LangChain
A RocketRide agent node for answering questions with a LangChain agent loop and the LLM and tools wired into the pipeline. Choose it when a single agent should use LangChain's structured tool-call flow, rather than coordinate CrewAI subagents.
About LangChain
LangChain is a framework for building applications that combine language models with tools and message-based workflows. This node uses its agent and chat-model interfaces while keeping model and tool execution on RocketRide connections.
What it does
The node accepts a question on questions, runs a LangChain agent using the
connected LLM and any connected tools, then writes the final text to answers.
It is also available to a parent agent as a tool. The LLM adapter asks the model
for a JSON envelope that represents either a tool call or a final answer, then
converts it into LangChain's structured message form. Choose this node for that
single-agent LangChain flow; use the CrewAI manager when work must be delegated
to configured specialist subagents.
Connections
| Connection | Required | Description |
|---|---|---|
llm | yes | LLM used by the agent. |
tool | no | Tools available to the agent through control-plane invocation. |
The llm connection is required. With no tool connection, the agent can
still produce a final answer, but has no pipeline tools to call.
Lanes
| Lane in | Lane out | Description |
|---|---|---|
questions | answers | Run the agent for each incoming question and emit its final answer. |
During a run, the node sends thinking SSE updates for agent startup,
LLM activity, and tool-call activity.
As a tool
The registered tool name is <nodeId>.run_agent, where <nodeId> is this
node's pipeline ID; it has no separately configured server-name prefix.
| Function | Description |
|---|---|
<nodeId>.run_agent | Run this LangChain agent for a delegated query and return its agent result. |
The input must be an object with required non-empty query: string and optional
context: object. The node stores supplied context as a
RocketRide.agent.tool_context.v1 context entry. The call returns
{content, meta, stack} to its caller instead of writing to answers.
Non-object input, a blank query, or a non-object context raises ValueError.
If the LLM does not produce a parsable envelope after three attempts, the
adapter supplies an explanatory final message rather than a tool call.
Configuration
The only profile is default: connect an LLM first, then add tools only when
the agent needs to take actions or retrieve information. Most pipelines can
leave the profile selection alone and set the fields that shape delegation and
answering behavior.
Agent description
This text is added to the registered run_agent tool description when it is
non-empty. Set it when another agent may choose this node, describing the
specialist job and the kinds of requests it should receive; leave it empty when
the node is only driven from its questions lane. A concrete description such
as “Summarizes retrieved policy documents” gives a parent a usable routing cue.
Instructions
Use the instruction list for additional guidance supplied by this node's configuration. Keep each entry narrowly scoped to the work the agent should do or the constraints it must honor. Put enduring routing information in Agent description instead, because that is the text exposed to parent agents.
Require tool call
This option is off by default. Turn it on for a workflow where an answer must follow at least one real tool invocation, for example a response that must be grounded in a connected store. Leave it off when the agent can validly answer without a tool; enabled runs that answer without a tool fail with the declared guard error.
Notes
JSON tool-call protocol
RocketRide's LLM seam is text-based, while the LangChain agent expects
structured tool calls. On each model turn, the adapter requests exactly one
JSON object: {"type":"tool_call","name":"server.tool","args":{...}} or
{"type":"final","content":"..."}. It retries malformed output twice with
a correction prompt; after the third invalid response it returns an explanatory
AI message. Connected tools receive dynamically built argument schemas from
their declared input schemas, with a generic input field as the fallback.
Upstream docs
Schema
| Field | Type | Description | Default |
|---|---|---|---|
agent_description | string | Agent description What does this agent do? Describe its purpose and capabilities, this helps parent agents select and invoke it correctly. | "" |
agent_langchain.profile | string | Profile | "default" |
instructions | array | Instructions Additional instructions to guide the agent. | |
require_tool_call | boolean | Require tool call Require the agent to invoke at least one tool before answering. When on, a run that answers without calling any tool fails with a guard error. Use for determinism-critical pipelines where an ungrounded or narrated answer must never be delivered. Off by default. | false |
Dependencies
langchainlangchain-core