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Gemini Enterprise Agent Platform
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Overview
Studio
Agents
Models
Notebooks
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Agent Platform
Generative AI
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Overview
Studio
Agents
Models
Notebooks
Pricing
More
Engineering Blog
Console
Overview
Get an API key
Get started
Build an agent with ADK and Agents CLI
Build
Overview
Create agents
Overview
Set up the environment
Prebuilt agents
Agent Garden
Gemini Deep Research Agent
Use Deep Research
Agent card
Create agents in the console
Agent Studio
Managed Agents API on Agent Platform
Overview
Create and manage agents
Interact with agents
Sandbox environment
Evaluate agents
Create agents with frameworks
Quickstart: Develop with agent frameworks on Agent Runtime
Agent Development Kit (ADK)
ADK Overview
Quickstart: Develop with Agent Development Kit on Agent Runtime
Develop an ADK agent
Agent2Agent
LangChain
LangGraph
AG2
LlamaIndex
Create a custom agent
Skill Registry
Overview
Create and manage skills
Authentication
Authenticate to services using 3-legged OAuth with auth manager
Authenticate to services using 2-legged OAuth with auth manager
Authenticate to services using API key with auth manager
RAG Engine
RAG overview
RAG quickstart
RAG Engine billing
Deployment modes in RAG Engine
Overview
Managing Spanner mode
Serverless mode
Switching between modes
Data ingestion
Supported models
Generative models
Embedding models
Document parsing
Supported documents
Fine-tune RAG transformations
Use Document AI layout parser
Use the LLM parser
Vector database choices in RAG
Overview of vector database choices
Use RagManagedDb with RAG
Use Agent Platform Vector Search 2.0 with RAG
Use Agent Platform Vector Search with RAG
Use Feature Store with RAG
Use Weaviate with RAG
Use Pinecone with RAG
Use Agent Platform Search with RAG
Reranking for RAG
Manage your RAG corpus
Filter with metadata search
Use CMEK with RAG
Use RAG in Gemini Live API
RAG Cross Corpus Retrieval
Tokenizer
List and count tokens
Use the Count Tokens API
Multimodal datasets
Use Agent Search
Perform vector similarity searches
Vector Search overview
Try it
Get started
Vector Search quickstart
Before you begin
Notebook tutorials
About hybrid search
Create and manage index
Input data format and structure
Create and manage your index
Storage-optimized indexes
Index configuration parameters
Update and rebuild index
Filter vector matches
Import index data from BigQuery
Embeddings with metadata
Deploy and query an index
Private Service Connect (recommended)
Set up Vector Search with Private Service Connect
Query
JSON Web Token authentication
Public endpoint
Deploy
Query
Private services access
Set up a VPC network peering connection
Deploy
Query
JSON Web Token authentication
Monitor a deployed index
Use custom organization policies
Get support
Agent Retrieval (formerly Vector Search 2.0) - Intelligent Retrieval for AI Applications
Overview
Try it
Migrate from Vector Search 1.0
Collections
Autogenerated Embeddings
Data Objects
Using ETags for Concurrency Control
Querying Collections for Data Objects
Searching for Data Objects
Reranking
Indexes
Use MCP to connect to AI and custom applications
Customer Managed Encryption Keys (CMEK)
Audit logging
Quotas
Get support
Scale
Overview
Agent Runtime
Overview
Deploy agents
Provision agents with Terraform
Manage deployed agents
Overview
Manage agent access
Manage revisions and traffic
Set up tracing
Set up logging
Set up monitoring
Use agents
Overview
Agent Development Kit
Agent2Agent
LangChain
LangGraph
AG2
LlamaIndex
Custom
Runtime contract
Agent Identity with Agent Runtime
Route traffic through Agent Gateway
Bidirectional streaming
Optimize and scale Agent Runtime performance
Use Private Service Connect (PSC) interfaces
Agent Runtime SDK migration
Sessions
Overview
Manage sessions with Agent Development Kit
Manage sessions using the console or API calls
