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Add these nodes with the pipeline builder: pipeline.add(name="...").<node>(...). Each entry lists the node’s configuration parameters. See the Pipeline reference for add, run, and lifecycle methods.

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chat_file_reader — Chat File Reader

Allows for document upload within chatbots (often connected to the LLM node).
Platform docs: Chat File Reader
Parameters
​
chunk_overlap
int
default:"200"
​
chunk_size
int
default:"1000"
​
file_parser
str
default:"'default'"
One of: contextual_ai, default, docling, llama_parse, mistral_ocr, reducto, textract
​
max_docs_per_query
int
default:"10"
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retrieval_unit
str
default:"'chunks'"
One of: chunks, documents, pages

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chat_memory — Chat Memory

Give connected nodes access to conversation history.
Platform docs: Chat Memory
Parameters
​
memory_type
str
default:"'Full - Formatted'"
The type of memory to use One of: Full - Formatted, Full - Raw, Message Buffer, Token Buffer, Vector Database
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memory_window
int
default:"2048"
The number of tokens to store in memory

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create_session — Create Session

Create a new session with an agent and participants
Platform docs: Create Session
Parameters
​
agent_id
AcceptsAgent
required
​
name
str
default:"''"

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data_collector — Data Collector

Allows a chatbot to collect information by asking the user to provide specific pieces of information (e.g., name, email, etc.).
Platform docs: Data Collector
Parameters
​
auto_generate
bool
default:"True"
If checked, the node will output questions in successive order until all fields are successfully collected. If unchecked, the node will output the data that is collected (often passed to an LLM with a prompt to ask successive questions to the user, along with specific instructions after all fields are collected) - e.g., {‘Field1’: ‘Collected_Data’, ‘Field2’: ‘Collected_Data’}
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provider
str
required
The model provider
​
model
str
default:"''"
The specific model for question generation
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prompt
str
default:"''"
Specific instructions of how the LLM should collect the information
​
query
str
required
The query to be analysed for data collection (passed to the LLM)
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data_collector_node_id
str
default:"''"
The ID of the data collector node
​
fields
ListType | list[FieldDefinition] | list[List[Dict[str, Any]]]
default:"[]"
The fields to be collected

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get_run_data — Get Run Data

Fetch run metrics (trace, latency, cost, tokens, status) for a session
Platform docs: Get Run Data
Parameters
​
session_id
str
required

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read_memory — Read Memory

Load the full content of a memory entry. Use this when:
Parameters
​
memory_id
str
required

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read_session_messages — Read Session Messages

Read all messages from a session with derived metadata
Platform docs: Read Session Messages
Parameters
​
message_type_filter
str
default:"'none'"
​
offset
int
default:"0"
​
session_id
str
required

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write_memory — Write Memory

Create or update a Memory object for use with context-aware agents. You can store text memory content and or references to object types like knowledge bases, knowledge base items, files and prompts.
Parameters
​
content
list[str]
required
​
memory_description
str
default:"''"
​
memory_id
str
default:"''"
​
name
str
required
​
scope_user_id
str
default:"''"
​
scopes
list[str]
default:"[]"