LangChain

About LangChain

LangChain is an open-source framework for building applications with large language models. It provides abstractions for prompts, model calls, retrieval, tool use, and memory, alongside a broad catalogue of integrations with model providers and vector stores. LangGraph, its companion library, adds a graph-based runtime for agent orchestration: nodes, edges, persistent state, and human-in-the-loop control for long-running workflows. Both are widely deployed in production LLM applications, in Python and JavaScript.

What the integration does

Hyperpriors wraps existing LangChain and LangGraph applications in its harness without requiring a rewrite. Your chains and graphs keep their structure; the harness adds the operational layer around them — retries, timeouts, fallbacks, and policy enforcement — as configuration rather than code changes.

Step-level traces map directly to chain and graph nodes, so a trace in Hyperpriors reads the way your application is actually built. Each node invocation appears as a discrete step with its inputs, outputs, token usage, cost, and latency. When a graph misbehaves in production, you inspect the node that failed rather than a flat log of model calls.

Evaluation suites run against your existing chains. Before you change a prompt, swap a model, or restructure a graph, the same suite runs against both versions and the results are compared side by side. Guardrails inspect chain inputs and outputs at runtime, enforcing policy independently of application code.

Get started

Connect your LangChain or LangGraph application to the Hyperpriors harness from the dashboard, or contact us to talk through your deployment.