Everything in CAP / Node.js
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Domain: AI & Agents
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Agent access to your codeSAP and community
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.
Before You Upgrade S/4HANA: Static UI5 Checks with MCPBefore You Upgrade S/4HANA: Static UI5 Checks with MCPA UI5 version upgrade checked against one app by pairing two MCP servers: the ui5_version_diff tool added to mcp-sap-docs, which reads a bundle from ui5-lib-diff downloaded once at setup and filters it by version range, change type, library or query text, and @ui5/mcp-server, whose get_project_info, get_version_info, run_ui5_linter and run_manifest_validation say what the app uses and what fails. Run with a ui5-versions-upgrade.md skill on the legacy app from From SEGW and Legacy UI5 to RAP with ARC-1, from SAPUI5 1.84.59 to 1.147.2, the diff range held 985 deprecations and 961 What's New entries, and the linting and manifest validation narrowed that to findings in this app, among them jQuery.sap.require, synchronous bootstrap, the sap_belize theme, sap.m.MessagePage and an old manifest _version. The checks read the source in a local checkout and never the deployed app, so the report is a first pass: a changelog entry is not proof that a workaround can be deleted, and the app still has to be run and tested.published 2026-05-20
Evaluating SAP’s new MCP servers: UI5, CAP, and Fiori tools in practiceEvaluating SAP’s new MCP servers: UI5, CAP, and Fiori tools in practiceSomeone trying SAP's three new MCP servers rather than repeating the announcement. It sets the official positioning first — MCP as the bridge from coding assistants to SAP tooling across Cursor, Windsurf, Copilot, Cline and Claude Code, with best-practice scaffolds and BTP-aligned guardrails — and then reports what using them is actually like. The rare kind of post that tests a vendor claim.published 2025-09-04
From SEGW and Legacy UI5 to RAP with ARC-1From SEGW and Legacy UI5 to RAP with ARC-1Three ARC-1 skills, run in Cursor with Composer-2 against a deliberately rough demo on an S/4HANA 2023 trial system, turn the SEGW service ZDEMO_MIG_PROJECTS_SRV into a managed, draft-enabled RAP business object published as OData V4, replace its ApproveProject function import and its direct UPDATE ... COMMIT WORK with the action approve_project written in EML in local mode, and rebuild the freestyle UI5 JavaScript app once in UI5 TypeScript and once as a Fiori elements list report and object page driven by metadata extensions. What the published transcripts add is how the agent got there: it found the generated MPC class names and the custom logic in DPC_EXT only by reading the system, the skills made it print the extracted model and stop for confirmation before writing, and @ui5/mcp-server, mcp-sap-docs and @sap-ux/fiori-mcp-server each had their own job beside it. The demo is small on purpose, and the post says a productive SEGW service that calls BAPIs, carries custom authorisation logic or supports deep inserts is not solved with one prompt.published 2026-05-11
From SEGW and Legacy UI5 to RAP with ARC-1
@sap-ux/fiori-mcp-server has been through 2 major lines since this was published: 0.7 (2026-05), 1 (2026-05). Affects Runnable code.
Agent guidanceSAP and community
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.
Teaching AI Agents Best Practices: A Skills Workspace for CAP DevelopmentTeaching AI Agents Best Practices: A Skills Workspace for CAP DevelopmentStates the gap this capability exists for more precisely than most: an agent already knows CAP well enough to generate models, handlers and deployments, but knowing a framework and following its current practice reliably are different things, because a model defaults to whatever it saw most of during training — which may be outdated, subtly wrong, or simply not how a given team works. Skills are offered as the closing mechanism, and the post describes a workspace pattern rather than a single file, with the architecture claimed to be framework-agnostic.daniel_schlachter, published 2026-06-09
Agent toolingSAP and community
AI-native North Star architecture — Platform LayerAI-native North Star architecture — Platform LayerThe platform layer of SAP's North Star architecture, the chapter that says where agents run and what a developer builds them with. It names an AI Golden Path built on the SAP Cloud Application Programming Model, the ABAP Cloud development model with RAP and the SAP Fiori design system, and a managed agent runtime on SAP BTP that is designed to provide software development kits for building agents in any framework, an agent sandbox, container-hosted execution, a skills registry and continuous evaluations, which is the ground agent tooling covers. Agents are extended without changing their code through four patterns, the first of them tool extensibility through the Model Context Protocol, the concern MCP servers is named for; the process-layer chapter adds that a pro-code path with a standardised agent SDK and a prompt-driven path through the Joule Studio solution produce the same code artifact on the same runtime. It describes a design SAP intends rather than products that exist, and the SDK and the registry it names have no entry here yet.published 2026-05-13
AI-native North Star architecture — Platform Layer
Agent tooling changed in @cap-js/agents 0.9.1, 2026-08-14. Affects A reference.
