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SAP-samples/btp-cap-dox-invoice-validationSAP-samples/btp-cap-dox-invoice-validationA CAP application on SAP BTP for invoice validation: Document Information Extraction reads an invoice PDF, a person corrects the entries, and a workflow passes it on while recording each change.sap official, cap
CAP app validating invoices with Document Information Extraction
Connecting SAP Integration Suite with SAP Document AI and SAP Business AIConnecting SAP Integration Suite with SAP Document AI and SAP Business AIWalks through a Cloud Integration flow in SAP Integration Suite that pulls PDF purchase orders from a Gmail mailbox, extracts header and line-item fields through SAP Document AI's job-submission and polling API, and validates the result with SAP AI Core's generative AI hub using GPT-5-mini as a quality check rather than the source of the extracted values. Groovy steps combine the Document AI confidence score and the model's score into one figure that routes low-scoring purchase orders to manual review, with no S/4HANA posting performed.subhojitSaha, published 2026-08-24
How to Use n8n for Agentic Integrations and Cool InsightsHow to Use n8n for Agentic Integrations and Cool InsightsA Devtoberfest 2026 session on one use case built with n8n, the workflow automation tool SAP announced a partnership with in May 2026: judging the attachments of an automotive warranty claim. A Fiori upload table carries named evidence rows — this line is the invoice, this one the quote — and each uploaded file goes as base64 to an n8n webhook together with the claim context: the position and diagnosis codes, the parts, the amounts and the conversation with the dealer. The flow splits PDF from image, tries text extraction before falling back to a vision prompt, and calls Anthropic's Sonnet with a prompt that names the role, the input, the scoring rules and the response shape; the demo shows a matching invoice scoring 95%, an unrelated dent-removal invoice scoring 5% with the mismatches listed, and a photograph of a rusted suspension arm accepted while an engine bay is rejected against the same claim. The second half is the n8n editor itself — nodes, credentials, the test trace with each node's input and output — and the closing advice is human-in-the-loop flows at the edges of a process rather than automatic adjudication inside it.SAP Developers, published 2026-09-25
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
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
What a Second Document AI Workshop Taught Me About GovernanceWhat a Second Document AI Workshop Taught Me About GovernanceFrom questions raised at a second SAP Document AI workshop: the retention period for extracted data is a setting in the service's administration rather than a fixed property, with the source file deleted after processing and the decommissioning period for the extracted data configured to the customer's own policy. It also draws a boundary readers kept merging: Document AI extracts across languages but does not translate, so turning a document into a reviewer's own language is a separate step, pointing on BTP at SAP Translation Hub.Nicolas_BacaStorni, published 2026-08-25
Why Our First Document AI Exercise Was a Résumé, Not an InvoiceWhy Our First Document AI Exercise Was a Résumé, Not an InvoiceWorking through a SAP Document AI workshop, the author explains why its exercises teach extraction on a résumé before an invoice: a résumé has no stable layout to map fields against, so it forces the point that the tool extracts from a written description of a schema rather than from position-matched templates. Describing the schema is shown to carry real judgment — what a missing end date should become, what a vague field name like "number" costs the model, who in a company should decide these things — and the piece argues that preparing for a project is chiefly a business task of writing down knowledge that otherwise stays in people's heads.Mariano_BacaStorni, published 2026-09-03
UI5con_2026_MCPAppsUI5con_2026_MCPAppsFive runnable MCP servers whose tools return a prebuilt UI beside their text result, from plain HTML through UI5 Web Components and UI Integration Cards to a UI5 linter workflow and an ARC-1 transport review.independent, ui5
Five MCP servers whose tools return a prebuilt UI
Neither the package manifest nor the repository tree declares a licence.
UI5 Has Entered the Chat: Building Interactive MCP Apps for SAPUI5 Has Entered the Chat: Building Interactive MCP Apps for SAPMCP Apps let an MCP tool point at a ui:// resource that a compatible host renders in a sandboxed iframe, and the post walks through the five samples of UI5con_2026_MCPApps, built with Mike Zaschka for UI5con, from a plain HTML time tool through UI5 Web Components and UI Integration Cards to UI5 lint findings from @ui5/mcp-server and an ARC-1 transport review. In the samples the model selects the tool and does not generate the interface, the prebuilt kind of generative UI, and the subject is the server side of MCP servers: register the resource, link it from the tool's _meta.ui.resourceUri, return text content as a fallback beside structuredContent, call callServerTool() only for tools of the server that owns the app, and send a message to the host when another server such as ARC-1 has to act. It stops at the host, whose support differs by client and whose iframe sandbox is not a complete security model, and Joule support for MCP Apps could not be verified at the time of writing.published 2026-07-13
@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
@ui5/webcomponents@ui5/webcomponentsSAP's UI elements as standard custom elements, ui5-button among them, implementing SAP Fiori design and usable from plain HTML, React, Angular or Vue.sap official, ui5
SAP's UI elements as standard custom elements
Not where the UI library itself is expected to bind data: the documentation leaves binding a component's content and events to the host framework. Not from a CDN script tag: the project publishes none, so it assumes a build step.
