Full-Stack Developer
Auto-Generated API Documentation — From Code to Docs in 3 Minutes
Key Takeaway
Our coding agent reads Django views, serializers, URL configs, and model definitions — then generates a complete OpenAPI spec and human-readable markdown API docs with example requests, responses, auth requirements, and error codes in 3 minutes.
The Problem
API documentation is always wrong.
Not on day one. Day one, someone writes beautiful docs. By day thirty, three new endpoints exist that aren't documented, two documented endpoints have changed their response shape, and the authentication section references a header format that was deprecated two sprints ago.
The gap between code and docs is a function of time and developer discipline. Time always wins. Discipline always loses.
At PyratzLabs, we run multiple products — each with its own API, its own consumers, its own integration partners. Stale docs mean broken integrations, support tickets, and the dreaded "just read the code" response to an external developer.
The Solution
Our coding agent reads the actual source code — views, serializers, URL configurations, model definitions, permissions, throttles — and generates documentation from truth. No manual writing. No drift. Runs on every merge to main, so docs are never more than one commit behind reality.
The Process
Step 1: Code Parsing
The agent traverses the Django project structure:
pythonShow code
# Agent reads these files programmatically:
# 1. URL configs → discovers all endpoints and their HTTP methods
# 2. Views → extracts permission classes, throttle rates, query params
# 3. Serializers → maps request/response schemas
# 4. Models → understands field types, constraints, relationships
# 5. Settings → auth backends, pagination defaults, rate limits
Step 2: OpenAPI Spec Generation
From the code analysis, it produces a valid OpenAPI 3.0 spec:
yamlShow code
openapi: 3.0.3
info:
title: EskimoAI API
version: 2.4.0
description: AI job processing and management API
paths:
/api/jobs/:
get:
summary: List user's jobs
security:
- bearerAuth: []
parameters:
- name: status
in: query
schema:
type: string
enum: [pending, in_progress, completed, failed]
- name: page
in: query
schema:
type: integer
default: 1
responses:
'200':
description: Paginated list of jobs
content:
application/json:
schema:
type: object
properties:
count: { type: integer, example: 42 }
next: { type: string, nullable: true }
results:
type: array
items: { $ref: '#/components/schemas/Job' }
example:
count: 42
next: "https://api.eskimoai.com/api/jobs/?page=2"
results:
- id: 1
status: "completed"
created_at: "2026-03-11T14:30:00Z"
model: "gpt-4"
tokens_used: 1523
'401':
description: Authentication required
content:
application/json:
example:
detail: "Authentication credentials were not provided."
Step 3: Markdown Docs
The agent generates human-readable documentation alongside the spec:
markdownShow code
## POST /api/jobs/
Create a new AI processing job.
**Authentication:** Bearer token required
**Rate limit:** 60 requests/minute
**Content-Type:** application/json
### Request Body
| Field | Type | Required | Description |
|-------|------|----------|-------------|
| prompt | string | Yes | The input prompt (max 4096 chars) |
| model | string | No | Model to use (default: gpt-4) |
| callback_url | string | No | Webhook URL for completion notification |
| priority | string | No | "low", "normal", "high" (default: normal) |
Step 4: CI Integration
The docs regenerate on every merge to main:
yamlShow code
# .gitlab-ci.yml
generate-docs:
stage: docs
script:
- python manage.py generate_openapi_spec > docs/openapi.yaml
- python manage.py generate_api_docs > docs/API.md
artifacts:
paths:
- docs/
only:
- main
The Results
| Metric | Manual Docs | Agent-Generated |
|---|---|---|
| Time to write | 3-5 days | 3 minutes |
| Accuracy on day 1 | 95% | 100% (from source) |
| Accuracy on day 30 | ~70% | 100% (auto-updated) |
| Endpoints documented | "Most of them" | All (from URL configs) |
| Example requests | Sometimes | Every endpoint |
| Error codes | Rarely | Every response type |
Try It Yourself
The principle is simple: documentation should be generated, not written. Use your serializers as the single source of truth for request/response schemas. Use your URL configs as the source of truth for endpoints. Use your permission classes as the source of truth for auth requirements. Everything else is derivable.
Documentation that drifts from code isn't documentation. It's fiction.
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