BrowseSAP Cloud Application Programming Model
Everything in CAP
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LLM integrationSAP and community
@cap-js/ai@cap-js/aiOut-of-the-box value-help recommendations powered by SAP RPT-1, SAP AI Core as a standard CAP service, and an experimental local embedding model behind SQLite's vector functions.sap official, cap
Value-help recommendations and SAP AI Core as a CAP service
@geertjanklaps/cds-toon-ai-protocol-adapter@geertjanklaps/cds-toon-ai-protocol-adapterA protocol adapter that serves CAP data as TOON — Token-Oriented Object Notation — instead of JSON, so a payload bound for a language model costs fewer tokens to send.independent, cap
Protocol adapter serving CAP data as TOON instead of JSON
@jerome-benoit/sap-ai-provider@jerome-benoit/sap-ai-providerA provider for the Vercel AI SDK that speaks to SAP AI Core through its orchestration or foundation models API, so an application written against that SDK runs on SAP's models without being rewritten.independent, cap
Vercel AI SDK provider for SAP AI Core
@sap-ai-sdk/ai-api@sap-ai-sdk/ai-apiThe AI Core API surface of SAP's AI SDK, covering deployments, configurations and the rest of the control plane, as opposed to calling a model.sap official, cap
AI Core control plane API surface of SAP's AI SDK
@sap-ai-sdk/core@sap-ai-sdk/coreThe shared internals that the packages of SAP's AI SDK build on, which its own readme says are not meant to be depended on directly.sap official, cap
Shared internals of SAP's AI SDK
@sap-ai-sdk/foundation-models@sap-ai-sdk/foundation-modelsThe package of SAP's AI SDK that calls foundation models through SAP AI Core and the Generative AI Hub, and the one an application actually reaches for.sap official, cap
Client for foundation models via SAP AI Core and Generative AI Hub
@sap-ai-sdk/orchestration@sap-ai-sdk/orchestrationThe Orchestration Service client of SAP's AI SDK, which applies templating, grounding, filtering and masking around a model call rather than in the application.sap official, cap
Orchestration Service client for templating, grounding and masking
@sap-ai-sdk/prompt-registry@sap-ai-sdk/prompt-registryA package for keeping prompts in SAP's prompt registry rather than in the codebase, so changing a prompt is not a deployment.sap official, cap
Client for prompts kept in SAP's prompt registry
@saptarishi/cds-plugin-llm@saptarishi/cds-plugin-llmA CAP service kind that puts eleven model providers behind one chat, stream, embed and batch interface, so which backend a service calls is a profile in package.json rather than a change to the handler.independent, cap
CAP service kind putting eleven model providers behind one API
@shahid.la/cds-db-nlquery-mcp@shahid.la/cds-db-nlquery-mcpMCP server that answers plain-English questions against the entities in db/schema.cds — it discovers the schema, generates CQN-backed SQL with real joins, and returns rows.independent, cap
MCP server answering plain-English questions against a CDS schema
cap-java/cds-aicap-java/cds-aiBridges CAP Java to SAP AI Core and adds AI-powered field recommendations to Fiori draft UIs, as two alpha Maven modules with mock fallbacks for local development.sap official, cap
Bridge from CAP Java to SAP AI Core, with Fiori field recommendations
cap-llm-plugincap-llm-pluginGenerative AI in a CAP application with the sensitive fields anonymised through SAP HANA before the prompt leaves, so the model never sees the confidential half.independent, cap
Generative AI in CAP with fields anonymised through SAP HANA
Not where the records sit anywhere but [SAP HANA Cloud](artifact:btp-hana-cloud): the similarity search and the single retrieval method are both written against its vector engine, and the embeddings and the chat call go through [SAP AI Core](artifact:btp-ai-core), so a project without both has nothing here to call. Not where the dependency has to be an SAP-supported one either: the README states that the plugin is not an official SAP product and is published as a community contribution.
SAP AI CoreSAP AI CoreSAP's AI service on BTP: the runtime for AI assets, and through the Generative AI Hub inside it the access to foundation models, the orchestration service with its templating, masking, filtering and grounding modules, the Prompt Registry and Document Grounding.sap official, btp
AI runtime on BTP with the Generative AI Hub
ui5-cap-ai-featuresui5-cap-ai-featuresBoth halves of an AI text feature in one package: a CDS plugin serving an AIService over SAP AI Core's Orchestration Service, and the be.wl.ai UI5 control library that calls exactly that service.independent, cap
CDS plugin and UI5 controls for AI text features via SAP AI Core
It is text processing, not an agent: there is no tool calling, no retrieval and no conversation. It needs SAP AI Core, and the backend half needs [@sap-ai-sdk/orchestration](artifact:sap-ai-sdk-orchestration) installed alongside it — an optional peer, so a frontend-only install stays lean.
cap-agent-ui5-webui-samplecap-agent-ui5-webui-sampleA worked example of the cap-agent-ui5-webui plugin, shown running locally, in hybrid mode against SAP AI Core, and deployed.independent, cap
Worked example of the cap-agent-ui5-webui plugin
Neither the package manifest nor the repository tree declares a licence.
cap-openaicap-openaiTwo actions on one CAP service that reach OpenAI's chat completions endpoint by two routes: the openai npm client with the key from the environment, and a CAP remote service of kind: rest that goes through a destination in production.independent, cap
CAP service actions calling OpenAI chat completions two ways
Neither the package manifest nor the repository tree declares a licence.
cap-ui5-gpt-chatcap-ui5-gpt-chatA working chat application on CAP and SAPUI5 against the OpenAI API — selectable models, per-user chat history in the database, maintainable chatbot personalities, and streaming responses as an option per chat.independent, cap
Chat app on CAP and SAPUI5 against the OpenAI API
It calls the OpenAI API directly with a key in the environment. It was written in early 2023 and last touched in May 2023, which is before SAP AI Core and the CAP LLM plugins existed — so read it for the shape of the application, not for how a CAP service should reach a model today.
custom-llm-clientcustom-llm-clientA local-first proof-of-concept chat client for SAP questions: a CAP backend runs a LangGraph planner over MCP servers and streams the answer with every tool call shown as it runs.independent, cap
Local chat client for SAP questions with a CAP LangGraph backend
Neither the package manifest nor the repository tree declares a licence.
Sample CAP Application with SAP Cloud SDK for AISample CAP Application with SAP Cloud SDK for AIA CAP backend-sample and fullstack-sample pair that call SAP AI Core through the SAP Cloud SDK for AI: a capital-of-country question and a templated chat completion.sap official, cap
CAP samples calling SAP AI Core through the SAP Cloud SDK for AI
Every CDS service in both samples is marked `@requires: 'any'` and left publicly accessible, which the README says simplifies the deployment process, and it asks for proper authentication mechanisms to be applied in their place.
SAP-samples/btp-cap-genai-ragSAP-samples/btp-cap-genai-ragA CAP reference application on SAP BTP for GenAI email support, built on SAP AI Core's Generative AI Hub and retrieval-augmented generation over SAP HANA Cloud's Vector Engine.sap official, cap
CAP reference app for GenAI email support with RAG on HANA Cloud
SAP-samples/cap-agentic-engineeredSAP-samples/cap-agentic-engineeredA Financial Risk Analyzer built to show what changes when a coding agent is grounded by SAP's MCP servers rather than left to its training data — and it ships the AGENTS.md and skills that did the grounding.sap official, cap
Financial Risk Analyzer built by an agent grounded in SAP MCP servers
SAP-samples/codejam-cap-llmSAP-samples/codejam-cap-llmA CodeJam that builds a CAP-based job posting service exposing SAP AI Core's generative AI capabilities, including vector embeddings, through an OData API.sap official, cap
CodeJam on a CAP job posting service using SAP AI Core over OData
ui5-ai-features-demoui5-ai-features-demoA product-review screen on CAP and SAPUI5 that consumes ui5-cap-ai-features and contains no library source at all — written to prove the package is enough on its own.independent, cap
Product-review screen consuming ui5-cap-ai-features on CAP
Neither the package manifest nor the repository tree declares a licence.