abap-agent-runtimeabap-agent-runtimeAn ABAP-native MCP endpoint inside the SAP system: an agent reads the screen of an opted-in abap2UI5 app as structured data, fills its fields and fires its events as the logged-on SAP user.community org, abap
ABAP-native MCP endpoint that drives opted-in abap2UI5 apps
Stateful apps cannot be operated: a stateful session lives in one HTTP request and an MCP call is one request, so `app_start` refuses them. The repository carries no release yet and states the addon has not been run in production use, the 7.02 downport is lint-only with no 7.02 system having run it, and the ABAP Cloud HTTP service setup is written down but still to verify on your own system.
abap-sdk-nwas-xabap-sdk-nwas-xAn ABAP client library for IBM's watsonx.ai and watsonx.ai Runtime APIs, installed into an SAP NetWeaver AS ABAP system through abapGit, generating and refreshing the IAM token from an API key.independent, abap
ABAP client library for IBM watsonx.ai APIs
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
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.
zllmzllmAn ABAP framework for chaining and orchestrating calls to large language models in pure ABAP, with templated prompts, routing by complexity, response caching and token estimation.independent, abap
ABAP framework for chaining and orchestrating LLM calls
ai-enabled-integrations-codejamai-enabled-integrations-codejamA CodeJam that processes customer support requests in SAP Cloud Integration with an LLM in SAP AI Core, then exposes APIs to an agent as MCP servers through API Management's MCP Gateway.sap official, btp
CodeJam on LLM-backed integration flows and MCP via API Management
aicore-genai-samplesaicore-genai-samplesA collection of sample applications that show how to use SAP AI Core's Generative AI Hub capabilities, each with its own README and meant for enablement, not production use.sap official, btp
Sample apps for SAP AI Core's Generative AI Hub capabilities
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.
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-btp-ai-best-practicessap-btp-ai-best-practicesSAP's collection of sample implementations for AI-powered applications on SAP BTP, one directory per topic under best-practices, in TypeScript, Python, Java and CAP.sap official, btp
Sample implementations for AI-powered applications on SAP BTP
SAP-easy-AISAP-easy-AIAn ABAP report that sends a prompt to a locally running LLM in LM Studio through a destination created in transaction SM59, shown with gpt-oss-20b and Gemma 3 12B.independent, abap
ABAP report sending a prompt to a local LLM via an SM59 destination
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
SAP-samples/codejam-code-based-agentsSAP-samples/codejam-code-based-agentsA CodeJam teaching code-based AI agents in Python or TypeScript, from a first agent through custom tools to deployment on SAP BTP and integration with Joule.sap official, btp
CodeJam on code-based AI agents in Python or TypeScript on SAP BTP
SAP-samples/generative-ai-codejamSAP-samples/generative-ai-codejamA CodeJam that deploys and queries a large language model on SAP AI Core's Generative AI Hub, then builds a retrieval-augmented chatbot on top of it.sap official, btp
CodeJam deploying an LLM on Generative AI Hub and building RAG
SAP-samples/teched2025-AI160SAP-samples/teched2025-AI160A hands-on session that builds a purchase-order escalation agent in JavaScript or Java with the SAP Cloud SDK for AI and the Generative AI Hub in AI Foundation, calling the S/4HANA Purchase Order API.sap official, btp
Hands-on building an AI agent with the SAP Cloud SDK for AI
teched2025-DA266teched2025-DA266SAP TechEd 2025 session material for two SAP Databricks scenarios in SAP Business Data Cloud: cashflow forecasting and payment-delay prediction with SHAP explanations.sap official, btp
TechEd 2025 material on two SAP Databricks scenarios in BDC
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.
ui5con-2026-chrome-built-in-aiui5con-2026-chrome-built-in-aiA hands-on UI5 workshop that adds Chrome's on-device AI APIs to a small TypeScript app for triaging multilingual support tickets: language detection, summaries, structured extraction, translation and replies.sap official, ui5
UI5 workshop adding Chrome's on-device AI APIs to a TypeScript app
🟣 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
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
Autonomous Diagnostics on Kyma Unplugged: Goose + LiteLLM + KRO + SAP AI Core in ProductionAutonomous Diagnostics on Kyma Unplugged: Goose + LiteLLM + KRO + SAP AI Core in ProductionAn in-cluster autonomous diagnostic pipeline on Kyma Unplugged (open-source Kyma, noted to also run on SAP BTP, Kyma runtime): a Goose agent with kubectl access calls SAP AI Core through a LiteLLM proxy, and most of the post is about the quirks of that route — drop_params silently swallowing unrecognised parameters and beta headers, streaming that has to be off when tools are attached, and prompt caching that sits on the Orchestration V2 API rather than the foundation-model path the LiteLLM provider hits. A second Node.js proxy reaches that API, and the author reports caching unconfirmed at production scale, which leaves the roughly $0.004 per static run a target rather than a measured figure. Spend metrics reach SAP Cloud Logging over OTLP through Kyma's Telemetry MetricPipeline while the health analysis itself lands in Kubernetes custom resources read in Busola, and the stack is declared as KRO Resource Group Definitions applied with one Terraform command.quovadis, published 2026-07-09
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.
