MCPOmni Connect

A universal command-line interface (CLI) gateway to the MCP ecosystem, integrating multiple MCP servers, AI models, and transport protocols.

The Open Production Agent Harness for Python
Parallel tool batches, structured observations, signature loop detection, MCP tools, memory, workspace files, subagents, background tasks, and REST/SSE serving.

What It Is-Quick Start-Choose Your Path-Use Cases-Why It Matters-Install-Cookbook-Features-Docs-Ask AI

model + prompt contract + reasoning loop + local tools + MCP tools + parallel tool batches + structured observations + memory + context control + workspace files + tool-output offloading + guardrails + events + subagents + background tasks + REST/SSE serving

If you prefer guided docs, start with theQuick Start. If you use AI coding tools, use theAI tools guidefor Ask AI,/llms.txt, hosted docs MCP, Cursor, VS Code, ChatGPT, Claude, and Perplexity.

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.", session_id="quickstart", ) print(result["response"]) await agent.cleanup() asyncio.run(main())

That is the smallest path: one agent, one model, one stable session, 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? TheCookbookhas progressive examples from hello world to production deployments.

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

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_stepsis 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."

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

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

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, telemetry events, 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.

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("fetch_document") def fetch_document(path: str) -> dict: """Fetch a domain document from an application-owned source.""" 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 fetch notes.md. Do both at once " "if neither depends on the other." ) print(result["response"]) await agent.cleanup() asyncio.run(main())

The runtime acceptssearch_webandfetch_documentin the same batch, returns both results together, and continues from one structured observation.

pip install omnicoreagent # Core runtime pip install "omnicoreagent[redis]" # Redis memory backend 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[tokenizer]" # Token-aware context budgeting pip install "omnicoreagent[otel]" # OTLP trace export pip install "omnicoreagent[langsmith]" # LangSmith trace export pip install "omnicoreagent[opik]" # Comet Opik trace export pip install "omnicoreagent[all]" # Everything

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

OmniCoreAgent's capabilities are backed by concrete runtime modules:

See theAgent Harness docsfor the full implementation map.

All examples live in theCookbookand are organized by use case.

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 }

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

git clone https://github.com/omnirexflora-labs/omnicoreagent.git cd omnicoreagent uv venv && source .venv/bin/activate uv sync --dev pre-commit install

SeeCONTRIBUTING.mdfor guidelines. PRs are welcome.

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

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his repository contains a fully functional MCP (Model Context Protocol) server, providing solutions for Constraint Satisfaction Problems (CSP) and Linear Programming (LP). It is based on the gurddy package and supports solving a variety of classic problems.