feat: generate-spec command article

blog/generate-openapi-from-traffic.md

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+---
+template: ../@theme/templates/BlogPost
+title: Generate OpenAPI from real traffic (with AI)
+description: The new generate-spec command infers an OpenAPI description from recorded HTTP traffic - with AI refinement.
+seo:
+  title: Generate OpenAPI from real traffic (with AI)
+  description: The new generate-spec command infers an OpenAPI description from recorded HTTP traffic - with AI refinement.
+author: adam-sobaniec
+publishedDate: "2026-08-05"
+categories:
+  - redocly:redocly-cli
+  - redocly:product-updates
+  - api-specifications:openapi
+---
+
+Plenty of production APIs have no OpenAPI description at all.
+The endpoints live in code, the knowledge lives in people's heads, and the closest thing to documentation is a wiki page nobody fully trusts.
+
+Human developers cope with that - they read the source, ask a colleague, poke around with curl.
+AI agents can't.
+They learn what your API can do from a machine-readable description of it, and without one its capabilities are invisible to them.
+
+So the blank page is worth removing, and it is usually the only thing in the way: writing a description by hand for dozens of existing endpoints is exactly the kind of backfill work that never makes it into a sprint.
+The new `generate-spec` command in Redocly CLI removes it.
+Your API already produces the raw material every time someone uses it: traffic.
+`generate-spec` reads recorded HTTP traffic and infers an OpenAPI 3.2 description from it.
+
+## From traffic to description
+
+The command accepts HAR files, Kong logs, Nginx/Apache JSON logs, and NDJSON - a file or a whole folder of them.
+Traffic parsing is shared with the [`drift` command](./catch-api-drift.md), so any log that works with `drift` works here too.
+
+From the recorded exchanges it builds a baseline deterministically - no AI involved yet, same traffic in, same description out:
+
+- Identifier-like path segments (numeric IDs, UUIDs, prefixed tokens like `prd_…`) become named path parameters, so a hundred URLs collapse into one templated path.
+- Request and response schemas are merged across all observations; a property becomes optional as soon as one sample omits it.
+- Alternative body shapes for the same operation are preserved as `oneOf` variants, and object shapes that repeat across the document are extracted into `components/schemas`.
+- String values are analyzed conservatively: consistent well-known patterns get a `format` (`uuid`, `date-time`, `email`, `uri`), and strings that only ever take a small set of repeated values become an `enum`.
+
+## Example: the Cafe API
+
+Here is what that looks like end to end, on Redocly Cafe - our public demo API, so you can follow along on the same traffic if you want.
+Pretend for a moment that its OpenAPI description doesn't exist, and let's reconstruct it from traffic.
+
+First, record some.
+The `proxy` command starts a local reverse proxy that captures everything passing through into a HAR file:
+
+```bash
+redocly proxy --target https://api.cafe.redocly.com --har ./cafe.har
+```
+
+```sh
+Proxy listening on http://127.0.0.1:4040 → forwarding to https://api.cafe.redocly.com/
+Recording traffic to ./cafe.har
+Press Ctrl+C to stop.
+```
+
+{% admonition type="warning" name="Record in a sandboxed environment" %}
+The proxy records complete exchanges - URLs, headers, cookies, and request and response bodies - so a capture taken against production holds whatever that traffic held, including credentials and personal data.
+Record against a test environment with synthetic data.
+This matters in every mode, not only with AI: observed values reach the generated description as enums and examples, so a description inferred from real user data isn't safe to pass around either.
+{% /admonition %}
+
+Send a few requests through it - browse the menu, filter it, download some menu item photos:
+
+```bash
+curl http://127.0.0.1:4040/menu
+curl "http://127.0.0.1:4040/menu?category=dessert"
+curl "http://127.0.0.1:4040/menu?category=beverage"
+curl -o tiramisu.png http://127.0.0.1:4040/menu-item-images/prd_0000000000seedtrams0000000
+curl -o tea.png http://127.0.0.1:4040/menu-item-images/prd_0000000000seedteabv0000000
+curl -o cheesecake.png http://127.0.0.1:4040/menu-item-images/prd_0000000000seedchesc0000000
+```
+
+Press <kbd>`Ctrl`</kbd> + <kbd>`C`</kbd> to stop the proxy and write the HAR file:
+
+```sh
+Captured 6 exchange(s) to ./cafe.har
+```
+
+Now ask for a description:
+
+```bash
+redocly generate-spec ./cafe.har --title "Cafe API" -o cafe-openapi.yaml
+```
+
+```sh
+Inferred a baseline OpenAPI description from traffic: 2 operation(s).