🟣 Bring AI into Your ABAP Applications with ISLM: Build, Integrate, and Consume AI Natively🟣 Bring AI into Your ABAP Applications with ISLM: Build, Integrate, and Consume AI NativelyDevtoberfest 2026 session AI-11, from the product manager and product owner of Intelligent Scenario Lifecycle Management: ISLM as the place an AI use case is registered, deployed and operated inside an SAP S/4HANA system, and what has been added to it for generative AI. The demo builds a custom scenario end to end — an intelligent scenario of type generative AI, a connection to SAP AI Core, a GPT-5 mini model, an orchestration flow template that configures data masking, input and output filtering and translation into the user's logged-in language, then prompts with a grounding query over five companies' return-policy documents — and consumes it from an ABAP report through the ABAP AI SDK, which calls the orchestration API and returns the summary as JSON. Two newer pieces follow: SAP RPT, the relational pre-trained transformer for tabular prediction, called with context rows and query rows and answering with a confidence value and no training step; and system-triggered agents, where an application job in the ABAP system starts a billing-creation agent that reaches S/4HANA OData APIs as tools through the agent gateway with principal propagation. The closing availability matrix states the generative AI capabilities for on-premise and private cloud from the 2021 release, with SAP notes to implement on lower ones.SAP Developers, published 2026-09-24
🟣 Enable Your AI application to Run on Multiple Models to Ensure Resilience and Compliance🟣 Enable Your AI application to Run on Multiple Models to Ensure Resilience and ComplianceDevtoberfest 2026 session AI-21. A cross product owner on the SAP AI Core team gives the reasons an AI application ends up on more than one model — a model down at the hyperscaler, a data centre that is not allowed to run it, a version being deprecated — and runs a joke app that answers from Gemini 2.5 Flash in EU10 and from Claude 4.5 Haiku in Asia-Pacific. The walk through AI Core's parts is where the edges hold: prompts kept as templates in the prompt registry, imported through the launchpad and the APIs or synced as YAML from a git repository (Prompt management); the evaluation service scoring one prompt across up to four models per run on built-in or model-as-judge metrics, and the prompt optimizer generating variants against one metric (Agent evals); and the orchestration service harmonising the providers behind one message format with a priority-sorted fallback list (LLM integration). The session also states where a metric has to be designed rather than picked: an exact-JSON match scores an email generator at zero, so the joke app's custom metric judges topic and family-appropriateness instead.SAP Developers, published 2026-10-01
A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 1/3A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 1/3The concepts before the code: what the GPT model and its API actually offer, and what the finished chat application looks like — personalities included. Written in the weeks when the ground was moving under it, and the post carries its own change log to prove it: GPT-3.5 added on 1 March, GPT-4 on 15 March, and an admission that a series planned as two posts became three.published 2023-02-22
A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 1/3
cap-ui5-gpt-chat: no published releases, repository quiet for 41 months. Affects Runnable code.
A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 2/3A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 2/3The backend: CAP with TypeScript, the entities holding chats and personalities, and the service that talks to the API. This is where the streamed completion and the repositories behind the handler come from, which is the part of the sample still worth lifting even though the model access has since been superseded by AI Core.published 2023-02-24
A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 2/3
cap-ui5-gpt-chat: no published releases, repository quiet for 41 months. Affects Runnable code.
A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 3/3A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 3/3The SAPUI5 frontend of the sample, and mostly an argument for writing UI5 in TypeScript: ES imports in place of sap.ui.define, a class with real private members in place of the underscore convention, and the service types generated from the CDS model copied into the UI package, so one IChats interface types the OData object the controller reads. The second half is structure — a formatter class and a singleton LayoutManager called from the XML view through core:require, a confirmation helper taking a callback, a dialog class resolving a promise, a generic createEntity<T> on a service class — all of it to keep every controller under 100 lines. What it says about arranging a UI5 app holds whatever model sits behind the chat; the tooling around it is of its date, and the post calls UI5's TypeScript support experimental.published 2023-02-27
A ChatGPT-like chat app built with modern SAP technologies (CAP, SAPUI5) – Part 3/3
cds2types. Affects Runnable code.
Agentic AI on BTP: a Multi‑Agent App with Pydantic AIAgentic AI on BTP: a Multi‑Agent App with Pydantic AISplits a single CAP/TypeScript agent that had grown to juggle five BTP MCP servers behind one bloated tool list into btp-multiagent-app, a Python and Pydantic AI rewrite where the Cloud Foundry and BTP Core MCP servers each get a dedicated specialist agent, with an audit log specialist scaffolded but left disabled, behind an orchestrator whose only tools are delegation calls to those specialists, which is the agent-tooling pattern the piece works through. Each specialist authenticates to its MCP server over Streamable HTTP with OAuth 2.1, and a shared module centralises the SAP AI Core GenAI Hub model setup behind two credential layouts, one for local runs and one for the Cloud Foundry service binding, the LLM integration side of the rewrite. Tool lists per agent drop from well over a hundred to a dozen or two, and routing failures become separable from tool-misuse failures by reading the trace.published 2026-05-03
Agentic AI on BTP: a Single Agent with LangGraphAgentic AI on BTP: a Single Agent with LangGraphA CAP TypeScript agent puts a LangGraph StateGraph in front of AzureOpenAiChatClient from @sap-ai-sdk/langchain, reaching SAP AI Core's Generative AI Hub, and lets its tool-calling loop draw on five deployed MCP servers — OData MCP Proxy, CI MCP Server, AI Core MCP Server, BTP MCP Server and BTP CF MCP Server — through @langchain/mcp-adapters' MultiServerMCPClient. Because agent, servers and APIs share one XSUAA tenant, the chat service forwards the signed-in user's own JWT instead of a service account, so each downstream server enforces that user's own authorizations. Past roughly a hundred combined tools the single prompt slows down and stops separating concerns, which is why the author's next post splits it into one specialist agent per MCP server behind an orchestrator.published 2026-05-03
Agentic CAP: Build MCP servers and enterprise AI agentsAgentic CAP: Build MCP servers and enterprise AI agentsCAP applications become AI agents over the Model Context Protocol and the Agent2Agent protocol, which is the serve your application over MCP concern, and SAP AI Core is the LLM integration through which AI is embedded in a CAP app, with recommendations from the SAP-RPT-1 model as the abstract's example. The abstract names the two protocols and the one model and does not say which packages carry them. A two-hour TechEd 2026 session in a hands-on room (Tuesday 27 October, Berlin), presented by Daniel Hutzel, Daniel Schlachter, Marc Huber and Sami Lechner, written here from SAP's abstract; the recording is not yet published.
Agentic memory: Knowledge graph engine and recall in SAP HANA CloudAgentic memory: Knowledge graph engine and recall in SAP HANA CloudThe knowledge graph engine in SAP HANA Cloud as an agent's memory: what the engine, the graph side of graph, spatial and document data, does in an agentic workflow, how an agent keeps what it learns there, the state agent tooling holds between turns, and the SAP AI Core integration through which the database calls LLMs and tabular foundation models such as SAP-RPT-1. The abstract names the engine, the memory role and the AI Core link and does not say how the memory is written or read. DA263 on Wednesday 28 October is a two-hour hands-on on the same engine with a different cast. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by Markus Fath and Shraddha Shetty, written here from SAP's abstract; the recording is not yet published.
AI-enabled administration: Automating log analysis with generative AI hubAI-enabled administration: Automating log analysis with generative AI hubA log analysis tool for incident work, built in SAP Business Application Studio with the generative AI hub feature, which SAP AI Core ships as its LLM integration by its own documentation: it automates root-cause analysis over system errors and masks sensitive data. SAP's abstract states the tool's jobs and not how the hub is called, what the masking covers or which model answers. A TechEd 2026 session (Wednesday 28 October, Berlin), with no speaker listed in the catalogue at the time of writing, written here from SAP's abstract; the recording is not yet published.
An Innovative Language Exchange App developed with SAP Build Apps, CAP, Kyma, and OpenAIAn Innovative Language Exchange App developed with SAP Build Apps, CAP, Kyma, and OpenAIA dormant internal app at SAP Japan revived with AI, built to solve a problem the team actually had — colleagues who could not talk to each other. Build Apps on the front, CAP on Kyma behind it, OpenAI in the middle.javieryw, published 2024-01-31
An Innovative Language Exchange App developed with SAP Build Apps, CAP, Kyma, and OpenAI
LLM integration changed in @cap-js/ai 1.0.0, 2026-04-28. Affects Runnable code.
Automate your Job Postings Using Generative AI (ChatGPT) & SAP Build Process Automation AppsAutomate your Job Postings Using Generative AI (ChatGPT) & SAP Build Process Automation AppsA process that drafts its own text: ChatGPT writing job postings, SAP Build Process Automation carrying them through approval, and CAP in between. Presented as a generic pattern the author expects to be adapted rather than copied.shivamshukla12, published 2023-08-10
Banking Sentinel: A Multi-Agent Credit-Risk Copilot on SAP BTP, Built on a Mixed AI StackBanking Sentinel: A Multi-Agent Credit-Risk Copilot on SAP BTP, Built on a Mixed AI StackThe risk it is chasing is a graph problem stated in banking terms: a borrower who looks safe alone and is the linchpin of a group ten times the size. Multi-agent, mixed AI stack, on BTP and CAP.Shahid, published 2026-06-24
Banking Sentinel: A Multi-Agent Credit-Risk Copilot on SAP BTP, Built on a Mixed AI Stack
Agent tooling changed in @cap-js/agents 0.9.1, 2026-08-14. Affects A recipe, Runnable code.
