llama-index-tools-eydii 0.1.1

EYDII Verify tools for LlamaIndex — verify every agent action before execution

llama-index-tools-eydii

Eydii tools for LlamaIndex -- verify every agent action before execution.

---

Why EYDII?

LlamaIndex agents can query data, call APIs, send emails, write files, and update databases -- all autonomously. But autonomy without oversight is a liability. EYDII sits between your agent's decision and the real-world action, verifying every sensitive operation against your security policies before it executes. If the action violates policy, EYDII blocks it and logs the attempt. If it passes, Eydii returns a cryptographic proof for your audit trail.

---

Install

pip install llama-index-tools-eydii

This installs the EYDII verification tools alongside the core veritera SDK. You will also need a LlamaIndex LLM provider:

pip install llama-index-tools-eydii llama-index-llms-openai

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Prerequisites: Create a Policy

Before using EYDII with LlamaIndex, create a policy that defines what your agent is allowed to do. You only need to do this once:

from veritera import Eydii

eydii = Eydii(api_key="vt_live_...")  # Get your key at id.veritera.ai

# Create a policy from code
eydii.create_policy_sync(
    name="finance-controls",
    description="Controls for document agents with action capabilities",
    rules=[
        {"type": "action_whitelist", "params": {"allowed": ["email.send", "refund.process", "crm.update"]}},
        {"type": "amount_limit", "params": {"max": 10000, "currency": "USD"}},
    ],
)

# Or generate one from plain English
eydii.generate_policy_sync(
    "Allow sending emails, processing refunds under $10,000, and updating CRM records. Block bulk data exports and account deletions.",
    save=True,
)

A default policy is created automatically when you sign up — it blocks dangerous actions like database drops and admin overrides. You can use it immediately with policy="default".

Tip: pip install veritera to get the policy management SDK. See the full policy docs.

---

Quick Start

import os
from llama_index.core.agent import FunctionAgent
from llama_index.core.tools import FunctionTool
from llama_index.llms.openai import OpenAI
from eydii_llamaindex import EydiiVerifyToolSpec

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

# Create EYDII verification tools
eydii = EydiiVerifyToolSpec(policy="finance-controls")  # create this policy first (see above) -- or use "default"
eydii_tools = eydii.to_tool_list()

# Your application tools
def send_payment(amount: float, recipient: str) -> str:
    """Send a payment to a recipient."""
    return f"Sent ${amount} to {recipient}"

app_tools = [FunctionTool.from_defaults(fn=send_payment)]

# Create agent with all tools
agent = FunctionAgent(
    tools=eydii_tools + app_tools,
    llm=OpenAI(model="gpt-4.1"),
    system_prompt=(
        "Before executing any sensitive action, ALWAYS call verify_action first. "
        "Only proceed if the result is APPROVED."
    ),
)

response = await agent.run("Send $500 to [e-mail]")
print(response)

The agent will call verify_action before send_payment. If the amount exceeds your policy threshold, Eydii denies it and the agent explains why it cannot proceed.

---

Tutorial: Building a Verified Document Agent

This walkthrough builds a practical RAG + action agent -- an agent that reads documents AND takes real-world actions (sends emails, updates CRM records), with EYDII ensuring every action is authorized.

Step 1: Define your application tools

These are the tools your agent needs to do its job. Some are read-only (safe), others mutate state (dangerous).

from llama_index.core.tools import FunctionTool


# -- Read-only tools (low risk) --

def search_documents(query: str) -> str:
    """Search the company knowledge base for relevant documents."""
    # In production, this would query a VectorStoreIndex
    return (
        "Policy DOC-2024-118: Refund requests over $1,000 require VP approval. "
        "Requests under $1,000 may be processed by any support agent."
    )


def lookup_customer(customer_id: str) -> str:
    """Look up a customer record by ID."""
    return (
        f"Customer {customer_id}: Acme Corp, tier=enterprise, "
        f"account_manager=[e-mail], balance_due=$4,200"
    )


