A comprehensive suite of AI-powered tools designed to solve complex enterprise challenges across various domains including legacy system integration, security, healthcare, finance, and more. This toolkit is specifically designed for LLM (Large Language Model) agents, providing essential connectors and tools to bridge the gap in the AI Agent ecosystem. It enables seamless integration with enterprise systems while maintaining compatibility with modern AI agent standards.
- MCP (Model Context Protocol): Native support for standardized model communication
- A2A (Agent-to-Agent): Built-in capabilities for multi-agent collaboration
- OASF (Open Agentic Schema): Standardized agent descriptions and capabilities
- OpenAI Function Calling: Broad compatibility with popular LLM frameworks
- Structured data extraction from legacy systems
- Secure enterprise-grade authentication and authorization
- Real-time data synchronization
- Standardized API interfaces for agent interaction
- Context-aware data processing and transformation
What it does: Extracts data from legacy SAP R/3 systems (orders, customers, warehouses) and exposes it through modern APIs.
How it works: RFC connections + SAP table parsing + REST API wrapper.
Why it's needed: 70% of large enterprises still run SAP R/3, but AI agents can't access it directly.
What it does: Connects to IBM z/OS mainframes, runs COBOL programs, and reads VSAM files.
How it works: 3270 terminal emulation + JCL job submission + screen scraping.
Why it's needed: Banks and insurers keep mission-critical data on 1970-80s mainframes.
What it does: Provides access to IBM AS/400 (iSeries) databases and RPG programs.
How it works: JDBC over SSL + SQL queries to DB2/400 + command execution.
Why it's needed: Many manufacturing plants run ERP/MRP systems on AS/400.
What it does: Automates legacy Oracle Forms applications by mimicking user actions.
How it works: Oracle Forms API + automatic field population + form navigation.
Why it's needed: Countless 2000-era enterprise apps rely on Oracle Forms; rewriting them costs millions.
What it does: Scans data for PII, checks consent, and tracks retention policies.
How it works: NLP-based PII detection + metadata tracking + automated consent management.
Why it's needed: GDPR fines reach 4% of annual revenue; automated compliance cuts risk.
What it does: Automatically documents financial transactions and changes for Sarbanes-Oxley audits.
How it works: Event logging + blockchain integrity + automatic report generation.
Why it's needed: U.S. public companies must meet SOX; manual audit trails are costly.
What it does: Examines current security processes and finds gaps against ISO 27001 requirements.
How it works: Security-control mapping + automated assessment + gap identification + remediation advice.
Why it's needed: ISO 27001 certification demands documentation of hundreds of controls.
What it does: Tracks data origin and transformations across all systems.
How it works: SQL parsing + API monitoring + metadata extraction + graph database.
Why it's needed: For compliance and debugging, knowing where data came from is essential.
What it does: Guarantees atomic operations across unrelated systems (CRM + ERP + Bank).
How it works: Two-phase commit protocol + transaction state management + automatic rollback.
Why it's needed: Critical business processes often span 5-10 systems and must stay consistent.
What it does: Analyzes real-world logs and compares them with designed workflows.
How it works: Event-log analysis + process discovery algorithms + deviation detection.
Why it's needed: Actual processes differ from documentation by 60-80%.
What it does: Tracks SLAs across processes and escalates breaches automatically.
How it works: Real-time monitoring + predictive SLA-breach detection + automated workflows.
Why it's needed: SLA penalties can reach millions; prevention saves money.
What it does: Converts medical data to the FHIR R4 standard and synchronizes between systems.
How it works: HL7 FHIR API + data transformation + profile validation.
Why it's needed: Interoperability across healthcare systems is mandated by regulations.
What it does: Flags dangerous drug interactions in real time.
How it works: Pharmaceutical database lookup + interaction algorithms + severity scoring + alerts.
Why it's needed: Prevents fatalities from incompatible medications.
What it does: Automatically assigns ICD-10 and CPT codes from diagnosis/procedure descriptions.
How it works: NLP + medical ontology mapping + code suggestions + validation.
Why it's needed: Accurate coding drives millions in insurance reimbursements.
What it does: Monitors access to medical data and detects privacy violations.
How it works: Access logging + pattern analysis + breach detection + incident reporting.
Why it's needed: HIPAA fines reach $1.5M per incident; continuous monitoring is vital.
What it does: Computes credit, market, and operational risk per Basel III rules.
How it works: Risk modeling + regulatory formulas + stress testing + capital-adequacy calculation.
Why it's needed: Mandatory reporting for banks; miscalculation triggers sanctions.
What it does: Detects money-laundering patterns in real time.
