Alation AI Agent SDK
Officialby
Enables AI agents to access and leverage metadata from the Alation Data Catalog for tasks such as asset curation, search, and data analysis.
Alation AI Agent SDK Overview
What is Alation AI Agent SDK about?
The SDK provides a set of Python libraries and server components that let AI agents query, retrieve, and modify catalog metadata stored in Alation. It includes core API client, LangChain adapters, and an MCP‑compatible server exposing catalog context to any MCP client.
How to use Alation AI Agent SDK?
- Install the required packages via pip:
pip install alation-ai-agent-sdk # core SDK pip install alation-ai-agent-langchain # LangChain integration pip install alation-ai-agent-mcp # MCP server - Configure authentication using either
ServiceAccountAuthParamsorUserAccountAuthParamsand instantiateAlationAPI. - Call tools such as
get_context,bulk_retrieval,lineage, etc., either directly through the core SDK or via LangChain agents. - Run the MCP server (if needed) after installing
alation-ai-agent-mcp; the server will expose the catalog context over the Model Context Protocol for external clients.
Key features of Alation AI Agent SDK
- Core SDK: Handles authentication, request building, and response parsing for the Alation API.
- MCP Integration: Provides an MCP‑compatible server that surfaces catalog context to any MCP client.
- LangChain Integration: Ready‑made adapters to plug the SDK into LangChain agents.
- Rich toolset: Includes
alation_context,get_data_products,bulk_retrieval,check_job_status,update_catalog_metadata,generate_data_product,lineage,get_custom_fields_definitions, andget_data_dictionary_instructions. - Signature‑based customization: Users can define signatures to control which fields and object types are returned.
- Support for async jobs, custom field handling, and lineage graphs (beta).
Use cases of Alation AI Agent SDK
- Data catalog‑driven agents that answer natural‑language questions about datasets.
- Automated data product creation using
generate_data_productand schema retrieval. - Metadata enrichment bots that update glossary terms or other catalog objects.
- Impact analysis via lineage retrieval to understand upstream/downstream dependencies.
- Bulk metadata extraction for reporting or downstream processing.
- Integration with existing LangChain workflows to add catalog awareness to complex agents.
FAQ from the Alation AI Agent SDK
Q: Which Python version is required?
A: Python 3.10 or higher.
Q: How do I authenticate if I don't have service‑account credentials?
A: Use UserAccountAuthParams as described in the authentication guide.
Q: Is there a way to retrieve custom field definitions?
A: Yes, the get_custom_fields_definitions tool returns all custom field metadata (admin) or built‑in fields (non‑admin).
Q: Can I run the MCP server in a container?
A: The MCP server is a Python package; you can containerize it like any Python app by installing the package and executing the provided entry point.
Q: What if I need lineage but the feature is in beta?
A: Enable the beta lineage feature on your Alation instance via support, then activate the lineage tool in the SDK.
Alation AI Agent SDK's README
Alation AI Agent SDK
The Alation AI Agent SDK enables AI agents to access and leverage metadata from the Alation Data Catalog.
Overview
This SDK empowers AI agents to:
- Easily integrate with Alation's Data Catalog
- Address use cases like Asset Curation, Search & Discovery, Role Based Agents, and Data Analyst Agents
- Use natural language to search for relevant metadata
- Integrate seamlessly with AI frameworks like MCP
Components
The project is organized into multiple components:
- Core SDK - Foundation with API client and context tools
- MCP Integration - Server implementation for Model Context Protocol
- LangChain Integration - Adapters for the LangChain framework
Core SDK (alation-ai-agent-sdk)
The core SDK provides the foundation for interacting with the Alation API. It handles authentication, request formatting, and response parsing.
LangChain Integration (alation-ai-agent-langchain)
This component integrates the SDK with the LangChain framework, enabling the creation of sophisticated AI agents that can reason about your data catalog.
Learn more about the LangChain Integration
MCP Integration (alation-ai-agent-mcp)
The MCP integration provides an MCP-compatible server that exposes Alation's context capabilities to any MCP client.
