Version: 1.14.x (Next)

Agentics

Deprecated

The Agentics components are deprecated and can't run in this version of Langflow, because the agentics-py SDK they depend on can't be installed alongside Langflow. For details, see Compatibility.

Don't run pip install agentics-py or uv pip install agentics-py in your Langflow environment. The install succeeds, but it downgrades langchain-core and other packages below the versions Langflow requires, and Langflow stops working.

The Agentics component bundle uses LLMs to transform tabular data. Add or fill columns row-by-row with the aMap component, collapse many rows into one with aReduce component, or generate synthetic rows with the aGenerate component.

Watch it in action with a sentiment analysis example that transforms customer reviews into structured insights:

Compatibility

The Agentics components need the agentics-py SDK at runtime, and no agentics-py release can be installed in the same Python environment as Langflow 1.9 or later:

The latest release, agentics-py 0.3.6, requires langchain-core earlier than 1.0 through its langchain-huggingface dependency. Langflow requires langchain-core 1.x.
agentics-py depends on crewai, and every crewai release since 0.140.0 pins json-repair or tomli below the versions Langflow requires.
Earlier agentics-py releases have the same conflicts, or they fail when imported.

When you run an Agentics component, it stops with an error that links to this page. Flows that contain Agentics components still open, so you can inspect the components and their settings.

The Agentics components are legacy components, so they are hidden in the visual editor by default. To show them, click Component settings, and then turn on the Legacy filter. If a compatible agentics-py release becomes available, the components can return to supported status. Otherwise, they can be removed in a future release.

The Agentics components also require an LLM. Supported providers include OpenAI, Anthropic, Google Generative AI, IBM WatsonX, and Ollama.

aGenerate component

aGenerate generates synthetic data from a schema or from an example DataFrame. Use it for test data, augmentation, or documentation examples.

For example, this schema definition creates the following DataFrame output:

Schema definition:

customer_name (str): Full name
email (str): Email address
age (int): Age between 18–80
purchase_categories (str, As List): List of product categories purchased

Output DataFrame (example, 10 rows generated):

customer_name	email	age	purchase_categories
Sarah Johnson	sarah.j@email.com	34	Electronics, Books, Home & Garden
Michael Chen	m.chen@email.com	28	Sports, Clothing, Electronics
...	...	...	...

Parameters

Some parameters are hidden by default in the visual editor. You can modify all component parameters through the component inspection panel that appears when you select a component.

Name	Type	Description
Language Model	Dropdown	Select the LLM provider and model. Use guided experience.
Input DataFrame	Table	Optional. Example DataFrame to learn from; only first 50 rows used. If not provided, Schema is used.
Schema	Table	Define columns to generate when no Input DataFrame is provided. See the component's schema definition.
Instructions	String	Optional instructions for generation. Hidden by default in the visual editor.
Number of Rows to Generate	Integer	How many synthetic rows to create. Default: 10.

aMap component

aMap transforms each row of input data using natural language instructions and a defined output schema (one row in, one row out). Use aMap for enriching data with LLM-generated columns such as sentiment, categories, and entity extraction.

Rows are processed concurrently; default batch size is 10. Token usage scales with number of rows.

For example, aMap keeps each input row and fills in sentiment, confidence, and key_topics with the connected LLM.

Input DataFrame:

review_id	text
1	Great product, fast shipping!
2	Terrible quality, broke after one use

Schema definition:

sentiment (str): "positive", "negative", or "neutral"
confidence (float): Confidence score 0–1
key_topics (str, As List): Main topics mentioned

Output DataFrame:

review_id	text	sentiment	confidence	key_topics
1	Great product, fast shipping!	positive	0.95	product quality, shipping
2	Terrible quality, broke after one use	negative	0.92	quality, durability

Parameters

Some parameters are hidden by default in the visual editor. You can modify all component parameters through the component inspection panel that appears when you select a component.

Name	Type	Description
Language Model	Dropdown	Select the LLM provider and model. Use guided experience.
Input DataFrame	Table	Input DataFrame (list of dicts or DataFrame). Each row is processed independently.
Schema	Table	Define the structure and types for generated columns. See the component's schema definition.
Instructions	String	Natural language instructions for transforming each row into the output schema.
As List	Boolean	If true, generate multiple instances of the schema per row and concatenate.
Keep Source Columns	Boolean	If `true`, append new columns to original data; if false, return only generated columns. Ignored if As List is true. Default: `true`.

aReduce component

aReduce aggregates all rows in the input DataFrame into a single row following the output schema. Use aReduce for summaries, reports, or consolidated insights. To aggregate rows into a list, set As List to true in the component.

All rows are sent in one request; token usage can be high for large DataFrames. Consider filtering or sampling first.

For example, aReduce takes all input rows and produces a single row with LLM-generated aggregates. It sums revenue into total_revenue, identifies the best-selling product in best_selling_product, and writes a short summary of the sales.

Input DataFrame:

date	product	revenue	units
2024-01-01	Widget A	1200	50
2024-01-02	Widget B	800	30
2024-01-03	Widget A	1500	60

Schema definition:

total_revenue (float): Sum of all revenue
best_selling_product (str): Product with highest units
summary (str): Natural language summary

Output DataFrame:

total_revenue	best_selling_product	summary
3500	Widget A	Over 3 days, Widget A was the best seller with 110 units, generating $2700 in revenue.

Parameters

Some parameters are hidden by default in the visual editor. You can modify all component parameters through the component inspection panel that appears when you select a component.

Name	Type	Description
Language Model	Dropdown	Select the LLM provider and model.
Input DataFrame	Table	Input DataFrame (list of dicts or DataFrame). Required.
Schema	Table	Define the structure and types for the aggregated output. See the component's schema definition.
As List	Boolean	If true, output is a list of instances of the schema.
Instructions	String	Optional instructions for aggregation. If omitted, the LLM infers from field descriptions.

Performance and troubleshooting

Token usage: aMap scales with rows; aReduce sends all rows in one call; aGenerate scales with instances. Use smaller batches or sample large datasets to reduce cost.
Batch size: Default 10; max 25. Larger batches improve throughput but increase latency per batch.
Errors: If a component reports that agentics-py isn't available, see Compatibility. Don't install agentics-py into your Langflow environment. For API/key errors, set the provider’s API key as a global variable or env var. For DataFrame errors, ensure input is a list of dicts or use a DataFrame component output.

Compatibility​

aGenerate component​

Parameters​

aMap component​

Parameters​

aReduce component​

Parameters​

Performance and troubleshooting​

See also​

Compatibility

aGenerate component

Parameters

aMap component

Parameters

aReduce component

Parameters

Performance and troubleshooting

See also