+Written to: cafe-openapi.yaml
+Done in 0s.
+```
+
+The result is a valid OpenAPI 3.2 description - about a hundred lines of it, from six requests.
+The frame: a server URL inferred from the capture, and one path per discovered endpoint:
+
+```yaml
+openapi: 3.2.0
+info:
+  title: Cafe API
+  version: 1.0.0
+servers:
+  - url: https://api.cafe.redocly.com
+paths:
+  /menu:
+    # …
+  /menu-item-images/{menu-item-imageId}:
+    # …
+```
+
+Rather than paste the whole file, let's zoom in on what the inference did.
+
+Start with the paths: the three photo URLs collapsed into one templated path, because the `prd_…` identifiers were recognized as IDs and became a required path parameter:
+
+```yaml
+/menu-item-images/{menu-item-imageId}:
+  get:
+    operationId: get-menu-item-images-menu-item-imageId
+    responses:
+      '200':
+        description: OK
+    parameters:
+      - name: menu-item-imageId
+        in: path
+        required: true
+        schema:
+          type: string
+```
+
+Inside the `/menu` response schema, every menu item property got a type, and the observed values were mined for more:
+
+```yaml
+properties:
+  # …
+  price:
+    type: integer
+  category:
+    type: string
+    enum:
+      - beverage
+      - dessert
+  createdAt:
+    type: string
+    format: date-time
+  photoUrl:
+    type: string
+    format: uri
+```
+
+`category` became an enum because every observed value was one of the two, and `createdAt` and `photoUrl` matched well-known patterns in every sample.
+The `category` query parameter on the same operation stayed a plain string - two observations aren't enough evidence, so the inference stays conservative rather than guessing:
+
+```yaml
+parameters:
+  - name: category
+    in: query
+    required: false
+    schema:
+      type: string
+```
+
+The merge across samples also noticed which properties come and go.
+Beverages have volume, desserts have calories, so `volume`, `containsCaffeine`, and `calories` are typed but absent from the `required` list:
+
+```yaml
+properties:
+  # …
+  volume:
+    type: integer
+  containsCaffeine:
+    type: boolean
+  calories:
+    type: integer
+required:
+  - id
+  - name
+  - price
+  - photoTextDescription
+  - category
+  - createdAt
+  - updatedAt
+  - object
+  - photoUrl
+```
+
+It's still a hypothesis, though - the description only knows what the traffic showed.
+`price` is an integer because every observed price happened to be whole.
+`id` and `name` came out as enums of the five menu items the capture contained, which is over-fitting rather than a rule of the API - the same mechanism that turns real values into published enums if you record real traffic.
+The photo response has no schema because its body is a PNG, not JSON.
+Endpoints nobody called don't exist, nothing has a human-readable description, and names like `{menu-item-imageId}` are derived mechanically - rename them when you review.
+More traffic tightens the hypothesis, and your e2e test suite is a free source of it: route the tests through `proxy` once, and the whole run becomes input.
+
+## Refine it with AI
+
+For everything determinism can't reach, add `--with-ai`:
+
+```bash
+redocly generate-spec ./cafe.har --title "Cafe API" --with-ai --ai-provider claude -o cafe-openapi.yaml
+```
+
+The command refines the baseline one operation at a time, sending each operation together with a shape-diverse sample of its real recorded exchanges to an AI provider.
+The AI narrows types, adds formats, enums, descriptions, and examples, and models alternative payloads explicitly with `oneOf` and `allOf`.
+
+Three providers are supported - `claude` (Claude Code), `codex` (Codex CLI), and `cursor` (Cursor CLI).
+Each runs the corresponding locally installed CLI in non-interactive mode, so the provider you already use and pay for is the one doing the work.
+`--ai-provider` is optional and defaults to `claude`; pick a model with `--ai-model` or let the provider use its default.