Behind the Autonomous Enterprise: SAP Knowledge Graph and SAP Domain ModelsBehind the Autonomous Enterprise: SAP Knowledge Graph and SAP Domain ModelsThe strategy behind SAP Business AI's intelligence layer: SAP Knowledge Graph for enterprise context, SAP Domain Models for SAP-specific expertise and tabular AI for predictions, unified on SAP AI Core with SAP Document AI. Knowledge Graph and Domain Models are not atlas entries, so the edges rest on the two products the abstract names as the infrastructure and on Predictive models for the tabular AI; nothing is stated about what any of them does in the layer. A TechEd 2026 session (Tuesday 27 October, Berlin; also ST103V, virtual), presented by Jan Portisch and Richard Grandpierre, written here from SAP's abstract; the recording is not yet published.
Bring "developers.sap.com" Into Your Dev Environment: SAP Developer Context for AI Coding AgentsBring "developers.sap.com" Into Your Dev Environment: SAP Developer Context for AI Coding AgentsDevtoberfest 2026 session FA-11: a tour of the rebuilt developers.sap.com by the team that runs it, from the administration screens behind the home page to the agent interfaces in front of it. The site is a CAP application on SAP HANA Cloud and SAP BTP, open-sourced in full, and the half of the session that places it here is how its content reaches an agent: every tutorial is also served as markdown, a coding agent is detected and given that version instead of the HTML, a well-known agent card advertises what the site offers, and SAP Developers MCP answers questions about tutorials, news and a reader's own completion history in Claude Code and Joule Desktop, over OAuth or a personal access token. Behind the answers sit two things working together: a knowledge graph in the HANA database over 1400 tutorials and 11,000 concepts, and SAP AI Core for the Joule interactions and the embeddings, with the agent harness written for the site rather than taken off a shelf. Where neither MCP nor A2A is available, the same content is published as skills.SAP Developers, published 2026-09-25
Build AI agents and applications natively on SAP HANA CloudBuild AI agents and applications natively on SAP HANA CloudSAP HANA Cloud as the base for agentic applications on SAP Business AI Platform: the session goes through CAP, the SAP HANA Cloud vector engine and SAP AI Core, with architecture patterns and lessons learned from building AI-supported applications. SAP's abstract names the three parts of that stack and the vector engine as the database's feature in it, and describes none of the patterns. A TechEd 2026 session (Wednesday 28 October, Berlin; also DA113V, virtual), presented by Christoph Morgen and Shraddha Shetty, written here from SAP's abstract; the recording is not yet published.
Build business differentiation with SAP AI CoreBuild business differentiation with SAP AI CoreSAP AI Core as the orchestration layer for enterprise AI, worked through with SAP's engineers: extend Joule Agents with models from its library, ground them in SAP Business Data Cloud products, embed them in SAP S/4HANA flows, then govern, scale and monitor them from prototype to production. The abstract says what the layer does for an agent and not which services carry each step. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by David Knaack, Franziska Dobrigkeit, Hadi Hares and Jonas Israel, written here from SAP's abstract; the recording is not yet published.
Building agentic applications with SAP HANA CloudBuilding agentic applications with SAP HANA CloudA CAP application extended with semantic search and agent features on top of SAP HANA Cloud's vector engine and SAP AI Core, built in a hands-on workshop. The abstract names the parts, CAP, the vector engine and AI Core, and the two additions: semantic search, the retrieval half of retrieval-augmented generation, and LLM-supported agent capabilities inside the application, which is agent tooling. Which embedding model, which CAP runtime and how the agent calls its tools are not stated. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Hui Li, Ritty Joseph, Shraddha Shetty and Tanuja Jadhav, written here from SAP's abstract; the recording is not yet published.
Building Agentic Workflows with n8n in Joule StudioBuilding Agentic Workflows with n8n in Joule StudioA Devtoberfest 2026 session, JS-31, on SAP-managed n8n inside Joule Studio, shown as a labs preview on a blocked-invoice scenario: a solution made of assets, an intent refined by the web agent into requirements and then a specification, and the generated workflow opened in the n8n editor that Joule Studio connects to its own n8n tenant, with the generated files committed through GitHub. The nodes carry the other edges — the human-in-the-loop node is built on SAP Task Center, and SAP AI Core is where model providers are configured centrally instead of per developer. It ends in Joule chat, where the approval task the workflow created is listed, opened and completed.SAP Developers, published 2026-10-08
Building an LLM Agent in CAP Java with Spring AIBuilding an LLM Agent in CAP Java with Spring AIThe Java counterpart to the author's own Node.js post, and written as one: connecting to AI Core with the SAP AI SDK, then calling an agent and a tool through Spring AI. Opens with a section on libraries and workflows — which framework does what, and what is left to you — before any code.published 2026-06-08
Building an LLM Agent in CAP Java with Spring AI
Agent tooling changed in @cap-js/agents 0.9.1, 2026-08-14. Affects Runnable code.
Building an LLM Agent using CAPBuilding an LLM Agent using CAPThe argument is that CAP is already most of what an agent framework wants — services, handlers, a typed model — so the agent is built inside it rather than beside it. AI Core through sap-ai-sdk, then Langchain for the agent and its tools. Long, with a TLDR at the top for people who want the shape without the walk.published 2026-05-27
Building an LLM Agent using CAP
Agent tooling changed in @cap-js/agents 0.9.1, 2026-08-14. Affects Runnable code.
Building Code-Based AI Agents on the BAIPBuilding Code-Based AI Agents on the BAIPA Devtoberfest 2026 session on writing the agent yourself rather than configuring one, which is the agent tooling edge: an agent is a model with tools, skills, memory and context around it, and the session reads the code of one built in CAP as a LangGraph state graph, with an agent node that calls the model, a tools node holding the MCP clients, and the edge between them looping until no tool is needed. It then shows the shorter route, a CDS service annotated for the agent protocol with its instructions and skills in an agent.md file. SAP AI Core carries both, with the CAP service in front of it as middleware so the credentials never reach the browser, and that is what cap-agent-ui5-webui is demonstrated against: added as a dependency, it gives the annotated service a UI5 chat interface that can be deployed rather than only previewed. A Python multi-agent application on Cloud Foundry, where an orchestrator delegates to per-stack specialists so no single model sees every tool, closes the session.published 2026-10-09
CAP in the age of AI #3 - from MCP to A2ACAP in the age of AI #3 - from MCP to A2AThe episode that finishes @cap-js/mcp with authorisation and then crosses to agents. @requires and @restrict are the same annotations CAP already has, and the plugin reads them to filter the tool list and the describe output per user and per request, so a model is not told about entities the caller cannot reach — where the OData metadata of the same service lists every exposed entity regardless. The second half annotates a service with @agent from @cap-js/agents, which serves it over A2A with an agent card and a chat preview, runs first against a mocked response and then against SAP AI Core bound with cds bind and a hybrid profile, and reuses the MCP adapter's tools rather than defining new ones.published 2026-08-21
CAP in the age of AI #5 - agents wrap-upCAP in the age of AI #5 - agents wrap-upThe agents topic closed out on what sits around the agent rather than inside it, now that the documentation has moved into capire. The chat preview's session is persisted in the database rather than kept in memory, traces go out through @cap-js/telemetry to a self-hosted MLflow under the OpenTelemetry conventions for generative AI, and the panel is explicit that @agent reuses the MCP adapter's code programmatically without serving an MCP endpoint. It ends in the xtravels sample, where a travel agent delegates to a hotel-service sub-agent and reaches a separately started flight service over MCP through the service binding CAP resolves anyway. Earlier in the hour the same agent answers from a model running on the presenter's laptop, reached as an OpenAI-compatible endpoint.published 2026-10-02
CAP in the age of AI #6 - vector embeddingsCAP in the age of AI #6 - vector embeddingsVector embeddings put to work in the incident management sample, answering whether this problem has been seen before. An embedding element of type Vector is calculated and stored from the vector_embedding function, so the database refreshes it whenever the text behind it changes, and cosine_similarity compares it with a freshly embedded string; because a title says little, a scheduled action first summarises each incident's conversation through a model on SAP AI Core and the embedding is taken from that summary. The similar-incidents list is a view joining incidents to themselves, with order and limit set in a before handler on the expanded query, and the vector element excluded from the service, since OData knows nothing about vectors — on SAP HANA Cloud the function is native, while SQLite needs packages added to the project.published 2026-10-09
CAP in the age of AI #7 - vector embeddings wrap-upCAP in the age of AI #7 - vector embeddings wrap-upThe same embeddings model taken from SQLite to SAP HANA Cloud, then handed to an agent. Locally it is @cap-js/ai that adds the vector functions SQLite lacks and downloads an all-MiniLM L6v2 ONNX model on first start; on HANA natural language processing has to be switched on, the model name is a mandatory argument, the vectors it returns are 768 dimensions, and dimensions must match on both sides of a comparison. HANA itself can be given SAP AI Core as a remote source for other embedding models, which a CDS.ql expression has to inject because the postfix notation only takes what the CDS model holds, and the hour ends with a search function over cosine similarity exposed over @cap-js/mcp, so an agent asked about low output from solar panels calls semantic search instead of writing the query itself.published 2026-10-16
CAP: The Road Ahead - Simplicity in an AI EraCAP: The Road Ahead - Simplicity in an AI EraDevtoberfest 2026 session CAP10, from the head of SAP's Cloud Application Programming Model: forty minutes of live building rather than slides, on the argument that the cheapest code in an AI project is code nobody has to generate. Claude Code builds an invoice-matching workspace from cds init onwards, and each step is one the framework answers rather than the agent: a 42-line domain model that @sap/cds serves as OData and Fiori elements with a status flow, data privacy and change-tracking annotations that produce audit log entries in the console and would reach the SAP Audit Log service once deployed, an imported S/4HANA supplier data product, and semantic matching from a local embedding model after @cap-js/ai is added. The last three steps are annotations: @mcp beside @odata turns the service into an MCP server that a second Claude session queries through @cap-js/mcp, an agent annotation adds the A2A protocol with an agent card and an audit log entry for the agent task, and a skill file gives the agent the matching policy.SAP Developers, published 2026-09-25
cds-toon-ai-protocol-adapter: a pragmatic way to cut AI token costs using SAP CAPcds-toon-ai-protocol-adapter: a pragmatic way to cut AI token costs using SAP CAPNames a cost most CAP-plus-LLM writing ignores: every JSON payload streamed to a model is billed by its verbosity, in latency as well as money. The plugin brings TOON — Token-Oriented Object Notation — to a CAP service so the wire format changes without the service code doing so, in a REST variant and an OData one, with token counts reported on both sides.published 2026-01-25
Content FilteringContent FilteringContent filtering screens a large language model's input and output for low-quality, irrelevant or unsafe content: the four categories it names being hateful, violent, sexual and vulgar content, checking a request once on the way in and once on the way back. The page states smaller models can do this filtering efficiently, reading perplexity scores to flag anomalies, with the aim that SAP AI Core produces content meeting a stated safety standard, which is the ground content filtering covers on the BTP door.
