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A ReAct loop built with LangChain and [LangGraph](external:langgraph) calls deterministic Python functions for the analysis itself and uses the model only to read the question and phrase the answer; daily digests and alerts are pushed to a Microsoft Teams channel through an incoming webhook instead of waiting for a recruiter to open a dashboard. It also ships a multi-currency compensation-analytics module and a toolkit for assessing a migration from [SAP SuccessFactors](external:sap-successfactors) Recruiting, and it runs on mock data with no credentials, falling back to keyword-based routing and template responses when SAP AI Core is unavailable."},"links":{"repo":"https://github.com/SAP-samples/smartrecruiters-ai-recruiting-custom-agent"},"identity":{"license":"Apache-2.0"},"source_available":true,"demonstrates":["capability:agent-tooling"],"requires":[{"ref":"artifact:btp-ai-core","for":"the model that reads the question and words the answer; the README offers a fallback mode and mock data for running without it.","requirement":"conditional","condition":"the agent's conversational answers; without SAP AI Core it falls back to keyword-based intent detection and template responses","sources":[{"url":"https://github.com/SAP-samples/smartrecruiters-ai-recruiting-custom-agent","title":"SAP-samples/smartrecruiters-ai-recruiting-custom-agent — README, Quick Start","checked":"2026-10-19","quote":"Works out of the box with mock data. 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