Ultimate Discord Intelligence Bot

A production-ready, multi-tenant intelligence system powered by autonomous AI agents for content analysis, fact-checking, and cross-platform monitoring. Built with CrewAI orchestration, 31 specialized agents, 111 specialized tools, and a modern 3-layer architecture.

🚀 Overview

The Ultimate Discord Intelligence Bot provides:

• Autonomous Content Analysis: AI-powered deep analysis of videos, articles, and social media content
• Fact-Checking & Verification: Multi-source claim verification with confidence scoring
• Multi-Platform Intelligence: Monitors YouTube, TikTok, Twitter/X, Reddit, Instagram, and Discord
• Real-Time Processing: Live stream analysis and real-time content monitoring
• Advanced Memory Systems: Qdrant vector storage, Neo4j graph memory, Mem0/HippoRAG continual learning
• Quality Assurance: Content validation, consistency checking, and quality gates
• Trajectory Evaluation: LangSmith-powered scoring with RL feedback loops for model routing
• Performance Optimization: Multi-level caching, intelligent model routing, prompt compression
• OpenAI Integration: Structured outputs, function calling, streaming, multimodal analysis
• Multi-Tenant Architecture: Complete workspace and resource isolation

🏗️ Architecture

3-Layer Architecture

The system employs a clean 3-layer architecture following domain-driven design principles:

1. Platform Layer (src/platform/): Infrastructure and cross-cutting concerns

   • Core protocols: StepResult pattern for unified error handling
   • HTTP layer: Resilient HTTP client with circuit breakers, retries, and timeout management
   • Cache layer: Multi-level caching (L1 in-memory, L2 Redis, L3 semantic)
   • LLM layer: Provider-agnostic routing with quality/cost/latency policies
   • RL layer: Reinforcement learning (contextual bandits) for adaptive model selection
   • Observability: Prometheus metrics, Langfuse tracing, structured logging
   • Security: Rate limiting, privacy controls, policy enforcement

2. Domain Layer (src/domains/): Business logic organized by bounded contexts

   • Orchestration (orchestration/): CrewAI agents, tasks, and crew management
   • Ingestion (ingestion/): Multi-platform content providers
     • YouTube, TikTok, Twitter/X, Reddit, Instagram, Discord, Twitch
   • Intelligence (intelligence/): Analysis and verification
     • Analysis: Content processing, sentiment, fallacy detection, timeline construction
     • Verification: Fact-checking, claim extraction, confidence scoring
   • Memory (memory/): Storage and retrieval systems
     • Vector: Qdrant integration for semantic search
     • Graph: Neo4j knowledge graphs
     • Continual: Mem0, HippoRAG for long-term learning

3. App Layer (src/app/): Application entry points and interfaces

   • Discord bot (discord/): Commands, events, and bot lifecycle management
   • Crew executor: Agent orchestration and task execution
   • Configuration: Agent definitions (YAML), feature flags, runtime settings
   • Main entry point (main.py)

Core Components

• CrewAI Framework: Orchestrates 31 specialized agents with role-based tool access across strategic, operational, and specialized domains
• 111 Tools: Comprehensive tool ecosystem organized across 9 categories
• Multi-Tenant Support: Complete tenant isolation and workspace management
• Advanced Memory: Vector stores, graph databases, and traditional storage
• Performance Optimization: Intelligent caching and model routing
• Quality Assurance: Comprehensive validation and consistency checking
• Observability Pipeline: Langfuse tracing, Prometheus metrics, and StepResult instrumentation

Agent Specialists

Executive & Strategic Layer (2 agents)

1. Executive Supervisor: Strategic oversight, resource allocation, and mission success
2. Workflow Manager: Task routing, dependency management, and load balancing across agent hierarchies

Coordination & Mission Control (1 agent)

1. Mission Orchestrator: End-to-end mission coordination, depth sequencing, and budget management

Content Acquisition & Processing (3 agents)

1. Acquisition Specialist: Multi-platform content download with rate-limit handling and quality fallbacks
2. Transcription Engineer: Audio/video transcription and searchable indexing
3. Analysis Cartographer: Linguistic, sentiment, and thematic signal mapping

Verification & Risk (2 agents)

1. Verification Director: Fact-checking, claim extraction, and defensible verdicts
2. Risk Intelligence Analyst: Deception scoring, trust metrics, and longitudinal tracking

