agentevalkit 0.6.0

Testing and evaluation framework for AI agents

AgentEval 🧪

Testing and evaluation framework for AI agents. Define test suites in YAML, grade agent outputs with 6 pluggable graders, track results over time, and detect regressions with statistical comparison.

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Why AgentEval?

AI agents are hard to test. They're non-deterministic, they call tools, and their outputs vary between runs. Traditional unit tests don't cut it.

• 🎯 YAML-based test suites — Define inputs, expected outputs, and grading criteria declaratively
• 📊 Statistical regression detection — Welch's t-test across multiple runs, not just pass/fail
• 🔌 6 built-in graders — Exact match, contains, regex, tool-check, LLM-judge, and custom
• 🔗 AgentLens integration — Import real production sessions as test cases
• 💰 Cost & latency tracking — Know what each eval costs in tokens and dollars
• 🗄️ SQLite result storage — Every run is persisted for historical comparison

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Quick Start

pip install agentevalkit

1. Define a test suite

# suite.yaml
name: my-agent-tests
agent: my_agent:run

cases:
  - name: basic-math
    input: "What is 2 + 2?"
    expected:
      output_contains: ["4"]
    grader: contains

  - name: tool-usage
    input: "Search for the weather in NYC"
    expected:
      tools_called: ["web_search"]
    grader: tool-check

  - name: format-check
    input: "List 3 colors"
    expected:
      pattern: "\\d\\.\\s+\\w+"
    grader: regex

2. Create your agent callable

# my_agent.py
from agenteval.models import AgentResult

def run(input_text: str) -> AgentResult:
    # Your agent logic here
    return AgentResult(
        output="The answer is 4.",
        tools_called=[{"name": "web_search", "args": {"query": "weather NYC"}}],
        tokens_in=12,
        tokens_out=8,
        cost_usd=0.0003,
    )

3. Run the eval

$ agenteval run --suite suite.yaml --verbose

============================================================
Suite: my-agent-tests  |  Run: c1c6493118d5
============================================================
  PASS  basic-addition (score=1.00, 150ms)
  PASS  capital-city (score=1.00, 200ms)
  PASS  quantum-summary (score=1.00, 350ms)
  PASS  tool-usage (score=1.00, 280ms)
  PASS  list-format (score=1.00, 120ms)

Total: 5  Passed: 5  Failed: 0  Pass rate: 100%
Cost: $0.0023  Avg latency: 220ms

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Features

🎯 6 Built-in Graders

Grader	What it checks	Expected fields
exact	Exact string match	output
contains	Substring presence	output_contains: [list]
regex	Pattern matching	pattern
tool-check	Tools were called	tools_called: [list]
llm-judge	LLM evaluates quality	criteria (free-form)
custom	Your own function	grader_config: {function: "mod:fn"}

📊 Statistical Comparison

Compare runs with Welch's t-test to detect statistically significant regressions:

$ agenteval compare c1c6493118d5,d17a2dce0222 4ee7e40601e3,ba5b0dde212b

============================================================================
Comparing: c1c6493118d5,d17a2dce0222 vs 4ee7e40601e3,ba5b0dde212b
Alpha: 0.05  Regression threshold: 0.0
============================================================================

Case                          Base   Target     Diff   p-value  Sig Status
----------------------------------------------------------------------------
  basic-addition             1.000    1.000   +0.000         —
  capital-city               1.000    0.500   -0.500    0.4533
  quantum-summary            1.000    0.500   -0.500    0.4533
  tool-usage                 1.000    0.000   -1.000    0.0000    * ▼ regressed
  list-format                1.000    0.500   -0.500    0.4533

Summary: 0 improved, 1 regressed, 4 unchanged

⚠ 1 regression(s) detected!

Run the same suite multiple times and compare groups: agenteval compare RUN_A1,RUN_A2 vs RUN_B1,RUN_B2. Uses scipy when available, falls back to pure Python.