+
+The AI's answer is never trusted blindly.
+A refined operation is only accepted when it keeps the operation's path and method, keeps every response status code observed in the traffic, and passes validation with the `spec` ruleset.
+A rejected refinement keeps its deterministic baseline, and if refinement fails entirely, the command falls back to the baseline description - you never end up with less than the deterministic run would have given you.
+
+{% admonition type="warning" name="Traffic leaves your machine" %}
+`--with-ai` sends samples of the recorded traffic - URLs, query strings, request and response bodies - to the selected AI provider.
+Beyond recording in a sandbox, make sure the traffic holds no secrets or personal data you are not allowed to share with that provider.
+{% /admonition %}
+
+## How much does `--with-ai` actually add?
+
+Fair question, and the Cafe API can answer it with ground truth: its real, handwritten [OpenAPI description](https://cafe.redocly.com/openapi/cafe) exists - we only pretended it doesn't.
+Whatever `generate-spec` reconstructs from traffic can be scored against what the API team actually wrote.
+
+So we recorded a fuller session than the six-request capture above - 30 requests covering every endpoint: the OAuth2 client registration dance, menu items created in both categories, orders placed, updated, and deleted, photo downloads, and the errors a real session produces along the way (a `400`, a few `404`s, even a `409`).
+Then we generated a description twice from that one capture - once deterministically, once with `--with-ai` - and scored both against the handwritten description.
+
+Two things are worth measuring separately:
+
+- **Precision** - of every checkable claim the generated description makes about a property the handwritten description also documents (its type, its format, its enum values, whether it's required), how many agree.
+  This is the "can I trust what it says" number.
+- **Recall** - of everything the handwritten description documents, how much the generated one recovered.
+  This is the "how much work is left" number.
+
+For response schemas:
+
+| Metric                                 | Deterministic | `--with-ai` |
+| -------------------------------------- | ------------- | ----------- |
+| Response properties recovered          | 97.5%         | 98.3%       |
+| Correct types                          | 100%          | 100%        |
+| Correct formats                        | 100%          | 100%        |
+| Correct enum values                    | 100%          | 100%        |
+| Correct `required`                     | 69.2%         | 72.2%       |
+| **Precision, all claims**              | **87.8%**     | **89.2%**   |
+| Formats documented, recovered          | 53.1%         | 62.5%       |
+| Enums documented, recovered            | 66.7%         | 66.7%       |
+| `required` documented, recovered       | 91.3%         | 94.2%       |
+| Properties carrying a description      | 0%            | 97.5%       |
+| Numeric and length constraints         | 0             | 21          |
+| Run time                               | under 1s      | 6-15 min    |
+
+Responses tell the same story as the walkthrough: the deterministic baseline is already *correct* - it never contradicted the handwritten description on a single type, format, or enum value - and what `--with-ai` adds there is everything determinism structurally cannot produce: descriptions on nearly every property, constraints, examples, and formats inferred from context rather than repetition.
+
+Request bodies are a different story:
+
+| Metric                              | Deterministic | `--with-ai` |
+| ----------------------------------- | ------------- | ----------- |
+| Request properties recovered        | 55.9%         | 61.8%       |
+| Correct types                       | 78.9%         | **100%**    |
+| Correct `required`                  | 81.8%         | **100%**    |
+| **Precision, all claims**           | **80.0%**     | **100%**    |
+| Properties carrying a description   | 0%            | 85.7%       |
+
+## What actually changed
+
+`POST /menu` accepts `multipart/form-data`, and in a multipart form every value is a string on the wire.
+The deterministic baseline can only write down what it saw:
+
+```yaml
+requestBody:
+  content:
+    multipart/form-data:
+      schema:
+        type: object
+        properties:
+          name:
+            type: string
+          price:
+            type: string
+          category:
+            type: string
+          volume:
+            type: string
+          containsCaffeine:
+            type: string
+          calories:
+            type: string
+        required:
+          - name
+          - price
+          - category
+```
+
+This is the one place in the whole experiment where the baseline was *wrong* rather than merely incomplete - `price`, `volume`, `calories`, and `containsCaffeine` are not strings - and it is exactly what the AI fixed.
