Faultmap vs LangChain

LangChain gives you the wiring to construct the agent. Faultmap maps where the agent will break before you write the first chain.

Side by side

Faultmapbefore the build

LangChainafter the build

When it runs

Before the build, in the design phase

At the build — you write chains, agents, and graphs, then execute them against a live LLM

What it needs

Your goal, personas, data, and tools

LLM API credentials, tool definitions, prompt templates, and the code to wire chains and agents together

What it produces

A map of where it breaks, plus the first test suite

A running agent application with structured outputs, tool call history, and conversation state

The question it answers

Where will this agent break, before I build it?

How do I build an LLM-powered agent from scratch?

We do not replace LangChain. Faultmap runs one step earlier. Keep using LangChain after the build.

See where YOUR agent breaks

Faultmap maps the likely breaks from your goal and data before you build. First cohort: agents that use SQL as the data source. First 20 tests free. Full validation dataset $10 one time, per agent.

Reserve your beta spot

What LangChain is

LangChain in one sentence. Where Faultmap fits with it.

An open-source Python and JavaScript framework for building LLM-powered agents and pipelines. Provides integrations with 200-plus models and tools, ReAct agent patterns via LangGraph, structured output handling, and middleware for memory and human-in-the-loop approval.

Agent orchestration framework

Where Faultmap fits before it

Run Faultmap in design to catch the breaks before you build.
Build the agent against the test suite Faultmap hands you.
Use LangChain after launch for agent orchestration framework.

Map the breaks before LangChain sees them.

Preview 20 agent-specific validation tests free. No card, no code.

Preview 20 tests