# -- Write/action tools (high risk -- Eydii must verify these) --

def send_email(to: str, subject: str, body: str) -> str:
    """Send an email to a customer or internal stakeholder."""
    # Production: calls your email service API
    return f"Email sent to {to}: '{subject}'"


def process_refund(customer_id: str, amount: float, reason: str) -> str:
    """Process a refund for a customer."""
    return f"Refund of ${amount:.2f} processed for customer {customer_id}: {reason}"


def update_crm_record(customer_id: str, field: str, value: str) -> str:
    """Update a field on a customer's CRM record."""
    return f"CRM updated: {customer_id}.{field} = {value}"


app_tools = [
    FunctionTool.from_defaults(fn=search_documents),
    FunctionTool.from_defaults(fn=lookup_customer),
    FunctionTool.from_defaults(fn=send_email),
    FunctionTool.from_defaults(fn=process_refund),
    FunctionTool.from_defaults(fn=update_crm_record),
]

Step 2: Add EYDII verification tools

import os
from eydii_llamaindex import EydiiVerifyToolSpec

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

eydii = EydiiVerifyToolSpec(
    agent_id="support-doc-agent",
    policy="customer-support",
)
eydii_tools = eydii.to_tool_list()

This gives the agent three additional tools: verify_action, get_proof, and check_health.

Step 3: Build the agent with a verification-aware system prompt

The system prompt is critical. It tells the agent exactly when and how to use Eydii.

from llama_index.core.agent import FunctionAgent
from llama_index.llms.openai import OpenAI

SYSTEM_PROMPT = """\
You are a customer support agent with access to company documents and customer records.

VERIFICATION RULES -- follow these exactly:
1. Reading documents and looking up customers does NOT require verification.
2. Before calling send_email, process_refund, or update_crm_record, you MUST
   call verify_action first with the action name and a JSON string of the parameters.
3. If verify_action returns APPROVED, proceed with the action.
4. If verify_action returns DENIED, do NOT execute the action. Explain the denial
   to the user and suggest next steps (e.g., escalate to a manager).
5. After completing a sensitive action, note the proof_id for the audit trail.

Example verification call:
  verify_action(action="process_refund", params='{"customer_id": "C-1001", "amount": 750, "reason": "defective product"}')
"""

agent = FunctionAgent(
    tools=eydii_tools + app_tools,
    llm=OpenAI(model="gpt-4.1"),
    system_prompt=SYSTEM_PROMPT,
)

Step 4: Run the agent

import asyncio

async def main():
    # Scenario 1: Small refund -- should be approved
    response = await agent.run(
        "Customer C-1001 (Acme Corp) wants a $400 refund for a defective shipment. "
        "Look up their account, check our refund policy, process the refund, "
        "and email the customer a confirmation."
    )
    print("--- Scenario 1 ---")
    print(response)

    # Scenario 2: Large refund -- should be denied by policy
    response = await agent.run(
        "Process a $5,000 refund for customer C-1001."
    )
    print("\n--- Scenario 2 ---")
    print(response)

asyncio.run(main())

What happens under the hood

Scenario 1 (approved):

1. Agent calls search_documents("refund policy")        --> reads policy (no verification needed)
2. Agent calls lookup_customer("C-1001")                --> reads record (no verification needed)
3. Agent calls verify_action("process_refund", ...)     --> Eydii returns APPROVED + proof_id
4. Agent calls process_refund("C-1001", 400, ...)       --> executes the refund
5. Agent calls verify_action("send_email", ...)         --> Eydii returns APPROVED + proof_id
6. Agent calls send_email("[e-mail]", ...)     --> sends confirmation
7. Agent responds with summary and proof IDs

Scenario 2 (denied):

1. Agent calls verify_action("process_refund", ...)     --> Eydii returns DENIED: "amount exceeds $1,000 limit"
2. Agent does NOT call process_refund
3. Agent responds: "I'm unable to process this refund. The amount exceeds the $1,000
   policy limit. Please escalate to a VP for approval."

---

Two Integration Points

Eydii for LlamaIndex provides two complementary approaches. Use one or both depending on your needs.

1. EydiiVerifyToolSpec -- explicit verification tools

EydiiVerifyToolSpec is a LlamaIndex BaseToolSpec that adds verification tools directly to your agent's toolbox. The agent decides when to call them based on your system prompt.

from eydii_llamaindex import EydiiVerifyToolSpec

spec = EydiiVerifyToolSpec(
    api_key="vt_live_...",           # or set VERITERA_API_KEY env var
    agent_id="my-agent",
    policy="finance-controls",
    fail_closed=True,
)
tools = spec.to_tool_list()

Tools provided:

Tool	Purpose
verify_action(action, params)	Check if an action is allowed by policy before executing it. Returns APPROVED or DENIED with a proof ID.
get_proof(proof_id)	Retrieve the full cryptographic proof record for a previous verification. Use for audits and compliance reporting.
check_health()	Test connectivity to the Eydii service. Useful for startup checks and monitoring dashboards.

