOps Automation | Docker, Prometheus, Grafana |
| **Deep Code Agent** | Code Analysis with Sandbox | Sandbox execution, session management |
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⚙️ Configuration
Environment Variables
# Required
LLM_API_KEY=your_api_key
# Optional: Memory backends
REDIS_URL=redis://localhost:6379/0
DATABASE_URL=postgresql://user:pass@localhost:5432/db
MONGODB_URI=mongodb://localhost:27017/omnicoreagent
# Optional: Observability
OPIK_API_KEY=your_opik_key
OPIK_WORKSPACE=your_workspaceAgent Configuration
agent_config = {
"max_steps": 15, # Max reasoning steps
"tool_call_timeout": 30, # Tool timeout (seconds)
"request_limit": 0, # 0 = unlimited
"total_tokens_limit": 0, # 0 = unlimited
"memory_config": {"mode": "sliding_window", "value": 10000},
"enable_advanced_tool_use": True, # BM25 tool retrieval
"enable_agent_skills": True, # Specialized packaged skills
"memory_tool_backend": "local" # Persistent working memory
}📖 Full configuration reference: Configuration Guide →
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🧪 Testing & Development
# Clone
git clone https://github.com/omnirexflora-labs/omnicoreagent.git
cd omnicoreagent
# Setup
uv venv && source .venv/bin/activate
uv sync --dev
# Test
pytest tests/ -v
pytest tests/ --cov=src --cov-report=term-missing---
🔍 Troubleshooting
| Error | Fix |
|---|---|
Invalid API key | Check .env: LLM_API_KEY=your_key |
ModuleNotFoundError | pip install omnicoreagent |
Redis connection failed | Start Redis or use MemoryRouter("in_memory") |
MCP connection refused | Ensure MCP server is running |
📖 More troubleshooting: Basic Usage Guide →
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📝 Changelog
See the full Changelog → for version history.
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🤝 Contributing
# Fork & clone
git clone https://github.com/omnirexflora-labs/omnicoreagent.git
# Setup
uv venv && source .venv/bin/activate
uv sync --dev
pre-commit install
# Submit PRSee CONTRIBUTING.md for guidelines.
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📄 License
MIT License — see LICENSE
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👨💻 Author & Credits
**Created by Abiola Adeshina**
- GitHub: @Abiorh001
- X (Twitter): @abiorhmangana
- Email: abiolaadedayo1993@gmail.com
🌟 The OmniRexFlora Ecosystem
| Project | Description |
|---|---|
| 🧠 OmniMemory | Self-evolving memory for autonomous agents |
| 🤖 OmniCoreAgent | Production-ready AI agent framework (this project) |
| ⚡ OmniDaemon | Event-driven runtime engine for AI agents |
🙏 Acknowledgments
Built on: LiteLLM, FastAPI, Redis, Opik, Pydantic, APScheduler
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Building the future of production-ready AI agent frameworks
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