example using GROQ

examples/README.md

@@ -24,8 +24,10 @@ The example consists of four agents:
     3. **ActionAgent**: This agent will write what received (the books suggested) in a file (books_suggested.txt). It will override the file each time. This agent code is not part of wise-agents framework, but it's defined for this example use only in `custom_agent.py` present in the example directory.
     4. **SequentialCoordinator**: Take care of coordinating the request handling from the user delagating the work to other agents in a predetermined order.
 
-
-
 * [run_examples_podman](./run_examples_podman/README.md)
 
     This guide walks you through running any of the example in the examples directory in podman containers
+
+* [memory_agentic_chatbot_groq](./memory_agentic_chatbot_groq/README.md)
+
+    This guide walks you through running a practical example of a multi-agent system using Wise Agents. In this example, two agents (a web interface agent and an intelligent agent) are started, allowing you to experiment with agent communication and interaction in a simulated environment. This is the same as described in [memory_agentic_chatbot](./memory_agentic_chatbot/), but instead of using a local LLM it uses [https://groq.com/](https://groq.com/) for model inference. Please obtain an API key from [https://console.groq.com/keys](https://console.groq.com/keys) and set the env variable GROQ_API_KEY to start playing with this example.

examples/memory_agentic_chatbot_groq/README.md

@@ -0,0 +1,109 @@
+
+# Wise Agents Example: Memory Agentic Chatbot with GROQ api
+
+This guide walks you through running a practical example of a multi-agent system using Wise Agents. In this example, two agents (a web interface agent and an intelligent agent) are started, allowing you to experiment with agent communication and interaction in a simulated environment. This is the same as described in [memory_agentic_chatbot](../memory_agentic_chatbot/), but instead of using a local LLM it uses [https://groq.com/](https://groq.com/) for model inference. Please obtain an API key from [https://console.groq.com/keys](https://console.groq.com/keys) and set the env variable GROQ_API_KEY to start playing with this example.
+
+
+## Example Overview
+
+The example consists of two main agents:
+
+1. **Web Interface Agent**: Simulates a web-based client for interacting with other agents.
+2. **Intelligent Agent**: Handles requests and provides intelligent responses based on memory and context.
+
+These agents are defined in YAML configuration files located in the `examples/memory_agentic_chatbot` directory.
+
+## Running the Example
+
+### Step 1: Clone the Repository
+
+If you haven't already, clone the Wise Agents repository from GitHub:
+
+```bash
+git clone https://github.com/wise-agents/wise-agents.git
+cd wise-agents
+```
+
+### Step 2: Configure and Start Redis
+
+In this step, we will set up Redis for agent context and registry.
+
+1. **Create a hidden directory `.wise-agents`** in the root of your project:
+
+   ```bash
+   mkdir .wise-agents
+   ```
+
+2. **Copy the Redis configuration file** as shown in the [`.wise-agents` directory](https://github.com/wise-agents/wise-agents/tree/main/.wise-agents) from the GitHub repo. Create a file named `redis-config.yaml` inside `.wise-agents`:
+
+   ```yaml
+   redis:
+     host: localhost
+     port: 6379
+   ```
+
+3. **Ensure Redis is installed and running**. You can start redis as a podman/docker image following instructions in the [Redis README.MD](../../redis/README.MD)
+
+### Step 3: Start Artemis 
+
+To support async communication between agents you need to have Artemis up and running
+
+1. Start the Artemis podman/docker image following the instructions in the [Artemis README.MD](../../artemis/README.MD)
+
+2. Set the environment variables for artemis secure login. If you haven't changed any configuration, starting Artemis following the previous point instructions they are:
+
+```bash
+export STOMP_USER=artemis
+export STOMP_PASSWORD=artemis
+```
+
+
+### Step 4: Start the Intelligent Agent
+
+In a second console, run the intelligent agent, also from the project’s home directory, using the following command:
+
+```bash
+python src/wiseagents/cli/wise_agent_cli.py examples/memory_agentic_chatbot_groq/intelligent-agent.yaml
+```
+
+This will initialize the intelligent agent, which will be ready to respond to requests sent by the web interface agent.
+
+
+### Step 6: Start the Web Interface Agent
+
+In your first console, navigate to the project’s home directory and run the web interface agent using the provided YAML configuration file:
+
+```bash
+python src/wiseagents/cli/wise_agent_cli.py examples/memory_agentic_chatbot_groq/web-interface.yaml
+```
+
+This will initialize the Assistant agent with its web interface. You should see logs indicating that the agent is started and waiting for requests. You will see in the console also a web server listening at [http://127.0.0.1:7860](http://127.0.0.1:7860)
+
+```plain-text
+Running on local URL:  http://127.0.0.1:7860
+```
+
+### Step 7: Interaction
+
+Once both agents are up and running, you can use the web interface agent as a chatbot and it will start sending requests to the intelligent agent. You will be able to see the interaction between the two agents through the logs in both consoles.
+
+### Step 8: Experiment
+
+You can experiment with different agent configurations or modify the agent behaviors by editing the YAML files located in the `examples/memory_agentic_chatbot_groq` directory. These configuration files define the agents' properties, including memory, communication methods, and response patterns.
+
+## Understanding the YAML Configuration
+
+- **web-interface.yaml**: Defines the web interface agent, which serves as the client interface for interacting with other agents.
+- **intelligent-agent.yaml**: Defines the intelligent agent, which processes the requests and generates responses based on the provided input. *Note:* This agent use an env variable GROQ_API_KEY you must set before launching it.
+
+These YAML files include the specific `WiseAgent` classes and configuration needed to run the agents. Feel free to explore and modify these files to customize the agents' behavior.
+
+## Additional Resources
+
+For more information about the architecture and advanced configurations of wise-agents, refer to the [Wise Agents Architecture Document](wise_agents_architecture.md), which provides insights into how the system can be scaled and deployed in distributed environments.
+
+## Conclusion
+
+By following these steps, you have successfully run a simple memory-agentic chatbot using Wise Agents. You can now explore further by modifying agent behaviors, adding new agents, or experimenting with different message flows.
+
+For any further assistance, feel free to refer to the official Wise Agents documentation or reach out to the repository maintainers.