Learn more about the MCP Integration
Getting Started
Prerequisites
- Python 3.10 or higher
- Access to an Alation Data Catalog instance
- A valid refresh token or client_id and secret. For more details, refer to the Authentication Guide.
Installation
# Install core SDK
pip install alation-ai-agent-sdk
# Install LangChain integration
pip install alation-ai-agent-langchain
# Install MCP integration
pip install alation-ai-agent-mcp
Usage
The library needs to be configured with your Alation instance credentials. Depending on your authentication mode, you can use either UserAccountAuthParams or ServiceAccountAuthParams.
Service Account Authentication (Recommended)
from alation_ai_agent_sdk import AlationAPI, ServiceAccountAuthParams
# Initialize the SDK with Service Account Authentication
auth_params = ServiceAccountAuthParams(
client_id="your_client_id",
client_secret="your_client_secret"
)
alation_api = AlationAPI(
base_url="https://your-alation-instance.com",
auth_method="service_account",
auth_params=auth_params
)
If you cannot obtain service account credentials (admin only), see the User Account Authentication Guide for instructions.
Supported Tools
alation_context
Functionality
- Accepts user questions in natural language
- Performs query rewrites to optimize search results
- Returns relevant catalog data in JSON format
- Can return multiple object types in a single response
Usage
response = alation_ai_sdk.get_context(
"What certified data set is used to make decisions on providing credit for customers?"
)
Input Parameters
question(string): The natural language querysignature(optional dict): The configuration controlling which objects and their fields
Returns
- JSON-formatted response of relevant catalog objects
get_data_products
Functionality
- Accepts product IDs for direct lookup
- Accepts user queries in natural language for discovery
- Returns relevant data products in JSON format
- Can return single or multiple results
Usage
response = alation_ai_sdk.get_data_products(
"12345" # Example product ID
)
response = alation_ai_sdk.get_data_products(
"Show me all data products related to sales"
)
Input Parameters
product_id(string, optional): The ID of the product for direct lookupquery(string, optional): A natural language query to discover data products
Returns
- JSON-formatted response of relevant data products
bulk_retrieval
Functionality
- Retrieve catalog objects without conversational queries.
- Useful for having an LLM decide which items to use from a larger set.
- Accepts a signature defining which objects and the fields required.
- Returns relevant catalog data in JSON format
- Can return multiple object types in a single response
Usage
# Get tables from a specific datasource
bulk_signature = {
"table": {
"fields_required": ["name", "description", "columns"],
"search_filters": {
"fields": {"ds": [123]} # Specific datasource
},
"limit": 100,
"child_objects": {
"columns": {
"fields": ["name", "data_type", "description"]
}
}
}
}
response = sdk.bulk_retrieval(signature=bulk_signature)
Input Parameters
signature(dict): The configuration controlling which objects and their fields
Returns
- JSON-formatted response of relevant data products
check_job_status
Functionality
- Used to monitor progress and completion of async jobs.
- Accepts a job id
- Returns the job detail object including status
Input Parameters
job_id(int): The identifier of the asychronous job.
Returns
- JSON-formatted response of the job details
update_catalog_metadata
Supported object types
glossary_term: Individual glossary terms (corresponds to document objects)glossary_v3: Glossary collections (corresponds to doc-folder objects, i.e., Document Hubs)
Functionality
- Creates an async job that updates one or more object field values.
Input Parameters
- A list of objects to be updated which include the
id,otype,field_id, and the newvalue.
Returns
- validation error (dict) A dictionary containing a "error" value.
- on success (dict) A dictionary containing a "job_id" value.
generate_data_product
Functionality
- Fetches the current Alation Data Product schema dynamically from your instance
- Includes detailed instructions for converting user input to valid YAML
Input Parameters
- No parameters required
Returns
- Complete instruction set with the latest schema from your Alation instance
lineage
NOTE: This BETA feature must be enabled on the Alation instance. Please contact Alation support to do this. Additionally, the lineage tool within the SDK must be explicitly enabled.
Functionality
- Access the object's upstream or downstream lineage.
