LangChain — $500 in LangSmith credits for Startups
Framework for building LLM-powered applications — chains, agents, RAG, and tool use with any model provider.
Reviewed within 48 hours
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Deal Highlights
What Is LangChain?
LangChain is the framework for building applications powered by large language models — providing the abstractions, tooling, and infrastructure for chains (sequential LLM calls), agents (LLMs that use tools), RAG (retrieval-augmented generation), and memory (conversation context). LangSmith, the companion platform, provides tracing, evaluation, and monitoring for LLM applications in production.
For AI startups building complex LLM-powered features, LangChain provides the development framework and LangSmith provides the production observability — together covering the full lifecycle from prototype to production.
What''s Included
- $500 in LangSmith credits — LLM application tracing, prompt evaluation, dataset management, production monitoring, and regression testing for AI applications.
Key Features
- LangSmith tracing: See every LLM call, tool use, and retrieval step in your AI pipeline
- Prompt evaluation: Test prompts against datasets and score outputs for quality
- Dataset management: Build test datasets for evaluating LLM application quality
- Production monitoring: Track latency, cost, and quality metrics for production AI features
- Regression testing: Ensure prompt changes don''t degrade output quality
Who Is This Deal For?
Early-Stage Startups
Seed and pre-seed companies looking to move fast without overspending on tools.
Growing SaaS Teams
Series A+ companies scaling their stack and optimizing software costs.
Solo Founders
Indie hackers and bootstrapped founders who need enterprise tools at startup prices.
Get $500 in LangSmith credits off LangChain
Apply now — reviewed within 48 hours.
!Eligibility Requirements
AI startup building LLM applications
Frequently Asked Questions
Everything you need to know about this startup deal.
LangChain is an open-source framework for building LLM applications. It provides abstractions for chains (sequential calls), agents (tool-using LLMs), RAG (search + generation), and memory. Available for Python and JavaScript/TypeScript.
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