+The same request body after `--with-ai`:
+
+```yaml
+requestBody:
+  content:
+    multipart/form-data:
+      schema:
+        oneOf:
+          - $ref: '#/components/schemas/BeverageCreate'
+          - $ref: '#/components/schemas/DessertCreate'
+        discriminator:
+          propertyName: category
+          mapping:
+            beverage: '#/components/schemas/BeverageCreate'
+            dessert: '#/components/schemas/DessertCreate'
+```
+
+```yaml
+BeverageCreate:
+  description: Creation request for a beverage menu item.
+  allOf:
+    - $ref: '#/components/schemas/MenuItemCreateBase'
+    - type: object
+      properties:
+        category:
+          type: string
+          enum:
+            - beverage
+        volume:
+          type: integer
+          minimum: 0
+          description: Serving volume in millilitres.
+          example: 180
+        containsCaffeine:
+          type: boolean
+          description: Whether the beverage contains caffeine.
+          example: true
+MenuItemCreateBase:
+  type: object
+  description: Attributes shared by every menu item creation request.
+  properties:
+    name:
+      type: string
+      description: Human-readable name of the menu item.
+      example: flat-white
+    price:
+      type: integer
+      minimum: 0
+      description: Price in the smallest currency unit (for example cents).
+      example: 450
+    # …
+  required:
+    - name
+    - price
+    - category
+```
+
+Every type is corrected, and constraints, descriptions, and examples appeared - but the bigger change is the shape itself.
+The AI noticed from the samples that beverages and desserts carry different fields and modeled the union explicitly: `allOf` composition over the shared attributes, selected by a `category` discriminator.
+The handwritten description models menu items exactly the same way - `oneOf` beverage or dessert, discriminated by `category`.
+Traffic plus AI converged on the design the API team chose by hand, where the baseline could only offer one merged object with everything optional.
+
+The rest of the delta, briefly:
+
+- Descriptions went from zero to 97.5% of response properties, plus a summary and description on every refined operation.
+- 21 numeric and length constraints appeared, along with realistic `example` values.
+- Identifier patterns were inferred on path parameters, such as `pattern: ^ord_[0-9a-z]+$` on the order ID.
+- The over-fitting the walkthrough warned about got cleaned up: the baseline turned the two observed order IDs into an `enum`, and the AI demoted it to a plain string with a description and a realistic `example` - while real enums like `category` and order `status` stayed.
+- `required` was cleaned up where the traffic over-claimed it - optional client-registration fields that every sample happened to include.
+
+The guardrails did real work during this run, too: one operation's refinement failed on a transient provider error and kept its deterministic baseline, so the output stayed valid and complete.
+
+One caveat travels from API to API: path parameters.
+Every Cafe path parameter was recognized, because its identifiers are prefixed tokens (`prd_…`, `ord_…`) that the deterministic inference detects.
+On APIs whose path segments are ordinary words - organization names, repository names, branches - those segments stay hardcoded, and AI refinement can't rescue them because a refined operation must keep its path.
+Reviewing paths by hand is the one non-optional step.
+
+{% admonition type="info" name="About these numbers" %}
+They come from one capture of one small API, scored by a script we wrote for the occasion, so treat them as an illustration rather than a benchmark.
+The handwritten description isn't perfect ground truth either: the traffic hit a `409` on `DELETE /menu/{menuItemId}` and a live `/health` endpoint that the handwritten description doesn't document - real API behavior that counts against the generated description as "not in the spec".
+{% /admonition %}
+
+{% admonition type="warning" name="Experimental" %}
+The `generate-spec` command is experimental.
+Flags, output, and behavior may change - including breaking changes - in upcoming releases while we shape it with your feedback.
+{% /admonition %}
+
+## Get started
+
+The `generate-spec` command is available now in the latest [Redocly CLI](https://redocly.com/docs/cli) - see the [command reference](https://redocly.com/docs/cli/commands/generate-spec) for all options.
+Run it on your traffic, review what comes out, lint it with your own ruleset, and tell us what you think on the [Redocly CLI GitHub repository](https://github.com/Redocly/redocly-cli/issues).
+
+And once you have a description, don't let it rot.
+Point the [`drift` command](./catch-api-drift.md) at next week's traffic and it tells you the moment reality moves.
+Generate, then guard.