Beyond Basic AI Error Handling in SAP CPI - A Rules-First Architecture with HANA Cloud LearningBeyond Basic AI Error Handling in SAP CPI - A Rules-First Architecture with HANA Cloud LearningBuilds a rules-first error-diagnosis pipeline on Cloud Integration in SAP Integration Suite: a Groovy classifier resolves known failures — auth, timeout, malformed payload — deterministically, and only genuinely novel errors reach an LLM through the AI Adapter, after a sanitizer script replaces every payload leaf value with a typed placeholder before it leaves the tenant. SAP HANA Cloud, reached over JDBC, holds a fingerprinted dedup cache and an accumulating table of past diagnoses. It also documents the AI Adapter configuration — Advanced mode, a Bearer-prefixed Secure Parameter, and a connection/processing URL split — needed to reach a non-SAP OpenAI-compatible endpoint, found by the author to be undocumented.Krishna_Karthik, published 2026-06-11
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 Your AI Agent prototype for free using SAP CAP, LangChain and Ollama cloudBuild Your AI Agent prototype for free using SAP CAP, LangChain and Ollama cloudA CAP Node.js service action calls an Ollama-hosted model through LangChain's ChatOllama, with the API key read from a .env file instead of any destination or credential binding — the hand-rolled-key route SAP's own SDK call exists to avoid. The same handler then builds a LangChain agent with createAgent and one mock tool, so a question about restaurants triggers a tool call and any other question does not, independent of the @cap-js/agents plugin's A2A-served harness.rajeevgoswami1, published 2026-08-29
Building a Pro-Code Agent on SAP BTP — Part 2: The Agent (LangGraph + GenAI Hub)Building a Pro-Code Agent on SAP BTP — Part 2: The Agent (LangGraph + GenAI Hub)Part 2 of a three-part series builds a separate, non-CAP Node/TypeScript agent that calls the Part 1 CAP service's MCP tools and reasons through SAP AI Core via @sap-ai-sdk/orchestration, using LangGraph's createAgent ReAct loop for provider-agnostic model calls across GPT, Claude and Gemini deployments. It explains why the tool's Zod schema and model-facing description are the agent's actual contract, the gotcha between a foundation-models deployment and an orchestration-scenario one, and how edge-case testing replaced date inference with a server-computed daysOverdue field after the agent wrongly called a delayed order 'on schedule'.rodfior, published 2026-08-20
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
Calling Claude from ABAP: How I Wired SAP AI Core into a Real Business ScenarioCalling Claude from ABAP: How I Wired SAP AI Core into a Real Business ScenarioA proof of concept wiring SAP AI Core's Claude deployment directly into ABAP with no CAP layer between them: two SM59 HTTP destinations, a hand-written OAuth 2.0 client-credentials exchange, and a regex to unwrap Claude's system/messages response envelope. Most of the effort goes into a system prompt that constrains the model to return one fixed JSON schema of filter fields, which ABAP deserialises with /UI2/CL_JSON and exposes through a RAP custom action so a Fiori search field can drive an OData V4 filter.PranayGaroo, published 2026-06-04
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
Connecting SAP Integration Suite with SAP Document AI and SAP Business AIConnecting SAP Integration Suite with SAP Document AI and SAP Business AIWalks through a Cloud Integration flow in SAP Integration Suite that pulls PDF purchase orders from a Gmail mailbox, extracts header and line-item fields through SAP Document AI's job-submission and polling API, and validates the result with SAP AI Core's generative AI hub using GPT-5-mini as a quality check rather than the source of the extracted values. Groovy steps combine the Document AI confidence score and the model's score into one figure that routes low-scoring purchase orders to manual review, with no S/4HANA posting performed.subhojitSaha, published 2026-08-24
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
How to implement business AI capabilities in ABAP CloudHow to implement business AI capabilities in ABAP CloudA Devtoberfest 2026 session (ABA-32) on embedding AI in an ABAP Cloud application and on making SAP Joule aware of what that application can do. The first demo implements two features of a RAP travel app with the ABAP AI SDK: a determination on an uploaded receipt that sends the image together with the allowed categories and transaction types to a model reached through an intelligent scenario, then writes the extracted description, category, currency and amount back to the expense entity; and a recommendation function, triggered by a side effect on the trip title, that proposes a trip type with a score the Fiori UI sorts the proposals by and leaves the business user to accept — which is LLM integration from ABAP code. The second demo builds a capability for SAP Joule in the Joule Studio code editor in VS Code, with a scenario that extracts a trip title from the user's intent and calls an OData service in the ABAP backend through a BTP destination, deploys it and asks the Joule test UI for the trip's details; the session states that agents reach ABAP only over governed remote APIs — OData, SQL and MCP — and that automatic grounding of custom OData services in the knowledge graph is upcoming rather than available.published 2026-10-05
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.