Creating a meal generator application with SAP Build, Cloud Application Programming and openAICreating a meal generator application with SAP Build, Cloud Application Programming and openAIIngredients in, meal suggestions out, assembled from BTP services plus OpenAI and Google APIs. A small application built for the pleasure of it, and useful here as the plainest example of CAP holding the middle of a generative flow.former_member793421, published 2023-07-17
Data MaskingData MaskingData masking replaces identifiable or confidential text in a prompt with placeholders such as <Person_0> or <Organization_1> before it reaches a large language model, using pattern matching and machine learning to find what to replace. The page states SAP AI Core supports it as a feature and sets out five steps for applying it, defining the masking scope, choosing a technique, preserving the prompt's intent, checking compliance and risk, then testing and monitoring the result. That is the shape data masking takes on the BTP door.
Document GroundingDocument GroundingDocument Grounding is a managed retrieval-augmented-generation service under the Generative AI Hub on BTP, which SAP's page on SAP AI Core places inside that service: it ingests documents from Amazon S3, SharePoint, SAP Document Management Service, SAP Work Zone or an SFTP server, and handles chunking, embedding and retrieval itself, refreshing content automatically when a source document changes. The page states this managed path is recommended for most enterprise scenarios because it covers the whole lifecycle end to end, and offers a separate Document Grounding Vector API for data that is already chunked and embedded, for cases that need finer control over chunking and metadata.
Elevating Data Privacy in SAP CAP based GenAI Applications with CAP LLM PluginElevating Data Privacy in SAP CAP based GenAI Applications with CAP LLM PluginThe question underneath the enthusiasm: what leaves your landscape when a prompt goes to a model. The plugin's answer is to anonymise the sensitive fields through HANA before the prompt is sent, and this is the post that explains why that is the design rather than an option.AkashAmarendra, published 2024-02-02
Empowering Your Data Analytics with GPT-3 and SAP CAP IntegrationEmpowering Your Data Analytics with GPT-3 and SAP CAP IntegrationFree text in, SQL out, answer back in a sentence — the model writes the query, CAP runs it, and the model turns the rows into prose. Written in the GPT-3 window, so the model access has dated, but the three-step shape has not, and it is a different bet from retrieval: nothing is embedded, the schema is the context.Former Member, published 2023-03-10
Enhancing Business Processes with SAP BTP AI in SAP Integration SuiteEnhancing Business Processes with SAP BTP AI in SAP Integration SuiteWalks through a post-exit iflow in SAP Integration Suite that calls an LLM through SAP AI Core's Generative AI Hub to turn a customer's free-text return comment into one of a fixed set of SAP rejection-reason codes, so an inbound ORDERS05 IDoc carries a decision (E1EDP01-ABGRU) the ERP can act on without pausing for manual review. The worked example is a Commerce Cloud returns flow into S/4HANA; the piece argues the same bounded-classification pattern fits other narrative fields — delivery-exception remarks, expense-claim justifications — where the cost of an LLM call is repaid by removing a human review step, and points to a colleague's post on the Generative AI Hub orchestration workflow for the guardrailing (prompt templating, data masking) it only names.arunmaarirajha_kv, published 2025-12-15
Enhancing Enterprise Applications: AI-powered User Experience with SAP CAP AI PluginEnhancing Enterprise Applications: AI-powered User Experience with SAP CAP AI PluginStarts from the blank form nobody wants to fill in, and asks what an enterprise application should do about it. The AI is in service of that rather than the subject of it.published 2026-04-29
Enriching AI models with SAP AI Core in SAP Business Data CloudEnriching AI models with SAP AI Core in SAP Business Data CloudThe integration between SAP Business Data Cloud and SAP AI Core, through which data products with their semantics feed AI models for benchmarking, custom AI development and agentic analytics and applications. The abstract names the two products and the three uses and states nothing about how the data reaches AI Core or what the integration consists of. A TechEd 2026 session (Tuesday 27 October, Berlin), presented by San Tran and Sandy Tran, written here from SAP's abstract; the recording is not yet published.
Generative AI on SAP BTPGenerative AI on SAP BTPThe reference shape for a generative AI application on BTP: a CAP backend, SAP HANA Cloud holding data and embeddings for similarity search through its vector engine, and the Generative AI Hub in SAP AI Core as the single access point to foundation models. It shows retrieval-augmented generation on that stack and names the CAP plugins and SDKs that connect the pieces, for both Cloud Foundry and Kyma.published 2025-09-02
Generative AI on SAP BTP
LLM integration changed in @cap-js/ai 1.0.0, 2026-04-28. Affects A reference.
HANA Cloud’s VECTOR EMBEDDING in CAP and Comparison with OpenAI EmbeddingHANA Cloud’s VECTOR EMBEDDING in CAP and Comparison with OpenAI EmbeddingTwo ways to produce the same embedding, run side by side: HANA Cloud's own VECTOR_EMBEDDING function against Azure OpenAI through the CAP LLM plugin. The CDS side is short — an element of type cds.Vector maps to REAL_VECTOR on deploy — so what remains is the comparison, which is the thing you cannot get from either product's documentation alone.lalitmohan, published 2025-04-09
How to Reduce Prompt Token Costs Using Toon = Save MoneyHow to Reduce Prompt Token Costs Using Toon = Save MoneyToken spend treated as a running cost rather than a detail, and TOON as the lever. Companion reading to the CAP adapter for the same format that is indexed here.Yogananda, published 2025-11-12
How to Reduce Prompt Token Costs Using Toon = Save Money
LLM integration changed in @cap-js/ai 1.0.0, 2026-04-28. Affects A recipe, Runnable code.
How to use Cloud SDK for AI in an existing CAP NodeJS Project in CommonJSHow to use Cloud SDK for AI in an existing CAP NodeJS Project in CommonJSA dependency that is ESM-only inside a project that is CommonJS, and the author's judgement that converting the project was too risky. What follows is the esbuild workaround, which is the honest answer when the ecosystem has moved and your codebase has not.Jacky_Liu1, published 2025-08-01
Integrating a Custom AI Model into SAP CAP Using AI LaunchpadIntegrating a Custom AI Model into SAP CAP Using AI LaunchpadYour own model rather than a hosted one: deployed to AI Core, made callable through AI Launchpad, then reached from CAP. The case a lot of companies ask about and few posts cover.egeAksoyek, published 2026-03-20
Integrating a Custom AI Model into SAP CAP Using AI Launchpad
LLM integration changed in @cap-js/ai 1.0.0, 2026-04-28. Affects Runnable code.
Joule and SAP Knowledge Graph: Context-aware AI across SAP solutionsJoule and SAP Knowledge Graph: Context-aware AI across SAP solutionsThe catalogue carries RA107's abstract under this title, and what it describes is SAP AI Core as the management and orchestration layer of an AI architecture: extending Joule Agents in SAP Joule, the model library in Joule Studio for contextual reasoning, connecting data products from SAP Business Data Cloud, and embedding AI in SAP S/4HANA. The SAP Knowledge Graph the title names does not appear in that text, so the edges rest on the abstract as published, and whether the session follows the title or the abstract is not known until it is recorded. A TechEd 2026 session (Wednesday 28 October, Berlin; also RA110V, virtual), presented by Jan Portisch and Pavithra G K, written here from SAP's abstract; the recording is not yet published.