Knowledge & Personas (2 agents)

1. Persona Archivist: Living dossiers with behavior, sentiment, and trust milestones
2. Knowledge Integrator: Memory consolidation across vector, graph, and continual memory

Signal Intelligence (2 agents)

1. Signal Recon Specialist: Cross-platform discourse and sentiment tracking
2. Trend Intelligence Scout: Emerging content detection and prioritization

Community & Research (4 agents)

1. Community Liaison: Community Q&A with verified intelligence and context retrieval
2. Argument Strategist: Steelman arguments, debate prep, and persuasive narratives
3. Research Synthesist: Deep background briefs harmonizing multiple perspectives
4. Intelligence Briefing Curator: Stakeholder-ready intelligence packets

System Reliability (2 agents)

1. System Reliability Officer: Pipeline health, dashboards, and operational visibility
2. Personality Synthesis Manager: Consistent tone, style, and persona alignment

Specialized AI Agents - Phase 3.1 (5 agents)

1. Visual Intelligence Specialist: Computer vision, image analysis, OCR, and visual fact-checking
2. Audio Intelligence Specialist: Speaker diarization, emotion analysis, and acoustic classification
3. Trend Intelligence Specialist: Real-time trend monitoring and long-term forecasting
4. Content Generation Specialist: AI-powered content creation, adaptation, and optimization
5. Cross-Platform Intelligence Specialist: Multi-platform correlation and propagation analysis

Creator Network Intelligence (6 agents)

1. Network Discovery Specialist: Social network mapping, collaboration patterns, and influence dynamics
2. Deep Content Analyst: Multimodal long-form analysis and narrative arc tracking
3. Guest Intelligence Specialist: Guest profiling, collaborator tracking, and monitoring recommendations
4. Controversy Tracker Specialist: Drama detection, conflict tracking, and early warnings
5. Insight Generation Specialist: Pattern synthesis and actionable intelligence delivery
6. Quality Assurance Specialist: Output validation, consistency checks, and re-analysis triggers

Discord AI Conversational (2 agents)

1. Conversational Response Agent: Natural, context-aware Discord responses
2. Personality Evolution Agent: RL-based personality optimization from interaction feedback

Import Patterns

After domains/ migration, use these import paths:

# Platform layer - infrastructure
from platform.core.step_result import StepResult, ErrorCategory
from platform.http.http_utils import resilient_get, resilient_post
from platform.cache.tool_cache_decorator import cache_tool_result
from platform.llm.llm_router import LLMRouter

# Tenancy - multi-tenant isolation
from ultimate_discord_intelligence_bot.tenancy.context import (
    TenantContext,
    with_tenant,
    current_tenant,
    require_tenant,
)

# Domain layer - business logic
from domains.intelligence.analysis.logical_fallacy_tool import LogicalFallacyTool
from domains.intelligence.verification.fact_check_tool import FactCheckTool
from domains.intelligence.acquisition.youtube_downloader_tool import YouTubeDownloaderTool
from domains.memory.vector_memory_tool import VectorMemoryTool
from domains.orchestration.crew.compat import CrewAICompatExecutor

# Observability
from ultimate_discord_intelligence_bot.obs.metrics import get_metrics

Deprecated paths (pre-migration):

• ❌ from ultimate_discord_intelligence_bot.crew import ...
• ❌ from ultimate_discord_intelligence_bot.tools import ...
• ✅ Use from domains.orchestration.crew import ... instead
• ✅ Use from domains.intelligence.* import ... for tools

🤖 OpenAI Integration

The bot includes comprehensive OpenAI integration with advanced AI capabilities:

Core Features

• Structured Outputs: Generate validated, structured responses using JSON schemas
• Function Calling: Enable AI models to call custom functions during analysis
• Streaming: Real-time streaming of AI responses for better user experience
• Voice Processing: Convert between text and speech using OpenAI's voice models
• Vision Analysis: Analyze images and visual content with advanced vision models
• Multimodal Analysis: Process content across multiple modalities (text, images, audio)

Services

• OpenAIStructuredOutputsService: Generate structured, validated responses
• OpenAIFunctionCallingService: Enable function calling capabilities
• OpenAIStreamingService: Stream responses in real-time
• OpenAIVoiceService: Text-to-speech and speech-to-text conversion
• OpenAIVisionService: Image analysis and visual content processing
• MultimodalAnalysisService: Cross-modal content analysis
• OpenAIIntegrationService: Main service combining all capabilities
• OpenAICostMonitoringService: Track API usage and costs

