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Know your users better.
Build experiences they remember.

Analytics and real-time context use cases, developed and managed with your agent of choice. Prompt your way from tracking plan to live pipeline to customer intelligence in your application.

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Deliver Agentic Context to Your Favorite Frameworks

Snowplow combines live behavioral streams, delivering what users are doing right now in under 10ms, alongside historical context from your warehouse for a full picture of who your users are to serve them better experiences.

Get Started in a Few Easy Steps

Define the attributes

count_product_views = Attribute(
    name="count_product_views",
    type="int32",
    events=[Event(name="snowplow_ecommerce_action")],
    criteria=Criteria(
        all=[
            Criterion.eq(
                property=EventProperty(
                    vendor="com.snowplowanalytics.snowplow.ecommerce",
                    name="snowplow_ecommerce_action",
                    major_version=1,
                    path="type",
                ),
                value="product_view",
            )
        ]
    ),
    aggregation="counter",
)

Each attribute defines which event it will be calculated from, and what kind of aggregation will be performed.

Create an attribute group

attribute_group = StreamAttributeGroup(
    name="ecom_attributes",
    version=1,
    attribute_key=domain_userid,
    attributes=[
        count_product_views,
        count_add_to_cart,
        total_cart_value,
    ],
    owner="user@company.com",
)

Attribute groups organize related attributes together. They can be considered as "tables" of attributes.

Create a service

stream_service = Service(
    name="ecom_attributes",
    attribute_groups=[attribute_group],
    owner="user@company.com",
)

Services provide an interface for applications to retrieve attributes. Create a service that includes your attribute group.

Demo

4 mins

Build Real-Time Personalized Experiences with Context-Aware AI Agents

Watch Demo

Agentic Accelerators

Each pairs a real framework - Vercel AI SDK, Google ADK, AWS Bedrock AgentCore - with the Snowplow calls it needs: event tracking for the agent itself, and Signals for context injected back into it mid-conversation.

Python SDK Tracking for Ecommerce App

Enable agents to intervene before users have to explain themselves

~30 mins

Real-time Context for an OpenAI Travel Agent

Personalize content and chatbot responses based on user behavior

~45 mins

ML-based Prospect Scoring for a SaaS Website

Deliver real-time predictions to your browser for adaptive experiences

~45 mins

Own Your Analytics Stack, End to End

Snowplow delivers validated, event-level data straight to your warehouse. Model it with open-source dbt packages. Query it with standard SQL or a data agent - no black boxes.

Get Started in a Few Easy Steps

Track your events

// JavaScript tracker
snowplow('trackSelfDescribingEvent', {
  event: {
    schema: 'iglu:com.yourcompany/conversion/jsonschema/1-0-0',
    data: {
      conversion_type: 'signup',
      plan: 'pro'
    }
  }
});

Schemas enforce validation at ingestion. If an event doesn't match the schema, it fails — no silent data corruption.

Model your tables

# packages.yml
packages:
  - package: snowplow/snowplow_unified
    version: [">=1.0.0", "<2.0.0"]


dbt deps
dbt run --select snowplow_unified

Install Snowplow's Unified Digital dbt package to generate view, session, and user derived tables across web and mobile, all within your warehouse.

Query Behavioral Data

SELECT
    session_identifier,
    engaged_time_in_s,
    views_in_session,
    first_page_title
FROM <target_schema>_derived.snowplow_unified_sessions
ORDER BY start_tstamp DESC
LIMIT 10;