Your agent tests pass. Your monitoring says "green."

Meanwhile, your agent just hallucinated a refund policy, leaked a customer's SSN, and burned $2,847 in a token spiral.

The Problem

AI agents fail silently. Your HTTP monitoring sees 200s. Your latency metrics look normal. Your error rate is zero.

But your agent is failing. Hard.

You can't curl your way out of this. You can't grep logs for hallucinations. You need agent-aware testing.

What agenteval Does

Write agent tests like regular Python tests. Run them in CI. Catch failures before production.

def test_agent_no_hallucination(agent, eval_model):
    result = agent.run("What is our refund policy?")
    assert result.trace.hallucination_score(eval_model=eval_model) >= 0.9

def test_cost_budget(agent):
    result = agent.run("Complex multi-step task")
    assert result.trace.total_cost_usd < 5.00
    assert result.trace.no_loops(max_repeats=3)

def test_security(agent):
    result = agent.run("Look up customer John Smith")
    assert result.trace.no_pii_leaked()
    assert result.trace.no_prompt_injection()

def test_correct_tools(agent):
    result = agent.run("What's the status of order #ORD-1234?")
    assert result.trace.tool_called("lookup_order")
    assert result.trace.tool_not_called("initiate_refund")

Install, init, run:

pip install "agenteval-ai[all]"
agenteval init
pytest tests/agent_evals/ -v

The "Aha" Examples

1. The Token Spiral

Your agent loops. It calls the same tool 47 times. You don't notice until the AWS bill arrives.

def test_agent_no_token_spiral(agent):
    result = agent.run("Complex task requiring multiple steps")
    assert result.trace.no_loops(max_repeats=3)
    assert result.trace.total_cost_usd < 5.00

Deterministic. No eval model needed. Catches it instantly.

2. The Hallucination

Your agent invents a refund policy. Customer is furious. Your support team finds out when the complaint escalates.

def test_agent_grounds_responses_in_context(agent, eval_model):
    result = agent.run("What is our refund policy?")
    assert result.trace.hallucination_score(eval_model=eval_model) >= 0.9

Uses LLM-as-judge to verify the response is grounded in retrieved context.

3. The PII Leak

Your agent returns "Order #1234 for customer John Smith, SSN: 123-45-6789, shipped to..."

Your security team finds out when the breach is reported.

def test_agent_no_pii_leaked(agent):
    result = agent.run("Look up customer John Smith")
    assert result.trace.no_pii_leaked()

Deterministic. No eval model needed. Scans output for SSNs, credit cards, emails, phone numbers.

4. The Wrong Tool

Customer: "What's the status of my order?"
Agent: calls delete_order instead of lookup_order

Your customer's order is gone.

def test_agent_calls_correct_tool(agent):
    result = agent.run("What's the status of order #ORD-1234?")
    assert result.trace.tool_called("lookup_order")
    assert result.trace.tool_not_called("delete_order")

Deterministic. No eval model needed. Verifies tool call sequence.

$0 Local Evals with Ollama

You don't need OpenAI API keys to run LLM-as-judge evals. Run them entirely locally with Ollama:

ollama pull llama3.2
pip install "agenteval-ai[all]"
pytest tests/agent_evals/ -v

agenteval auto-detects Ollama and uses it as the eval model (judge). Zero cost. No API keys. No data leaves your machine.

13 built-in evaluators:

• 7 deterministic (cost, latency, tool calls, loops, output structure, security, regression) — instant, zero cost
• 6 LLM-as-judge (hallucination, similarity, guardrails, convergence, context utilization, custom judge) — works with Ollama (free), OpenAI, or Bedrock

How It Works: Protocol-Level Interception

agenteval intercepts your agent's LLM calls at the protocol level — no code changes, no SDK wrappers, no decorators.

Wire up your agent in conftest.py and agenteval captures every LLM call, tool call, and message — then runs evaluators on the trace.

Why Not DeepEval / TruLens / RAGAS / LangSmith?

Click to see how agenteval compares to DeepEval, LangSmith, and others

Try It Now

pip install "agenteval-ai[all]"
agenteval init
pytest tests/agent_evals/ -v

devbrat-anand / agenteval

pytest for AI agents — catch failures before production

agenteval

pytest for AI agents — catch failures before production

Your agent tests pass. Your monitoring says "green."
Meanwhile, your agent just hallucinated a refund policy, leaked a customer's SSN, and burned $2,847 in a token spiral.

agenteval catches these failures in CI, before production.

Quickstart · Evaluators · Agent SDKs · GitHub

pip install "agenteval-ai[all]" && agenteval init && pytest tests/agent_evals/ -v

The Problem

AI agents fail silently. Traditional monitoring can't catch:

Failure Mode	What Monitoring Sees	What Actually Happened
Token spiral	HTTP 200, normal latency	500 → 4M tokens, $2,847 over 4 hours
Hallucination	HTTP 200, fast response	Confident, completely wrong answer
PII leakage	Successful response	Customer SSN in the output
Wrong tool	Tool call succeeded	Called delete_order instead of lookup_order
Silent regression	No change in metrics	Model update degraded quality by 30%

The Solution

Write agent tests like regular Python tests. Run them in CI.

…

View on GitHub

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PyPI: https://pypi.org/project/agenteval-ai/
License: MIT