AI Agent Library

Overview

I built a modular AI tool library that makes it fast to assemble and orchestrate LLM-powered agents. Using LangChain for reasoning and LangGraph for graph-based orchestration, the framework lets a developer combine tools, APIs, databases, and custom functions into reusable agent pipelines instead of wiring the same plumbing from scratch every time.

On top of the core, I shipped pre-built agents for web research and crawling, lead generation, marketing outreach, and customer-service automation — each one extensible, so a new workflow starts from a working template rather than a blank file.

The problem

Building agents from scratch means re-solving the same hard parts on every project: tool calling, state management, retries, and keeping LLM behavior predictable. Most teams reinvent that wheel for each automation, which is slow and produces inconsistent, brittle results.

Approach

• Composable tool layer — every capability (search, scrape, query, call an API) is a drop-in tool an agent can be granted.
• Graph orchestration — LangGraph models each workflow as an explicit state machine, so branching, loops, and human-in-the-loop steps are first-class rather than buried in prompt spaghetti.
• Templates over boilerplate — pre-built agents cover common jobs and are meant to be forked and customized.
• API-first — FastAPI endpoints expose any agent so it can drop into a product or be triggered from a UI.

What I learned

This project sharpened how I think about abstraction: hiding orchestration complexity without taking flexibility away from the developer. Working closely with LangGraph taught me how much of “reliable AI” is really good state design — making an LLM’s behavior reproducible is an engineering problem, not a prompting trick.

What’s next

I’m packaging the framework with a hosted playground where anyone can run an agent in the browser, plus a small evaluation harness that scores agent runs on task-completion and cost — so the quality story is measured, not asserted.