LangChain

Founded by Harrison Chase in 2022

Build, test, monitor, and deploy custom AI agents

Pricing model

Free Tier, Paid

Rating

★ Positive

Time to value

Immediate

You can use LangChain to create, test, and deploy custom AI agents and workflows. The framework connects large language models to your existing data sources, APIs, and custom computation. With its companion tools like LangGraph and LangSmith, you can construct cyclical agent workflows, trace detailed execution steps, and debug errors across production runs. It provides standardized evaluation datasets, controls API spend through gateways, monitors performance metrics, and isolates code execution so your applications run reliably at scale without vendor lock-in.

What LangChain does

Create custom agents using pre-built templates or graph architecturesDebug failing agent runs with detailed visual trace treesSet up automated test suites to measure response qualityTrack token usage, cost, and latency metrics across modelsEnforce cost limits and data security rules across LLM endpointsRun code generated by agents inside isolated sandboxesStep-by-step trace debugging for complex agent workflowsMulti-agent coordination with low-level state control via LangGraphAutomated evaluation loops converting production traces into test datasetsGateway controls to limit model spending and guard sensitive dataIsolated execution sandboxes for running agent-generated codePersistent memory and context management across long-running sessionsPre-built connectors for major model providers and vector databases