Add English translation of the README after the Russian section
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README.md
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README.md
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@ -171,3 +171,180 @@ npm run mock # мок TeamoRouter на http://127.0.0.1:4123/v1 (кл
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## Лицензия
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## Лицензия
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MIT
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MIT
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---
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---
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# English
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Custom n8n nodes for the **TeamoRouter** gateway (`https://api.teamorouter.com/v1`) — an OpenAI-compatible router in front of GPT, Claude, Gemini, DeepSeek, GLM, Grok, Kimi and other models.
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The package ships two nodes and one credential type:
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| Node | Type | Purpose |
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| --- | --- | --- |
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| **TeamoRouter Chat Model** (`lmChatTeamRouter`) | sub-node, `ai_languageModel` | Plugs into **AI Agent**, **Basic LLM Chain**, **Question and Answer Chain** and any other node that takes a Language Model |
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| **TeamoRouter** (`teamorouter`) | regular node | Three operations: `Chat → Message a Model`, `Image → Generate an Image`, `Model → List Models` |
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| **TeamoRouter API** (`teamorouterApi`) | credentials | API key + Base URL |
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---
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## 1. Installation
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### Option A. Through the n8n UI (recommended)
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Community nodes in n8n are installed from npm, so the package has to be published:
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1. Push this folder to your git repository.
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2. Publish to npm:
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```bash
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npm install
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npm run build
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npm publish --access public
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```
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3. In n8n: **Settings → Community nodes → Install a community node**, enter `n8n-nodes-teamorouter` and confirm the warning.
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4. No restart is needed — the nodes appear right away.
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The Owner/Admin role and enabled community nodes are required. Community nodes are installed instance-wide.
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### Option B. Copying the folder (if you have access to the n8n server)
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1. Run `npm install && npm run build` — you need the resulting `dist/` folder.
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2. Put the **whole package folder** (it must contain `package.json`) into the custom extensions directory, for example:
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* in Docker: `/home/node/.n8n/custom/n8n-nodes-teamorouter/`
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* via an environment variable: `N8N_CUSTOM_EXTENSIONS=/path/to/custom-nodes`, with the package folder inside it
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3. Inside the package folder run `npm install --omit=dev` once, so that `n8n-workflow` (a peer dependency) is reachable from your folder.
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4. Restart n8n.
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### Option C. Local development
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```bash
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npm install
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npm run build:watch # tsc in watch mode
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export N8N_CUSTOM_EXTENSIONS="$(pwd)"
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npx n8n start
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```
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---
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## 2. Credentials
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**Settings → Credentials → New → TeamoRouter API**
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| Field | Value |
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| --- | --- |
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| **API Key** | the key from the TeamoRouter dashboard, it starts with `sk-teamo-` |
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| **Base URL** | `https://api.teamorouter.com/v1` (the default; change it only for the `.cn` mirror or your own router) |
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The test button issues `GET /v1/models` with the `Authorization: Bearer <key>` header.
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---
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## 3. TeamoRouter Chat Model (for the AI Agent)
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1. Add an **AI Agent** (or a **Basic LLM Chain**).
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2. Add **TeamoRouter Chat Model** into the **Chat Model** slot.
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3. Pick a model from the list — it is read live from `GET /v1/models`. The label of each model shows the provider and the price per 1M tokens (when the router reports them).
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### Options
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| Option | What it does |
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| --- | --- |
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| **Sampling Temperature** | randomness of the answer, 0…2 |
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| **Maximum Number of Tokens** | generation limit; `-1` = decided by the model |
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| **Top P** | nucleus sampling; change either temperature or topP, not both |
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| **Force JSON Response** | asks the model to answer with a JSON object (the prompt must mention JSON) |
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| **Service Tier → Fast** | TeamoRouter Fast mode: up to 2.5x faster, billed at 2x |
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| **Extra Body** | arbitrary JSON fields for the request body, e.g. `{"top_k": 40}`; overrides everything above |
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| **Timeout** | 600000 ms by default (the router allows answers up to 600 s) |
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| **Max Retries** | retries on failure, 2 by default |
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### A model that is missing from the list
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The **Model** field can be switched to expression mode (the `Expression` toggle in the top right corner of the field) and filled with the ID by hand:
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```
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{{ 'some-new-model-id' }}
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```
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Useful when the router already serves a new model but the list in the UI has not been reloaded yet.