When to use: You want the agent to reason about verification explicitly. The agent sees the approval/denial and can adapt its behavior -- explaining denials to users, suggesting alternatives, or noting proof IDs in its response.

2. EydiiEventHandler -- automatic audit trail

EydiiEventHandler hooks into LlamaIndex's instrumentation system to intercept and verify every tool call automatically. No changes to your agent's prompt or tool list required.

from eydii_llamaindex import EydiiEventHandler
import llama_index.core.instrumentation as instrument

handler = EydiiEventHandler(
    api_key="vt_live_...",           # or set VERITERA_API_KEY env var
    agent_id="my-agent",
    policy="finance-controls",
    block_on_deny=True,              # raise ValueError on denied actions
    fail_closed=True,
)

dispatcher = instrument.get_dispatcher()
dispatcher.add_event_handler(handler)

Behavior:

• Every tool call the agent makes fires an instrumentation event.
• EydiiEventHandler intercepts tool call events and sends them to EYDII for verification.
• If block_on_deny=True and Eydii denies the action, a ValueError is raised, preventing execution.
• If block_on_deny=False, denied actions are logged but still execute (audit-only mode).
• All verifications (approved and denied) are recorded in your Eydii audit log.

When to use: You want a safety net that catches everything regardless of what the system prompt says. Useful as a defense-in-depth layer -- even if the agent skips the verify_action call, the event handler still catches and blocks unauthorized actions.

---

Using Both Together

For maximum protection, combine both integration points. The ToolSpec gives the agent awareness of verification (so it can communicate denials gracefully), while the EventHandler acts as a backstop that catches anything the agent misses.

import os
import llama_index.core.instrumentation as instrument
from llama_index.core.agent import FunctionAgent
from llama_index.core.tools import FunctionTool
from llama_index.llms.openai import OpenAI
from eydii_llamaindex import EydiiVerifyToolSpec, EydiiEventHandler

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

# --- Layer 1: ToolSpec (agent-aware verification) ---
eydii_spec = EydiiVerifyToolSpec(
    agent_id="billing-agent",
    policy="billing-controls",
)
eydii_tools = eydii_spec.to_tool_list()

# --- Layer 2: EventHandler (automatic backstop) ---
handler = EydiiEventHandler(
    agent_id="billing-agent",
    policy="billing-controls",
    block_on_deny=True,
)
dispatcher = instrument.get_dispatcher()
dispatcher.add_event_handler(handler)

# --- Application tools ---
def charge_customer(customer_id: str, amount: float) -> str:
    """Charge a customer's payment method."""
    return f"Charged ${amount:.2f} to customer {customer_id}"

def issue_credit(customer_id: str, amount: float) -> str:
    """Issue a credit to a customer's account."""
    return f"Issued ${amount:.2f} credit to customer {customer_id}"

app_tools = [
    FunctionTool.from_defaults(fn=charge_customer),
    FunctionTool.from_defaults(fn=issue_credit),
]

# --- Agent with dual protection ---
agent = FunctionAgent(
    tools=eydii_tools + app_tools,
    llm=OpenAI(model="gpt-4.1"),
    system_prompt=(
        "You are a billing agent. Before any charge or credit, call verify_action. "
        "Only proceed if APPROVED. Report the proof_id in your response."
    ),
)

# Even if the LLM ignores the system prompt and calls charge_customer directly,
# the EydiiEventHandler will intercept and block unauthorized actions.
response = await agent.run("Charge customer C-5021 $12,000")

How the two layers interact:

Scenario	ToolSpec	EventHandler	Result
Agent calls verify_action first, gets APPROVED	Tells agent "approved"	Sees charge_customer call, verifies, allows	Action executes with two verification records
Agent calls verify_action first, gets DENIED	Tells agent "denied"	Never fires (agent stops)	Action blocked gracefully with explanation
Agent skips verify_action, calls tool directly	Not invoked	Intercepts tool call, verifies, blocks if denied	Safety net catches the gap

---

Configuration Reference

EydiiVerifyToolSpec

Parameter	Type	Default	Description
api_key	str	None	EYDII API key. Falls back to VERITERA_API_KEY env var.
base_url	str	https://id.veritera.ai	EYDII API endpoint. Override for self-hosted deployments.
agent_id	str	llamaindex-agent	Identifier for this agent in audit logs. Use a unique name per agent.
policy	str	None	Default policy to evaluate actions against. Can be overridden per call.
fail_closed	bool	True	If True, deny actions when the EYDII API is unreachable. Set to False for fail-open (not recommended for production).
timeout	float	10.0	HTTP timeout in seconds for EYDII API calls.

