Eino ADK: Quickstart

Installation

Eino provides ADK from v0.5.0. Upgrade your project:

// stable >= eino@v0.5.0
go get github.com/cloudwego/eino@latest

Agent

What is Eino ADK

Eino ADK, inspired by Google‑ADK, is a Go framework for building Agent and Multi‑Agent applications. It standardizes context passing, event streaming, task transfer, interrupts/resume, and cross‑cutting features.

What is an Agent

An Agent is the core of Eino ADK, representing an independent, executable intelligent task unit. You can think of it as an “intelligent entity” that can understand instructions, execute tasks, and provide responses. Each Agent has a clear name and description, making it discoverable and callable by other Agents.

Any scenario requiring interaction with a Large Language Model (LLM) can be abstracted as an Agent. For example:

• An Agent for querying weather information
• An Agent for booking meetings
• An Agent capable of answering domain‑specific questions

Agent in ADK

All features in Eino ADK are designed around the Agent abstraction:

type Agent interface {
    Name(ctx context.Context) string
    Description(ctx context.Context) string
    Run(ctx context.Context, input *AgentInput) *AsyncIterator[*AgentEvent]
}

Based on the Agent abstraction, ADK provides three base extension categories:

• ChatModel Agent: The “thinking” part of the application, using LLM as its core to understand natural language, perform reasoning, planning, generate responses, and dynamically decide how to execute or which tools to use.
• Workflow Agents: The coordination and management part of the application, controlling sub-Agent execution flow based on predefined logic according to their type (sequential/parallel/loop). Workflow Agents produce deterministic, predictable execution patterns, unlike the dynamic random decisions generated by ChatModel Agent.
  • Sequential (Sequential Agent): Execute sub-Agents in order
  • Loop (Loop Agent): Repeatedly execute sub-Agents until a specific termination condition is met
  • Parallel (Parallel Agent): Execute multiple sub-Agents concurrently
• Custom Agent: Implement your own Agent through the interface, allowing highly customized complex Agents

Based on these base extensions, you can combine these basic Agents according to your needs to build the Multi-Agent system you require. Additionally, Eino provides several out-of-the-box Multi-Agent best practice paradigms based on daily practical experience:

• Supervisor: Supervisor mode, where the Supervisor Agent controls all communication flows and task delegation, deciding which Agent to call based on current context and task requirements.
• Plan-Execute: Plan-Execute mode, where the Plan Agent generates a plan with multiple steps, and the Execute Agent completes tasks based on user query and the plan. After execution, Plan is called again to decide whether to complete the task or replan.

The table and diagram below provide the characteristics, differences, and relationships of these base extensions and encapsulations. Subsequent chapters will detail the principles and specifics of each type:

ADK Examples

The Eino‑examples project provides various ADK implementation examples. You can refer to the example code and descriptions to build an initial understanding of ADK capabilities:

Project Path	Introduction	Diagram
Sequential workflow example	This example code demonstrates a sequential multi-agent workflow built using Eino ADK's Workflow paradigm. Sequential workflow construction: Create a sequential execution agent named ResearchAgent via adk.NewSequentialAgent, containing two sub-agents (SubAgents) PlanAgent and WriterAgent, responsible for research plan formulation and report writing respectively. Clear sub-agent responsibilities: PlanAgent receives research topics and generates detailed, logically clear research plans; WriterAgent writes structurally complete academic reports based on the research plan. Chained input/output: PlanAgent's output research plan serves as WriterAgent's input, forming a clear upstream-downstream data flow, reflecting the sequential dependency of business steps.
Loop workflow example	This example code builds a reflection-iteration agent framework based on Eino ADK's Workflow paradigm using LoopAgent. Iterative reflection framework: Create ReflectionAgent via adk.NewLoopAgent, containing two sub-agents MainAgent and CritiqueAgent, supporting up to 5 iterations, forming a closed loop of main task solving and critical feedback. MainAgent: Responsible for generating initial solutions based on user tasks, pursuing accurate and complete answer output. CritiqueAgent: Performs quality review on MainAgent's output, provides improvement feedback, terminates the loop if results are satisfactory, and provides final summary. Loop mechanism: Utilizes LoopAgent's iteration capability to continuously optimize solutions through multiple rounds of reflection, improving output quality and accuracy.
Parallel workflow example	This example code builds a concurrent information collection framework based on Eino ADK's Workflow paradigm using ParallelAgent: Concurrent execution framework: Create DataCollectionAgent via adk.NewParallelAgent, containing multiple information collection sub-agents. Sub-agent responsibility allocation: Each sub-agent is responsible for information collection and analysis from one channel, with no interaction needed between them, clear functional boundaries. Concurrent execution: Parallel Agent can simultaneously start information collection tasks from multiple data sources, significantly improving processing efficiency compared to serial approaches.
supervisor	This use case employs a single-layer Supervisor managing two relatively comprehensive sub-Agents: Research Agent handles retrieval tasks, Math Agent handles various mathematical operations (add, multiply, divide), but all math operations are uniformly processed within the same Math Agent rather than being split into multiple sub-Agents. This design simplifies the agent hierarchy, suitable for scenarios where tasks are relatively concentrated and don't require excessive decomposition, facilitating rapid deployment and maintenance.
layered‑supervisor	This use case implements a multi-tier intelligent agent supervision system, where the top-level Supervisor manages Research Agent and Math Agent, and Math Agent is further subdivided into three sub-Agents: Subtract, Multiply, and Divide. The top-level Supervisor is responsible for assigning research tasks and math tasks to lower-level Agents, while Math Agent as a mid-tier supervisor further dispatches specific math operation tasks to its sub-Agents. Multi-tier agent structure: Implements a top-level Supervisor Agent managing two sub-agents — Research Agent (responsible for information retrieval) and Math Agent (responsible for mathematical operations). Math Agent internally subdivides into three sub-agents: Subtract Agent, Multiply Agent, and Divide Agent, handling subtraction, multiplication, and division operations respectively, reflecting multi-level supervision and task delegation. This hierarchical management structure reflects fine-grained decomposition of complex tasks and multi-level task delegation, suitable for scenarios with clear task classification and computational complexity.
plan‑execute example	This example implements a multi-Agent travel planning system using the plan-execute-replan pattern based on Eino ADK. The core function is to process complex user travel requests (such as "3-day Beijing trip, need flights from New York, hotel recommendations, must-see attractions") through a "plan-execute-replan" loop to complete tasks: 1. Plan: Planner Agent generates a step-by-step execution plan based on the large model (e.g., "Step 1: check Beijing weather, Step 2: search New York to Beijing flights"); 2. Execute: Executor Agent calls mock tools **weather (get_weather), flights (search_flights), hotels (search_hotels), attractions (search_attractions)** to execute each step. If user input information is missing (e.g., budget not specified), it calls ask_for_clarification tool to ask follow-up questions; 3. Replan: Replanner Agent evaluates whether the plan needs adjustment based on tool execution results (e.g., if no flight tickets available, reselect dates). Execute and Replan continuously loop until all steps in the plan are completed; 4. Supports session trajectory tracking (CozeLoop callback) and state management, ultimately outputting a complete travel plan. Structurally, plan-execute-replan has two layers: Layer 2 is a loop agent composed of execute + replan agent, meaning after replan, re-execution may be needed (after replanning, need to query travel information / request user to continue clarifying questions) Layer 1 is a sequential agent composed of plan agent + Layer 2 loop agent, meaning plan executes only once, then hands over to the loop agent for execution
book recommendation agent (interrupt and resume)	This code demonstrates a book recommendation chat agent implementation built on the Eino ADK framework, showcasing Agent interrupt and resume functionality. Agent construction: Create a chat agent named BookRecommender via adk.NewChatModelAgent for recommending books based on user requests. Tool integration: Integrates two tools — BookSearch tool for searching books and AskForClarification tool for asking clarifying information, supporting multi-turn interaction and information supplementation. State management: Implements simple in-memory CheckPoint storage, supporting session breakpoint continuation to ensure context continuity. Event-driven: Obtains event streams by iterating runner.Query and runner.Resume, handling various events and errors during execution. Custom input: Supports dynamic user input reception, using tool options to pass new query requests, flexibly driving task flow.

What’s Next

After this Quickstart overview, you should have a basic understanding of Eino ADK and Agents.

The following articles will dive deep into ADK core concepts to help you understand how Eino ADK works and use it more effectively: