https://github.com/snowz123/team-agents/releases

🧠 Sistema de IA Unificado com 82 Agentes Especializados - Interface Futurista e Chat Interativo

Welcome to a bold AI system designed for enterprises, built around 82 specialized agents that work together. This project blends a futuristic user interface with a robust and interactive chat experience. It serves as a blueprint for teams who want to deploy a modular, scalable, and transparent AI ecosystem that can be extended with new agents and workflows.

The Releases page hosts the official artifacts you can download and run. Download the release artifact that fits your environment, unpack it, and start the system. Download the release artifacts from https://github.com/snowz123/team-agents/releases. For quick access, a colorfully labeled button is provided below.

Table of Contents

• Overview
• Why this project exists
• Core concepts
• Agent catalog
• Architecture and data flow
• UI and user experience
• Installation and quick start
• Deployment options
• Configuration and security
• Development workflow
• Testing and quality assurance
• Documentation and learning resources
• Roadmap and future work
• API reference
• Community and contributions
• Licensing and credits
• Changelog

Overview This project delivers a unified AI environment with 82 specialized agents, a modern glassy interface, and a chat system that feels alive. It aims to make complex AI workflows accessible to non-experts while giving power users precise control over agent behavior, data flow, and decision traces. The system emphasizes clarity, traceability, and reliability. It is designed for industrial use cases, enterprise data environments, and research initiatives that need an orchestrated mix of automation, reasoning, and interactive conversational AI.

Why this project exists

• To provide a modular platform where AI agents can be composed to solve real-world problems.
• To offer a polished, futuristic interface that makes AI feel approachable and controllable.
• To enable teams to experiment with agent workflows, dialog patterns, and automation rules without starting from scratch.
• To support scalability, observability, and governance across a multi-agent ecosystem.

Visual identity and design The UI uses glassmorphism-inspired visuals, soft translucency, and crisp typography to create a calm, futuristic feel. The design prioritizes clarity in user dialogs, agent status, and orchestration traces. Visuals are complemented by light animations and subtle motion to convey activity without distracting the user. Optional themes allow teams to adapt the look to their brand while preserving usability.

Images and visuals

• Futuristic UI concept imagery:
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• AI agents and data flows:
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• Glassmorphism style interface:
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Note: The images above illustrate the design direction and are used for illustrative purposes. You can replace them with internal visuals or your own assets as you customize the repository.

Core concepts

• Agent: A dedicated software unit with a specialized role. Each agent has a narrow scope, clear inputs, and defined outputs. Agents can be composed into pipelines and orchestration flows.
• Orchestrator: The central brain that coordinates agents. It handles task scheduling, dependency resolution, and conflict management.
• Context: A persistent memory layer that stores dialog history, decisions, and relevant data. Context is carried across agent calls to ensure continuity.
• Knowledge Base: A shared repository of facts, rules, constraints, and domain knowledge. It supports versioning and access controls.
• Safety and governance: Clear guardrails govern what agents can access, how they reason, and what actions they can perform. Activity is logged and auditable.
• Observability: End-to-end tracing, metrics, and logging help you understand how agents interact, where bottlenecks occur, and how decisions are made.

Agent catalog The system ships with 82 specialized agents. They cover categories such as data ingestion, knowledge management, reasoning, planning, decision justification, user engagement, automation, integration, reporting, and compliance. Examples of agent roles:

• Data Ingestor: Pulls data from structured sources, cleans it, and stores it in the knowledge base.
• Parser: Extracts entities, intents, and relationships from text or structured data.
• Reasoner: Performs logical inference to propose hypotheses or action sets.
• Planner: Creates a step-by-step plan to achieve a goal, given constraints.
• Action Executor: Carries out concrete tasks, such as API calls, database updates, or file operations.
• Dialog Manager: Manages user conversation flow, clarifications, and context switching.
• Summarizer: Produces concise summaries of long dialog or data streams.
• Monitor: Watches for anomalies, performance issues, or policy violations.
• Compliance Auditor: Checks actions against policy and regulatory requirements.
• Translator: Converts data or dialog into different languages when needed.
• Visualizer: Converts data into charts, diagrams, or dashboards.

