crewai-forge 0.1.0

Forge Verify tools and guardrails for CrewAI — verify every agent action before execution

crewai-forge

Forge Verify tools and guardrails for CrewAI. Verify every AI agent action against your policies before execution.

Install

pip install crewai-forge

Quick Start

import os
from crewai import Agent, Task, Crew
from forge_crewai import ForgeVerifyTool, forge_task_guardrail

os.environ["VERITERA_API_KEY"] = "vt_live_..."

# Create a Forge verification tool
verify = ForgeVerifyTool(policy="finance-controls")

# Give it to your agent
analyst = Agent(
    role="Financial Analyst",
    goal="Process financial transactions safely",
    tools=[verify],
)

# Add a task guardrail for output validation
task = Task(
    description="Process the refund for order #12345",
    agent=analyst,
    guardrail=forge_task_guardrail(policy="finance-controls"),
    guardrail_max_retries=3,
)

crew = Crew(agents=[analyst], tasks=[task])
result = crew.kickoff()

Three Integration Points

1. Agent Tool — verify before acting

from forge_crewai import ForgeVerifyTool

tool = ForgeVerifyTool(policy="finance-controls")
agent = Agent(role="Analyst", tools=[tool])

The agent calls forge_verify(action="payment.create", params='{"amount": 500}') before executing sensitive actions.

2. Task Guardrail — validate output

from forge_crewai import forge_task_guardrail

task = Task(
    description="Draft customer email",
    agent=support_agent,
    guardrail=forge_task_guardrail(policy="communication-policy"),
    guardrail_max_retries=3,
)

If the output violates your policy, CrewAI automatically retries the task.

3. LLM Hooks — intercept at the model level

from forge_crewai import forge_before_llm, forge_after_llm

# Block LLM calls that violate policy
forge_before_llm(policy="safety-controls", max_iterations=10)

# Audit all LLM responses
forge_after_llm(policy="audit-trail")

License

MIT — Veritera AI