examples/memory_agentic_chatbot_groq/intelligent-agent.yaml

@@ -0,0 +1,13 @@
+---
+!wiseagents.agents.ChatWiseAgent
+_description: This is another test agent
+_llm: !wiseagents.llm.OpenaiAPIWiseAgentLLM
+  _model_name: llama-3.1-70b-versatile
+  _remote_address: https://api.groq.com/openai/v1
+  _api_key: ${GROQ_API_KEY}
+  _system_message: Answer my greeting saying Hello and my name
+_name: WiseIntelligentAgent
+_transport:  !wiseagents.transports.StompWiseAgentTransport
+    _host: localhost
+    _port: 61616
+    _agent_name: WiseIntelligentAgent

examples/memory_agentic_chatbot_groq/web-interface.yaml

@@ -0,0 +1,11 @@
+---
+!wiseagents.agents.AssistantAgent
+_description: This is a test agent
+_name: AssistantAgent
+_destination_agent_name: WiseIntelligentAgent
+_transport:  !wiseagents.transports.StompWiseAgentTransport
+    _host: localhost
+    _port: 61616
+    _agent_name: AssistantAgent
+
+

src/wiseagents/llm/openai_API_wise_agent_LLM.py

@@ -49,6 +49,7 @@ def __getstate__(self) -> object:
 
     def connect(self):
         '''Connect to the remote machine.'''
+        print(f"Connecting to WiseAgentLLM on remote machine at {self.remote_address} with API key {self.api_key}")
         self.client = openai.OpenAI(base_url=self.remote_address, 
                 api_key=self.api_key)