Ready-made templates

A template here is a complete scenario: nodes, wires and part of the settings that you can drop into your account in two clicks and then edit as your own. There are ten of them, and all of them are built around chat.

All ten import and run on the free plan: no template requires a higher tier, none uses model keys of your own and none contains nodes a plan gates. The limits still apply — five scenarios in total, one published at a time — while all six channels are open. The full table is in “Plan limits”.

Three ways to get a ready-made graph

Importing is an ordinary scenario creation, so it obeys the same limits: the scenario count quota and the plan’s node allow-list are checked on the server. If the quota is exhausted, the import fails exactly the same way the “create” button would.

What you will have to configure

No template sets a model

In all nine templates that contain an LLM node, its configuration is empty. This is deliberate: the list of available models depends on the plan and on which keys are connected, so a model hard-wired into a template simply would not exist for some readers, and the import would fail before the editor even opened.

Neither validation nor the run complains about it. The “Model” field has a default — the first model of the catalog — so an imported template starts on exactly that one, graph validation (Validate) stays silent and the report comes back empty. Picking a model yourself is still worth it — not because you would otherwise get an error, but because the catalog default may not be what this scenario needs: a different price, a different quality, a different language. It takes ten seconds: open the LLM node and pick a model from the catalog. How the settings dialog opens is described in “Node configuration”.

REPLACE_WITH_YOUR_KB_ID in two templates

In rag-kb and support-rag-agent the Knowledge base node has the kb_id field filled with the literal REPLACE_WITH_YOUR_KB_ID. The field is not empty, so the validator considers the knowledge source specified and stays silent too.

And the run does not fail — which is worse than failing. Search honestly reports that the base was not found: a string like Knowledge base 'REPLACE_WITH_YOUR_KB_ID' not found travels down the graph as context. In rag-kb you see it directly in the answer. In support-rag-agent only the model sees it: the agent gets that string instead of documents and answers based on it — politely, confidently and wrongly. Meanwhile the run counts as successful and is billed in full.

The fix: create a knowledge base (see “Knowledge bases and RAG”) and select it in the Knowledge base node instead of the literal.

Ten templates

Four chat starters on one skeleton

Simple LLM chat, FAQ bot, Text summarizer and Translator are the same graph of four nodes: Entry → AI Agent → Exit, plus LLM connected to the agent by the link_llm configuration wire. They differ only in the agent’s system prompt: in “Simple LLM chat” it is empty, in the other three it is written for the task (the FAQ bot’s prompt even says “tune this for your product”).

• Execute + Data
• LLM

The practical takeaway: if you need a “bot about X”, take any of the four and rewrite the prompt — the difference between them is entirely in the text. Why the model attaches from the side rather than in the chain is explained in “Wires”.

Chat with conversation memory

The same skeleton plus Window memory on the agent’s Memory port. Memory keeps the latest messages (ten by default) within the thread and mixes them into every request; without it the bot does not remember the previous turn. To configure: the model and, optionally, the window size. What exactly counts as a “session” is covered in “State”.

RAG over a knowledge base

The only template with no model at all: Entry → RAG query → Exit, with Knowledge base connected to the search by a data wire and setting its parameters (five fragments by default, relevance threshold 0). What travels to the user is the found text itself, without a model retelling it. This is useful as a search-quality check: if the right paragraphs are not found here, adding an agent on top will not save them. To configure: the knowledge base, then top_k and the threshold.

Support bot (RAG + agent)

The full chain: Entry → RAG query → AI Agent → Exit, where Knowledge base feeds the search and LLM feeds the agent. The user’s question goes into search, the found context arrives at the agent’s input, and the agent formulates the answer. The agent’s system prompt is empty — that is the first thing worth writing: what the bot must not do, and what to do when the context comes back empty.

• Execute + Data
• Data
• LLM

Agent with web search

The chat starter skeleton plus Web search plugged into the agent’s Agent extensions port: in the template the node is already set to As Tool mode, so it has no execution ports. The agent decides on its own whether search is needed and calls it, and what comes back is both a written answer and the sources with excerpts. The node settings hold the number of results (five by default). The search is paid: every call spends quarks on the search itself, and the model’s tokens are counted on top — covered in “Web search”. To configure: the model.

Structured data extraction

Entry → Structured output LLM → Exit; the model is attached to the node by the same link_llm. The template already contains a sample schema of three fields — name, email, summary, one of which is required (summary). The output is JSON following that schema rather than text, so it is convenient to route onward into a database, an email or a CRM. To configure: the model and the schema for your own fields.

Agent answer through a template

Entry → AI Agent → Template → Exit. The template contains {{ nodes.agent.output }} wrapped in a heading, i.e. the agent’s answer is inserted into a fixed shape — that is how you get a consistent answer format (signature, disclaimer, markup). The nodes.agent reference points at the agent node’s id; when you rename an id right in the editor, references in templates are rewritten automatically, but when assembling a graph by hand you have to watch for this yourself. The syntax is in “Templates”. To configure: the model and the template text.

Small things that come up in practice

• Nodes in templates are labelled in Russian (Вход, Агент, Ответ) — these are labels inside the graph, not interface elements, and the interface language does not affect them. You can rename a node by double-clicking its header.
• A template is a copy, not a link. After importing, the scenario is yours: edits to the template on the platform side do not reach you, and your edits break nothing.
• Importing does not publish. A scenario created from a template is a draft; to make it usable you have to save and publish it on the Deploy tab (see “Publishing” and “Web chat”).
• Be careful with the graph boundaries. One Entry and at least one Exit is a hard requirement; details in “Entry and exit”.