Skip to main content

A framework for conflict detection and resolution in multi-agent systems.

Project description

Logo of OVADARE

OVADARE

🤖 OVADARE: Cutting-edge framework for detecting, classifying, and resolving conflicts between AI agents autonomously. Designed to integrate seamlessly with AutoGen, CrewAI and other AI orchestration platforms, OVADARE empowers multi-agent systems to handle complex tasks collaboratively and efficiently.

Homepage | Documentation | Examples

GitHub Repo stars License: MIT

Table of contents

Why OVADARE?

In a world where multi-agent systems are becoming the backbone of AI-driven solutions, OVADARE stands out by providing robust conflict detection, classification, and resolution capabilities. Whether it's resolving task overlaps, prioritization issues, or behavioral conflicts among agents, OVADARE ensures seamless collaboration within your AI ecosystem.

Getting Started

To get started with OVADARE, follow these simple steps:

1. Installation

Ensure you have Python >=3.8 installed on your system. OVADARE can be installed via pip:

'' pip install ovadare ''

2. Setting Up OVADARE

To integrate OVADARE into your project, create an OVADARE conflict resolution manager:

'' from ovadare.conflicts import ConflictDetector, ConflictResolver

detector = ConflictDetector() resolver = ConflictResolver()

Detect conflicts

conflicts = detector.detect_conflicts(agent_data)

Resolve conflicts

resolved = resolver.resolve_conflicts(conflicts) ''

3. Configuring Policies

OVADARE allows you to define custom policies for conflict resolution:

'' from ovadare.policies import PolicyManager, Policy

policy_manager = PolicyManager()

custom_policy = Policy( name="TaskPrioritization", rules=[ "If two agents are assigned the same task, prioritize based on expertise.", "Resolve resource allocation conflicts using weighted scoring." ] )

policy_manager.add_policy(custom_policy) ''

4. Running with Autogen

OVADARE integrates seamlessly with Autogen for multi-agent orchestration. Define agents and tasks using Autogen and let OVADARE handle the conflicts:

'' from autogen import Agent, Task

agents = [ Agent(name="Agent1", role="Planner"), Agent(name="Agent2", role="Executor") ]

tasks = [ Task(name="Plan Project", assigned_to="Agent1"), Task(name="Execute Project", assigned_to="Agent2"), ]

Detect and resolve conflicts before execution

conflicts = detector.detect_conflicts(tasks) resolved_tasks = resolver.resolve_conflicts(conflicts) ''

Key Features

  • Comprehensive Conflict Handling: Detect, classify, and resolve agent-level conflicts.
  • Customizable Policies: Define dynamic policies tailored to your specific needs.
  • Seamless Integration: Works out-of-the-box with platforms like Autogen.
  • Extensibility: Easily extend functionality with custom rules and modules.
  • Advanced Analytics: Monitor and visualize conflict trends and resolution effectiveness.

Examples

Conflict Detection

Detect conflicts in a multi-agent system with ease:

'' conflicts = detector.detect_conflicts(agent_tasks) print(conflicts) ''

Policy Adjustment

Dynamically adjust policies based on feedback loops:

'' policy_manager.adjust_policy("TaskPrioritization", new_rules=["If deadlines conflict, prioritize by urgency."]) ''

Resolution Automation

Automate resolution using AI-powered decision-making engines:

'' resolved = resolver.resolve_conflicts(conflicts, method="ai-assisted") ''

Integration with Autogen

OVADARE enhances Autogen by adding robust conflict resolution capabilities to its agent orchestration framework. By integrating OVADARE, you can ensure that your agents collaborate effectively without stepping on each other's toes.

Contribution

OVADARE is open-source, and we welcome contributions. To contribute:

  1. Fork the repository.
  2. Create a new branch for your feature.
  3. Add your feature or improvement.
  4. Send a pull request.

Running Tests

'' pytest tests/ ''

Static Analysis

'' mypy ovadare/ ''

Packaging

'' python setup.py sdist bdist_wheel ''

License

OVADARE is released under the MIT License.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ovadare-0.1.4.tar.gz (72.4 MB view details)

Uploaded Source

Built Distribution

ovadare-0.1.4-py3-none-any.whl (33.0 kB view details)

Uploaded Python 3

File details

Details for the file ovadare-0.1.4.tar.gz.

File metadata

  • Download URL: ovadare-0.1.4.tar.gz
  • Upload date:
  • Size: 72.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.4

File hashes

Hashes for ovadare-0.1.4.tar.gz
Algorithm Hash digest
SHA256 b2010eb2025780238ef97d966f1f59c7b02594b333be7ab23cf378a872f7262c
MD5 806265e7bbec97ce40541f94712ba58c
BLAKE2b-256 9fa692e5d21394792183a991738e54dc97c1e5974b1ab00e01d2c09a0414ab4c

See more details on using hashes here.

File details

Details for the file ovadare-0.1.4-py3-none-any.whl.

File metadata

  • Download URL: ovadare-0.1.4-py3-none-any.whl
  • Upload date:
  • Size: 33.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.4

File hashes

Hashes for ovadare-0.1.4-py3-none-any.whl
Algorithm Hash digest
SHA256 661f0dc4b1db79d03f9ff9d6c9a2efeb9bf897b60eb06db85e24e66f8997fb92
MD5 7501cfaee2d41dd46df05700af9fe634
BLAKE2b-256 0b963ff29a1cba2af0c5ed4689afdfdf206b435d7d37b854cc0d1a5df1ba56da

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page