ARC-1 with Joule Studio: Bringing Real ABAP System Context into JouleARC-1 with Joule Studio: Bringing Real ABAP System Context into JouleBrings the same BTP-deployed ARC-1 endpoint into Joule through a Joule Studio, classic edition agent configured with Anthropic Claude Sonnet 4 and two MCP servers, ARC-1 and mcp-sap-docs, for work that starts in SAP where the previous part started in Microsoft 365. Two read-only showcases carry the ABAP Cloud readiness edge: a clean core check of one class that reports it is not released, uses USR02 and BUT000 directly and has no ATC findings, and a modernisation backlog for package ZSD_LEGACY which, as that package did not exist, grouped 28 ZSD* objects into risk buckets instead. The route through SAP Integration Suite API Management or a future MCP gateway is presented as a target architecture and not yet a productive setup, because Joule Studio's documentation requires streamable HTTP destinations and does not support MCP servers that need interactive OAuth user authorisation.published 2026-05-06
ARC-1 with Joule Studio: Bringing Real ABAP System Context into Joule
arc-1 has been through 2 major lines since this was published: 0.9 (2026-05), 1 (2026-07). Affects Runnable code.
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.
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 Durable AI Agents - Announcing the cds-langgraph-persistence PluginBuilding Durable AI Agents - Announcing the cds-langgraph-persistence PluginThe announcement, and the argument behind it. Treating an agent as a software architecture rather than a model with tools makes a list of production requirements — identity, authn and authz, multitenancy, persistence, event-driven flows, scalability, observability, CI/CD — and the author's point is that CAP already answers most of them for BTP, which is what makes LangGraph inside CAP Node.js worth doing. The post then narrows to one of those requirements, durability, and the checkpointer that supplies it. Written from a year of building agents next to real business systems rather than as a framework tour.merbinjanselm, published 2026-07-15
Building Durable AI Agents - Announcing the cds-langgraph-persistence Plugin
@mi8y/cds-langgraph-persistence has been through 3 major lines since this was published: 0.5 (2026-07), 0.6 (2026-07), 0.7 (2026-09). Affects Runnable code.
Integrating and Extending JouleIntegrating and Extending JouleSAP's reference architecture for SAP Joule across SAP cloud applications: a request is checked against a Scenario Catalog of the applications' skills and a Knowledge Catalog answered by retrieval-augmented generation, combined with the user's roles and history, sent to an LLM available through the generative AI hub, and, where a Joule function is needed, turned into a call to the backend. Its identity page is the authentication path: each user sits in the Identity Directory of Cloud Identity Services with a Global User ID, Identity Provisioning synchronises role assignments into SAP Build Work Zone, whose navigation service decides which functions a user may call, and the Destination service exchanges the user's token to call a function in the user's name. Its extension page covers Joule Studio in SAP Build, where Joule skills automate rule-based tasks over APIs and AI agents plan and reason with models and document grounding from SAP AI Core, the ground agent tooling covers; a later Joule Studio page describes a managed Joule Studio Runtime on the SAP Business AI Platform, which has no entry here.published 2026-03-16
Document extractionSAP only
Integrate CAP application as outbound channel for SAP Document AIIntegrate CAP application as outbound channel for SAP Document AIDocument AI will call you back when extraction finishes, and the callback is where this goes wrong: the CAP application has to accept an unauthenticated POST from outside without dropping CSRF protection for everything else. The answer is a middleware hooked into the CAP bootstrap, and the post spends its length on the token lifecycle rather than on the extraction.sdeissenboeck, published 2026-04-22
Integrating SAP Document Information Extraction (Document AI) with SAP CAP – Step by StepIntegrating SAP Document Information Extraction (Document AI) with SAP CAP – Step by StepThe forward direction, in full: a PDF or scan arriving at a CAP service, sent to Document Information Extraction, and the fields coming back into the model. Long, and step-by-step in the literal sense — the service binding and the request shapes are all written out.furkan5, published 2025-10-09
SAP Document AISAP Document AISAP's reference architecture for SAP Document AI, first as SAP composes the service on BTP for document extraction: OCR, pretrained models, customer-defined schemas and LLM calls through SAP AI Core and its Prompt Registry in one pipeline, with tenant-isolated persistence in SAP HANA Cloud, PostgreSQL and an object store. Its scenario pages then lay out a solution in three layers: intake through Outlook and SharePoint inbound channels, the workspace UI, the Joule Work mobile app or the upload API; classification and extraction with a confidence score per field, automatic confirmation above a threshold and review of the rest in the workspace; and posting through outbound channels, directly to a REST API, through an SAP Integration Suite flow, or through a CAP service that maps the fields and calls SAP S/4HANA over OData. Enrichment beyond the service's own master data lookup is left to a CAP application or an Integration Suite flow triggered from an outbound channel, and direct API posting leaves error handling, retries and monitoring to the application.published 2026-06-03
LLM integrationSAP and community
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.
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.
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.
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.
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
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
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
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.
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.
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.
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
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.
RAG on CDSSAP and community
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 governanceCommunity only
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.
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