Michael Jackson: AI Cognitive Pipeline on SAP BTP, SAP CAP, ElevenLabs, Claude, HANA DB, SolaceMichael Jackson: AI Cognitive Pipeline on SAP BTP, SAP CAP, ElevenLabs, Claude, HANA DB, SolaceA pipeline that listens to a song, identifies the historical events it references, retrieves knowledge about each and builds an emotional arc across the whole track. Not an enterprise use case, and useful precisely for that: the plumbing — CAP, HANA, Solace, Claude, ElevenLabs — is doing something the vendor demos never ask of it.Shahid, published 2026-05-18
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-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
SAP Integration Suite: Intelligent Integration with AI AdapterSAP Integration Suite: Intelligent Integration with AI AdapterThe AI Adapter adds generative AI as a receiver step inside a Cloud Integration flow, connecting through either AICore-GenAIHub — a single API onto SAP AI Core's Generative AI Hub and its foundation models — or Custom-OpenAI-Rest, a plain REST call out to a provider such as OpenAI. SAP Integration Suite starts the call from the iFlow and feeds the model's response into the next step, which is the mechanics behind the sentiment-analysis, summarisation and routing use cases the piece lists without walking through any one of them.meena_choudhary, published 2026-01-28
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
What Happens to Your Data When You Enable SAP's AI Features?What Happens to Your Data When You Enable SAP's AI Features?Traces where a prompt goes when Joule, the Generative AI Hub or the orchestration service calls a third-party model through SAP AI Core: the prompt and the business context around it travel to a model such as GPT-4o, Claude or Gemini for inference, and masking through SAP Data Privacy Integration is an optional orchestration module rather than a default, so with no masking block in the @sap-ai-sdk/orchestration client config nothing is masked. On the masking options, which the author says he confirmed with SAP AI Core product management, anonymization discards the original value and pseudonymization keeps it reversible; DPI recognizes person names, email addresses and phone numbers, plus custom entities defined by regex, which leaves commercially sensitive non-PII such as a revenue forecast or a project codename to pass through unless someone writes a pattern for it, and whether that data is covered at all is one of three answers the post marks as still pending. The "no training" assurance for third-party providers is read as contractual rather than technical, and models hosted and managed by SAP, Mistral among them, are named as a path other than a US-hyperscaler-hosted one.jaripie, published 2026-04-23
ai-core-samplesai-core-samplesSAP-authored tutorial notebooks and workflow templates that walk through a first machine learning project on SAP AI Core, from its object store and Docker images to workflow templates.sap official, btp
Tutorial notebooks and templates for a first SAP AI Core project
aicore-multioutput-regression-restaurant-inspectionsaicore-multioutput-regression-restaurant-inspectionsA boilerplate machine learning service on SAP AI Core that turns restaurant-inspection violation text into several criticality scores.sap official, btp
ML service on SAP AI Core scoring restaurant-inspection violations
analytics-cloud-rpt15-prediction-demoanalytics-cloud-rpt15-prediction-demoA FastAPI backend that triggers SAP-RPT-1.5 payment-delay predictions from an SAP Analytics Cloud Multi Action and writes the results back to HANA.sap official, btp
FastAPI backend for SAP-RPT-1.5 predictions from SAP Analytics Cloud
hana-apl-apis-runtimeshana-apl-apis-runtimesSQL code examples for every SAP HANA Automated Predictive Library API, plus reference runtimes for scoring an exported model in C++, Java or JavaScript.sap official, btp
SQL examples for the HANA APL APIs plus scoring runtimes
hana-ml-sampleshana-ml-samplesSAP-authored code examples for predictive and machine learning scenarios on SAP HANA, from Predictive Analysis Library SQL to hana-ml Python notebooks.sap official, btp
Code examples for predictive and ML scenarios on SAP HANA
sap-rpt-samplessap-rpt-samplesBruno API collections and code samples for calling SAP's Relational Pretrained Transformer models on SAP AI Core for predictions on tabular data, one subdirectory per model.sap official, btp
Bruno collections and code for SAP-RPT predictions on tabular data
SAP-samples/hana-ml-py-codejamSAP-samples/hana-ml-py-codejamSAP CodeJam material that walks through a first machine learning workflow in SAP HANA Cloud from Python, in Jupyter notebooks from HANA DataFrames to hyperparameter tuning and AutoML.sap official, btp
CodeJam on a first machine learning workflow in SAP HANA Cloud
teched2023-DA263teched2023-DA263An SAP TechEd 2023 session on SAP HANA features over the HANA Deployment Infrastructure: in-memory column tables, data tiering, spatial, the document store, graph and AutoML.sap official, btp
TechEd 2023 session on SAP HANA features over HDI
teched2025-DA261teched2025-DA261An SAP TechEd 2025 hands-on workshop on SAP HANA Cloud's AI capabilities: outlier analysis, text embeddings with machine learning, and the knowledge graph engine.sap official, btp
TechEd 2025 workshop on SAP HANA Cloud's AI capabilities
teched2025-DA266teched2025-DA266SAP TechEd 2025 session material for two SAP Databricks scenarios in SAP Business Data Cloud: cashflow forecasting and payment-delay prediction with SHAP explanations.sap official, btp
