pytest plugin for HTTP testing using JSON files
Project description
pytest-httpchain
A pytest plugin for testing HTTP endpoints.
Overview
pytest-httpchain is an integration testing framework for HTTP APIs based on httpx lib.
It aims at helping with common HTTP API testing scenarios, where user needs to make several calls in specific order using data obtained along the way, like auth tokens or resource ids.
Why pytest-httpchain?
Testing HTTP APIs with plain pytest often leads to these pain points:
- Boilerplate accumulates — Every test repeats the same setup: create client, set headers, make request, parse response, assert. The actual test intent gets buried.
- Data threading is manual — When one call returns a token or ID needed by the next, you end up with fragile helper functions passing state around.
- Common patterns get copy-pasted — Auth flows, base URLs, shared headers end up duplicated across test files. Fixtures might help, but they are not designed for that.
- Code reviews are noisy — The actual test logic is rarely clear because of all the boilerplate and helpers, following changes gets overwhelming quickly.
pytest-httpchain offers a more structured approach.
Features
Declarative JSON format
Test scenarios are JSON documents that describe what to test, not how. No setup code to scroll through — the request and assertions are right there.
$ref with deep merging
Reuse arbitrary parts of your scenarios with JSONRef. Properties merge with type checking, so you can compose scenarios from shared fragments (auth flows, common headers, base URLs).
Multi-stage execution
Each scenario contains 1+ stages executed in order. One stage failure stops the chain. Use always_run for cleanup stages that should execute regardless.
Common data context
A key-value store persists throughout scenario execution. Variables, fixtures, and saved response data all live here. Use template expressions ({{ var }}) anywhere in your requests — substitution happens dynamically before each stage.
Response processing
- JMESPath — Extract values from JSON responses directly
- JSON Schema — Validate response structure against a schema
- User functions — Call Python functions for custom extraction, verification, or authentication
Full pytest integration
Markers, fixtures, parametrization, and other plugins work as expected. You're not locked into a separate ecosystem.
Quick Start
Create a JSON test file named like test_<name>.<suffix>.json (default suffix is http):
# conftest.py
import pytest
from datetime import datetime
@pytest.fixture
def now_utc():
return datetime.now()
{
"substitutions": [
{
"vars": {
"user_id": 1
}
}
],
"stages": {
"get_user": {
"request": {
"url": "https://api.example.com/users/{{ user_id }}"
},
"response": [
{
"verify": {
"status": 200
}
},
{
"save": {
"jmespath": {
"user_name": "user.name"
}
}
}
]
},
"update_user": {
"fixtures": ["now_utc"],
"request": {
"url": "https://api.example.com/users/{{ user_id }}",
"method": "PUT",
"body": {
"json": {
"user": {
"name": "{{ user_name }}_updated",
"timestamp": "{{ str(now_utc) }}"
}
}
}
},
"response": [
{
"verify": {
"status": 200
}
}
]
},
"cleanup": {
"always_run": true,
"request": {
"url": "https://api.example.com/cleanup",
"method": "POST"
}
}
}
}
Scenario we created:
- common data context is seeded with the first variable
user_id - get_user
url is assembled usinguser_idvariable from common data context
HTTP GET call is made
we verify the call returned code 200
assuming JSON body is returned, we extract a value by JMESPath expressionuser.nameand save it to common data context underuser_namekey - update_user
now_utcfixture value is injected into common data context
url is assembled usinguser_idvariable from common data context
we create JSON body in place using values from common data context, note thatnow_utcis converted to string in place
HTTP PUT call with body is made
we verify the call returned code 200 - cleanup
finalizing call meant for graceful exit
always_runparameter means this stage will be executed regardless of errors in previous stages
For detailed usage guide see the full documentation.
Installation
Install normally via package manager of your choice from PyPi:
pip install pytest-httpchain
or directly from Github, in case you need a particular ref:
pip install 'git+https://github.com/aeresov/pytest-httpchain@main'
Configuration
- Test file discovery is based on this name pattern:
test_<name>.<suffix>.json. Thesuffixis configurable as pytest ini option, default value is http. $refinstructions can point to other files using relative paths; absolute paths are rejected for security. You can limit the depth of relative path traversal usingref_parent_traversal_depthini option, default value is 3.- Template expressions support list/dict comprehensions. You can limit the maximum comprehension length using
max_comprehension_lengthini option, default value is 50000. - Parallel stage iterations (repeat/foreach) have a safety limit configurable via
max_parallel_iterationsini option, default value is 10000.
HAR export
Pass --output-dir DIR on the pytest command line to write an HAR file (and a "HAR File" report section) capturing each test's HTTP traffic:
pytest --output-dir ./har-output
HAR files contain full requests/responses including credential headers and saved tokens — nothing is redacted, so scrub them before sharing. See the HAR export docs.
AI agent support
pytest-httpchain ships a scenario validator to help AI coding agents (and humans) author and check test scenarios.
Scenario validation
Validate scenario files for structure and common problems — undefined variables, variables referenced before they are saved (data-flow ordering), duplicate stage names, fixture/variable conflicts, no-op verify steps, and contradictory body checks:
uvx pytest-httpchain validate tests/test_login.http.json
Each finding carries a stable diagnostic code (HTTPCHAINxxx) and a severity. It exits non-zero when any file is invalid, so it doubles as a CI gate. Use --format json for machine-readable output (editor/CI integration):
uvx pytest-httpchain validate --format json tests/test_login.http.json
The same checks also run automatically at pytest collection time — semantic errors fail collection and warnings are reported — so pytest --collect-only validates every scenario in your suite.
For deeper, opt-in checks, add --deep: it imports your module:func references to confirm they resolve, checks their call signatures (including the injected response for save/verify functions), and verifies referenced files and schemas exist. Because it imports your code it is never run at collection time; pair it with --strict to fail CI on any warning, and --syspath to add import roots:
uvx pytest-httpchain validate --deep --strict tests/test_login.http.json
Editor schema
A JSON Schema is published for as-you-type validation and autocomplete. Reference it from your test files:
{
"$schema": "https://aeresov.github.io/pytest-httpchain/schema/scenario.schema.json"
}
The hosted schema tracks the latest release; to pin the schema matching your installed version (e.g. for CI), emit it locally:
uvx pytest-httpchain schema > scenario.schema.json
Inspecting scenarios
More read-only commands help author and debug scenarios offline — no network, no test run:
# Print a scenario with all $ref/$include/$merge inlined and deep-merged
uvx pytest-httpchain resolve tests/test_login.http.json
# Summarize stages and the variable data-flow (which stage saves what, who consumes it)
uvx pytest-httpchain show tests/test_login.http.json
# Render the stage data-flow as a Mermaid flowchart
uvx pytest-httpchain graph tests/test_login.http.json
Documentation
- Full Documentation - Complete usage guide
- Changelog - Release notes
Thanks
This project was inspired by Tavern and pytest-play.
httpx does comms.
Pydantic keeps structure.
simpleeval powers templates.
pytest-order sorts chain.
pytest-datadir saved me a lot of elbow grease while testing.
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