Building Agents with Core Bioinformatics Tools

Meet your instructors, review the course focus on practical AI agent building with LLMs, and learn how to set up and use your Vocareum OpenAI API key for hands-on labs.

Extend AI agents beyond text with tool integrations, enabling reliable real-time actions and data access.

Learn to build Python agents using LangChain and OpenAI function calling, enabling LLMs to use external tools, parse data, and autonomously analyze clinical or drug safety signals.

Discover structured outputs in AI: transform responses into actionable JSON for integration. Utilize schemas, parsers, and function calls to enhance reliability and automation in workflows.

Learn to extract, validate, and structure genomic variant data from free-text using Pydantic and LLMs, enabling reliable, machine-readable outputs for bioinformatics workflows.

Explore agent state management with state machines. Learn how agents track user input, instructions, and tool use for complex workflows, ensuring adaptability and reliability.

Learn to manage agent state with LangGraph by building iterative state machines for life science workflows, using tools, Pydantic models, and conditional logic for controlled loops.

Explore short-term memory in AI agents, enhancing coherence via state, ephemeral, and ephemeral memory strategies for efficient context retention in active sessions.

Learn how to implement short-term agent memory in LangChain, enabling AI to recall clinical context and patient details across chat turns using local or cloud LLMs and session state.

Explore using external APIs for real-time data, dynamic actions, and authenticating agents. Discover MCP, a protocol standardizing AI’s tool interoperability and safety.

Learn to integrate external biomedical APIs with LangChain, enabling agents to query, analyze, and summarize data for clinical questions using PubMed and ClinVar.

Equip agents to search web for real-time, unstructured info. Ground responses in evidence using APIs, handle noise, and avoid hallucination for credible answers.

Learn to build a web-aware agent with LangChain that fetches, clusters, and summarizes life-sciences literature from arXiv and Google Scholar into concise, actionable digests.

Equip agents to access and modify structured data by using SQL for interaction and vector databases for semantic tasks, ensuring seamless integration with private systems.

Learn to build LangGraph database agents that use AI to convert natural language into SQL queries, enabling users to query databases conversationally without writing SQL.

Discover Agentic RAG: Enhance RAG by enabling reflection, query reformulation, and intelligent adaption for nuanced answers. Master retrieval, reasoning, and retry loops.

Learn to build agentic RAG systems for biomedical questions using ChromaDB—extracting key terms, retrieving evidence, and refining LLM answers with evidence-based, iterated workflows.

Explore long-term agent memory: understand semantic, episodic, and procedural memories. Learn storage strategies and best practices for personalized, coherent interactions.

Learn to build a scientific agent that summarizes, stores, and retrieves miRNA–gene literature with long-term memory using vector search and domain filters.

Agent Evaluation guides assessing an agent’s task completion, quality, tool use, and system metrics using response, step, or trajectory strategies to ensure reliable and efficient operations.

Learn to evaluate biomedical agents by testing citation integrity, retrieval quality, quantitative and entity accuracy, and source attribution to ensure reliability and safety.