Files
agentforge/tests/unit/test_runtime_graph.py
T

129 lines
4.3 KiB
Python

"""Tests del grafo compilado: camino feliz con MockProvider y pausa HITL."""
from datetime import UTC, datetime
from pathlib import Path
from uuid import uuid4
from agentforge_core.domain.agent import AgentDefinition, LLMConfig
from agentforge_core.domain.policy import PolicyDefinition
from agentforge_core.guardrails.guardrails_ai import GuardrailsAIEngine
from agentforge_core.llm.mock import MockProvider
from agentforge_core.runtime.checkpointer import build_checkpointer
from agentforge_core.runtime.graph import build_graph
def _agent() -> AgentDefinition:
return AgentDefinition(
name="incident_analyzer",
version="v1",
owner="Juan",
purpose="Análisis de incidentes",
state="active",
guardrails=["default"],
llm=LLMConfig(provider="mock"),
system_prompt="Eres un analista. Responde SIEMPRE con JSON.",
output_schema={"type": "object"},
risk_threshold_for_hitl=4,
updated_at=datetime.now(UTC),
)
def _policy_min() -> PolicyDefinition:
return PolicyDefinition(
name="min",
version="v1",
description="t",
input_validators=[],
output_validators=[],
)
def _initial_state(trace_id: str, agent: AgentDefinition, user_input: str) -> dict:
return {
"trace_id": trace_id,
"agent_name": agent.name,
"agent_version": agent.version,
"user_input": user_input,
"messages": [],
"raw_llm_output": None,
"parsed_output": None,
"proposed_actions": [],
"violations": [],
"decision_path": [],
"status": "running",
"error": None,
"human_decision": None,
"final_output": None,
}
async def test_grafo_completa_camino_feliz_sin_hitl(tmp_path: Path) -> None:
agent = _agent()
trace_id = str(uuid4())
config = {"configurable": {"thread_id": trace_id}}
async with build_checkpointer(tmp_path) as cp:
graph = build_graph(
agent_def=agent,
policy=_policy_min(),
provider=MockProvider(),
engine=GuardrailsAIEngine(),
checkpointer=cp,
)
final = await graph.ainvoke(
_initial_state(trace_id, agent, "degradación MOS pool SBC"), # risk=2 → no HITL
config=config,
)
assert final["status"] == "completed"
assert final["final_output"]["approved_actions"] # propagó la acción no riesgosa
async def test_grafo_pausa_en_hitl_si_riesgo_alto(tmp_path: Path) -> None:
agent = _agent()
trace_id = str(uuid4())
config = {"configurable": {"thread_id": trace_id}}
async with build_checkpointer(tmp_path) as cp:
graph = build_graph(
agent_def=agent,
policy=_policy_min(),
provider=MockProvider(),
engine=GuardrailsAIEngine(),
checkpointer=cp,
)
await graph.ainvoke(
_initial_state(trace_id, agent, "caída registros sip"), # act-1 risk=4 → HITL
config=config,
)
# En interrupt, ainvoke devuelve el snapshot; el grafo queda con un paso pendiente.
state = await graph.aget_state(config)
assert state.next # interrumpido en approve_gate, esperando resume()
assert state.values["status"] == "running"
async def test_grafo_bloquea_por_guardrail_de_entrada(tmp_path: Path) -> None:
agent = _agent()
policy = PolicyDefinition(
name="block-pii",
version="v1",
description="bloquea emails en la entrada",
input_validators=[
{"type": "detect_pii", "config": {"entities": ["EMAIL_ADDRESS"], "severity_on_match": "block"}}
],
output_validators=[],
)
trace_id = str(uuid4())
config = {"configurable": {"thread_id": trace_id}}
async with build_checkpointer(tmp_path) as cp:
graph = build_graph(
agent_def=agent,
policy=policy,
provider=MockProvider(),
engine=GuardrailsAIEngine(),
checkpointer=cp,
)
final = await graph.ainvoke(
_initial_state(trace_id, agent, "manda correo a juan@example.com"),
config=config,
)
assert final["status"] == "blocked_by_guardrail"
assert final["raw_llm_output"] is None # nunca llegó al LLM