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---
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## 4. The TeamoRouter node (regular operations)
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### Chat → Message a Model
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Sends `POST /v1/chat/completions` and returns the JSON answer as is.
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| Field | Description |
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| --- | --- |
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| **Model** | the list is read from `GET /v1/models` (text models only; image models are hidden) |
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| **Prompt** | the user message |
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| **Options → System Prompt** | system instruction |
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| **Options →** the rest | temperature, topP, max tokens, JSON answer, Service Tier Fast, Extra Body, Timeout |
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The answer text is available as `{{ $json.choices[0].message.content }}`, the token usage as `{{ $json.usage }}`.
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### Image → Generate an Image
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Sends `POST /v1/images/generations` and puts the result into a **binary field** (`data` by default, PNG).
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| Field | Description |
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| --- | --- |
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| **Model** | `gpt-image-2` by default; the list contains image models only; the ID can be entered as an expression |
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| **Prompt** | description of the image |
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| **Put Output File in Field** | name of the binary property |
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The file can then go to **Write Binary File**, Telegram, S3, and so on. Long generations are awaited for up to 5 minutes.
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### Model → List Models
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Returns the models of the router, one item per model (`id`, `object`, `owned_by`, `pricing` and more). Handy for building your own catalogue or for counting costs.
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---
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## 5. Known details and limitations
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* **Requests go to `/v1/chat/completions`.** The exception is the `gpt-5.6*` model family and anything containing `codex`: LangChain inside n8n switches such IDs to `/v1/responses` on its own (this behaviour is hardcoded in `@langchain/openai` and cannot be turned off by settings). TeamoRouter supports `/v1/responses`, but **for GPT models only**, so the scheme works. Claude/Gemini requests use plain Chat Completions.
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* **Claude through the OpenAI format gives up prompt caching.** That is a limitation of the router's format, not of the node: prompt caching is available only through the native Anthropic protocol (the TeamoRouter README says so explicitly).
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* **`max_tokens`, not `max_completion_tokens`.** The node sends `max_tokens`, exactly as in the TeamoRouter documentation example. If a particular model requires `max_completion_tokens`, add it through **Extra Body**.
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* **The JSON response format** is passed as `response_format`, which is a Chat Completions parameter. For models that go to `/v1/responses` it may be ignored.
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* **The list of image models** is detected by name: `*-image`, `gpt-image*`, `nano-banana*`, `dall-e*`, `imagen*`. If the router names a model differently, it will land in the chat list (or the other way round); the field can always be filled in by hand as an expression.
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* **Prices in the model list** are a hint. The router may report a price per token or already per 1M tokens; the node treats values below `0.005` as “per token”. This is display only and does not affect requests.
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* The list of the **Model** fields is loaded when the field is opened and is not cached: if a model has just appeared, reload the list or use an expression.
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---
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## 6. Development and checks
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```bash
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npm install
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npm run build # tsc + copying the icons into dist/
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npm test # package shape validation + node tests against a mock server
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npm run typecheck
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npm run mock # mock TeamoRouter on http://127.0.0.1:4123/v1 (key: test-key)
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```
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`npm test` runs two suites:
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1. `test/validate-nodes.mjs` — imitates n8n loading the package: checks the node descriptions, the presence of the icons, the implementation of every `loadOptionsMethod`, the credential references, and the `ai_languageModel` connection type of the sub-node.
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2. `test/run-node-tests.mjs` — starts the mock router (`test/mock-server.mjs`, dependency-free) and drives the compiled nodes with real HTTP and real `@langchain/openai`: the model list and the filtering of image models, `supplyData` plus a real model call, the passing of options (`service_tier`, `extra_body`, limits), both operations of the regular node, image generation into a binary field, the handling of 401/402/404 errors, `Continue On Fail` and the timeout.
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The tests need no TeamoRouter key. The `fail-401`, `fail-402`, `fail-404` and `fail-500` models in the mock return an error on purpose, and `slow-model` answers after 2.5 s.
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---
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## 7. First run and troubleshooting
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* The package is built and covered by the automated tests (see `npm test`), but it **has not been run inside a live n8n yet** — do the first run on a test workflow.
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* When installing through `Settings → Community nodes`, the UI may show a warning about a community node — that is normal.
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* If n8n reports that the node failed to load, check `Help → About` (the n8n version) and the container logs: the usual cause is the unreachable `n8n-workflow` peer dependency (Option B, step 3).
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## License
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MIT
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