Legal MultiAgent System is a Python-based open-source platform that orchestrates multiple AI agents specialized for legal workflows. Each agent handles discrete tasks like document parsing, contract drafting, citation retrieval, compliance verification, and Q&A. Agents communicate via a central orchestrator, enabling parallel processing and collaborative analysis. By integrating with popular LLM APIs and allowing custom module development, it streamlines legal research, automates repetitive tasks, and ensures consistent output. The system’s modular architecture supports easy extension, so organizations can tailor agents to specific jurisdictions, practice areas, or compliance frameworks, achieving scalable and accurate legal automation.

Granite Retrieval Agent provides developers with a flexible platform to build retrieval-augmented generative AI agents that combine semantic search and large language models. Users can ingest documents from diverse sources, create vector embeddings, and configure Azure Cognitive Search indexes or alternative vector stores. When a query arrives, the agent retrieves the most relevant passages, constructs context windows, and calls LLM APIs for precise answers or summaries. It supports memory management, chain-of-thought orchestration, and custom plugins for pre- and post-processing. Deployable with Docker or directly via Python, Granite Retrieval Agent accelerates the creation of knowledge-driven chatbots, enterprise assistants, and Q&A systems with reduced hallucinations and enhanced factual accuracy.