Discord Commands

• !openai-health - Check OpenAI service health
• !openai-analyze-structured <content> - Perform structured analysis
• !openai-analyze-multimodal <prompt> - Analyze multimodal content
• !openai-voice-command - Process voice commands

CrewAI Integration

The OpenAI capabilities are available as CrewAI tools:

• OpenAIEnhancedAnalysisTool: Enhanced analysis with OpenAI capabilities
• Integrated with analysis agents for advanced content processing
• Supports structured outputs, function calling, and multimodal analysis

For detailed documentation, see OpenAI Integration Features.

🛠️ Installation

Prerequisites

• Python 3.10+
• Docker and Docker Compose
• Discord Bot Token
• OpenAI/OpenRouter API Key
• Qdrant Vector Database

Quick Start

1. Clone the repository

   git clone <repository-url>
   cd ultimate-discord-intelligence-bot

2. Set up environment

   cp .env.example .env
   # Edit .env with your configuration

3. Install dependencies

   # Recommended for development
   pip install -e '.[dev]'
   
   # Minimal install (production)
   pip install -e .
   
   # Optional feature sets
   pip install -e '.[metrics]'     # Prometheus metrics
   pip install -e '.[whisper]'     # Whisper transcription
   pip install -e '.[vllm]'        # VLLM inference
   pip install -e '.[mcp]'         # Model Context Protocol

4. Start services

   # Development compose
   docker compose -f config/docker/docker-compose.yml up -d

5. Run the bot

   # Ensure virtualenv is active; sitecustomize.py auto-adds src/ to PYTHONPATH when launched from repo root
   source .venv/bin/activate
   python -m app.main run
   
   # OR use explicit PYTHONPATH
   PYTHONPATH=src python -m app.main run
   
   # OR call the file directly
   python src/app/main.py run

   Troubleshooting: If you see a Python error like "Could not find platform independent libraries " or sys.prefix shows "/install" and "No module named 'encodings'", your virtual environment is corrupted (often due to an external Python shim such as uv). Fix by recreating a clean venv with your system Python:

   # from repo root
   deactivate || true
   rm -rf .venv
   python3 -m venv .venv
   source .venv/bin/activate
   pip install -U pip wheel setuptools
   pip install -e .
   # then:
   python -m app.main run

   Note on stdlib platform module conflict: This repository contains a top-level platform/ package. Some environments ship a system sitecustomize.py that loads before project-level startup hooks, which can cause from platform.core ... imports to resolve the stdlib module instead. We've implemented two safeguards:

   • Entry-point bootstrap: app/main.py and server/app.py call a tiny bootstrap (ultimate_discord_intelligence_bot.core.bootstrap.ensure_platform_proxy()) at process start to make the stdlib platform module behave as a package exposing our platform/* submodules.
   • User customizations: a minimal src/usercustomize.py performs the same augmentation on Python startup when supported by your environment.

   With these in place, imports like from platform.core.step_result import StepResult work consistently across shells, venvs, and containers.

🛡️ Compliance Guards

This project enforces automated compliance checks to maintain code quality and architectural standards:

Automated Enforcement

• Local Development: Pre-commit hooks validate all changes before commit
• CI Pipeline: GitHub Actions workflow runs on every PR and push to main branches
• Merge Blocking: PRs cannot merge if compliance checks fail (when branch protection enabled)

Five Guards Enforced

1. HTTP Wrapper Guard - Prevents direct requests.* calls; enforces resilient HTTP wrappers with circuit breakers and retries
2. Tools Export Guard - Ensures all tools are registered in __all__ for dynamic discovery (112 tools validated)
3. Metrics Instrumentation Guard - Enforces consistent metric naming conventions and observability patterns
4. Deprecated Directories Guard - Blocks new files in deprecated paths during architecture migration
5. Dispatcher Usage Guard - Validates tenant context propagation in async/thread boundaries

Quick Start

# One-time setup: Install pre-commit hooks
make setup-hooks

# Manual execution: Run all guards
make guards

# Check specific guard
python scripts/validate_http_wrappers_usage.py

# Generate compliance report
python scripts/generate_guard_report.py --input <(make guards 2>&1) --output report.json --pretty

Emergency Bypass

For critical hotfixes only:

# Bypass local hook (CI still enforces)
git commit --no-verify -m "hotfix: critical issue"

Note: Bypassed commits still run in CI. Document bypass reason in commit message.