How it works: ML models + pattern recognition + suspicious-activity scoring + regulatory reports.
Why it's needed: AML violations lead to multi-billion-dollar fines and lost licenses.
What it does: Generates credit scores using alternative data (social, behavioral).
How it works: Alternative-data ingestion + ML scoring + explainable AI + decision automation.
Why it's needed: Traditional scoring excludes 45% of adults; alternative data expands the market.
What it does: Automatically creates core regulatory reports for central banks (COREP, FINREP, etc.).
How it works: Data aggregation + regulatory templates + validation + automated submission.
Why it's needed: Banks spend $1B+ yearly on reporting; 70% can be automated.
What it does: Connects to industrial equipment via OPC-UA and streams sensor data.
How it works: OPC-UA client + real-time streaming + protocol translation + edge computing.
Why it's needed: Industry 4.0 requires linking legacy machines to AI systems.
What it does: Analyzes equipment vibrations to predict failures.
How it works: FFT analysis + ML anomaly detection + failure prediction + maintenance scheduling.
Why it's needed: Predictive maintenance cuts downtime by 40% and costs by 25%.
What it does: Tracks supply-chain risks: geopolitics, weather, supplier finances.
How it works: Multi-source data ingestion + risk scoring + impact analysis + alternate-supplier suggestions.
Why it's needed: Disruptions cost firms $184B per year.
What it does: Uses computer vision to inspect product quality automatically.
How it works: CV defect detection + classification + statistical QC.
Why it's needed: Human QC misses 15% of defects; AI finds 99.9%.
What it does: Extracts data from any document type: PDFs, scans, handwriting, audio, video.
How it works: OCR + handwriting recognition + speech-to-text + video-frame analysis + NLP extraction.
Why it's needed: 80% of business data is unstructured; unified processing unlocks it.
What it does: Extracts key moments from video calls, presentations, and training sessions.
How it works: Computer vision + speech recognition + sentiment analysis + topic extraction + summaries.
Why it's needed: Companies hold 23M meetings daily, and insights often get lost.
What it does: Identifies emotional states—stress, anger, satisfaction—from voice.
How it works: Voice-feature extraction + emotion classification + trend analysis + alerts.
Why it's needed: Customer-service quality rises 40% with emotion insights.
What it does: Understands spatial context in AR/VR to deliver relevant assistance.
How it works: 3D scene understanding + object recognition + spatial mapping + contextual AI help.
Why it's needed: As AR/VR adoption grows, context-aware assistance becomes essential.
What it does: Tracks long-term sentiment trends of customers/employees across channels.
How it works: Multi-channel sentiment aggregation + trend analysis + correlation detection + predictive modeling.
Why it's needed: Provides early warnings of product or HR issues before they become critical.
What it does: Tailors AI responses to cultural specifics of different regions.
How it works: Cultural knowledge base + context-aware generation + cultural-sensitivity scoring.
Why it's needed: Global firms lose customers due to culturally insensitive AI.
What it does: Crafts empathetic replies for customer support based on emotional state.
How it works: Emotion detection + empathy modeling + personalized responses + satisfaction optimization.
Why it's needed: Empathetic support boosts customer satisfaction by 40%.
What it does: Facilitates conflict resolution in teams and suggests compromises.
How it works: Conflict-pattern recognition + mediation strategies + personality modeling + resolution tracking.
Why it's needed: Workplace conflict cuts productivity by 25%; HR spends 40% of its time on disputes.
What it does: Tunes AI agents for speed, accuracy, and cost efficiency.
How it works: Performance monitoring + bottleneck detection + resource optimization + auto-tuning.
Why it's needed: Poorly tuned agents can burn 10× more resources.
What it does: Distributes tasks among multiple agents for optimal performance.
How it works: Load monitoring + capability matching + dynamic routing + failover management.
Why it's needed: Uneven loads cause time-outs and bad UX.
What it does: Tracks agent "health": availability, response time, error rates.
How it works: Health-metric collection + anomaly detection + predictive failure analysis + auto-recovery.
Why it's needed: Agent downtime costs enterprises $100K+ per hour; proactive monitoring prevents it.
What it does: Minimizes API-call expenses by choosing the most cost-effective models.
How it works: Usage tracking + cost analysis + model-selection optimization + budget management.
Why it's needed: AI costs are skyrocketing; intelligent cost control is critical.
Each tool in this toolkit can be deployed independently or as part of an integrated solution. Please refer to the individual tool documentation for setup and configuration instructions.
This project is licensed under the MIT License - see the LICENSE file for details.
Contributions are welcome! Please read our contributing guidelines for details on our code of conduct and the process for submitting pull requests.