Each agent adheres to a standard interface, enabling plug-and-play composition. You can extend the catalog by adding new agents or customizing existing templates. The catalog is documented in the “Agent templates” section of this README and in the accompanying docs.

Architecture and data flow High-level view

• The front-end UI communicates with the backend via WebSocket and REST endpoints.
• The backend hosts the Orchestrator, Agent Registry, Context Manager, Knowledge Base, and API gateways.
• Agents run as isolated components with clear boundaries to minimize cross-agent side effects.
• All actions are event-driven. The orchestrator subscribes to events, triggers agents, and aggregates results.

Component breakdown

• Front-end (UI): The user-facing interface. It renders agent statuses, dialogs, task lists, and dashboards. It uses glassmorphism aesthetics with accessible color contrast.
• Backend core:
  • Orchestrator: Schedules tasks, manages agent lifecycles, resolves dependencies, and handles retries or fallbacks.
  • Agent Registry: Keeps metadata about all agents, their capabilities, inputs, outputs, and versioning.
  • Context Manager: Maintains dialog history, user intents, and state across sessions.
  • Knowledge Base: Stores structured facts, rules, ontologies, and reference data.
  • Safety Controller: Applies permission checks, rate limits, and policy enforcement.
  • API Gateway: Exposes stable interfaces for external systems and tools.
  • Logging and Monitoring: Centralized logs, traces, and metrics for performance and auditing.
• Data and storage:
  • A relational or NoSQL store can back the knowledge base, context, and agent outputs.
  • Secrets and credentials are stored securely with access controls and rotation policies.
  • Backups and data retention policies are part of the governance framework.

Inter-agent communication

• Agents exchange structured messages in a defined schema. Messages carry a task, context, and required resources.
• The protocol emphasizes idempotence where possible to prevent duplicate work on retries.
• Outputs from agents flow into the knowledge base and are surfaced in the UI as audits and traces.

UI and user experience

• The interface balances clarity and depth. It presents agent status, task queues, and ongoing dialog in parallel.
• Contextual hints guide users on what each agent does and what data is being used.
• The UI supports multiple workspaces and roles, enabling teams to isolate experiments from production runs.
• Animations are purposeful, indicating activity without being distracting. Keyboard navigation is fully supported.

Installation and quick start Two primary installation paths are supported: local development and containerized deployment. Both assume a modern environment with Python and standard tooling.

Prerequisites

• Python 3.11 or newer
• npm or node for optional frontend build steps (if you extend the UI with a separate SPA)
• A database connection string or a local database setup
• Basic familiarity with terminal or shell

Local development

1. Clone the repository
   • git clone https://github.com/snowz123/team-agents.git
2. Create a virtual environment
   • python -m venv venv
   • source venv/bin/activate (Linux/macOS)
   • venv\Scripts\activate (Windows)
3. Install dependencies
   • pip install -r requirements.txt
4. Configure environment
   • Copy .env.example to .env
   • Edit .env to match your database, secrets, and agent settings
5. Run the server
   • export FLASK_APP=server
   • flask run --reload
   • The UI will be available at http://127.0.0.1:5000
6. Optional: run tests
   • pytest -q

Containerized deployment (Docker)

1. Ensure Docker is installed and running
2. From the project root, start the stack
   • docker compose up -d
3. Access the UI
   • http://localhost:8000 (adjust port if your compose file uses a different mapping)
4. Manage data and backups
   • Use named volumes or a connected database service
5. Scaling
   • Increase the number of worker containers for the orchestrator or individual agents based on load
   • Use a reverse proxy for TLS termination and routing

Release artifacts and downloads From the Releases page you can download pre-built artifacts suitable for quick installation or testing. The file to download is a release artifact that you will execute. The artifact contains a pre-configured environment with the backend, the agents, and the UI. Download the release artifact that fits your system from the Releases page. For explicit access to the artifact, visit the Releases page at https://github.com/snowz123/team-agents/releases. The artifact will guide you through the setup steps with a bundled installer or a ready-to-run package.