Documentation

• Full Guide: docs/compliance-guards.md - 557 lines covering setup, troubleshooting, examples
• CI Workflow: .github/workflows/guards-ci.yml - Automated enforcement with PR comments
• Implementation: F011_F014_GUARD_CI_INTEGRATION_COMPLETE_2025-11-12.md

⚙️ Configuration

Environment Variables

Core Services

# Discord Bot
DISCORD_BOT_TOKEN=your_discord_bot_token
DISCORD_PRIVATE_WEBHOOK=your_private_webhook_url

# LLM Services
OPENAI_API_KEY=your_openai_api_key
OPENROUTER_API_KEY=your_openrouter_api_key
OPENAI_BASE_URL=https://api.openai.com/v1

# Vector Database
QDRANT_URL=http://localhost:6333
QDRANT_API_KEY=your_qdrant_api_key

LLM Provider Routing (new)

Configure the provider-agnostic router that selects the best model per request.

# Comma-separated provider allowlist used to build routing candidates
# Examples: openai,anthropic,google,openrouter,groq,together,cohere,mistral,fireworks,perplexity,xai,deepseek,azure_openai,bedrock
LLM_PROVIDER_ALLOWLIST=openai,anthropic,google,openrouter

# Router policy: quality_first | cost | latency
ROUTER_POLICY=quality_first

# Comma-separated task names that should force quality-first routing
QUALITY_FIRST_TASKS=analysis,reasoning,coding

These values are also available at runtime via get_config() from platform.config.configuration as llm_provider_allowlist (parsed list) and router_policy.

Feature Flags

# Core Features
ENABLE_DEBATE_ANALYSIS=true
ENABLE_FACT_CHECKING=true
ENABLE_MULTI_PLATFORM_MONITORING=true

# Advanced Features
ENABLE_UNIFIED_KNOWLEDGE=true
ENABLE_UNIFIED_METRICS=true
ENABLE_UNIFIED_ORCHESTRATION=true
ENABLE_UNIFIED_ROUTER=true

# Performance Features
ENABLE_CACHE_OPTIMIZATION=true
ENABLE_MODEL_ROUTING=true
ENABLE_PERFORMANCE_OPTIMIZATION=true

# OpenAI Integration Features
ENABLE_OPENAI_STRUCTURED_OUTPUTS=true
ENABLE_OPENAI_STREAMING=true
ENABLE_OPENAI_VOICE=false
ENABLE_OPENAI_VISION=false
ENABLE_OPENAI_MULTIMODAL=false
ENABLE_OPENAI_FUNCTION_CALLING=true
ENABLE_OPENAI_FALLBACK=true

# Evaluation & Observability
ENABLE_TRAJECTORY_EVALUATION=1  # Set to 1 to enable runtime scoring
ENABLE_TRAJECTORY_FEEDBACK_LOOP=0  # Set to 1 to push scores into RL router
RL_FEEDBACK_BATCH_SIZE=25  # Optional: number of feedback items processed per tick
RL_FEEDBACK_LOOP_INTERVAL_SECONDS=15  # Optional: delay (seconds) between feedback loop runs
ENABLE_LANGSMITH_EVAL=false
ENABLE_LANGFUSE_EXPORT=false

Performance Settings

# CrewAI Configuration
CREW_MAX_RPM=10
CREW_EMBEDDER_PROVIDER=openai

# Cache Settings
CACHE_TTL=3600
CACHE_MAX_SIZE=1000

# Rate Limiting
RATE_LIMIT_REQUESTS_PER_MINUTE=60
RATE_LIMIT_BURST_SIZE=10

Performance Optimization

The codebase includes comprehensive performance analysis and optimization tools. See Performance Initiative Summary for details.