🔗 AgentLens Integration

Import real agent sessions from AgentLens as test suites:

# From AgentLens SQLite database
agenteval import --from agentlens --db sessions.db --output suite.yaml --grader contains

# From AgentLens server API
agenteval import-agentlens --url http://localhost:3000 --output suite.yaml --grader contains

# With filtering and interactive review
agenteval import --from agentlens --db sessions.db --output suite.yaml --filter-tag production --auto-assertions --interactive

Import modes:

• SQLite mode (import --from agentlens --db path) — reads directly from an AgentLens database file
• Server mode (import-agentlens --url URL) — fetches sessions via the AgentLens HTTP API

Sessions are converted to eval cases with input/output mapping and optional tool-call assertions. Use --auto-assertions to automatically generate expected fields from session data, and --interactive to review each case before saving.

Turn production traffic into regression tests — no manual test writing needed.

💰 Cost & Latency Tracking

Every eval tracks tokens and cost. Your agent callable returns AgentResult with tokens_in, tokens_out, and cost_usd, and AgentEval aggregates them per run.

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YAML Suite Format

Full annotated example:

name: my-agent-tests           # Suite name (shown in reports)
agent: my_module:my_agent      # Default agent callable (module:function)

defaults:                       # Defaults applied to all cases
  grader: contains
  grader_config:
    ignore_case: true

cases:
  - name: basic-math            # Unique case name
    input: "What is 2 + 2?"     # Input passed to agent
    expected:                    # Grader-specific expected values
      output_contains: ["4"]
    grader: contains             # Override default grader
    tags: [math, basic]          # Tags for filtering (--tag math)

  - name: tool-usage
    input: "Search for weather"
    expected:
      tools_called: ["web_search"]
    grader: tool-check

  - name: quality-check
    input: "Explain gravity"
    expected:
      criteria: "Should mention Newton or Einstein, be scientifically accurate"
    grader: llm-judge
    grader_config:
      model: gpt-4o-mini         # LLM judge model
      api_base: https://api.openai.com/v1

  - name: custom-validation
    input: "Generate a JSON object"
    expected: {}
    grader: custom
    grader_config:
      function: my_graders:validate_json  # Your grader function

---

CLI Reference

agenteval run

agenteval run --suite suite.yaml [--agent module:fn] [--verbose] [--tag math] [--timeout 30] [--db agenteval.db]

• --suite — Path to YAML suite file (required)
• --agent — Override the agent callable from the suite
• --verbose / -v — Show per-case pass/fail details
• --tag — Filter cases by tag (repeatable)
• --timeout — Per-case timeout in seconds (default: 30)
• --db — SQLite database path (default: agenteval.db)

Exit code is 1 if any case fails.

agenteval list

agenteval list [--suite-filter name] [--limit 20] [--db agenteval.db]

$ agenteval list --limit 5

ID             Suite                Passed   Failed   Rate     Created
--------------------------------------------------------------------------------
aeccd5e53f03   math-agent-demo      2        3        40%      2026-02-12T21:12:12
4f3e380f622c   math-agent-demo      3        2        60%      2026-02-12T21:12:12
bd4ef3a0727b   math-agent-demo      1        4        20%      2026-02-12T21:12:12
e2ca43e99852   math-agent-demo      3        2        60%      2026-02-12T21:12:11
32ed650cab6d   math-agent-demo      2        3        40%      2026-02-12T21:12:11

agenteval compare

agenteval compare RUN_A RUN_B [--alpha 0.05] [--threshold 0.0] [--stats/--no-stats]
agenteval compare RUN_A1,RUN_A2 vs RUN_B1,RUN_B2   # Multi-run comparison

agenteval import

agenteval import --from agentlens --db sessions.db --output suite.yaml [--grader contains] [--limit 100]

---

Grader Reference

exact

Compares result.output exactly with expected.output. Config: ignore_case: bool.

expected:
  output: "The answer is 42."
grader: exact
grader_config:
  ignore_case: true

contains

Checks that all substrings in expected.output_contains appear in the output.