TechEd 2025 material on two SAP Databricks scenarios in BDC
Decision Logic Modernization in SAP Integrations: Using SAP-RPT-1 for Intelligent Routing DecisionsDecision Logic Modernization in SAP Integrations: Using SAP-RPT-1 for Intelligent Routing DecisionsWalks through embedding SAP-RPT-1 — SAP's foundation model for classification and regression over tabular ERP data, hosted on SAP AI Core — into a Cloud Integration flow that picks the optimal 3PL logistics partner for an outbound delivery. The iflow sends historical deliveries from SAP HANA Cloud plus the new delivery as context rows to the model's in-context learning, with no training step, and routes on the returned prediction and confidence score, the mechanism predictive models's core coverage describes for SAP-RPT-1. The author is explicit that SAP-RPT-1 differs from general-purpose language models trained on text, which keeps this apart from an LLM-integration scenario.arunmaarirajha_kv, published 2026-02-16
How We Make Tabular Predictions in 2026 with TabPFNHow We Make Tabular Predictions in 2026 with TabPFNA Devtoberfest 2026 session, AI-32, on why a table needs a model of its own: a language model reasons its way to a plausible number, while a tabular foundation model takes the table as context in one forward pass and learns from its features statistically. That is predictive models from both ends — a scikit-learn-shaped fit and predict in a notebook over a heart-disease data set, predicted class probabilities and SHAP values for which features drove the outcome, then the same model reached as an MCP tool by a coding agent that is asked which current customers are most likely to churn. The presenter is from the lab that publishes TabPFN; the Q&A puts it beside SAP's own tabular foundation model, which she says she has not used.SAP Developers, published 2026-10-08
SAP-RPT-1.5 Is Now Generally Available: More Accurate Predictions, Better Explainability, What's NewSAP-RPT-1.5 Is Now Generally Available: More Accurate Predictions, Better Explainability, What's NewAnnounces general availability of SAP-RPT-1.5, the next release of SAP's foundation model for predictive models over tabular enterprise data: in-context classification and regression with no training step, now with stronger accuracy on high-cardinality columns, a maximum API batch of 512 rows (up from 128), and an "explain by example" mode that surfaces the historical rows that most influenced a prediction rather than ranking feature importance alone. Two variants ship, a default model and a -large model recommended past 100 columns or where accuracy is the priority, and the post previews a coming "tabular orchestration" workflow, targeted for general availability in August 2026, that is meant to select the relevant context rows from millions of historical records automatically.RanjeetKr, published 2026-08-14
Transforming Enterprise Data Strategy with SAP Business Data CloudTransforming Enterprise Data Strategy with SAP Business Data CloudSAP's layered picture of SAP Business Data Cloud, from lake storage and compute up to a knowledge core and agents, with eight sub-pages that each work one part through. The data products page is the cataloguing and lineage part: datasets described by Open Resource Discovery metadata, listed in the BDC catalog and shared over Delta Sharing to SAP Databricks and SAP HANA Cloud, with SAP Datasphere as a consumer on the intelligent content page; other pages install intelligent content, bring SAP BW data in through the Data Product Generator, provision groups by SCIM from Cloud Identity Services into SAP Databricks, which is identity provisioning, and train predictive models in SAP Databricks that SAP AI Core then serves for batch and real-time scoring. The main page states direction more than mechanism, and the BW page marks SQL Share for derived and OData for analytical data products as to be available.published 2026-05-12
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
@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 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.
sap-btp-ai-best-practicessap-btp-ai-best-practicesSAP's collection of sample implementations for AI-powered applications on SAP BTP, one directory per topic under best-practices, in TypeScript, Python, Java and CAP.sap official, btp
Sample implementations for AI-powered applications on SAP BTP
🟣 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
@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
sap-knowledge-pipelinesap-knowledge-pipelineA Python library that reads selected records out of an SAP OData service or an SAP HANA SELECT and renders them as chunked documents, each carrying the entity set and business key it came from.independent, btp
Python library turning OData or HANA records into chunked documents
An early alpha release whose README says to pin the version and test recipes against non-production data first. HANA synchronisation is snapshot-only and does not reconcile deleted rows, so a document for a row deleted in the source stays in the index. A recipe's field allow-list does not decide what the service account may read: the README states that field allow-listing does not replace SAP authorization, and that the package infers no SAP module semantics, authorization scope or safe fields from table names. Only Business Partner has a built-in recipe, every other dataset needs a `KnowledgeRecipe` written for it, and [Qdrant](external:qdrant) local mode is stated to be for development and smaller indexes.