Quick Performance Check:

# Analyze performance issues
python3 scripts/performance_improvements.py --report

# Check for performance regressions
python3 -m ruff check src --select PERF

Documentation:

• Performance Improvement Recommendations - Comprehensive analysis of 258 performance issues
• Performance Best Practices - Best practices for high-performance Python code
• Performance Fixes Applied - Summary of automated fixes

Benchmarking:

# Run performance benchmarks
python3 benchmarks/performance_benchmarks.py

# Profile tool imports
python3 profile_tool_imports.py

🎯 Usage

Discord Commands

Basic Commands

• !analyze <url> - Analyze content from any supported platform
• !factcheck <claim> - Fact-check a specific claim
• !monitor <creator> - Start monitoring a creator
• !status - Get system status and metrics

Advanced Commands

• !deep_analysis <url> - Comprehensive content analysis
• !verify_claims <url> - Extract and verify all claims
• !trend_analysis <topic> - Analyze trends for a topic
• !quality_assess <content> - Assess content quality

API Usage

Content Analysis

# Recommended: domain-based import
from domains.orchestration.crew import get_crew, UltimateDiscordIntelligenceBotCrew

# Factory pattern
crew_adapter = get_crew()
crew_obj = crew_adapter.crew()
result = crew_obj.kickoff(inputs={"url": "https://youtube.com/watch?v=example"})

# Or instantiate directly
crew_instance = UltimateDiscordIntelligenceBotCrew()
result = crew_instance.crew().kickoff(inputs={"url": "https://youtube.com/watch?v=example"})

Fact-Checking

result = crew.crew().kickoff(inputs={
    "claim": "The Earth is flat",
    "context": "Scientific discussion"
})

📊 Monitoring & Observability

Langfuse Tracing

• Feature-gated exporter capturing traces and spans for every pipeline step via LangfuseService
• Configure with ENABLE_LANGFUSE_EXPORT, LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY, and optional LANGFUSE_BASE_URL
• StepResult-aware sanitization ensures payload safety and tenant metadata tagging
• Validate instrumentation with python run_observability_tests.py or enable live export in staging

Metrics Dashboard

• Real-time performance metrics
• Agent execution statistics
• Tool usage analytics
• Error rates and success rates

Logging

• Structured logging with tenant isolation
• Performance tracing
• Error tracking and alerting
• Audit trails for compliance

Health Checks

• System health endpoints
• Service availability monitoring
• Resource usage tracking
• Automated alerting

🧪 Testing

Run Tests

# Run all tests
make test

# Run specific test categories
pytest tests/tools/          # Tool tests
pytest tests/agents/         # Agent tests
pytest tests/services/       # Service tests
pytest tests/integration/    # Integration tests

Trajectory Evaluation Suite

• Validates LangSmith adapters and heuristic fallbacks.
• Execute pytest tests_new/unit/eval/test_langsmith_adapter.py for focused coverage.
• Enable ENABLE_TRAJECTORY_EVALUATION=1 and ENABLE_TRAJECTORY_FEEDBACK_LOOP=1 to exercise reinforcement feedback paths in integration environments.

Quality Gates

make format     # Code formatting
make lint       # Linting
make type       # Type checking
make test       # Testing
make docs       # Documentation validation

For detailed quality gate requirements, see docs/quality-gates.md.

📚 Documentation

Core Documentation

• Architecture Overview
• Agent Reference
• Tools Reference
• Configuration Guide

Specialized Guides

• Quality Assurance Tools
• Performance Optimization
• Compliance Tools
• MCP Integration

API Documentation

• Tools API
• Agents API
• Services API

🚀 Deployment

Docker Deployment

# Build and deploy (production)
docker compose -f config/docker/docker-compose.prod.yml up -d

# Scale services
docker compose -f config/docker/docker-compose.prod.yml up -d --scale bot=3

Kubernetes Deployment (optional)

Kubernetes manifests are not included by default in this repository. If you maintain your own manifests, apply them in your deployment repository or ops layer.

Environment-Specific Configs

• config/docker/docker-compose.yml - Development
• config/docker/docker-compose.prod.yml - Production

