Mcp_omni_connect

<p align="center"> <img src="assets/IMG_5292.jpeg" alt="OmniCoreAgent Logo" width="250"/> </p> <h1 align="center">OmniCoreAgent</h1> <p align="center"> <strong>Open Python Agent Harness and Runtime for Production AI Agents</strong><br /> <em>Everything around the model: parallel tool batches, structured observations, loop detection, memory, workspace files, MCP tools, subagents, background tasks, and serving.</em> </p> <p align="center"> <a href="https://pepy.tech/projects/omnicoreagent"><img src="https://static.pepy.tech/badge/omnicoreagent" alt="PyPI Downloads"></a> <a href="https://badge.fury.io/py/omnicoreagent"><img src="https://badge.fury.io/py/omnicoreagent.svg" alt="PyPI version"></a> <a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/python-3.10+-blue.svg" alt="Python Version"></a> <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-green.svg" alt="License"></a> </p> <p align="center"> <a href="#quick-start">Quick Start</a> - <a href="#choose-your-path">Choose Your Path</a> - <a href="#what-makes-it-different">What Makes It Different</a> - <a href="#install-only-what-you-need">Install</a> - <a href="./cookbook">Cookbook</a> - <a href="#features">Features</a> - <a href="https://docs-omnicoreagent.omnirexfloralabs.com/docs">Docs</a> </p>

---

Quick Start

pip install omnicoreagent

export LLM_API_KEY=your_api_key

import asyncio
from omnicoreagent import OmniCoreAgent

agent = OmniCoreAgent(
    name="assistant",
    system_instruction="You are a helpful assistant.",
    model_config={"provider": "openai", "model": "gpt-4o"},
)

async def main():
    result = await agent.run("Research the top 3 open-source agent runtimes and summarize them.")
    print(result["response"])
    await agent.cleanup()

asyncio.run(main())

That is the smallest path: one agent, one model, the harness loop, session memory, guardrails, workspace files, error handling, and metrics around each run.

Context management, tool output offloading, BM25 tool retrieval, subagents, skills, cloud workspace storage, and production backends are opt-in so a small agent stays small.

Ready to go deeper? The Cookbook has progressive examples from hello world to production deployments.

---

Choose Your Path

---

What Makes It Different

Most agent libraries stop at "LLM plus tool loop." OmniCoreAgent is built around the runtime problems that show up after that: slow sequential tools, noisy observations, stuck loops, context exhaustion, MCP server tools, durable workspace files, and runtime serving.

1. Agents call tools in batches instead of forced sequences

The usual tool loop looks like this:

LLM -> call tool A -> wait -> result -> LLM -> call tool B -> wait -> result

OmniCoreAgent lets the model request independent tools together:

LLM -> [tool A + tool B + tool C in parallel] -> one structured observation -> LLM

The model gets one complete view of the batch before it reasons again. A failed tool is represented beside the successful tools instead of silently collapsing the whole step.

Native function calling alone is not the runtime. OmniCoreAgent uses its own tool-call contract, parser, resolver, parallel runner, and result formatter so the harness controls the full execution path.

2. Tool results become structured observations

Raw tool output is often too noisy for the next reasoning step. Large payloads, errors, irrelevant fields, and prompt-injection content can all distort the loop.

OmniCoreAgent routes tool results through an observation pipeline:

tool output -> parse -> format -> guardrail check -> offload when configured -> observation -> model

The model receives the signal it needs to continue the task, not an unbounded dump of every byte returned by a tool. When tool offloading is enabled, large outputs are written into the active workspace and the model receives a readable preview plus a path it can use later.

3. Loop detection uses signatures beyond step counts

max_steps is still useful, but it is a blunt instrument. It stops an agent that is making progress just as quickly as one that is stuck.

OmniCoreAgent tracks SHA256-backed tool-call signatures across the loop. Each signature is based on the tool name, input, and output for the call. The runtime detects:

• Consecutive loops: the same tool call returns the same result repeatedly.
• Pattern loops: the same tool repeats a small interaction pattern.

When the harness stops a loop, the agent gets a reason. That makes debugging the agent behavior much easier than "max iterations reached."

4. The harness is already assembled

OmniCoreAgent ships as a working agent harness, not a bag of disconnected pieces:

model + prompt + loop + tools + memory + context + workspace + guardrails + events

Keep it small for simple agents, then turn on the heavier harness pieces when the workload needs them: MCP tools, BM25 tool retrieval, dynamic subagents, skills, cloud workspace storage, Redis/Postgres/MongoDB memory, event streams, and OmniServe.

5. Context is managed before the model call

When context management is enabled, OmniCoreAgent checks the active message history before every LLM request. If the configured threshold is crossed, the harness automatically applies the selected strategy before calling the model:

messages -> threshold check -> truncate or summarize+truncate -> LLM

The system prompt is preserved, recent messages are preserved, and older middle history is either summarized or removed depending on configuration. If you set the budget below your model's real context window, the harness acts before the provider rejects the request.

