Agent-To-Agent (A2A) Integration

The Inference Gateway supports Agent-To-Agent (A2A) integration, enabling Large Language Models (LLMs) to seamlessly coordinate with external specialized agents. This powerful feature allows LLMs to access and utilize a wide range of external tools and services through standardized agent interfaces.

What is Agent-To-Agent (A2A)?

Agent-To-Agent (A2A) is a protocol that enables LLMs to discover, communicate with, and coordinate multiple specialized agents simultaneously. Each agent can provide specific capabilities (called "skills") that the LLM can automatically discover and use to fulfill user requests.

Key Features

Automatic Agent Discovery: The LLM automatically discovers available agents and their capabilities
Multi-Agent Coordination: Coordinate multiple agents in a single conversation
Specialized Skills: Each agent provides specialized tools for specific domains
Distributed Architecture: Agents can run as separate services and scale independently
Natural Language Integration: Users interact naturally while the LLM handles agent coordination
Protocol Standardization: Based on standardized A2A protocol for interoperability

How A2A Works

When a user makes a request:

Request Analysis: The LLM analyzes the user's request
Agent Discovery: Available agents and their skills are discovered
Task Decomposition: The request is broken down into tasks for specific agents
Agent Coordination: Multiple agents are called simultaneously or sequentially
Result Integration: Results from all agents are integrated into a coherent response

Using A2A with the Inference Gateway CLI

The best way to use A2A is through the Inference Gateway CLI. The CLI provides seamless integration with A2A agents, allowing you to delegate specialized tasks to external agents directly from your chat sessions.

Getting Started

First, install and initialize the CLI:

Terminal

# Install the CLI
curl -fsSL https://raw.githubusercontent.com/inference-gateway/cli/main/install.sh | bash

# Initialize your project
infer init

# Start interactive chat
infer chat

Adding A2A Agents

The CLI provides dedicated commands for managing A2A agents through the infer agents command.

Browse available agents at registry.inference-gateway.com

Terminal

# Initialize agents configuration
infer agents init

# Add a remote A2A agent
infer agents add <agent-name> <agent-url>

# Add a local agent with Docker support
infer agents add <agent-name> <agent-url> \
  --oci <oci-image> \
  --run

# Add an agent with environment variables
infer agents add <agent-name> <agent-url> \
  --environment KEY=value

# List configured agents
infer agents list

# Show details for a specific agent
infer agents show <agent-name>

# Remove an agent
infer agents remove <agent-name>

Agents can be configured at two levels:

Project-level: .infer/agents.yaml - Agents specific to the current project
Userspace: ~/.infer/agents.yaml - Global agents available across all projects (use --userspace flag)

Delegating Tasks to A2A Agents

Within the CLI, you can delegate specialized tasks to A2A agents. The CLI handles agent discovery, coordination, and result integration automatically.

Terminal

infer chat
> "Schedule a team meeting for tomorrow at 2 PM using the calendar agent"
> "Check my calendar for conflicts this week"

For autonomous task execution with A2A agents:

Terminal

infer agent "Analyze my calendar and suggest optimal meeting times for the team"

A2A Tools

The CLI provides four A2A tools that LLMs can use to interact with agents:

A2A_QueryAgent: Query agent capabilities and metadata for discovery
A2A_SubmitTask: Submit tasks to specialized agents for processing
A2A_QueryTask: Check the status and results of submitted tasks
A2A_DownloadArtifacts: Download files and artifacts from completed tasks

Viewing Connected Agents

During a chat session, you can use the /a2a shortcut to view all connected A2A agents and their capabilities:

Terminal

infer chat
> /a2a

This displays information about each configured agent, including their available skills and status.

Why Use the CLI for A2A?

The Inference Gateway CLI acts as an A2A agent client, providing a seamless interface for interacting with A2A-compatible agents. The CLI provides several advantages for A2A integration:

A2A Client Implementation: The CLI implements the A2A client protocol, handling all communication with agents
Dedicated Agent Management: Add, list, show, and remove agents with simple commands
Automatic Discovery: Agents are discovered and coordinated automatically
Safety Controls: Built-in approval workflows for sensitive operations
Interactive Experience: Rich TUI with real-time feedback on agent interactions
Flexible Modes: Switch between Standard, Plan, and Auto-Accept modes for different workflows
Docker Support: Run agents locally with OCI image support

Learn more about the CLI in our CLI Documentation.

Available Agents

Google Calendar Agent

The Google Calendar Agent provides comprehensive calendar management capabilities:

Skills:

list-events: List calendar events for specified time periods
create-event: Create new calendar events with natural language parsing
update-event: Update existing calendar events
delete-event: Delete calendar events

Features:

Natural language date/time parsing ("tomorrow at 2pm", "next Monday")
Smart attendee extraction ("meeting with John and Sarah")
Location detection and parsing
Google Calendar API integration

Repository: github.com/inference-gateway/google-calendar-agent

Creating Custom Agents

To create your own A2A-compatible agent, implement these endpoints:

Required Endpoints

/.well-known/agent.json - Agent capabilities and metadata
/a2a - Main A2A protocol endpoint
/health - Health check endpoint

Agent Capabilities Schema

Your agent must expose its capabilities via the /.well-known/agent.json endpoint:

JSON

{
  "capabilities": {
    "skills": [
      {
        "id": "your-skill-id",
        "name": "Your Skill Name",
        "description": "Description of what your skill does"
      }
    ]
  }
}

A2A Protocol Implementation

Implement the A2A protocol at the /a2a endpoint to handle:

message/send - Send a message and receive response
message/stream - Send a streaming message
task/get - Get task status (optional)
task/cancel - Cancel a running task (optional)

Best Practices

Agent Design

Single Responsibility: Each agent should focus on a specific domain
Stateless Operations: Design agents to be stateless for better scalability
Error Handling: Implement robust error handling and fallback mechanisms
Documentation: Clearly document agent capabilities and expected inputs

Security

Authentication: Implement proper authentication between gateway and agents
Input Validation: Validate all inputs to prevent injection attacks
Rate Limiting: Implement rate limiting to prevent abuse
Network Security: Use secure communication channels (HTTPS/TLS)

Performance

Caching: Implement caching for frequently accessed data
Timeouts: Set appropriate timeouts for agent communications
Load Balancing: Use load balancing for high-availability deployments
Monitoring: Monitor agent performance and availability

Awesome A2A
A2A Protocol Specification
Inference Gateway CLI
A2A Registry - Discover available A2A agents and their capabilities