Knowledge graph and agent memory: Build an agent that remembersKnowledge graph and agent memory: Build an agent that remembersThe knowledge graph engine in SAP HANA Cloud as an agent's memory: what the engine, the graph side of graph, spatial and document data, does in an agentic workflow, how an agent keeps what it learns there, the state agent tooling holds between turns, and the SAP AI Core integration through which the database calls LLMs and tabular foundation models such as SAP-RPT-1. The abstract names the engine, the memory role and the AI Core link and does not say how the memory is written or read. DA205 on Tuesday 27 October is the 45-minute talk on the same engine, with a near-identical abstract and a different cast. A two-hour hands-on TechEd 2026 session (Wednesday 28 October, Berlin), presented by Christoph Morgen, Jan Zwickel, Markus Fath and Thomas Hammer, written here from SAP's abstract; the recording is not yet published.
Lets Talk to the Database - CAP & SQLite Powered by Google Gemini-2.5-ProLets Talk to the Database - CAP & SQLite Powered by Google Gemini-2.5-ProNatural language against a CAP service on SQLite, with a non-SAP model doing the translation. Written as a learning exercise rather than a pattern, and the choice of Gemini over anything in the AI hub is the interesting part of it.ShivamShuklaSAP, published 2025-04-16
Natural Language Queries for SAP CAP via MCP: The LLM Translates, CDS ExecutesNatural Language Queries for SAP CAP via MCP: The LLM Translates, CDS ExecutesThe division of labour is the argument, and it is in the subtitle: the model turns English into a CDS query and CDS runs it, so nothing generates SQL and nothing hallucinates a join. Schema annotations do the resolving. Long, with the package, the repository and the MCP registry entry all linked.Shahid, published 2026-06-25
Prompt TemplatingPrompt TemplatingPrompt templating replaces a static prompt with a structured, reusable template whose placeholders are filled in at runtime, which the page says helps LLM responses stay consistent and lets the same instructions be adapted across tasks. On SAP AI Core this is implemented through the Prompt Registry, and the page states that storing prompts as plain strings in code is possible but not recommended. That is prompt management as a stored and managed object rather than a string embedded in the caller.
Prompt-Driven Development: Building Full-Stack SAP CAP Apps from User StoriesPrompt-Driven Development: Building Full-Stack SAP CAP Apps from User StoriesA user story in, a scaffolded full-stack CAP application out, through SAP Build Code and Joule. The question it is actually asking is how much of the foundational code is worth generating, and it answers by generating all of it.AnuradhaDhingan, published 2025-12-11
RAG with SAP HANA Cloud Vector Engine, GenAI Hub & CAPRAG with SAP HANA Cloud Vector Engine, GenAI Hub & CAPThe end-to-end version: a CAP service exposing the vector engine and the AI hub behind a couple of endpoints, answering where a CodeJam is on a given date. It has aged in a way the author has marked in the page itself — the section on the CAP LLM plugin now carries "(Outdated)" in its own heading — so read the embedding preparation and the retrieval shape, and check the plugin part against the package.KevinR, published 2024-05-21
SAP AI Core deep dive: Models, agents, and business context at scaleSAP AI Core deep dive: Models, agents, and business context at scaleSAP AI Core read as the management and orchestration layer of an AI architecture, beyond the model runtime: SAP's abstract lists extending Joule Agents, the model library in Joule Studio for contextual reasoning, connecting data products from SAP Business Data Cloud, and embedding AI in SAP S/4HANA. It names the connections and not the APIs behind them. A TechEd 2026 session (Tuesday 27 October, Berlin; also RA107V, virtual), presented by Franziska Dobrigkeit and Hadi Hares, written here from SAP's abstract; the recording is not yet published.
SAP Joule for Consultants: Unified knowledge for requirements and designsSAP Joule for Consultants: Unified knowledge for requirements and designsFit-to-standard analysis as a consultant on a cloud transformation project: connecting an organisation's data to SAP Joule for Consultants through SAP AI Core, getting answers from SAP knowledge and that content, then building an expert, loading project files and running guided prompts. The edges are to SAP Joule, of which Joule for Consultants is a part, and to SAP AI Core, named as the connection path; how the data is grounded is left to the session. A TechEd 2026 hands-on session (Wednesday 28 October, Berlin), presented by Anantharaman Ravi, Natalie Han, Sachin Kaura and Yi Hsuan Lin, written here from SAP's abstract; the recording is not yet published.
SAP-RPT and TabPFN: Get started with tabular foundation modelsSAP-RPT and TabPFN: Get started with tabular foundation modelsTabular foundation models, SAP-RPT and TabPFN, in a hands-on with SAP and Prior Labs: the model families first, then predictions run through SAP AI Core with a workflow built on tabular orchestration and connected to the participant's own data for real-time predictions. A predictive models session with the models as the subject; the abstract does not name the SDK or API the workflow is built with. A TechEd 2026 hands-on session (Thursday 29 October, Berlin), presented by Eliott Kalfon, Janick Zaehringer-Frasch, Johannes Hoehne, Katharina Schell, Nilay Mehta and Sherene Tan, written here from SAP's abstract; no recording is announced.
Ship your own sandboxed AI operations agent to Kyma in two hoursShip your own sandboxed AI operations agent to Kyma in two hoursAn operations agent built and then broken on purpose: the abstract has participants deploy a reasoning agent to SAP BTP, Kyma runtime, connect it to SAP AI Core, give it observability over the runtime and cause a failure for it to diagnose, the operations automation concern with an agent doing the diagnosis. Patterns for cost control and monitoring are promised at the end; the agent framework and how the sandbox is drawn are not named. A TechEd 2026 session (Tuesday 27 October, Berlin, two hours), presented by Christian Lechner, Piotr Tesny, Thorsten Sandfuchs and Vipin VijayKumar, written here from SAP's abstract; the recording is not yet published.
Stop guessing, start counting: How generative AI fixed an inventory messStop guessing, start counting: How generative AI fixed an inventory messAn inventory-counting application a business built on SAP Business AI Platform with SAP HANA Cloud and generative AI, to replace recount lists kept by gut feel across SAP ERP Central Component, SAP S/4HANA and Microsoft Excel; the talk promises the app, its architecture and what went wrong. The generative AI part is LLM integration from application code; the abstract names HANA Cloud and generative AI as the two building blocks and says nothing about how either is used. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Ranjeet Kumar, written here from SAP's abstract; the recording is not yet published.
Streaming LLM Output with CAP and WebSocketsStreaming LLM Output with CAP and WebSocketsTokens arriving from the AI SDK's orchestration module, forwarded to the browser as they come rather than after the model finishes. Covers all four pieces — enabling WebSocket support in CAP, a service with actions and events, the streaming itself, and a frontend on the native WebSocket API — which is what makes it the clearest case for why a CAP service would want a socket at all.RobinP, published 2025-11-18
Streaming LLM Output with CAP and WebSockets
LLM integration changed in @cap-js/ai 1.0.0, 2026-04-28. Affects A recipe, Runnable code.
UI5 AI Features library is now an npm PackageUI5 AI Features library is now an npm PackageThe release note for a demo turning into a dependency, and it is explicit about why that matters: the earlier version worked, but using it meant cloning the repo and copying both halves, which stops working the moment one of them changes. The post walks through what shipping the CAP plugin and the be.wl.ai control library in one npm version buys — a client and server contract that cannot drift — then lists the eleven controls by group, shows the two-line configuration that activates each half, and notes that this version is actually deployed on BTP behind both a standalone approuter and Work Zone. It closes on the demo repository, written with no library source in it as the test that the package stands alone.published 2026-08-31
UI5 AI Features library is now an npm Package
cds 8, line ended. Affects Runnable code.
