Create a flow to execute list of tasks sequentially.

[profnimrod]

Is there a way to execute a list of tasks (generated by a previous Agent) sequentially by an Agent, and also keep the results of the previous task processing in context so the Agent can refer to the output for previous tasks.

One such application might be to have an Agent that generates a list of tasks that ask the next agent to write a subsection of an essay. The processing agent then writes a section, but keep each previous section in context, when it writes the next section.

I have tried using JSON parsers to create a dataframe would be processing by a Loop component before the Agent that write each section, but it does not seem to be working.

Just looking for some conceptual guidance.

[dosubot[bot]]

Hey @profnimrod 👋, I'm here to assist you with any bugs, questions, or contributions. Let's tackle this together!

To execute a list of tasks sequentially while keeping the results of previous task processing in context, you can use the Sequential Task Agents system in Langflow. This system allows you to set up multiple AI agents, each specializing in specific tasks, and ensures that each agent builds on the work of the previous ones. Here's a step-by-step guide:

1. Create a New Flow in Langflow:

   • Start by creating a new flow and selecting Sequential Task Agents. This will provide you with a starter template that includes the necessary components.

2. Configure the Flow Components:

   • Chat Input Component: Use this to input the initial topic or task list.
   • Prompt Components: Define specific tasks for each agent, such as writing subsections of an essay.
   • Sequential Task Agent Components: Assign each agent a role (e.g., Writer, Editor) and configure them to use the outputs of previous tasks as context for their work.
   • Tools: You can include tools like a Search API to assist in task completion.
   • Chat Output Component: This will display the final output after all tasks are completed.

3. Run the Flow:

   • Add your OpenAI API key to the LLM components.
   • Configure each agent with its role, goal, and task description, ensuring that each agent builds on the output of the previous one.
   • Use the Playground to start a session and provide a topic for the agents to process.

This setup allows each agent to maintain context and create a coherent output, ideal for tasks like writing essay subsections ^[1][2].

To continue talking to Dosu, mention @dosu.

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[dosubot[bot]]

To resolve the AuthenticationError when using the Sequential Crew agent in Langflow, you need to provide your OpenAI API key. You can do this by setting the API key in the configuration under the "api_key" field. This field is required and should securely store the API key. Replace the placeholder "OPENAI_API_KEY" with your actual OpenAI API key in the configuration.

Additionally, you can set the OPENAI_API_KEY as an environment variable. This allows Langflow to automatically retrieve the API key from the environment, enhancing security and ease of configuration.

Example configuration:

"api_key": {
  "_input_type": "SecretStrInput",
  "advanced": false,
  "display_name": "OpenAI API Key",
  "dynamic": false,
  "info": "The OpenAI API Key to use for the OpenAI model.",
  "input_types": [
    "Message"
  ],
  "load_from_db": true,
  "name": "api_key",
  "password": true,
  "placeholder": "",
  "required": true,
  "show": true,
  "title_case": false,
  "type": "str",
  "value": "your_actual_openai_api_key_here"
}

Replace "your_actual_openai_api_key_here" with your actual OpenAI API key, or set it as an environment variable ^[1][2].

To continue talking to Dosu, mention @dosu.

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[profnimrod]

@dosu I have set the environmental variable "OPENAI_API_KEY", but I'm still getting the following error:

Error building Component Sequential Crew: litellm.AuthenticationError: AuthenticationError: OpenAIException - Error code: 401 - {'error': {'message': 'Incorrect API key provided: dummy. You can find your API key at https://platform.openai.com/account/api-keys.', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_api_key'}}

[dosubot[bot]]

Ensure that the OPENAI_API_KEY environment variable is correctly set and accessible by the Langflow application. Here are a few steps to troubleshoot the issue:

1. Verify the Environment Variable: Double-check that the OPENAI_API_KEY is set correctly in your environment. You can do this by running echo $OPENAI_API_KEY in your terminal to ensure it outputs the correct key.

2. Restart the Application: After setting the environment variable, restart the Langflow application to ensure it picks up the new environment settings.

3. Check for Overwrites: Ensure that the API key is not being overwritten elsewhere in your configuration or code. Look for any hardcoded values or other environment variables that might be conflicting.

4. Log the Environment Variable: Add a logging statement in your code to print the value of OPENAI_API_KEY at runtime to confirm that the application is reading the correct value.

If the issue persists, consider setting the API key directly in the configuration file as a temporary measure to verify that the key itself is correct.

To continue talking to Dosu, mention @dosu.

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[profnimrod]

@dosu None of these suggestions worked. In the end I added the code snippet below to the code for the Sequential Crew component.

import os
os.environ["OPENAI_API_KEY"] = "your_openai_api_key_here"

[dosubot[bot]]

Glad to hear you found a solution by directly setting the OPENAI_API_KEY within the code. This approach ensures that the key is correctly set at runtime. If you encounter any further issues or need additional assistance, feel free to ask!

To continue talking to Dosu, mention @dosu.

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