A Groq-Powered Agentic Research Assistant with LangGraph, Tool Calling, Sub-Agents, and Agentic Memory: Lets Built It

Lets Build a Grok-Powered agentic research assistant with LangGraph — here's the architecture that makes it work.

Most agent demos stop at a single LLM call. This one goes deeper:

< Inference: Groq's OpenAI-compatible endpoint with `llama-3.3-70b-versatile` — just swap the base URL, no wrapper changes needed

< Agent loop: LangGraph `StateGraph` cycling between an `agent` node and a `ToolNode` until no tool calls remain

< Sub-agent delegation: the lead agent spawns isolated assistants with scoped tool sets for focused subtasks — keeps the main context lean

< Skill-based dispatch: structured `SKILL.md` files define reusable workflows. Agent calls `list_skills` → `load_skill` before tackling complex tasks

< Persistent memory: a flat JSON store handles cross-session fact retention via `remember()` / `recall()` — no vector DB needed for basic continuity

< Sandboxed execution: all file I/O and Python execution are path-constrained with explicit escape prevention

The graph topology is simple. The real complexity lives in the tools and the system prompt — which is the right place for it.

Check out the full Tutorial Article here: https://www.marktechpost.com/2026/05/06/a-groq-powered-agentic-research-assistant-with-langgraph-tool-calling-sub-agents-and-agentic-memory-lets-built-it/

Notebook: https://github.com/Marktechpost/AI-Agents-Projects-Tutorials/blob/main/Agentic%20AI%20Codes/groq_agentic_research_assistant_langgraph_Marktechpost.ipynb