UI5 MCP Server Got Extended for UI Integration Cards GenerationUI5 MCP Server Got Extended for UI Integration Cards GenerationVersion 0.2.1 of the UI5 MCP Server adds three tools for UI Integration Cards: get_integration_cards_guidelines feeds an agent current best practices and API versions, create_integration_card scaffolds a new card project, and run_manifest_validation checks the generated manifest against the schema. The post frames these as closing the same gaps as the server's existing tools — an LLM's outdated or wrong card API knowledge — this time for scaffolding and validating a card rather than a full app.daniel_vladinov, published 2026-01-22
Unlocking Tabular Foundation Models: From Experimentation to ProductionUnlocking Tabular Foundation Models: From Experimentation to ProductionA Devtoberfest 2026 session (AI-41) on taking tabular prediction from a playground to a deployment. It sets tabular foundation models against a model trained per use case and against language models reading rows as text, explains in-context learning as examples plus the rows whose target column is missing, and tries SAP RPT 1.6 on a train-delay dataset in the RPT playground, including the column and row importance it returns with the prediction. The same prediction is then rebuilt on SAP AI Core, where the tabular orchestration service plays the part the orchestration service plays for language models: a data destination, a table artifact with its CSN schema and a scenario configuration registered once at design time, after which a predict call passes only the query rows, the scenario configuration and a context-selection strategy. The last demo runs those same calls in natural language through the aicore-skills plugin in a coding agent, and one of them only resolves once the agent is told which scenario configuration to use.SAP Developers, published 2026-10-15
What an AI infra engineer sees when opening SAP's AI stack (1/3): the model gatewayWhat an AI infra engineer sees when opening SAP's AI stack (1/3): the model gatewayA first look at SAP AI Core's Generative AI Hub from someone who builds gateways outside SAP for a living: the orchestration service harmonizes provider calls onto one API (a model name string switches providers, and SAP Note 3437766 carries the current model list, token rates and deprecation dates), runs a fixed, non-reorderable module pipeline — templating, masking through SAP Data Privacy Integration, grounding, content filtering, then the model call — and retries down an ordered fallback list whose entries can each carry their own model and rules. It places @sap-ai-sdk/orchestration among the official JavaScript, Java and Python SDKs, and notes SAP's own LiteLLM provider as a way to put a different gateway in front of it.amishkush, published 2026-09-30
Prompt managementSAP and community
@sap-ai-sdk/orchestration@sap-ai-sdk/orchestrationThe Orchestration Service client of SAP's AI SDK, which applies templating, grounding, filtering and masking around a model call rather than in the application.sap official, cap
Orchestration Service client for templating, grounding and masking
@sap-ai-sdk/prompt-registry@sap-ai-sdk/prompt-registryA package for keeping prompts in SAP's prompt registry rather than in the codebase, so changing a prompt is not a deployment.sap official, cap
Client for prompts kept in SAP's prompt registry
@saptarishi/cds-plugin-llm@saptarishi/cds-plugin-llmA CAP service kind that puts eleven model providers behind one chat, stream, embed and batch interface, so which backend a service calls is a profile in package.json rather than a change to the handler.independent, cap
CAP service kind putting eleven model providers behind one API
SAP AI CoreSAP AI CoreSAP's AI service on BTP: the runtime for AI assets, and through the Generative AI Hub inside it the access to foundation models, the orchestration service with its templating, masking, filtering and grounding modules, the Prompt Registry and Document Grounding.sap official, btp
AI runtime on BTP with the Generative AI Hub
SAP BTP AI Best Practice Demo - Prompt Registry - CAPSAP BTP AI Best Practice Demo - Prompt Registry - CAPA CAP backend-sample and fullstack-sample that create, use and delete a template in SAP AI Core's Prompt Registry instead of embedding the prompt in code.sap official, cap
CAP samples using SAP AI Core's Prompt Registry for templates
Every CDS service in both samples is marked `@requires: 'any'` and left publicly accessible, which the README says simplifies the testing and deployment process, and it asks for proper authentication mechanisms to be applied in their place.
SAP BTP AI Best Practice Demo - Prompt Templating - CAPSAP BTP AI Best Practice Demo - Prompt Templating - CAPA CAP backend-sample and fullstack-sample that fill a country placeholder in a prompt template through the SAP Cloud SDK for AI and send it to SAP AI Core.sap official, cap
CAP samples filling a prompt template sent to SAP AI Core
Every CDS service in both samples is marked `@requires: 'any'` and left publicly accessible, which the README says simplifies the testing and deployment process, and it asks for proper authentication mechanisms to be applied in their place.
RAG on CDSSAP and community
@cap-js/ai@cap-js/aiOut-of-the-box value-help recommendations powered by SAP RPT-1, SAP AI Core as a standard CAP service, and an experimental local embedding model behind SQLite's vector functions.sap official, cap
Value-help recommendations and SAP AI Core as a CAP service
@mi8y/cap-agents-aicore-vectorstore@mi8y/cap-agents-aicore-vectorstoreA LangChain vector store backed by SAP AI Core Document Grounding: the collection holds the documents and makes the embeddings, and the CAP application stores no vectors of its own.independent, cap
LangChain vector store on SAP AI Core Document Grounding
@mi8y/cap-agents-cds-vectorstore@mi8y/cap-agents-cds-vectorstoreA LangChain vector store whose documents and embeddings are CDS entities, so retrieval runs as a CDS query against the database the CAP service is already bound to.independent, cap
LangChain vector store with CDS entities for embeddings
@sap/cds@sap/cdsThe CAP Node.js runtime, which compiles the CDS model, serves the services declared in it, answers generic requests before any handler runs, and loads every cds-plugin.sap official, cap
CAP Node.js runtime
@saptarishi/cds-plugin-llm@saptarishi/cds-plugin-llmA CAP service kind that puts eleven model providers behind one chat, stream, embed and batch interface, so which backend a service calls is a profile in package.json rather than a change to the handler.independent, cap
CAP service kind putting eleven model providers behind one API
@saptarishi/cds-plugin-vector-hana@saptarishi/cds-plugin-vector-hanaVector storage and similarity search on SAP HANA from CAP, packaged as a plugin so the embedding column and the query are modelled rather than hand-written.independent, cap
Vector storage and similarity search on SAP HANA from CAP
cap-llm-plugincap-llm-pluginGenerative AI in a CAP application with the sensitive fields anonymised through SAP HANA before the prompt leaves, so the model never sees the confidential half.independent, cap
Generative AI in CAP with fields anonymised through SAP HANA
Not where the records sit anywhere but [SAP HANA Cloud](artifact:btp-hana-cloud): the similarity search and the single retrieval method are both written against its vector engine, and the embeddings and the chat call go through [SAP AI Core](artifact:btp-ai-core), so a project without both has nothing here to call. Not where the dependency has to be an SAP-supported one either: the README states that the plugin is not an official SAP product and is published as a community contribution.
SAP AI CoreSAP AI CoreSAP's AI service on BTP: the runtime for AI assets, and through the Generative AI Hub inside it the access to foundation models, the orchestration service with its templating, masking, filtering and grounding modules, the Prompt Registry and Document Grounding.sap official, btp
AI runtime on BTP with the Generative AI Hub
SAP HANA CloudSAP HANA CloudThe managed database under most of this: CAP's default target, the store Datasphere and Analytics Cloud sit on, and a service with plans and components.sap official, btp
Managed SAP HANA database service on BTP
Provisioned per plan and per component, so capacity and cost are decisions taken before any model exists. And it is the database, not a warehouse — modelling, loading and cataloguing are the products above it, and reaching it directly bypasses every semantic they add.
cap-bp-ragcap-bp-ragAn event-driven RAG application: Cloud Integration embeds a Business Partner event taken off a broker, and the CAP service stores the vector in SAP HANA Cloud and answers questions from it.independent, cap
Event-driven RAG app with CAP, Cloud Integration and SAP HANA Cloud
Neither the package manifest nor the repository tree declares a licence.
cap-embedding-functioncap-embedding-functionA CAP service that computes a note's Vector(768) embedding in its own handlers, selecting SAP HANA Cloud's vector_embedding as a column from a Dummy entity.independent, cap
CAP service computing embeddings with HANA Cloud vector_embedding
cap-vector-hccap-vector-hcA sample that declares VECTOR_EMBEDDING in a CDS entity: an FAQ model whose embedding column HANA Cloud generates and stores, then answers questions by similarity search.independent, cap
VECTOR_EMBEDDING in a CDS entity for FAQ similarity search
Neither the package manifest nor the repository tree declares a licence.
SAP BTP AI Best Practice Demo - Vector Embedding (RAG) - CAPSAP BTP AI Best Practice Demo - Vector Embedding (RAG) - CAPA CAP backend-sample and fullstack-sample that parse an uploaded CSV of science data, embed each row through SAP AI Core and store the vectors in SAP HANA Cloud's vector store.sap official, cap
CAP samples embedding CSV rows into the SAP HANA Cloud vector store
Every CDS service in both samples is marked `@requires: 'any'` and left publicly accessible, which the README says simplifies the testing and deployment process, and it asks for proper authentication mechanisms to be applied in their place.
SAP BTP AI Best Practice Demo - Vector Query (RAG) - CAPSAP BTP AI Best Practice Demo - Vector Query (RAG) - CAPA CAP backend-sample and fullstack-sample that run a similarity query over the vectors in SAP HANA Cloud and pass the closest science-data rows to the model as context.sap official, cap
CAP samples running RAG similarity queries over SAP HANA Cloud vectors
Every CDS service in both samples is marked `@requires: 'any'` and left publicly accessible, which the README says simplifies the testing and deployment process, and it asks for proper authentication mechanisms to be applied in their place.