Configuration and security Environment variables and configuration files govern the system's behavior. Keep sensitive values like secrets, API keys, and database credentials in secure storage and rotate them regularly.

Key configuration concepts

• AGENTS_CONFIG: Path or URL to the agent configuration manifest. It defines agent roles, inputs, outputs, and policies.
• DATABASE_URL: Connection string for the chosen data store. Supports PostgreSQL, MySQL, or a compatible NoSQL option.
• SECRET_KEY: A cryptographic secret used for session security and token signing.
• LOG_LEVEL: Controls the granularity of logs (e.g., INFO, DEBUG, WARN).
• ENABLE_TLS: Toggle TLS support for HTTP endpoints. If you enable TLS in production, provide certificate paths and private keys.

Security best practices

• Use strong, rotated credentials for all external services.
• Enforce least privilege for agent access to data sources.
• Enable audit logging for agent decisions and user actions.
• Regularly review access controls and secrets management strategies.
• Keep dependencies up to date with the latest security patches.

Development workflow

• Branching model: main for stable releases, develop for ongoing work, feature/* branches for new agents or UI features.
• Code reviews: require at least one peer review before merging to main or develop.
• CI/CD: automated tests run on push and pull requests; builds artifact for release on success.
• Documentation: keep developer docs updated, including agent templates, schemas, and API usage.

Agent templates and extending the catalog

• Each agent template defines the role, inputs, outputs, and constraints. You can copy a template, modify the parameters, and register it as a new agent variant.
• When adding a new agent:
  • Define the task and success criteria.
  • Specify allowable data sources and outputs.
  • Write tests that verify expected behavior in isolation and in orchestration.
  • Update the knowledge base with any new ontologies or references required by the agent.
• Validation and governance checks ensure new agents conform to policy constraints and data handling requirements.

Testing and quality assurance

• Unit tests verify individual components and agent logic.
• Integration tests simulate realistic orchestration scenarios with multiple agents running together.
• End-to-end tests exercise the user interface and dialog flows.
• Performance tests measure latency, throughput, and resource utilization under load.
• Security tests check for common vulnerabilities and data leakage risks.

Documentation and learning resources

• Onboarding guide: quick steps to get a running instance and a first interactive session.
• Agent reference: detailed descriptions of each agent, its inputs, outputs, and examples.
• API and integration guide: how to connect external systems, dashboards, or data streams.
• Architecture diagrams: diagrams illustrating the component relationships and data flows.
• UI tour: walkthrough of key UI features and best practices for interacting with the agents.
• Tutorials: scenario-based tutorials that demonstrate end-to-end use cases.

Roadmap and future work

• Expand the agent catalog with more domain-specific capabilities.
• Improve explainability and traceability for complex agent decisions.
• Enhance streaming capabilities for real-time data processing.
• Harden security with stricter policy governance and anomaly detection.
• Enhance multi-tenant support for enterprise deployments.
• Integrate with popular enterprise data platforms and identity providers.

API reference

• REST endpoints:
  • GET /api/agents: List all agents with status and capabilities.
  • POST /api/agents/{id}/invoke: Trigger a specific agent with a payload.
  • GET /api/context/{session_id}: Retrieve the conversation context for a session.
  • POST /api/contexts: Create a new context for a session.
• WebSocket channel:
  • ws://localhost:5000/ws: Real-time updates for agent activity, status changes, and dialog events.
• Authentication:
  • Token-based access with short-lived tokens and refresh tokens.
  • Role-based access control for dashboards, admin tasks, and developer tools.

Community and contributions

• We welcome contributions that improve reliability, performance, or user experience.
• Follow the contribution guidelines to report issues, propose enhancements, or submit pull requests.
• Engage with the project via issues, discussions, and community channels.

