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    Omnicoreagent

    OmniCoreAgent is the complete AI development platform that combines two powerful systems into one revolutionary ecosystem.

    192 stars
    Python
    Updated Nov 3, 2025
    agent
    fastapi
    mcp
    mcp-client
    multi-agent-systems
    orchestration

    Table of Contents

    • 🎬 See It In Action
    • ⚡ Quick Start
    • 🎯 What Makes OmniCoreAgent Different?
    • 🎯 Core Features
    • 📚 Examples & Cookbook
    • 🏆 Showcase: Full Production Applications
    • ⚙️ Configuration
    • Environment Variables
    • Agent Configuration
    • 🧪 Testing & Development
    • 🔍 Troubleshooting
    • 📝 Changelog
    • 🤝 Contributing
    • 📄 License
    • 👨‍💻 Author & Credits
    • 🌟 The OmniRexFlora Ecosystem
    • 🙏 Acknowledgments

    Table of Contents

    • 🎬 See It In Action
    • ⚡ Quick Start
    • 🎯 What Makes OmniCoreAgent Different?
    • 🎯 Core Features
    • 📚 Examples & Cookbook
    • 🏆 Showcase: Full Production Applications
    • ⚙️ Configuration
    • Environment Variables
    • Agent Configuration
    • 🧪 Testing & Development
    • 🔍 Troubleshooting
    • 📝 Changelog
    • 🤝 Contributing
    • 📄 License
    • 👨‍💻 Author & Credits
    • 🌟 The OmniRexFlora Ecosystem
    • 🙏 Acknowledgments

    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

    python
    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*

    ---

    ⚡ Quick Start

    bash
    pip install omnicoreagent
    bash
    echo "LLM_API_KEY=your_api_key" > .env
    python
    from 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.

    ---

    🎯 What Makes OmniCoreAgent Different?

    FeatureWhat It Means For You
    Runtime Backend SwitchingSwitch Redis ↔ MongoDB ↔ PostgreSQL without restarting
    Cloud Workspace StorageAgent files persist in AWS S3 or Cloudflare R2 ⚡ NEW
    Context EngineeringSession memory + agent loop context + tool offloading = no token exhaustion
    Tool Response OffloadingLarge tool outputs saved to files, 98% token savings
    Built-in GuardrailsPrompt injection protection out of the box
    MCP NativeConnect to any MCP server (stdio, SSE, HTTP with OAuth)
    Background AgentsSchedule autonomous tasks that run on intervals
    Workflow OrchestrationSequential, Parallel, and Router agents for complex tasks
    Production ObservabilityMetrics, tracing, and event streaming built in

    ---

    🎯 Core Features

    📖 Full documentation: docs-omnicoreagent.omnirexfloralabs.com/docs

    #FeatureDescriptionDocs
    1OmniCoreAgentThe heart of the framework — production agent with all featuresOverview →
    2Multi-Tier Memory5 backends (Redis, MongoDB, PostgreSQL, SQLite, in-memory) with runtime switchingMemory →
    3Context EngineeringDual-layer system: agent loop context management + tool response offloadingContext →
    4Event SystemReal-time event streaming with runtime switchingEvents →
    5MCP ClientConnect to any MCP server (stdio, streamable_http, SSE) with OAuthMCP →
    6DeepAgentMulti-agent orchestration with automatic task decompositionDeepAgent →
    7Local ToolsRegister any Python function as an AI tool via ToolRegistryLocal Tools →
    8Community Tools100+ pre-built tools (search, AI, comms, databases, DevOps, finance)Community Tools →
    9Agent SkillsPolyglot packaged capabilities (Python, Bash, Node.js)Skills →
    10Workspace MemoryPersistent file storage with S3/R2/Local backendsWorkspace →
    11Sub-AgentsDelegate tasks to specialized agentsSub-Agents →
    12Background AgentsSchedule autonomous tasks on intervalsBackground →
    13WorkflowsSequential, Parallel, and Router agent orchestrationWorkflows →
    14BM25 Tool RetrievalAuto-discover relevant tools from 1000+ using BM25 searchAdvanced Tools →
    15GuardrailsPrompt injection protection with configurable sensitivityGuardrails →
    16ObservabilityPer-request metrics + Opik distributed tracingObservability →
    17Universal Models9 providers via LiteLLM (OpenAI, Anthropic, Gemini, Groq, Ollama, etc.)Models →
    18OmniServeTurn any agent into a production REST/SSE API with one commandOmniServe →

    ---

    📚 Examples & Cookbook

    All examples are in the **Cookbook** — organized by use case with progressive learning paths.

    CategoryWhat You'll BuildLocation
    Getting StartedYour first agent, tools, memory, eventscookbook/getting_started
    WorkflowsSequential, Parallel, Router agentscookbook/workflows
    Background AgentsScheduled autonomous taskscookbook/background_agents
    ProductionMetrics, guardrails, observabilitycookbook/production
    🏆 ShowcaseFull production applicationscookbook/showcase

    🏆 Showcase: Full Production Applications

    ApplicationDescriptionFeatures
    **OmniAudit**Healthcare Claims Audit SystemMulti-agent pipeline, ERISA compliance
    **DevOps Copilot**AI-Powered DevOps AutomationDocker, Prometheus, Grafana
    **Deep Code Agent**Code Analysis with SandboxSandbox execution, session management

    ---

    ⚙️ Configuration

    Environment Variables

    bash
    # 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_workspace

    Agent Configuration

    python
    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 →

    ---

    🧪 Testing & Development

    bash
    # 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

    ErrorFix
    Invalid API keyCheck .env: LLM_API_KEY=your_key
    ModuleNotFoundErrorpip install omnicoreagent
    Redis connection failedStart Redis or use MemoryRouter("in_memory")
    MCP connection refusedEnsure MCP server is running

    📖 More troubleshooting: Basic Usage Guide →

    ---

    📝 Changelog

    See the full Changelog → for version history.

    ---

    🤝 Contributing

    bash
    # 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 PR

    See CONTRIBUTING.md for guidelines.

    ---

    📄 License

    MIT License — see LICENSE

    ---

    👨‍💻 Author & Credits

    **Created by Abiola Adeshina**

    • GitHub: @Abiorh001
    • X (Twitter): @abiorhmangana
    • Email: abiolaadedayo1993@gmail.com

    🌟 The OmniRexFlora Ecosystem

    ProjectDescription
    🧠 OmniMemorySelf-evolving memory for autonomous agents
    🤖 OmniCoreAgentProduction-ready AI agent framework (this project)
    ⚡ OmniDaemonEvent-driven runtime engine for AI agents

    🙏 Acknowledgments

    Built on: LiteLLM, FastAPI, Redis, Opik, Pydantic, APScheduler

    ---

    Building the future of production-ready AI agent frameworks

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