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-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-btp-ai-best-practicessap-btp-ai-best-practicesSAP's collection of sample implementations for AI-powered applications on SAP BTP, one directory per topic under best-practices, in TypeScript, Python, Java and CAP.sap official, btp
Sample implementations for AI-powered applications on SAP BTP
sap-genai-hub-with-sap-hana-cloud-vector-enginesap-genai-hub-with-sap-hana-cloud-vector-engineA hands-on workshop that works through SAP HANA Cloud's vector engine, SAP Generative AI Hub and the Python LangChain framework in one Jupyter notebook.sap official, btp
Workshop on HANA Cloud vector engine, Generative AI Hub and LangChain
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
SAP-samples/generative-ai-codejamSAP-samples/generative-ai-codejamA CodeJam that deploys and queries a large language model on SAP AI Core's Generative AI Hub, then builds a retrieval-augmented chatbot on top of it.sap official, btp
CodeJam deploying an LLM on Generative AI Hub and building RAG
teched2025-DA261teched2025-DA261An SAP TechEd 2025 hands-on workshop on SAP HANA Cloud's AI capabilities: outlier analysis, text embeddings with machine learning, and the knowledge graph engine.sap official, btp
TechEd 2025 workshop on SAP HANA Cloud's AI capabilities
🟣 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
A Use Case for HANA Cloud Knowledge Graph: AI‑Driven Tender AnalysisA Use Case for HANA Cloud Knowledge Graph: AI‑Driven Tender AnalysisMatching unstructured text against internal data, using both HANA engines at once — the Knowledge Graph for the relationships and the Vector Engine for similarity — with CAP holding the application and its data. Notes that the Knowledge Graph Engine is off by default and has to be enabled when the instance is created, which is the kind of thing that stops a weekend.giacomo_dantonio, published 2026-03-19
Building a Generative AI-powered Inventory Assistant with SAP CAP, RAG, and Vector DatabaseBuilding a Generative AI-powered Inventory Assistant with SAP CAP, RAG, and Vector DatabaseAn existing CAP stock application turned into a question-answering one, and the part worth reading is the schema: three tables, not one — the products, an archive of the text that was embedded, and a separate lookup table holding the vectors. Retrieval finds candidate products by similarity and then reads the real rows, so the answer is grounded in records rather than in the embedded text.furkan5, published 2025-11-07
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
Michael Jackson: AI Cognitive Pipeline on SAP BTP, SAP CAP, ElevenLabs, Claude, HANA DB, SolaceMichael Jackson: AI Cognitive Pipeline on SAP BTP, SAP CAP, ElevenLabs, Claude, HANA DB, SolaceA pipeline that listens to a song, identifies the historical events it references, retrieves knowledge about each and builds an emotional arc across the whole track. Not an enterprise use case, and useful precisely for that: the plumbing — CAP, HANA, Solace, Claude, ElevenLabs — is doing something the vendor demos never ask of it.Shahid, published 2026-05-18
Semantic search in CAP (Node.js): vector embeddings and cosine similaritySemantic search in CAP (Node.js): vector embeddings and cosine similarityFinding the best semantic match for a prompt inside an entity, using local vector embeddings and cosine similarity — and the finding the author leads with is that it works without a vector database at all. Reported as experiments rather than as a pattern, which is the honest register for it: the interesting claim is how little infrastructure the simple case needs.published 2025-12-09
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.
arc-mcp-hubarc-mcp-hubA hub on SAP BTP that puts several arc-1 instances, one per SAP system, behind one login and a route each, exchanging the caller's token through a destination so each system still sees the real user.independent, btp
Hub on SAP BTP putting several arc-1 instances behind one login
Archived The declared repository is archived.
Functional boundary With one SAP system the README says to point the client at that arc-1 directly. Version 1 exchanges tokens within a single subaccount, so backends spread across subaccounts are out; it keeps its session map in memory, so more than one instance needs sticky sessions or a shared store. It routes deterministically and runs no model, which is a different thing from an assistant that reasons across systems. The `/all` endpoint drops the per-connection isolation, and the maintainer's answer to that is a PROD backend set read-only at the SAP end rather than a tool surface that hides the writes.
bw-modeling-mcpbw-modeling-mcpReads, creates and modifies BW/4HANA modelling objects through the same internal APIs Eclipse BWMT and the BW/4HANA Cockpit use — and can run centrally on BTP with each caller reaching BW as themselves.independent, btp
MCP server for BW/4HANA modelling objects
Its README says it plainly and so does this entry: this is not a simulation, every tool call reaches a live BW system, and write operations produce real changes. BW modelling objects are the definition of a warehouse — the blast radius of a wrong one is every report built on it.
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
Maherd18/sap-mcp-serverMaherd18/sap-mcp-serverAn MCP server for ABAP development over the ADT resources: eight tools, each checked against a JSON policy file before the call reaches the system, with activation and transport release held for a person to approve.independent, abap
Policy-checked MCP server for ABAP development over ADT
Development and sandbox systems only: the README warns against pointing it at production. Transport release is never executed, since the tool exists to show the policy stopping it, and separation of duties is checked against the `identity.approvers` list in the policy file rather than against SAP authorisations, so it holds as far as that file does and no further. The write path has been tested with ABAP classes and other object types may need adjustments, there is deliberately no tool that creates or deletes an object, and one server instance serves one SAP system.
pyrfc-mcppyrfc-mcpAn MCP server for calling an SAP system's RFC-enabled function modules from an agent, built so that discovering a function does not authorise calling it: a read or a write is denied unless the function name matches an allowlist the server is configured with.independent, abap
MCP server calling RFC-enabled function modules behind an allowlist
Real SAP calls need the proprietary [SAP NetWeaver RFC SDK](external:sap-nw-rfc-sdk) and the `pyrfc==3.3.1` release the README calls archived and yanked, on Python 3.11, so without those only the demo backend answers. Not over HTTP as it stands: credentials are read from the environment for stdio, and the README says not to expose the server over HTTP without implementing the MCP authorization specification and deployment-level rate limiting. Multi-tenant auth and RBAC beyond the one server-wide policy, rate limiting, metrics and tracing, and deployment packaging are listed as not built. The result limits protect the client and not the system, since the README states they cannot reduce work already performed inside SAP, and the MCP tool annotations that describe read and write risk are advisory metadata rather than an authorisation boundary, so the service account's `S_RFC` and table authorisations are still what decides.