SAP-samples/btp-cap-genai-ragSAP-samples/btp-cap-genai-ragA CAP reference application on SAP BTP for GenAI email support, built on SAP AI Core's Generative AI Hub and retrieval-augmented generation over SAP HANA Cloud's Vector Engine.sap official, cap
CAP reference app for GenAI email support with RAG on HANA Cloud
SAP-samples/btp-cap-genai-semantic-searchSAP-samples/btp-cap-genai-semantic-searchA basic CAP sample that builds a semantic search engine on SAP BTP using Generative AI Hub and SAP HANA Cloud's Vector Engine.sap official, cap
CAP sample for semantic search with Generative AI Hub and HANA Cloud
Building agentic applications with SAP HANA CloudBuilding agentic applications with SAP HANA CloudA CAP application extended with semantic search and agent features on top of SAP HANA Cloud's vector engine and SAP AI Core, built in a hands-on workshop. The abstract names the parts, CAP, the vector engine and AI Core, and the two additions: semantic search, the retrieval half of retrieval-augmented generation, and LLM-supported agent capabilities inside the application, which is agent tooling. Which embedding model, which CAP runtime and how the agent calls its tools are not stated. A TechEd 2026 session (Thursday 29 October, Berlin), presented by Hui Li, Ritty Joseph, Shraddha Shetty and Tanuja Jadhav, written here from SAP's abstract; the recording is not yet published.
CAP in the age of AICAP in the age of AISeven live episodes of Hands-on SAP Dev in which DJ Adams works through CAP's AI features with the people who build them, each one a screen-share against a sample project. The first three are @cap-js/mcp: the @mcp annotation on a service, the describe, query and call tools, CQL as the query format, and what authorisation takes out of the tool list. From episode three the run moves to A2A and @cap-js/agents — human approval of an action, agents.md and skills, session persistence, telemetry and sub-agents — and the last two episodes are vector embeddings in CDS, local on SQLite and native on SAP HANA Cloud.published 2026-08-07
CAP in the age of AI #6 - vector embeddingsCAP in the age of AI #6 - vector embeddingsVector embeddings put to work in the incident management sample, answering whether this problem has been seen before. An embedding element of type Vector is calculated and stored from the vector_embedding function, so the database refreshes it whenever the text behind it changes, and cosine_similarity compares it with a freshly embedded string; because a title says little, a scheduled action first summarises each incident's conversation through a model on SAP AI Core and the embedding is taken from that summary. The similar-incidents list is a view joining incidents to themselves, with order and limit set in a before handler on the expanded query, and the vector element excluded from the service, since OData knows nothing about vectors — on SAP HANA Cloud the function is native, while SQLite needs packages added to the project.published 2026-10-09
CAP in the age of AI #7 - vector embeddings wrap-upCAP in the age of AI #7 - vector embeddings wrap-upThe same embeddings model taken from SQLite to SAP HANA Cloud, then handed to an agent. Locally it is @cap-js/ai that adds the vector functions SQLite lacks and downloads an all-MiniLM L6v2 ONNX model on first start; on HANA natural language processing has to be switched on, the model name is a mandatory argument, the vectors it returns are 768 dimensions, and dimensions must match on both sides of a comparison. HANA itself can be given SAP AI Core as a remote source for other embedding models, which a CDS.ql expression has to inject because the postfix notation only takes what the CDS model holds, and the hour ends with a search function over cosine similarity exposed over @cap-js/mcp, so an agent asked about low output from solar panels calls semantic search instead of writing the query itself.published 2026-10-16
Document GroundingDocument GroundingDocument Grounding is a managed retrieval-augmented-generation service under the Generative AI Hub on BTP, which SAP's page on SAP AI Core places inside that service: it ingests documents from Amazon S3, SharePoint, SAP Document Management Service, SAP Work Zone or an SFTP server, and handles chunking, embedding and retrieval itself, refreshing content automatically when a source document changes. The page states this managed path is recommended for most enterprise scenarios because it covers the whole lifecycle end to end, and offers a separate Document Grounding Vector API for data that is already chunked and embedded, for cases that need finer control over chunking and metadata.
Generative AI on SAP BTPGenerative AI on SAP BTPThe reference shape for a generative AI application on BTP: a CAP backend, SAP HANA Cloud holding data and embeddings for similarity search through its vector engine, and the Generative AI Hub in SAP AI Core as the single access point to foundation models. It shows retrieval-augmented generation on that stack and names the CAP plugins and SDKs that connect the pieces, for both Cloud Foundry and Kyma.published 2025-09-02
Generative AI on SAP BTP
LLM integration changed in @cap-js/ai 1.0.0, 2026-04-28. Affects A reference.
HANA Cloud’s VECTOR EMBEDDING in CAP and Comparison with OpenAI EmbeddingHANA Cloud’s VECTOR EMBEDDING in CAP and Comparison with OpenAI EmbeddingTwo ways to produce the same embedding, run side by side: HANA Cloud's own VECTOR_EMBEDDING function against Azure OpenAI through the CAP LLM plugin. The CDS side is short — an element of type cds.Vector maps to REAL_VECTOR on deploy — so what remains is the comparison, which is the thing you cannot get from either product's documentation alone.lalitmohan, published 2025-04-09
RAG with SAP HANA Cloud Vector Engine, GenAI Hub & CAPRAG with SAP HANA Cloud Vector Engine, GenAI Hub & CAPThe end-to-end version: a CAP service exposing the vector engine and the AI hub behind a couple of endpoints, answering where a CodeJam is on a given date. It has aged in a way the author has marked in the page itself — the section on the CAP LLM plugin now carries "(Outdated)" in its own heading — so read the embedding preparation and the retrieval shape, and check the plugin part against the package.KevinR, published 2024-05-21
Using HANA Cloud’s "VECTOR_EMBEDDING" Function in CAPUsing HANA Cloud’s "VECTOR_EMBEDDING" Function in CAPEmbeddings generated inside the database rather than fetched from AI Core or an external model — a Q4 2024 HANA Cloud function, used from CAP. The shift it describes is where the embedding step lives, which changes what has to be deployed and what has to be called.published 2025-03-02
Tool governanceSAP and community
a2a-plugina2a-pluginExposes an annotated CDS service as an Agent-to-Agent protocol agent — @a2a opts the service in, @a2a.Skill declares what it offers, and an agent handler answers.independent, cap
Exposes an annotated CDS service as an Agent-to-Agent agent
No public repository is declared on the package, so the README is the documentation.
API ManagementAPI ManagementAPI proxies in front of backend services, with security and traffic management policies attached to them, and Developer Hub where consumers subscribe.sap official, btp
API proxies with security and traffic policies and Developer Hub
cds-agentscds-agentsTurns a CAP service into a tool layer for agent frameworks — a CAPToolkit bound to a named service, with a tool strategy controlling how much of it becomes callable.independent, cap
Toolkit turning a CAP service into tools for agent frameworks
SAP API Policy Evidence SkillSAP API Policy Evidence SkillAssesses whether a described SAP API usage aligns with the SAP API Policy, gathering live evidence from SAP's own sources and returning a sourced assessment with a stated confidence — and routing the legal questions away from itself.independent, btp
Skill assessing API usage against the SAP API Policy
It says so itself, in bold, and the atlas repeats it: not legal advice and not a compliance decision. Only SAP, your contract, or SAP support can answer bindingly, and the skill routes legal, commercial and roadmap questions there rather than guessing at them. Treat the output as evidence gathered, not as a verdict.
A Clearer View of Your SAP Integrations Under the New API PolicyA Clearer View of Your SAP Integrations Under the New API PolicyThe SAP API Policy Evidence Skill assesses one SAP interface or usage pattern at a time against SAP API Policy v.4.2026a, reading evidence from five MCP servers, among them servers for the Business Accelerator Hub and SAP Notes, mcp-sap-docs and a read-only ARC-1, and returns one of four labels with a confidence level, an evidence table, residual risk and questions for SAP. It gives no compliant or not compliant answer on purpose, because SAP's FAQ (Q49) declines to publish a binding decision matrix, and it sends legal, commercial and roadmap questions to SAP and lowers its confidence when a source is not connected. Four sample runs show the method, among them ODP-RFC extraction labelled likely not aligned under SAP Note 3255746 and the same API_SALES_ORDER_SRV assessed differently for an autonomous agent behind a custom gateway and for a Boomi middleware flow; the SAP Notes and Business Accelerator Hub servers read SAP sites through a browser login, which the post says to use at your own risk.published 2026-06-02
Agentic Engineering for SAP BTP ExtensionsAgentic Engineering for SAP BTP ExtensionsAn agent harness coordinates code generation across isolated Git worktrees, using a skills registry, SAP CAP MCP Server, Fiori MCP Server and UI5 Web Components MCP Server for development context. The diagram shows LiteLLM as a model proxy in front of SAP AI Core, while automated checks and review gate the generated changes. The worked scenario is CAP-based extensions on BTP, with specifications written before generation and reusable skills governed across teams.
