Multi-Agent Orchestration in Plain Python

from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict

class State(TypedDict):
    text: str

def planner(state): return {"text": "planned"}
def writer(state): return {"text": "written"}
def reviewer(state): return {"text": "reviewed"}

g = StateGraph(State)
g.add_node("planner", planner)
g.add_node("writer", writer)
g.add_node("reviewer", reviewer)
g.add_edge(START, "planner")
g.add_edge("planner", "writer")
g.add_edge("writer", "reviewer")
g.add_edge("reviewer", END)
app = g.compile()
result = app.invoke({"text": "prompt"})

from selectools import Agent, AgentConfig, tool
from selectools.providers import OpenAIProvider
from selectools.guardrails import GuardrailsPipeline, PIIGuardrail
from selectools.audit import AuditLogger, PrivacyLevel

@tool
def search_docs(query: str) -> str:
    """Search the knowledge base."""
    return db.search(query)

agent = Agent(
    provider=OpenAIProvider(),
    tools=[search_docs],
    config=AgentConfig(
        model="gpt-4.1-mini",
        guardrails=GuardrailsPipeline(
            input=[PIIGuardrail(action="redact")],
        ),
        screen_tool_output=True,
        coherence_check=True,
        audit=AuditLogger(
            path="./audit",
            privacy=PrivacyLevel.HASH,
        ),
    ),
)

result = agent.run("Find our refund policy")

from selectools.evals import EvalSuite, TestCase

suite = EvalSuite(agent=agent, cases=[
    TestCase(
        input="Cancel my subscription",
        expect_tool="cancel_sub",
        expect_contains="cancelled",
        expect_no_pii=True,
    ),
    TestCase(
        input="What's my balance?",
        expect_tool="check_balance",
        expect_latency_ms_lte=500,
        expect_cost_usd_lte=0.01,
    ),
])

report = suite.run()
print(report.accuracy)     # 1.0
print(report.latency_p50)  # 142ms
print(report.total_cost)   # $0.002

report.to_html("report.html")