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.
SuperClaude for SAPSuperClaude for SAPA Claude Code plugin that installs and wires its own ABAP MCP server, then puts a two-layer write guard between the agent and any system profile marked QA or production.independent, abap
Claude Code plugin with an ABAP MCP server and write guards
It is a plugin for one server — its own — so it is a stack rather than a component. And the guard is only as good as the profile labels: a production system registered as `DEV` is a production system with the safety off.
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
ABAP + MCP: Let AI Do Real Development in Your SAP SystemABAP + MCP: Let AI Do Real Development in Your SAP Systemabap-adt-mcp is an open-source MCP server that gives an AI agent full ABAP development capability over ADT: it searches, reads, edits, activates and unit-tests objects, and runs ATC checks, on a connected S/4HANA Cloud or on-premise system, acting as the signed-in developer. It runs on abap-adt-api, the same REST client Eclipse's own ADT tooling uses, and sits beside SAP's own ADT MCP Server, shipped with ADT for VS Code and Eclipse under a different server key, and marianfoo's abap-docs, a read-only server over the ABAP Keyword Documentation and Clean ABAP. Each destination carries a policy — read-only, allowed packages, denied tables and tools — the server checks before every call, and an optional audit file logs each tool call with its outcome and policy gate.Willian_Saez, published 2026-09-03
ABAP and Agentic AI: The Hidden Problem in Real ProjectsABAP and Agentic AI: The Hidden Problem in Real ProjectsAI in SAP development splits on three questions, how the model gets context, where writing happens and who controls the setup: UI5 and CAP work in local files where git, reviews, tests and pipelines already gate a change, while on-stack ABAP writes land directly in a shared development system. Six patterns follow, framed after a paper by Lars Hvam, from AI in the IDE to off-stack development with abapGit, and the post settles on a hybrid where the agent reads the system over ADT or MCP and writes through a gated git flow. Read access alone is put at maybe 80 percent of the work, and what agent access to your code still lacks is named as tool governance: read-only defaults, package restrictions, blocking free SQL, per-user identity and auditing.published 2026-04-22
Agentic AI & AI AgentsAgentic AI & AI AgentsSAP's target picture for AI agents: Joule as the engagement layer — Joule Work, Joule Assistants, Joule Agents — with custom agents from customers, partners and SAP teams connecting through A2A for agent-to-agent collaboration and MCP for tool connectivity, on the SAP Business AI Platform. It says up front that parts are not generally available yet, in particular bidirectional communication with third-party agents through the Agent Gateway, so it reads as direction with the building blocks named rather than a design to copy today.published 2026-08-27
ARC-1 on SAP BTP: Secure ABAP Agentic Development Beyond the LaptopARC-1 on SAP BTP: Secure ABAP Agentic Development Beyond the LaptopThe BTP deployment of ARC-1: one Cloud Foundry app per SAP system, reached by MCP clients through XSUAA and reaching the backend through the SAP Destination service, the SAP Connectivity service and Cloud Connector, with a BasicAuth destination for startup probing and a Principal Propagation destination for per-user requests, and SAP_PP_STRICT=true so a propagation problem fails instead of falling back to the shared user. Tool governance is the three gates it spells out, the server ceiling set by environment variables, the user's permission from XSUAA role collections, OIDC scopes or API-key profiles, and the SAP backend authorisation, all of which must allow an action, and the staged rollout from read-only to writes in $TMP, then selected packages, then transport writes. It recommends the MTA deployment over Docker on Cloud Foundry or a buildpack push, treats BTP ABAP Environment as a separate service-key scenario with no Cloud Connector, and is not a full setup guide: the commands are left to the ARC-1 deployment docs.published 2026-04-29
ARC-1 on SAP BTP: Secure ABAP Agentic Development Beyond the Laptop
arc-1 has been through 3 major lines since this was published: 0.8 (2026-05), 0.9 (2026-05), 1 (2026-07). Affects A recipe.
