Each example demonstrates how to expose agents built with various AI frameworks through ACP, preserving their specialized capabilities while leveraging ACP’s standardized communication interface.

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Basic Components Of ACP

A basic collection of standalone implementation of the various types of ACP clients and servers.

The complete source code is available on GitHub.

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BeeAI Framework Chat

Integrates the BeeAI framework’s ReAct agent architecture with ACP by mapping BeeAI events to ACP protocol messages. This enables tool-based reasoning steps and structured final outputs within ACP.

The complete source code is available on GitHub.

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Prompt-Chaining With BeeAI

Implementation of prompt chaining pattern using the ACP SDK and BeeAI framework. In this system, a main agent chains two ReAct agents: the first agent generates marketing copy for a product, and then another agent translates this marketing copy into Spanish. This setup showcases how multiple agents can be chained together within ACP.

The complete source code is available on GitHub.

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BeeAI Language Translation Using Routing

Demonstrates a translation system using BeeAI framework’s ReAct agent with ACP. In this example, a router agent efficiently routes translation requests to specialized agents for Spanish or French translations using the TranslationTool. Exhibits the handling of multilingual translations and the conversion of these translations into structured ACP messages.

The complete source code is available on GitHub.

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Handoff Pattern With BeeAI

Ilustrates a multi agent handoff system, using the BeeAI framework’s ReAct agent architecture with ACP. Based on the input language, the main agent delegates tasks to specialized agents for Spanish and English responses using the HandoffTool. This setup allows for flexible and handling of multilingual inputs via ACP.

The complete source code is available on GitHub.

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BeeAI Framework Canvas

Another example using BeeAI framework, this time directly calling an LLM and employing a custom logic for parsing the output. It utilizes the ACP’s artifact functionality to include generated files in responses, allowing the user to iterate over the generated files.

The complete source code is available on GitHub.

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CrewAI Song Writer

A reference implementation of a Crew using crew.ai, exposed via ACP. This Crew generates a song based on a website provided by the user, showcasing collaborative agent behavior exposed as a unit via ACP.

The complete source code is available on GitHub.

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GPT Researcher

Integrates GPT Researcher with ACP using a custom handler that transforms outputs into structured ACP messages. This allows real-time progress updates throughout research tasks, enhancing transparency and control.

The complete source code is available on GitHub.

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LangGraph Greeting Agent

An example of a LangGraph agent that returns a time-of-day specific greeting based on a provided name. This demonstrates how LangGraph can enable context-aware, responsive agents within ACP.

The complete source code is available on GitHub.

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ACP Agent Generator

This example demonstrates how to create an agent that dynamically generates other agents using the Agent Communication Protocol (ACP).

The complete source code is available on GitHub.

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Slack Agent using MCP

This example demonstrates an implementation of an ACP-compatible Slack Agent using the BeeAI framework. It leverages the framework’s tool-calling agent and integrates with the MCP Server for Slack API, enabling activities on Slack via the Agent Communication Protocol (ACP).

The complete source code is available on GitHub.