iointel 1.0.1

A framework to create agents, tasks, and workflows.

Agent Framework

This repository provides a flexible system for building and orchestrating agents and workflows. It offers two modes:

• Client Mode: Where tasks call out to a remote API client (e.g., your client.py functions).
• Local Mode: Where tasks run directly in the local environment, utilizing run_agents(...) and local logic.

It also supports loading YAML or JSON workflows to define multi-step tasks.

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Table of Contents

1. Overview
2. Installation
3. Concepts
   • Agents
   • Tasks
   • Client Mode vs Local Mode
   • Workflows (YAML/JSON)
4. Usage
   • Creating Agents
   • Creating an Agent with custom Persona
   • Building Tasks
   • Running a Local Workflow
   • Running a Remote Workflow (Client Mode)
   • Uploading YAML/JSON Workflows
5. Examples
   • Simple Summarize Task
   • Chainable Workflows
   • Custom Workflow
   • Loading From a YAML File
6. API Endpoints
7. License

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Overview

The framework has distilled Agents into 3 distinct pieces:

• Agents
• Tasks
• Workflows

The Agent can be configured with:

• Model Provider (e.g., OpenAI, Llama, etc.)
• Tools (e.g., specialized functions)

Users can define tasks (like council, sentiment, translate_text, etc.) in a local or client mode. They can also upload workflows (in YAML or JSON) to orchestrate multiple steps in sequence.

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Installation

1. Clone the Repo:

   git clone https://github.com/webcoderz/agents-framework.git
   cd agents-framework
   

2. Install Dependencies:

   uv pip install -r requirements.txt
   

3. Set Environment Variables:

   • OPENAI_API_KEY for the default OpenAI-based ChatOpenAI.
   • LOGGING_LEVEL (optional) to configure logging verbosity: DEBUG, INFO, etc.

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Concepts

Agents

• They can have a custom model provider (e.g., ChatOpenAI, a Llama-based model, etc.).
• Agents can have tools attached, which are specialized functions accessible during execution.
• Agents can have a custom Persona Profile configured.

Tasks

• A task is a single step in a workflow, e.g., council, schedule_reminder, sentiment, translate_text, etc.
• Tasks are managed by the Tasks class in tasks.py.
• Tasks can be chained for multi-step logic (e.g., tasks(text="...").council().sentiment().run_tasks()).

Client Mode vs Local Mode

• Local Mode: The system calls run_agents(...) directly in your local environment.
• Client Mode: The system calls out to remote endpoints in a separate API.
  • In client_mode=True, each task (e.g. sentiment) triggers a client function (sentiment_analysis(...)) instead of local logic.

This allows you to switch between running tasks locally or delegating them to a server.

Workflows (YAML/JSON)

• You can define multi-step workflows in YAML or JSON.
• The endpoint /upload-workflow accepts a file (via multipart form data).
  • First tries parsing JSON.
  • If that fails, it tries YAML.
• The file is validated against a WorkflowDefinition Pydantic model.
• Each step has a type (e.g., "sentiment", "custom") and optional parameters (like agents, target_language, etc.).

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Usage

Creating Agents

from iointel.src.agents import Agent

my_agent = Agent(
    name="MyAgent",
    instructions="You are a helpful agent.",
    model_provider="default"   # or use a callable for custom model
)

Creating an Agent with a Persona

from iointel.src.agent_methods.data_models.datamodels import PersonaConfig
from iointel.src.agents import Agent

my_persona = PersonaConfig(
    name="Elandria the Arcane Scholar",
    age=164,
    role="an ancient elven mage",
    style="formal and slightly archaic",
    domain_knowledge=["arcane magic", "elven history", "ancient runes"],
    quirks="often references centuries-old events casually",
    bio="Once studied at the Grand Academy of Runic Arts",
    lore="Elves in this world can live up to 300 years",
    personality="calm, wise, but sometimes condescending",
    conversation_style="uses 'thee' and 'thou' occasionally",
    description="Tall, silver-haired, wearing intricate robes with arcane symbols"
    emotional_stability: 0.85,
    friendliness: 0.45,
    creativity: 0.68,
    curiosity: 0.95,
    formality: 0.1,
    empathy: 0.57,
    humor: 0.99,
)

agent = Agent(
    name="ArcaneScholarAgent",
    instructions="You are an assistant specialized in arcane knowledge.",
    persona=my_persona
)

print(agent.instructions)

Building Tasks

In Python code, you can create tasks by instantiating the Tasks class and chaining methods:

from iointel.src.tasks import Tasks

tasks = Tasks(text="This is the text to analyze", client_mode=False)
(
  tasks
    .sentiment(agents=[my_agent])
    .council()   # a second step
)

results = tasks.run_tasks()
print(results)

Because client_mode=False, everything runs locally.

Running a Local Workflow

tasks = Tasks(text="Breaking news: local sports team wins!", client_mode=False)
tasks.summarize_text(max_words=50).run_tasks()

Running a Remote Workflow (Client Mode)

tasks = Tasks(text="Breaking news: local sports team wins!", client_mode=True)
tasks.summarize_text(max_words=50).run_tasks()

Now, summarize_text calls the client function (e.g., summarize_task(...)) instead of local logic.

Uploading YAML/JSON Workflows

1.	Create a YAML or JSON file specifying tasks:

name: "My YAML Workflow"
text: "Large text to analyze"
workflow:
  - type: "sentiment"
  - type: "summarize_text"
    max_words: 20
  - type: "moderation"
    threshold: 0.7
  - type: "custom"
    name: "special-step"
    objective: "Analyze the text"
    instructions: "Use advanced analysis"
    context:
      extra_info: "some metadata"

2.	Upload via the /upload-workflow endpoint (multipart file upload).

The server reads it as JSON or YAML and runs the tasks sequentially in local mode.

Examples

Simple Summarize Task

tasks = Tasks("Breaking news: new Python release!", client_mode=False)
tasks.summarize_text(max_words=30).run_tasks()

Returns a summarized result.

Chainable Workflows

tasks = Tasks("Tech giant acquires startup for $2B", client_mode=False)
(tasks
   .council()
   .translate_text(target_language="es")
   .sentiment()
)
results = tasks.run_tasks()

1.	Council step,
2.	Translate to Spanish,
3.	Sentiment analysis.

Custom Workflow

tasks = Tasks("Analyze this special text", client_mode=False)
tasks.custom(
    name="my-unique-step",
    objective="Perform advanced analysis",
    instructions="Focus on entity extraction and sentiment",
    agents=[my_agent],
    **{"extra_context": "some_val"}
)
results = tasks.run_tasks()

A "custom" task can reference a custom function in the CUSTOM_WORKFLOW_REGISTRY or fall back to a default behavior.

Loading From a YAML File

curl -X POST "http://<your server>/upload-workflow" \
     -F "yaml_file=@path/to/workflow.yaml"

API Endpoints

Here are some of the key endpoints if you integrate via REST:

• POST /council: Runs a council vote with ScheduleRequest.task.
• POST /reasoning: Runs a reasoning step with TextRequest.
• POST /summarize: Summarizes text in TextRequest.
• POST /sentiment: Performs sentiment analysis on TextRequest.
• POST /extract-entities: Extracts categorized entities.
• POST /translate: Translates text.
• POST /classify: Classifies text.
• POST /moderation: Moderation checks with a threshold.
• POST /custom-workflow: Runs a single “custom” step from CustomWorkflowRequest.
• POST /upload-workflow: Accepts JSON or YAML for multi-step workflows.