Building a Policy-Governed Multi-Agent Financial Research Workflow with Omnigent

In this tutorial, we build and execute a multi-agent workflow with Omnigent using a reliable, isolated Python environment created with uv. We configure a financial research lead agent that retrieves a live USD-to-EUR exchange rate from an external API, prepares a concise client-ready summary, and delegates its draft to a dedicated text-auditing sub-agent for clarity and length validation. We define reusable Python functions as callable agent tools, describe the complete agent structure in YAML, and use the Claude Agent SDK as the execution harness. We also manage the Anthropic API key securely through environment variables, apply non-interactive policies that limit tool calls and control session costs, and run the workflow directly from Colab without requiring Node.js, tmux, or an interactive terminal. Through this implementation, we explore how Omnigent combines agents, tools, delegation, live data access, and governance within a single configurable system. Copy CodeCopiedUse a different Browserimport os, sys, subprocess, textwrap, pathlib, getpass def sh(cmd, **kw): """Run a command, and on failure show the ACTUAL error, not just a code.""" print("$", " ".join(map(str, cmd))) p = subprocess.run(cmd, text=True, capture_output=True, **kw) if p.returncode != 0: print(p.stdout or "", p.stderr or "", sep="\n") raise RuntimeError(f"Command failed ({p.returncode}): {' '.join(map(str, cmd))}") return p WORKDIR = pathlib.Path("/content/omnigent_tutorial") WORKDIR.mkdir(parents=True, exist_ok=True) VENV = WORKDIR / ".venv" subprocess.run([sys.executable, "-m", "pip", "install", "-q", "uv"], check=True) if not (VENV / "bin" / "python").exists(): sh(["uv", "venv", "--python", "3.12", str(VENV)]) PY = str(VENV / "bin" / "python") sh(["uv", "pip", "install", "--python", PY, "-q", "omnigent", "requests"]) OMNI = str(VENV / "bin" / "omnigent") print("\n", subprocess.run([OMNI, "--version"], capture_output=True, text=True).stdout.strip()) We import the required Python modules and define a helper function that executes shell commands while displaying detailed error information when a command fails. We create a dedicated working directory and use uv to build an isolated Python 3.12 virtual environment that avoids Colab’s ensurepip limitation. We then install Omnigent and Requests inside the environment, locate the Omnigent CLI executable, and verify the installation by printing its version. Copy CodeCopiedUse a different Browserif not os.environ.get("ANTHROPIC_API_KEY"): os.environ["ANTHROPIC_API_KEY"] = getpass.getpass("Anthropic API key: ") env = os.environ.copy() env["OMNIGENT_NO_UPDATE_CHECK"] = "1" We securely collect the Anthropic API key only when it is not already available in the notebook environment. We store the credential in the current process environment so that Omnigent can detect it without writing sensitive information to a file. We also create a separate environment configuration for the subprocess and turn off Omnigent’s automatic update check during execution. Copy CodeCopiedUse a different Browser(WORKDIR / "agent_tools.py").write_text(textwrap.dedent(''' """Local tools exposed to the Omnigent agents in this tutorial.""" import requests def get_exchange_rate(base_currency: str, target_currency: str) -> dict: """Look up the latest FX rate between two ISO-4217 currency codes.""" r = requests.get( "https://api.frankfurter.app/latest", params={"from": base_currency.upper(), "to": target_currency.upper()}, timeout=10, ) r.raise_for_status() data = r.json() return { "base": base_currency.upper(), "target": target_currency.upper(), "rate": data["rates"][target_currency.upper()], "date": data["date"], } def word_count(text: str) -> int: """Count the words in a piece of text.""" return len(text.split()) ''')) We generate a Pyth