---

See It In Action

import asyncio
from omnicoreagent import MemoryRouter, OmniCoreAgent, ToolRegistry

tools = ToolRegistry()

@tools.register_tool("search_web")
def search_web(query: str) -> dict:
    """Search the web for information."""
    return {"results": [f"Result for: {query}"]}

@tools.register_tool("read_file")
def read_file(path: str) -> dict:
    """Read a local project file."""
    return {"path": path, "content": f"Contents of {path}"}

agent = OmniCoreAgent(
    name="research-agent",
    system_instruction=(
        "You are a research assistant. Use tools in parallel when the calls are "
        "independent and you can reason over the results together."
    ),
    model_config={"provider": "openai", "model": "gpt-4o"},
    local_tools=tools,
    memory_router=MemoryRouter("in_memory"),
    agent_config={
        "max_steps": 20,
        "context_management": {"enabled": True},
        "tool_offload": {"enabled": True},
        "enable_subagents": True,
        "enable_advanced_tool_use": True,
    },
)

async def main():
    result = await agent.run(
        "Search for recent AI agent papers and read notes.md. Do both at once "
        "if neither depends on the other."
    )
    print(result["response"])
    await agent.cleanup()

asyncio.run(main())

The runtime accepts search_web and read_file in the same batch, returns both results together, and continues from one structured observation.

---

Install Only What You Need

pip install omnicoreagent                    # Core runtime
pip install "omnicoreagent[redis]"           # Redis memory + event streams
pip install "omnicoreagent[postgres]"        # PostgreSQL / SQL memory
pip install "omnicoreagent[mongodb]"         # MongoDB memory
pip install "omnicoreagent[s3]"              # S3 / R2 workspace storage
pip install "omnicoreagent[serve]"           # OmniServe REST/SSE API
pip install "omnicoreagent[all]"             # Everything

Production backends are installable extras. Install only what the agent actually uses.

---

Features

---

Implementation Map

OmniCoreAgent's capabilities are backed by concrete runtime modules:

See the Agent Harness docs for the full implementation map.

---

Cookbook

All examples live in the Cookbook and are organized by use case.

---

Configuration

Environment Variables

For the first run, most hosted model providers only need LLM_API_KEY. OmniCoreAgent defaults memory and events to in-memory storage, workspace files to local disk, and optional production integrations stay off until you configure them.

export LLM_API_KEY=your_api_key

Add backend-specific variables only when you opt into Redis, MongoDB, SQL database storage, S3, R2, or OmniServe deployment settings.

Full Harness Config Example

The defaults keep the first agent small: workspace files and guardrails are on, conversation memory is in-memory, and advanced harness pieces stay off until you enable them. This example shows the production-style switches together.

agent_config = {
    "max_steps": 15,
    "tool_call_timeout": 30,
    "request_limit": 0,                  # 0 = unlimited
    "total_tokens_limit": 0,             # 0 = unlimited
    "memory_config": {
        "mode": "sliding_window",
        "value": 10000,
        "summary": {"enabled": False},
    },
    "enable_workspace_files": True,      # Default on
    "guardrail_mode": "full",            # Default
    "context_management": {"enabled": True},  # Default off
    "tool_offload": {"enabled": True},        # Default off
    "enable_advanced_tool_use": True,         # Default off
    "enable_subagents": True,                 # Default off
    "enable_agent_skills": True,              # Default off
}

When enable_subagents is true, workspace files are enabled automatically so subagents write outputs, notes, todos, and artifacts into the active workspace.

Full reference: Configuration Guide

---

Development

git clone https://github.com/omnirexflora-labs/omnicoreagent.git
cd omnicoreagent

uv venv && source .venv/bin/activate
uv sync --dev

pytest tests/ -v
pytest tests/ --cov=src --cov-report=term-missing

---

Troubleshooting

More help: Basic Usage Guide

---

Contributing

git clone https://github.com/omnirexflora-labs/omnicoreagent.git
cd omnicoreagent

uv venv && source .venv/bin/activate
uv sync --dev
pre-commit install

See CONTRIBUTING.md for guidelines. PRs are welcome.

---

License

MIT - see LICENSE.

---

Author

Built by Abiola Adeshina.

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

The OmniRexFlora Ecosystem

Built On

LiteLLM - FastAPI - Redis - Pydantic

---

<p align="center"> <a href="https://github.com/omnirexflora-labs/omnicoreagent">Star on GitHub</a> - <a href="https://github.com/omnirexflora-labs/omnicoreagent/issues">Report Bug</a> - <a href="https://github.com/omnirexflora-labs/omnicoreagent/issues">Request Feature</a> - <a href="https://docs-omnicoreagent.omnirexfloralabs.com/docs">Documentation</a> </p>