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Behavioral testing framework for AI agents — pytest for AI agents
The Behavioral Testing Framework for AI Agents
"pytest for AI Agents" — Write tests. Run agents. Ship with confidence.
Quick Start • Features • Documentation • Contributing
Teams building AI agents face a critical gap in their development workflow:
| What Exists | What's Missing |
|---|---|
| Observability (LangSmith, Langfuse) | Behavioral test runners |
| Evaluation dashboards | CI/CD pass/fail gates |
| LLM output quality metrics | Agent execution path testing |
AgentAssert fills this gap. It's an open-source, framework-agnostic, local-first behavioral test framework designed specifically for AI agent pipelines.
git push → test → deploy workflowspip install agentassert
# tests/test_my_agent.py
from agentassert import agent_test, expect, mock_tool, contains
@agent_test
def test_research_agent_workflow(agent_harness):
"""Test that the research agent follows the correct tool sequence."""
# 1. Create mock tools with deterministic responses
search = mock_tool("web_search", returns={"results": ["AI breakthrough news"]})
summarize = mock_tool("summarize", returns="Key finding: AI is advancing rapidly")
# 2. Run your agent under test
trace = agent_harness.run(
agent=my_research_agent,
input="Find the latest AI news",
tools=[search, summarize]
)
# 3. Assert on behavioral expectations
expect(trace).tool("web_search").was_called()
expect(trace).tool("web_search").called_before("summarize")
expect(trace).tool("web_search").called_with(query=contains("AI"))
expect(trace).completed_within_steps(10)
expect(trace).output.not_empty()
$ agentassert run tests/
AgentAssert v0.1.0 — Behavioral Testing Framework for AI Agents
collecting ... 3 tests
tests/test_my_agent.py
✓ test_research_agent_workflow (2 steps, $0.002, 0.3s)
✓ test_handles_api_failure (1 steps, $0.001, 0.1s)
✓ test_stays_within_budget (4 steps, $0.008, 0.5s)
════════════════════════════════════════════════════════════
3 passed in 0.9s
════════════════════════════════════════════════════════════
Create deterministic tool responses for predictable testing:
# Static response
search = mock_tool("web_search", returns={"results": ["item1", "item2"]})
# Sequential responses
api = mock_tool("api_call", returns_sequence=[
{"status": "pending"},
{"status": "processing"},
{"status": "complete"}
])
# Simulate failures
flaky_api = mock_tool("external_service", raises=ConnectionError("timeout"))
# Rate limiting simulation
limited_api = mock_tool("rate_limited_api", returns="ok", fail_after=5)
Assert on how your agent behaves, not just what it outputs:
# Tool invocation assertions
expect(trace).tool("search").was_called()
expect(trace).tool("search").was_not_called()
expect(trace).tool("search").called_exactly(3)
expect(trace).tool("search").called_at_least(1)
# Execution order assertions
expect(trace).tool("fetch_data").called_before("process_data")
expect(trace).tool("cleanup").called_after("main_task")
# Argument matching with flexible matchers
expect(trace).tool("api").called_with(
query=contains("search term"),
limit=greater_than(0),
filters=has_key("category")
)
# Execution behavior assertions
expect(trace).completed()
expect(trace).completed_within_steps(15)
expect(trace).failed_gracefully()
# Output assertions
expect(trace).output.not_empty()
expect(trace).output.contains("success")
expect(trace).output.is_valid_json()
Flexible matchers for complex assertion scenarios:
from agentassert import (
# String matchers
contains, matches, starts_with, ends_with, any_string,
# Numeric matchers
greater_than, less_than, between,
# Collection matchers
has_key, has_length, contains_item,
# Logical matchers
all_of, any_of, not_, anything
)
# Combine matchers for precise assertions
expect(trace).tool("search").called_with(
query=all_of(
starts_with("user:"),
contains("search"),
not_(contains("admin"))
)
)
# Run all tests in current directory
agentassert run
# Run tests in a specific directory
agentassert run tests/
# Run a specific test file
agentassert run tests/test_research_agent.py
# Filter tests by keyword
agentassert run -k "search"
# Verbose output with full tracebacks
agentassert run -v
# Minimal output (dots only)
agentassert run -q
# Set random seed for reproducibility
agentassert run --seed 12345
# Stop on first failure
agentassert run -x
AgentAssert is designed to work with any agent framework:
|
LangChain
|
Custom Agents
|
name: Agent Tests
on: [push, pull_request]
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install dependencies
run: |
pip install agentassert
pip install -r requirements.txt
- name: Run agent tests
run: agentassert run tests/ -v
agent-tests:
image: python:3.11
script:
- pip install agentassert
- agentassert run tests/
| Feature | AgentAssert | LangSmith | Langfuse | DeepEval |
|---|---|---|---|---|
| Local execution | Yes | No | Partial | Yes |
| No account required | Yes | No | No | Yes |
| Framework agnostic | Yes | No | Yes | Yes |
| Behavioral assertions | Yes | No | No | No |
| Tool call testing | Yes | Partial | Partial | No |
| CI/CD native | Yes | Partial | Partial | Yes |
| Deterministic replay | Yes | No | No | No |
We welcome contributions from the community. Please read our Contributing Guidelines before submitting PRs.
AgentAssert is released under the MIT License. See LICENSE for details.
Built by Kaushik Dhola
pip install agentassert
Download counts and popularity will appear here as the collector receives them.
| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 0.1.1 | 29 Mar 2026 | 14 |
This describes our collected data; it is not a security assessment.
Observed since 20 Oct, 21:30 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
package:be6d3d906c622c5fff782d9b4860909f71400b9793ce0b63aaf750d305c7fbe7