EydiiEventHandler

Parameter	Type	Default	Description
api_key	str	None	EYDII API key. Falls back to VERITERA_API_KEY env var.
base_url	str	https://id.veritera.ai	EYDII API endpoint. Override for self-hosted deployments.
agent_id	str	llamaindex-agent	Identifier for this agent in audit logs.
policy	str	None	Policy to evaluate actions against.
block_on_deny	bool	True	If True, raise ValueError when an action is denied, preventing execution. Set to False for audit-only mode.
fail_closed	bool	True	If True, block actions when the EYDII API is unreachable.

---

How It Works

  User Request
       |
       v
  +-----------+
  |  LlamaIndex |
  |   Agent     |
  +------+------+
         |
    (1) Agent decides to call send_email(...)
         |
    (2) verify_action("send_email", '{"to": "[e-mail]"}')
         |                                          |
         v                                          |
  +-------------+                                   |
  | EYDII API   |  <-- evaluates against policy     |
  +------+------+                                   |
         |                                          |
    APPROVED + proof_id                             |
         |                                          |
    (3) Agent proceeds with send_email(...)         |
         |                                          |
    (4) EydiiEventHandler intercepts (backup)  <----+
         |
    (5) Action executes
         |
         v
  Audit log: proof_id, timestamp, action, verdict, agent_id

1. The agent receives a user request and plans which tools to call.
2. Following the system prompt, the agent calls verify_action with the action name and parameters.
3. EYDII evaluates the action against your configured policy and returns APPROVED or DENIED with a cryptographic proof ID.
4. If approved, the agent calls the real tool. The EydiiEventHandler (if configured) provides a second verification as a safety net.
5. Every verification is recorded in your Eydii audit log with a tamper-proof proof ID for compliance.

---

Error Handling

EYDII API unreachable

By default, both EydiiVerifyToolSpec and EydiiEventHandler operate in fail-closed mode. If the EYDII API is unreachable, actions are denied:

# ToolSpec returns an error string the agent can read
"ERROR: Verification unavailable -- ConnectionError: ..."

# EventHandler raises ValueError (if block_on_deny=True)
ValueError("Eydii: Action 'send_email' blocked -- verification unavailable.")

To switch to fail-open (not recommended for production):

spec = EydiiVerifyToolSpec(fail_closed=False)
handler = EydiiEventHandler(fail_closed=False, block_on_deny=False)

Invalid JSON in params

If the params argument to verify_action is not valid JSON, the tool gracefully wraps it:

# This still works -- the raw string is sent as {"raw": "some text"}
verify_action(action="email.send", params="not valid json")

Missing API key

A ValueError is raised immediately at initialization if no API key is found:

ValueError("EYDII API key required. Pass api_key= or set VERITERA_API_KEY env var.")

---

Environment Variables

Variable	Required	Description
VERITERA_API_KEY	Yes (unless passed via api_key=)	Your EYDII API key. Get one at veritera.ai/dashboard.
OPENAI_API_KEY	For OpenAI LLM	Required if using llama-index-llms-openai as your LLM provider.

---

LlamaHub

This package follows the llama-index-tools-* naming convention for LlamaIndex community tool integrations. It is compatible with LlamaHub for discovery and can be installed directly from PyPI:

pip install llama-index-tools-eydii

The package registers the EydiiVerifyToolSpec tool spec and EydiiEventHandler instrumentation handler, both importable from eydii_llamaindex:

from eydii_llamaindex import EydiiVerifyToolSpec, EydiiEventHandler

---

Other Eydii Integrations

Eydii provides verification packages for all major agent frameworks:

Framework	Package	Repository
OpenAI Agents SDK	eydii-openai	GitHub
LangGraph	eydii-langgraph	GitHub
CrewAI	eydii-crewai	GitHub
Python SDK	veritera	GitHub
JavaScript SDK	@veritera/sdk	GitHub

Learn more at veritera.ai/docs.

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License

MIT -- Eydii by Veritera AI