Building an Enterprise-Ready SAP AI Agent with Open SourceBuilding an Enterprise-Ready SAP AI Agent with Open SourceA self-hosted chat agent for SAP systems: LibreChat as UI and orchestrator with Entra ID sign-in and SharePoint connected, a local LLM through Ollama, and two MCP servers, mcp-sap-docs for public documentation and the ADT MCP server vibing-steampunk for the system itself. The ADT server runs in its read-only mode under a technical user with display authorisations only, so the agent can find a function module and pull its source, or locate a RAP business object with its behaviour definition and projection view, but cannot change anything. Two workflows show it, explaining what BAPI_DISPUTE_CREATE does in one system and planning a RAP action from a PDF specification, where the generated ABAP is copied into Eclipse by hand; the full setup is in the marianfoo/llm-client-sap-system-integration-sharepoint repository.published 2026-03-03
Building MCP Servers with SAP Integration Suite: What to Design, Not Just How to ConnectBuilding MCP Servers with SAP Integration Suite: What to Design, Not Just How to ConnectArgues that the tool design, not the connection, is the hard part of putting an API in front of an agent. Works one request — create a customer — through eight endpoint-shaped tools, where the agent carries ids between calls and rebuilds the business rules each run, then through a single intent-shaped create_customer that hides the sequence; and says what separates the two: a business operation for a name, parameters a caller can supply without a lookup, the rules in the server rather than the prompt, and a description written for an agent choosing a tool. The SAP Integration Suite half is short — three creation paths, an iFlow behind the API artifact when a tool has to cross systems — and it closes on a checklist to run before publishing.published 2026-07-07
From APIs to Agent Tools: How MCP Gateway changes what Integration meansFrom APIs to Agent Tools: How MCP Gateway changes what Integration meansThe opening session of the Devtoberfest 2026 integration track, in which two product managers for SAP Integration Suite set out why the suite puts an MCP gateway between agents and backend systems, and then build one. The demo turns an HTTP endpoint and its OpenAPI specification into MCP tools on the integration cell runtime, does the same on top of an existing API-centric integration, and shows the policies tab where authentication through an external provider, quotas, spike arrest and IP filtering sit, plus the trace of the JSON-RPC calls in monitoring and the agent they are earmarked against in analytics. That is where the serve your application over MCP and tool governance edges hold; RFCs and BAPIs as sources are stated as supported, and MCP federation, custom domains and edge integration cell as roadmap.SAP Developers, published 2026-09-23
From SAP Integration Suite iFlow to MCP Tools: Exposing OData APIs to AI AgentsFrom SAP Integration Suite iFlow to MCP Tools: Exposing OData APIs to AI AgentsBuilds an MCP Server artifact inside SAP Integration Suite's Integration Cell runtime that fronts a Northwind OData V4 API through a Cloud Integration iFlow, turning each OpenAPI operation into a tool an agent can call. Walks through the OAuth2 client-credentials flow against XSUAA and the authorization policy scope — ESBMessaging.send in place of the default API.invoke — that gates access to the MCP server, then tests the server from Claude Code.published 2026-07-14
HANA AI Toolkit — Local MCP ServerHANA AI Toolkit — Local MCP ServerAn MCP server that turns the hana-ml Python library into governed tools an agent can call against a HANA Cloud instance, shipped in the hana-ai toolkit and exposed over stdio, SSE and streamable HTTP. Its one firm rule is attributable execution: an audit module writes the MCP-side identity — agent, model, session, tool, redacted arguments — into the HANA connection before any SQL runs, and fails the call if that write fails. A worked example of governance on an MCP tool surface, from SAP's data side rather than CAP's.published 2026-06-15
Introducing ARC-1: A Secure ADT MCP Server for Enterprise SAP DevelopmentIntroducing ARC-1: A Secure ADT MCP Server for Enterprise SAP DevelopmentWhy ARC-1 exists beside earlier ADT MCP servers such as vibing-steampunk, fr0ster/mcp-abap-adt and dassian-adt: one centrally deployed instance per SAP system instead of a server on each developer laptop, with the MCP client to ARC-1 hop and the ARC-1 to SAP hop authenticated separately. It sets out the restrictive defaults (read-only, no free SQL or named table preview, writes confined to $TMP), the layering in which ARC-1's configuration, the user's role and SAP's own authorisation must all allow an action, Principal Propagation so SAP sees the real user, and audit events to the SAP Audit Log service; that is tool governance as much as agent access to your code. It leaves the BTP setup to the next part, and places ARC-1 beside SAP's ADT MCP Server, which SAP's configuration guide describes as a local HTTP server hosted by the IDE, and beside SAP API Policy 4.2026a, where ARC-1's stated position is to stay close to documented ADT behaviour and ask SAP before production use.published 2026-04-27
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
Powering Enterprise-Ready Agentic AI with SAP Integration SuitePowering Enterprise-Ready Agentic AI with SAP Integration SuiteA Devtoberfest 2026 session (INT-31) from SAP Integration Suite product management on the MCP gateway. The demo creates an MCP server in a design package from an API already deployed on the tenant, exposes two of its 300-odd resources as tools, attaches authentication and quota policies on a policies tab, and publishes the deployed endpoint as an AI product in Developer Hub of API Management, where an agent subscribes and receives a token, URL, key and secret; the calls then show up in monitoring, in message processing logs and in the analytics dashboard, which is the MCP exposure and the tool governance the edges rest on. Three AI features inside Cloud Integration follow: a content migration agent that converts a PI/PO Java mapping into a Groovy script and generates a second integration flow for its lookup, memory profiling during a flow simulation, and root-cause analysis on a failed message processing log.SAP Developers, published 2026-10-07