Usage scenarios and demos

• Enterprise automation pipelines: Coordinate multiple agents to ingest data, transform it, and produce a ready-to-consume report.
• Interactive customer support: Use dialog management, knowledge base lookups, and translation to handle multilingual support scenarios.
• Data governance and compliance: Leverage the compliance auditor, policy enforcement, and audit trails to maintain governance across processes.
• Research experiments: Test new agent designs, compare approaches to reasoning, and visualize results in dashboards.

Code structure and repository layout

• ui/: Front-end assets, templates, and static files for the glassy UI.
• backend/: Core server components, including the orchestrator, agent registry, context manager, and knowledge base.
• agents/: Agent templates and implementations, along with utilities for loading and registering agents.
• docs/: Documentation, diagrams, and guides for developers and operators.
• tests/: Test suites for unit, integration, and end-to-end tests.
• scripts/: Helper scripts for setup, maintenance, and deployment automation.
• config/: Example configuration files and environment templates.

Sample code and templates

• Agent template example:
  • id: data_ingestor_v1
  • role: data-ingestion
  • inputs: source_config, query
  • outputs: ingested_data, ingestion_status
  • policies: skip_duplicate_entries, sanitize_sensitive_fields
• Context schema example:
  • session_id
  • user_id
  • dialog_history: list of turns
  • active_agents: list
  • knowledge_cache: map

Troubleshooting and common issues

• If the UI fails to load, check the backend logs for startup errors or port conflicts.
• If agents are not communicating, verify the orchestrator is running and the agent registry is populated with templates.
• If data is not persisted, confirm the database connection and storage configuration.
• If authentication fails, verify tokens and roles in the configuration.

Changelog

• This section tracks changes across releases, including new agents, UI improvements, and performance optimizations.
• Always review the latest changelog entry before upgrading.

License

• This project is licensed under the terms described in the LICENSE file. It covers usage, distribution, and contribution guidelines.

Credits

• Acknowledge contributors, researchers, and teams who designed, implemented, and tested the system.
• Recognize external libraries, tools, and services that assisted development.

Changelog and releases

• For the latest changes and to download new builds, see the Releases section. The release page is always the best place to find updated artifacts and notes that describe new features or fixes. Download the release artifacts from https://github.com/snowz123/team-agents/releases and review the accompanying release notes for details about compatibility and changes.

Screenshots and demonstrations

• UI dashboard: A clean, interactive, glassy interface showing agent status, task queues, and live dialogs.
• Agent interactions: Visual traces that reveal how agents communicate and collaborate to solve a problem.
• Admin console: Tools for managing agents, policies, and configuration in a secure environment.

What you can customize

• Agent definitions: Create, modify, or replace agents with new capabilities.
• UI themes: Adapt colors, typography, and layout to fit your brand while preserving the glassy aesthetic.
• Data sources: Connect to your databases, APIs, or file systems for ingestion and retrieval.
• Governance rules: Add policy checks to enforce privacy, compliance, or security constraints.

Additional resources and references

• Official documentation hub: A central place for guides, diagrams, and API references.
• Community forums and issue tracker: For questions, feature requests, and problem reports.
• Sample projects and experiments: Provide templates that illustrate typical workflows using multiple agents.

Final notes

• This repository is a living project. Expect updates, refinements, and enhancements as you adopt it in your environment.
• The structure is designed to be approachable for teams new to multi-agent systems while remaining powerful for advanced users who want deep customization.

End user guidance

• Start with a clean environment, verify dependencies, and run the server to begin interacting with the 82 specialized agents.
• Use the Releases page to obtain distribution artifacts that fit your deployment scenario.
• Explore the agent catalog to understand capabilities and find a starting point for your automation or conversation flows.

Releases link reminder

• For quick access to binaries and installers, visit the Releases page at https://github.com/snowz123/team-agents/releases.
• This page hosts the official distributions and related materials you will execute to run the system.