Give your agent real work. Keep control.
Every action checked before it runs. Every run survives a crash without silently redoing anything. Every step on the record. An agent that does real work (writes code, runs commands, changes systems, calls APIs) needs more around it than a loop and tools. OmniCoreAgent is an agent runtime for Python. It includes the harness, the loop, tools, context, workspace and sub-agents around the model, and adds what running an agent for real needs: a policy on every action, a sandbox as the boundary for its commands, runs that survive a crash, budgets, and a record of every step. You use it from Python, from the command line, or as an HTTP server: one agent object, from a first script to a governed background worker, and onto a benchmark. How it fits together walks one run through every layer.It stays in bounds
Before each action, the policy decides: allow it, ask a person, or
refuse it, and names the rule; the policy is on by default. Budgets on
tokens, cost, model calls and tool calls, once you set them, are checked
before a call is made, not added up after.
It picks up where it stopped
A run pauses for a person or a top-up and continues where it stopped; with
a durable store, even after a crash, in another process. It never silently
redoes an action it already started.
It's all on the record
Every step is kept: what the model saw, what it did, what the policy
decided and who approved, what it cost, and outcomes that arrive later, to
review, evaluate and train on.
Start in three steps
1
Install
python --version. On 3.10 or
3.11, the install stops and says so.2
Set your model key
3
Build something
Quickstart — 5 minutes
An agent, a tool, a memory, and the evidence of its run.
Tour — 15 minutes
A policy that asks a person, a sandbox, a budget.
See it
This agent is governed by default: the
permissive-dev policy allowed its
tool, and would have refused reading raw secrets, shell commands on the host,
unrestricted network and package installs. There is no budget until you set
one. The guardrail, privacy redaction and the trace are on too; see
the defaults.Find your way
- Build
- Make it safe
- Run it
- See and improve
Tools
Your Python functions as tools, in parallel batches.
MCP servers
stdio, SSE and streamable HTTP, with OAuth.
Code mode
The model writes a program that calls your tools.
Skills
Packaged instructions and scripts the agent can use.
Memory
Sessions in memory, Redis, Postgres or MongoDB.
Workspace files
Files for notes and artifacts, local, S3 or R2.
Context
Long tasks without running out of context.
Sub-agents
Workers the lead agent spawns and reads back.
Models
OpenAI, Anthropic, Gemini, Groq, DeepSeek, Ollama and more.
Events and streaming
Follow a run as it happens.
Using an AI coding agent?
Use these docs with AI tools
Ask AI,
llms.txt, copy as Markdown, the docs MCP server, Cursor and VS Code.AGENTS.md
A map of the repository for coding agents: what lives where, and how to work in it.