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patchy631--ai-engineering-hub/acp-code/README.md
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2026-07-13 12:37:47 +08:00

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# Summary Generator multi-agent workflow with ACP
A simple demonstration of the Agent Communication Protocol (ACP), showcasing how two agents built using different frameworks (CrewAI and Smolagents) can collaborate seamlessly to generate and verify a research summary.
---
## Setup and Installation
1. **Install Ollama:**
```bash
# Setting up Ollama on linux
curl -fsSL https://ollama.com/install.sh | sh
# Pull the Qwen2.5 model
ollama pull qwen2.5:14b
```
2. **Install project dependencies:**
Ensure you have Python 3.10 or later installed on your system.
First, install `uv` and set up the environment:
```bash
# MacOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
```
Install dependencies:
```bash
# Create a new directory for our project
uv init acp-project
cd acp-project
# Create virtual environment and activate it
uv venv
source .venv/bin/activate # MacOS/Linux
.venv\Scripts\activate # Windows
# Install dependencies
uv add acp-sdk crewai smolagents duckduckgo-search ollama
```
You can also use any other LLM providers such as OpenAI or Anthropic. Create a `.env` file and add your API keys
```
OPENAI_API_KEY=your_openai_key
ANTHROPIC_API_KEY=your_anthropic_key
```
## Usage
Start the two ACP servers in separate terminals:
```bash
# Terminal 1
uv run crew_acp_server.py
# Terminal 2
uv run smolagents_acp_server.py
```
Run the ACP client to trigger the agent workflow:
```bash
uv run acp_client.py
```
Output:
A general summary from the first agent
A fact-checked and updated version from the second agent
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---
## Contribution
Contributions are welcome! Please fork the repository and submit a pull request with your improvements.