--- title: "Khan Academy uses Langfuse's AI Engineering platform to build Khanmigo AI" seoTitle: "How Khan Academy Builds Khanmigo with Langfuse" date: August 1st, 2025 description: How Khan Academy uses Langfuse to debug and improve Khanmigo AI, a student tutor and teaching assistant. ogImage: tag: customer-story author: Felix customerLogo: "/images/customers/khan-academy/khan-academy-logo.png" customerLogoDark: "/images/customers/khan-academy/khanacademy_light.png" customerQuote: "Langfuse has enabled our developers to get extremely fast feedback. It's fundamental to how our developers understand their AI implementations." customerQuoteHighlight: "extremely fast feedback" quoteAuthor: "Walt Wells" quoteRole: "Staff Software Engineer" quoteCompany: "Khan Academy" quoteAuthorImage: "/images/customers/khan-academy/walt-wells.jpeg" showInCustomerIndex: true --- [Customer stories](/users) › Khan Academy ![Khan Academy Website](/images/customers/khan-academy/khanmigo.png) ## About Khan Academy [Khan Academy](https://www.khanacademy.org/) is the beloved personalized learning system with over 150 million registered learners across 190 countries in 56 languages. It provides free online courses, lessons, and practice exercises covering subjects from elementary math to college-level topics like calculus, physics, economics, and computer science. Khan Academy also provides tools for teachers and parents to track student progress. Khan Academy's mission: "A free, world-class education for anyone, anywhere." ## Pioneering AI in Education Khan Academy started their journey working with large language models in 2022 as an early access partner of OpenAI's GPT-4. They have learned many hard-won lessons and shipped dozens of cornerstone AI experiences led by their flagship AI product: [Khanmigo](https://www.khanmigo.ai/). ## Challenge: Go-Based Infrastructure Meets AI Running a nonprofit at this scale demands extreme resource efficiency. Khan Academy migrated to Go from Python2 starting in 2019. When it came time to implement AI observability, they faced a unique challenge. Most tracing tools required specific SDKs or wrappers that didn't fit their Go-based architecture. “What we really loved about Langfuse was the [open API](/docs/api-and-data-platform/features/public-api),” says Walt from Khan Academy's engineering team. “While many tracing tools require specific SDKs or wrappers, Langfuse's open API enabled us to build our own Golang client around it. This was huge for our Go-based services.” ## LLM Observability Across Product Teams Since deploying Langfuse in April 2024 as one of Langfuse's first enterprise customers, adoption has spread to over **100 users** across **7 product and 4 infrastructure teams** within Khan Academy. The platform isn't providing deep [observability](/docs/observability/overview) for just one AI feature but dozens of AI interactions across different products. The integration runs deep: - **Internal UIs** link directly to Langfuse traces for user experience analysis - **Community support teams** access traces for incident investigation - **Engineers** share Langfuse URLs for collaborative debugging - **Senior leadership** uses the platform for product decision analysis. ## Results: Speed Without Compromise For Khan Academy, Langfuse solved multiple challenges simultaneously. The company avoided the engineering overhead of hosting their own tracing infrastructure while maintaining the flexibility to work within their Go ecosystem. **Stack Flexibility** Langfuse plays well with Khan Academy's Go stack. They've built their custom Go client around the Langfuse API. - [Half a Million Lines of Go](https://blog.khanacademy.org/half-a-million-lines-of-go/) **Development Speed** Deep observability is the foundation for rapid iteration and debugging capabilities across products. **Hosting Simplicity** The nonprofit avoided the engineering overhead of hosting its own tracing infrastructure. **Platform across Teams** Development teams across the organization now share a common observability platform, preventing tool fragmentation. ## Ready to get started with Langfuse? Join thousands of teams building better LLM applications with Langfuse's open-source observability platform. - [Start free](/cloud) - [Documentation](/docs) - [Talk to an expert](/talk-to-us) --- ## Agent Instructions This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/users/khan-academy.md`. ### Querying these docs If the answer is not on this page, query the documentation instead of guessing: - **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question: ```bash curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?" ``` - **Index of every page**: , with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt). ### Before writing Langfuse code - **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results. - **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application. - **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples. - **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx langfuse-cli api `) to read or write traces, prompts, datasets, and scores from the terminal. 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