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# 🌈 A2A‑Go
> _"Combine A2A and MCP to create advanced, distributed agentic systems!"_
A2A-Go is a framework for building scalable, distributed agentic AI systems. It promotes a microservice architecture where agents and tools operate as independent services, deployable locally or across a network. The framework implements the Agent-to-Agent (A2A) protocol and utilizes the Model Context Protocol (MCP) for standardized tool interaction and data exchange.
[](https://github.com/theapemachine/a2a-go/actions/workflows/main.yml)
[](https://goreportcard.com/report/github.com/theapemachine/a2a-go)
[](https://godoc.org/github.com/theapemachine/a2a-go)
[](https://opensource.org/licenses/MIT)
[](https://sonarcloud.io/summary/new_code?id=TheApeMachine_a2a-go)
[](https://sonarcloud.io/summary/new_code?id=TheApeMachine_a2a-go)
[](https://sonarcloud.io/summary/new_code?id=TheApeMachine_a2a-go)
[](https://sonarcloud.io/summary/new_code?id=TheApeMachine_a2a-go)
[](https://sonarcloud.io/summary/new_code?id=TheApeMachine_a2a-go)
[](https://sonarcloud.io/summary/new_code?id=TheApeMachine_a2a-go)
[](https://sonarcloud.io/summary/new_code?id=TheApeMachine_a2a-go)
[](https://sonarcloud.io/summary/new_code?id=TheApeMachine_a2a-go)
[](https://sonarcloud.io/summary/new_code?id=TheApeMachine_a2a-go)
**a2a‑go** is a reference Go implementation of the [**Agent‑to‑Agent (A2A)**
protocol](https://google.github.io/A2A/#/) by Google, including the proposed
interoperability with the [**Model Context Protocol (MCP)**](https://modelcontextprotocol.io).
> 🚧 **Work in progress** 🚧 Consider this project a proof of concept at best, and subject
> to sudden changes.
## ✨ Features
- [x] **Agent‑to‑Agent (A2A)** protocol implementation
- [x] _Send Task_ to send a new task to an agent
- [x] _Get Task_ to retrieve a task by ID
- [x] _Cancel Task_ to cancel a task
- [x] _Stream Task_ to stream the task results
- [x] _Set Push Notification_ to configure push notifications for a task
- [x] _Get Push Notification_ to retrieve the push notification configuration for a task
- [x] _Structured Outputs_ to return structured data from an agent
- [x] _Fine‑tuning_ to fine‑tune an agent on a dataset
- [x] _Image Generation_ to generate images with an agent
- [x] _Audio Transcription_ to transcribe audio
- [x] _Text‑to‑Speech_ to convert text to speech
- [x] **Model Context Protocol (MCP)** interoperability
- [x] _Tool Calling_ to call tools and receive the results
- [x] _List Prompts_ to retrieve a list of prompts from an agent
- [x] _Get Prompt_ to retrieve a prompt by ID
- [x] _Set Prompt_ to create or update a prompt
- [x] _Delete Prompt_ to delete a prompt by ID
- [ ] _List Resources_ to retrieve a list of resources from an agent
- [ ] _Get Resource_ to retrieve a resource by ID
- [ ] _Set Resource_ to create or update a resource
- [ ] _Delete Resource_ to delete a resource by ID
- [x] _Sampling_ to sample a task from an agent
- [x] _Roots_ to get the root task for a task
- [x] **Built‑in tools**
- [x] _Browser_ to browse the web
- [x] _Docker_ to run Docker commands
- [x] _GitHub_ to search GitHub
- [x] _Memory_ to store and retrieve memories
- [x] _Qdrant_ to store and retrieve vectors
- [x] _Neo4j_ to store and retrieve graph data
---
## 🚀 Quick Start
Use the `Makefile` to run a full containerized distributed system, demonstrating A2A and MCP interoperability.
```bash
make server
make client
```
The `ui` agent is mapped to port `3212`. If you run the TUI outside the Docker
network, update `agent.ui.url` in `~/.a2a-go/config.yml` to point to
`http://localhost:3212`.