expected:
  output_contains: ["Paris", "France"]
grader: contains

regex

Matches result.output against expected.pattern (Python regex). Config: flags: [IGNORECASE, DOTALL, MULTILINE].

expected:
  pattern: "\\d+\\.\\d+"
grader: regex
grader_config:
  flags: [IGNORECASE]

tool-check

Verifies expected tools were called. Config: ordered: bool for sequence matching.

expected:
  tools_called: ["web_search", "calculator"]
grader: tool-check
grader_config:
  ordered: true

llm-judge

Sends the input, output, and criteria to an LLM for evaluation. Requires OPENAI_API_KEY or compatible API.

expected:
  criteria: "Response should be helpful, accurate, and concise"
grader: llm-judge
grader_config:
  model: gpt-4o-mini

custom

Imports and calls your own grader function. Must accept (case: EvalCase, result: AgentResult) -> GradeResult.

grader: custom
grader_config:
  function: my_module:my_grader

---

Adapters

Adapters let you test agents built with popular frameworks without writing a custom callable.

pip install agentevalkit[langchain]   # LangChain
pip install agentevalkit[crewai]      # CrewAI
pip install agentevalkit[autogen]     # AutoGen

Adapter	Framework Method	Install Extra
langchain	agent.invoke(input)	[langchain]
crewai	crew.kickoff(inputs={"input": ...})	[crewai]
autogen	agent.run(input) or agent.initiate_chat(message=...)	[autogen]

Usage with YAML suite defaults:

# suite.yaml
name: my-tests
agent: my_module:my_chain
defaults:
  adapter: langchain

Or via CLI:

agenteval run --suite suite.yaml --adapter langchain

Each adapter extracts output, tool calls, and token usage from the framework's response format into a standard AgentResult.

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Distributed Execution

Scale eval suites across multiple workers using Redis as a broker.

Setup

pip install agentevalkit[distributed]

Start Workers

# Terminal 1: Start a worker
agenteval worker --broker redis://localhost:6379 --agent my_module:my_agent

# Terminal 2: Start another worker
agenteval worker --broker redis://localhost:6379 --agent my_module:my_agent

Run with Workers

agenteval run --suite suite.yaml --workers redis://localhost:6379 --worker-timeout 60

How It Works

1. The coordinator pushes eval cases to a Redis queue
2. Workers pop cases, execute the agent, and push results back
3. The coordinator collects results and builds the final EvalRun
4. If no workers are detected, execution falls back to local mode automatically

Configuration

• --workers URL — Redis broker URL (supports redis:// and rediss:// for TLS)
• --worker-timeout N — Seconds to wait for worker results (default: 30)
• Workers register heartbeats and are automatically detected by the coordinator

Security: Use rediss:// URLs with authentication for production deployments. See docs/troubleshooting.md for Redis security guidance.

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Troubleshooting

See docs/troubleshooting.md for solutions to common issues including:

• Agent callable import errors (module:function format)
• Missing dependency extras ([distributed], [langchain], etc.)
• OpenAI API key setup for llm-judge grader
• Compare command syntax
• Redis connection issues for distributed execution

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Contributing

Contributions welcome! This project uses:

• pytest for testing
• ruff for linting
• src layout (src/agenteval/)

git clone https://github.com/amitpaz1/agenteval.git
cd agenteval
pip install -e ".[dev]"
pytest

🧰 AgentKit Ecosystem

Project	Description
AgentLens	Observability & audit trail for AI agents
Lore	Cross-agent memory and lesson sharing
AgentGate	Human-in-the-loop approval gateway
FormBridge	Agent-human mixed-mode forms
AgentEval	Testing & evaluation framework	⬅️ you are here
agentkit-mesh	Agent discovery & delegation
agentkit-cli	Unified CLI orchestrator
agentkit-guardrails	Reactive policy guardrails

License

MIT — see LICENSE.