MCP Servers are the New Z-TablesMCP Servers are the New Z-TablesA single CAP service exposed as three overlapping MCP servers tools, queried by the same prompt nineteen times, returned the same tool every time — evidence, the author argues, that tool selection rests on the resemblance between a prompt and a tool's description rather than on any binding a where-used list could enumerate before retiring it. It contrasts that setup with @cap-js/mcp, which exposes a whole service as three generic tools instead of one per operation, and quotes the adapter's own beta documentation admitting no rate limiting, audit logging, input validation or approval workflow, and no use as a gateway to SAP Application APIs. From there it names four small Tool governance practices it has adopted: an owner recorded in the tool's own metadata, logging every invocation, deprecating inside the description, and exposing fewer tools.published 2026-08-21
Third-Party MCP Server Access to SAP Solutions on BTPThird-Party MCP Server Access to SAP Solutions on BTPDescribes external MCP servers and custom servers on BTP, assigning their operators responsibility for authentication, scoped identity, API limits, logging and lifecycle. The permitted use remains conditional on the SAP API Policy, while SAP Integration Suite and Joule Studio are described as SAP-provided paths. Hosting a server on BTP does not automatically supply its governance controls.Dhawal Joshi, Harut Ter-Minasyan, Mirko Günther
Audit loggingSAP only
@cap-js/audit-logging@cap-js/audit-loggingIntegration with the SAP Audit Log service, so operations on personal data are recorded in the form an auditor expects without the application writing the entries.sap official, cap
CAP integration with the SAP Audit Log service
com.sap.cds:cds-feature-auditlog-ngcom.sap.cds:cds-feature-auditlog-ngA CAP Java plugin that sends audit log events to the SAP Audit Log service in the format the Audit Log Event Catalog expects.sap official, cap
CAP Java plugin sending audit log events to SAP Audit Log service
com.sap.cds:cds-feature-auditlog-v2com.sap.cds:cds-feature-auditlog-v2The CAP Java handler that sends the SDK's AuditLog events to the SAP Audit Log service over its API version 2, once a service binding is present.sap official, cap
CAP Java handler for SAP Audit Log service API version 2
com.sap.cds:cds-services-bomcom.sap.cds:cds-services-bomThe Maven bill of materials for CAP Java, pinning every com.sap.cds module in a release, from the API and core runtime to the Spring Boot starters and the CQN engine, to the same version.sap official, cap
Maven bill of materials pinning CAP Java module versions
SAP Audit Log serviceSAP Audit Log serviceIngests compliance audit data from BTP services and applications and keeps it for ninety days by default; retrieval and longer retention are separate services.sap official, btp
Ingests and retains compliance audit data from BTP
SAP-samples/btp-secure-developmentSAP-samples/btp-secure-developmentA hands-on OWASP Top 10 workshop that exploits and then fixes security flaws in a CAP Node.js application, covering broken access control, SQL injection and audit logging for sensitive data.sap official, cap
OWASP Top 10 workshop exploiting and fixing flaws in a CAP Node.js app
teched2025-XP260teched2025-XP260An SAP TechEd 2025 workshop on embedding security into CAP applications on SAP BTP, worked through three of the OWASP Top 10 risks: broken access control, SQL injection and logging failures.sap official, cap
TechEd 2025 workshop on OWASP Top 10 risks in CAP applications
BTP Audit Log MCP Server: Security Monitoring with AI AssistantsBTP Audit Log MCP Server: Security Monitoring with AI AssistantsWouter Lemaire builds an MCP server over SAP BTP's Audit Log Retrieval API using odata-mcp-proxy, with a single entity set and zero custom code, so an AI assistant can answer natural-language questions like "Were there any configuration changes yesterday?" by calling AuditLogRecords_list against a BTP destination authenticated with the Audit Log service's service key. It covers the destination setup, the entity-set configuration that maps the PascalCase entity name to a lowercase auditlogrecords path, and handling the API's 500-record pagination limit.published 2026-03-13
CAP: The Road Ahead - Simplicity in an AI EraCAP: The Road Ahead - Simplicity in an AI EraDevtoberfest 2026 session CAP10, from the head of SAP's Cloud Application Programming Model: forty minutes of live building rather than slides, on the argument that the cheapest code in an AI project is code nobody has to generate. Claude Code builds an invoice-matching workspace from cds init onwards, and each step is one the framework answers rather than the agent: a 42-line domain model that @sap/cds serves as OData and Fiori elements with a status flow, data privacy and change-tracking annotations that produce audit log entries in the console and would reach the SAP Audit Log service once deployed, an imported S/4HANA supplier data product, and semantic matching from a local embedding model after @cap-js/ai is added. The last three steps are annotations: @mcp beside @odata turns the service into an MCP server that a second Claude session queries through @cap-js/mcp, an agent annotation adds the A2A protocol with an agent card and an audit log entry for the agent task, and a skill file gives the agent the matching policy.SAP Developers, published 2026-09-25
How to enable SAP Audit Log Viewer service to collect all Audit logs from BTP Services - Part 1How to enable SAP Audit Log Viewer service to collect all Audit logs from BTP Services - Part 1Walks through subscribing to the SAP Audit Log service's viewer app from the SAP BTP cockpit and creating a RoleCollection from the auditlog-management and auditlog-viewer role templates, assigned to a user or by a rule on the IDP's SAML assertion, to read audit logging events in a subaccount. The post's own update notes that a BTP release from 19 February 2026 removed the need for that role-assignment step. It also lists which Cloud Foundry services write audit log events, pointing to each service's own auditing page rather than detailing them here.Yogananda, published 2025-03-07
How to implement SAP BTP Audit log service in a CAP ApplicationHow to implement SAP BTP Audit log service in a CAP ApplicationThe setup half: binding the BTP Audit Log service to a CAP application and getting the annotated path working, which is the step before anything custom becomes a question.KrishnaSharma7, published 2025-01-01
How to implement SAP BTP Audit log service in a CAP Application
@cap-js/audit-logging has been through 2 major lines since this was published: 0.9 (2025-06), 1 (2025-07). Affects A recipe.
Implementing Custom Audit Logging in SAP CAP ApplicationsImplementing Custom Audit Logging in SAP CAP ApplicationsWhere the annotations stop. CAP logs personal-data operations automatically once a model is annotated; this is about the events that are not that — an unauthorised access attempt, a business action somebody wants a trail for — and what writing those yourself involves.gopalanand, published 2025-10-09
Integrate SAP Business Technology Platform audit logs with IBM Security QRadarIntegrate SAP Business Technology Platform audit logs with IBM Security QRadarWalks through pulling entries from the SAP Audit Log service for a Cloud Foundry subaccount, via its retrieval API and a service key downloaded from the SAP Authorization and Trust Management service, into IBM Security QRadar as a Universal Cloud REST API log source. A sample audit event, a data read event under audit.data-access, drives a custom QRadar DSM mapping event ID and category, then the walkthrough deletes a user from a Kyma role collection and shows the matching audit logging entry arrive in QRadar as an offense.TusharTrivedi12, published 2024-01-03
Integrating SAP Cloud Identity Services (Identity Authentication - IAS) with SAP Audit Log ViewerIntegrating SAP Cloud Identity Services (Identity Authentication - IAS) with SAP Audit Log ViewerWalks through wiring an Identity Authentication tenant of Cloud Identity Services into the SAP Audit Log service on SAP BTP, Cloud Foundry: from the tenant's Monitoring & Reporting tile, adding the target BTP subaccount's tenant ID and subdomain ties the two together, and after a 15-20 minute wait the resulting Audit Log Viewer link surfaces logs of personal-data changes and of successful and failed authentications. Logs are then filtered by date and keyword, each entry carrying its event category, event and object type, timestamp, and the user who performed the action.Yogananda, published 2025-03-23
Introducing the Cloud Application Programming Model (CAP)Introducing the Cloud Application Programming Model (CAP)SAP's own general-availability announcement for CAP, laid out as a Golden Path: a data model and service defined in CDS gets a generated service provider that serves metadata publishing and CRUD requests, fills in primary keys and audit data, and writes trace and audit logs without application code. The same CDS model carries annotations that translate to OData vocabularies and generate a Fiori elements app skeleton, which is how Fiori annotations work in CAP.Daniel7, published 2018-06-05
Nice patch SAP! Revisiting your SAP BTP security measures after AI Core vulnerability fixNice patch SAP! Revisiting your SAP BTP security measures after AI Core vulnerability fixWritten after the SAPwned disclosure in SAP AI Core, it argues SAP BTP's shared-responsibility model still requires the customer's own hardening: scope SAP BTP, Cloud Foundry Runtime and SAP BTP, Kyma runtime app access, rotate OAuth2 client-credentials secrets, and enforce MFA and Conditional Access through a corporate identity provider rather than relying on SAP's patch alone. It then explains how the SAP Audit Log service writes standardized events for most Cloud Foundry BTP services, and walks through onboarding a BTP subaccount into Microsoft Sentinel for SAP BTP to query those logs with Kusto and build detection rules that flag unexpected login countries or known-bad IPs and trigger automatic user blocks.published 2024-07-25
SAP BTP Audit log Enable and retrieve logsSAP BTP Audit log Enable and retrieve logsWalks through enabling and retrieving records from the SAP Audit Log service on SAP BTP's Cloud Foundry environment: the default oAuth2 service plan retains audit data for 90 days at no cost, while the premium plan adds a configurable retention period and lets an application write its own audit data. Covers the three subscription plans (premium, default for a subaccount, central for a global account) and the CLI steps to create a service instance and key, then retrieve records as JSON through the Auditlog Management Service's REST API using an OAuth2 client-credentials token in Postman.anikesh_jyotishi2, published 2024-07-12