Developer Tools & APIs
Developer Tools & APIs on AI Demos focuses on structured, agent-ready building blocks that turn messy inputs into usable data. Tools like Firecrawl for web scraping and data extraction, LlamaParse and Airparser for resume parsing and schema extraction, and AskYourDatabase for natural-language SQL queries show how teams can move from raw documents and web pages to clean outputs fast. If you’re shipping AI workflows, support bots, or data pipelines, this category helps you compare APIs by real behavior—not hype—so you can plug the right tool into production with confidence.
26 resources across tools, rankings, comparisons & guides
Strongest balance of selective retrieval, visible memory usage, scope isolation, and cross-session continuity.
See the test →Highest structural quality across the three live tests, especially on noisy and JS-heavy pages, with slower runs and some recording-sync fragility.
See the test →Strong schema adherence and complete row/line-item extraction, but it invents sequential transaction IDs and occasionally misreads alphanumeric codes.
See the test →Voiceflow was the strongest overall performer, combining the best multi-document reasoning in the test with excellent follow-up context, proactive answers, and the most responsible crisis handling.
See the test →Most consistent across all document types; production-ready default choice.
See the test →Affinda is a professional-grade resume-parsing API with 100+ configurable fields, skill-taxonomy metadata via EMSI IDs, language-proficiency extraction, and both a web UI and a REST API. It is built for HR-tech platforms, ATS vendors, and recruitment-automation pipelines that need structured JSON at scale.
See the test →What we learned testing this category
Stateful agent memory is judged by selective retrieval and clean forgetting, not by how much context it can store. The memory-tool test favored Hindsight because it balanced selective retrieval, visible memory usage, scope isolation, and cross-session continuity. The same test also checked delete/forget requests, which shows that a good memory tool must avoid leaking stale context as well as remember useful details.
For web scraping, structural quality on hard pages mattered more than raw speed. Skyvern won because it produced the highest-quality output across noisy and JavaScript-heavy pages, including a cluttered recipe blog, a JS-heavy product page, and a protected jobs page. The verdict also notes slower runs and recording-sync fragility, so the best scraper was not the fastest one.
In document extraction, schema adherence and complete row-level extraction were more important than perfect field correctness. Landing AI won by staying close to the target schema and capturing rows and line items completely across a bank statement and an invoice. The same verdict says it still invented sequential transaction IDs and occasionally misread alphanumeric codes, so even the winner was optimized more for structured completeness than flawless entity accuracy.
Knowledge-base chatbots are differentiated by multi-document reasoning and follow-up context, not just single-turn retrieval. Voiceflow was strongest because it combined the best multi-document reasoning with excellent follow-up context across a seven-document knowledge base. The test also singled out proactive answers and responsible crisis handling, showing that grounded support behavior mattered alongside retrieval quality.
For PDF-to-markdown APIs, consistency across document types is the main production requirement. Extend AI won because it was the most consistent across a hybrid annual report, a table-heavy financial report, and an image-only scanned paper. The test framing makes clear that usable markdown needs OCR, tables, charts, and reading order preserved well enough for downstream RAG, search, and reuse.
Query Live Databases Using Plain English with AI
This use case shows how business users can query a live database using plain English instead of writing SQL. We tested five AI database tools on the same ecommerce database and found AskYourDatabase to be the best practical workflow because it showed generated SQL, returned readable business answers, handled follow-ups well, and surfaced operational risks like pending-but-paid orders. The page also covers real outputs, limitations, and when users should still review the SQL before acting on results.