🔧 Development

Project Structure

src/
├── platform/               # Platform layer: Infrastructure & cross-cutting concerns
│   ├── core/               # Core protocols (StepResult, config)
│   ├── http/               # Resilient HTTP client (circuit breakers, retries)
│   ├── cache/              # Multi-level caching (memory, Redis, semantic)
│   ├── llm/                # LLM providers, routing, structured outputs
│   ├── rl/                 # Reinforcement learning (contextual bandits)
│   ├── observability/      # Metrics, tracing, logging
│   ├── security/           # Security, privacy, rate limiting
│   ├── prompts/            # Prompt engineering (DSPy)
│   └── rag/                # RAG capabilities (LlamaIndex)
│
├── domains/                # Domain layer: Business logic by bounded context
│   ├── orchestration/      # CrewAI agents, tasks, crew
│   │   ├── crewai/         # CrewAI-specific components
│   │   └── legacy/         # Legacy orchestration code
│   ├── ingestion/          # Multi-platform content ingestion
│   │   ├── pipeline/       # Ingestion pipeline orchestrator
│   │   └── providers/      # Platform-specific providers
│   ├── intelligence/       # Analysis & verification
│   │   ├── analysis/       # Content analysis tools
│   │   └── verification/   # Fact-checking & verification
│   └── memory/             # Memory systems
│       ├── vector/         # Vector storage (Qdrant)
│       ├── graph/          # Graph memory (Neo4j)
│       └── continual/      # Continual learning (Mem0, HippoRAG)
│
├── app/                    # App layer: Application-specific code
│   ├── main.py             # Application entry point
│   ├── crew_executor.py    # CrewAI crew execution
│   ├── discord/            # Discord bot integration
│   │   ├── bot.py          # Bot implementation
│   │   ├── commands/       # Discord commands
│   │   └── events/         # Event handlers
│   └── config/             # Application configuration
│       ├── settings.py     # Global settings
│       └── agents.yaml     # Agent definitions
│
└── ultimate_discord_intelligence_bot/  # Legacy: being migrated to domains/
    ├── tools/              # 111 specialized tools (registry)
    ├── pipeline_components/# Pipeline orchestrator
    └── tenancy/            # Multi-tenant context management

Adding New Tools

1. Create tool class inheriting from BaseTool
2. Implement _run method returning StepResult
3. Add to tools/__init__.py exports
4. Register in crew.py with appropriate agent
5. Add comprehensive tests

Adding New Agents

1. Define agent in crew.py with @agent decorator
2. Specify role, goal, backstory, and tools
3. Add to crew construction in crew() method
4. Update agent documentation

🤝 Contributing

Development Workflow

1. Fork the repository
2. Create feature branch
3. Implement changes with tests
4. Run quality gates
5. Submit pull request

Code Standards

• Follow PEP 8 and project formatting rules
• Include comprehensive type hints
• Write tests for all new functionality
• Update documentation for new features
• Use StepResult for all tool returns

Quality Requirements

• All quality gates must pass
• 100% test coverage for new code
• Documentation updates required
• No increase in MyPy error baseline

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

Getting Help

• Check documentation for detailed guides
• Review troubleshooting guide
• Open an issue for bugs or feature requests
• Join our Discord server for community support

Reporting Issues

When reporting issues, please include:

• System information (OS, Python version)
• Error logs and stack traces
• Steps to reproduce
• Expected vs actual behavior

🗺️ Roadmap

Phase 1: Core System ✅

• Tool registration and organization
• Agent architecture implementation
• Basic functionality testing

Phase 2: Advanced Features ✅

• Quality assurance systems
• Performance optimization
• Enhanced MCP integration

Phase 3: Production Readiness 🚧

• Complete system configuration
• Comprehensive testing
• Documentation updates
• Performance optimization

Phase 4: Scale & Optimize 📋

• Advanced monitoring
• Auto-scaling capabilities
• Advanced analytics
• Machine learning optimization

🏆 Features

✅ Implemented

• 111 specialized tools across 9 categories (ingestion, analysis, verification, memory, observability, etc.)
• 31 autonomous AI agents with role-based tool access
• Multi-platform content ingestion (YouTube, TikTok, Twitter/X, Reddit, Instagram, Discord, Twitch)
• Advanced fact-checking with claim extraction and multi-source verification
• Multi-level memory systems (Qdrant vectors, Neo4j graphs, Mem0/HippoRAG continual learning)
• Quality assurance with validation, consistency checking, and quality gates
• Performance optimization with semantic caching, prompt compression, and model routing
• Multi-tenancy support with complete workspace and resource isolation
• Observability with Prometheus metrics, Langfuse tracing, and structured logging
• Reinforcement learning for adaptive model selection via contextual bandits

🚧 In Development

• Advanced analytics dashboard
• Real-time collaboration features
• Enhanced security features
• Advanced workflow automation

📋 Planned

• Machine learning model training
• Advanced visualization tools
• Enterprise integrations
• Mobile applications

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Built with ❤️ using CrewAI, Discord.py, and modern Python technologies.