OmniCoreAgent

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

What It Is

An LLM is not an agent by itself. The model provides intelligence; the harness gives that intelligence a working environment.

OmniCoreAgent is the application-facing harness layer around a model:

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

That is the difference between an agent harness and a generic agent library. A library gives you pieces to assemble. A harness gives you the runtime boundary that makes a model usable inside an application.

OmniCoreAgent keeps that boundary explicit:

Start with the core harness. Turn on heavier production pieces only when the workload needs them.

If you prefer guided docs, start with the Quick Start. If you use AI coding tools, use the AI tools guide for Ask AI, /llms.txt, hosted docs MCP, Cursor, VS Code, ChatGPT, Claude, and Perplexity.

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.",
        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? The Cookbook has progressive examples from hello world to production deployments.

Choose Your Path

What You Can Build

OmniCoreAgent is for application builders who need the agent runtime to hold together after the prototype works.

The core idea is simple: one harness entry point, many application membranes. You bring the domain instructions, tools, and business logic. OmniCoreAgent provides the execution boundary around them.

Why It Matters

Most demos stop at "LLM plus tool loop." Production agents fail in the layer around that loop: slow sequential tool calls, noisy observations, repeated actions, context exhaustion, unsafe tool output, missing workspace state, uninspectable background work, and weak serving boundaries.

OmniCoreAgent exists for that layer.

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 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.

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("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 accepts search_web and fetch_document 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 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.

Features

Core Runtime

Production Harness

Integrations

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

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