- Graph is filterable by object type.
- Helpful for root cause and impact analysis
- Enables custom field value propagation
Input Parameters
root_node(dict) The starting object. Must containidandotype.direction(upsteam|downstream) The direction to resolve the lineage graph from.limit(optional int) Defaults to 1,000.batch_size(optional int) Defaults to 1,000.max_depth(optional int) The maximumn depth to transerve of the graph. Defaults to 10.allowed_otypes(optional string[]) Controls which types of nodes are allowed in the graph.pagination(optional dict) Contains information about the request including cursor identifier.show_temporal_objects(optional bool) Defaults to false.design_time(optional 1,2,3) 1 for design time objects. 2 for run time objects. 3 for both design and run time objects.excluded_schema_ids(optional int[]) Remove nodes if they belong to these schemas.time_from(optional timestamp w/o timezone) Controls the start point of a time period.time_to(optional timestamp w/o timezone) Controls the ending point of a time period.
Returns
- (dict) An object containing the lineage graph, the direction, and any pagination values.
get_custom_fields_definitions
Functionality
- Retrieves all custom field definitions created by the organization
- Provides metadata about field types, allowed values, and object compatibility
- Returns built-in fields for non-admin users with appropriate messaging
- Includes usage guidance for implementing custom fields in applications
Input Parameters
No parameters required
Returns
- Admin users: JSON-formatted response with all custom fields plus built-in fields
- Non-admin users: Built-in fields only (title, description, steward) with informational message
get_data_dictionary_instructions
Functionality
- Dynamically fetches current custom field definitions from your instance
- Provides complete CSV format specifications with required headers
- Includes object hierarchy grouping requirements and validation rules
- Generates field-specific examples and transformation guidelines
- Returns ready-to-use instructions for LLMs and developers
Input Parameters
No parameters required
Returns
Complete instruction set with custom fields and examples for generating data dictionary.
Shape the SDK to your needs
The SDK's alation-context and bulk_retrieval tools support customizing response content using signatures. This powerful feature allows you to specify which fields to include and how to filter the catalog results. For instance:
# Define a signature for searching only tables that optionally
# include joins and filters if relevant to the user question
signature = {
"table": {
"fields_required": ["name", "title", "description"],
"fields_optional": ["common_joins", "common_filters"]
}
}
# Use the signature with your query
response = sdk.get_context(
"What are our sales tables?",
signature
)
For more information about signatures, refer to Using Signatures
Guides and Example Agents
General
- Planning an Integration - Practical considerations for getting the most out of your agents and the Alation Data Catalog.
- Using Signatures - How to customize your agent with concrete examples.
- Supported Object Types and Fields - See what's available.
Core SDK
Direct usage examples for the Alation AI Agent SDK:
- Basic Usage Example - Simple example showing SDK initialization and context queries.
- QA Chatbot Example - Interactive chatbot demonstrating conversation context and signature usage.
Model Context Protocol (MCP)
Enable agentic experiences with the Alation Data Catalog.
- MCP Integration - Getting the Alation MCP server up and running.
- Integration with Code Editors - Use the tools directly in your code editor.
- Testing with MCP Inspector - Steps for debugging and verification.
- Claude Desktop Integration - Leverage the Alation MCP server within Claude Desktop.
- LibreChat Integration - Create assistants and agents alike.
LangChain
Harness the SDK to build complex agents and workflows.
- LangChain Integration - How to integrate the SDK into your LangChain agents.
- Basic Usage Example - A simple example.
- Multi Agent Example - A multi agent workflow with several SDK integration points.
Integrating with other toolkits
The number of published agent frameworks and toolkits appears to be increasing every day. If you don't happen to see the framework or toolkit you're using here, it's still possible to adapt alation-ai-agent-sdk to your needs. It may be as simple as writing a wrapping function where a decorator is applied.
While we want to reach as many developers as possible and make it as convenient as possible, we anticipate a long tail distribution of toolkits and won't be able to write adapters for every case. If you'd like support for a specific toolkit, please create an issue to discuss.
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