joule-a2a-agent

name

joule-a2a-agent

description

Generate and deploy a pro-code AI agent using LangGraph and SAP GenAI Hub, deploy it to SAP BTP Cloud Foundry, create a Joule capability with A2A (Agent-to-Agent) action, and deploy it to Joule. Supports TypeScript (Express or CAP) and Python. Use this skill whenever the user mentions: building a custom agent for Joule, deploying an AI agent to BTP Cloud Foundry, A2A protocol integration with Joule, pro-code agent extensibility, LangGraph agent on SAP BTP, connecting an external agent to Joule, "bring your own agent" for Joule, creating a Joule capability with A2A action, CAP agent, MTA deployment, or any combination of LangGraph/A2A/Joule/BTP/Cloud Foundry/CAP in an agent development context. Also trigger when the user wants to scaffold, modify, or redeploy an existing A2A agent project.

Joule A2A Agent on SAP BTP Cloud Foundry

This skill generates a complete pro-code AI agent project that:

1. Implements an agent with A2A protocol v0.3.0 support using LangGraph + @a2a-js/sdk 0.3.10 and SAP GenAI Hub (OrchestrationClient) as the LLM provider
2. Deploys to SAP BTP Cloud Foundry as a REST API (via cf push or MTA)
3. Creates a Joule capability with an A2A action using the multi-file DTA format
4. Provides CLI commands to compile and deploy the capability to Joule

How This Works — The Big Picture

SAP Joule can orchestrate external agents using the A2A (Agent-to-Agent) protocol. The flow is:

1. Your agent runs as a TypeScript or Python web service on Cloud Foundry, exposing an A2A-compatible endpoint
2. Joule discovers your agent by reading its Agent Card at /.well-known/agent.json
3. When a user's prompt matches your agent's capability, Joule sends an A2A task to your agent
4. Your agent processes the request using LangGraph (with tools, LLM reasoning via SAP GenAI Hub, etc.) and returns the result
5. Joule presents the result to the user in the chat

The connection between Joule and your agent uses a BTP Destination + a Joule capability YAML that defines an agent-request action type.

Prerequisites

Joule DTA Schema Version 3.28.0+ Required: Code-based agents (BYOA) using the agent-request action type are supported as of DTA schema version 3.28.0. The capability.sapdas.yaml must specify schema_version: "3.28.0".

If your Joule tenant runs an older schema version, the compile will succeed but deploy will fail with: "Schema version defined in config file is greater than the current schema version of Joule".

How to check your tenant's version:

1. Login: joule login --use-env (or joule login )
2. Check status: joule status
3. Try deploying — if you get the schema error, your tenant needs an update
4. Contact your BTP admin to request a Joule service update

Other prerequisites:

• SAP BTP subaccount with Cloud Foundry enabled
• AI Core service instance with a GenAI Hub model deployed
• Joule CLI installed: npm install -g @sap/joule-studio-cli
• CF CLI installed and logged in (cf login or cf login )
• Joule CLI logged in (joule login or joule login --use-env)
• Joule App2App IAS flow configured for Joule Studio CLI (assign required roles)
• BTP roles: extensibility_developer + capabilityadmin for Joule deployments
• For CAP agents: mbt (MTA Build Tool): npm install -g mbt
• For CAP agents: MTA CF CLI plugin: cf install-plugin multiapps (required for cf deploy with .mtar files)

Before you start, make sure Claude Code is started with the toolkit plugin and you are logged in to both CLIs:

# Start Claude Code with the SAP A2A Agent Toolkit plugin
claude --plugin-dir <path-to-sap-a2a-agent-toolkit>

# Log in to Cloud Foundry
cf login -a https://api.cf.<landscape>.hana.ondemand.com 

# Log in to Joule
joule login 

Workflow

When the user asks to create/modify a Joule A2A agent, follow these steps:

Step 1: Gather Requirements

Ask the user:

• Language — TypeScript (default) or Python?
• Framework — Express (default, lightweight) or CAP (enterprise, MTA deploy)? Note: CAP is TypeScript-only.
• Agent name and purpose — what should this agent do?
• Tools/APIs — what external services or APIs should the agent call?
• SAP GenAI Hub model — which model deployed on AI Core? (default: gpt-4.1)
• BTP landscape details — CF API endpoint, org, space (if they know them)

If the user is unsure about BTP details, generate the project anyway with placeholder values.

Step 2: Generate the Agent Project

The project can be generated in three configurations:

a) TypeScript + Express (default)

Run the scaffold script:

bash <skill-path>/scripts/scaffold-ts.sh \
  --name <agent-name> \
  --framework express \
  --namespace <company-namespace> \
  --output <output-dir> \
  --description "<agent description>" \
  --landscape <cf-landscape>

Or manually using references/langgraph-a2a-agent-typescript.md + references/cf-deployment-typescript.md.

<agent-name>/
├── src/
│   ├── index.ts              # Express + A2A server entry point
│   ├── agent.ts              # LangGraph.js ReAct agent
│   ├── executor.ts           # A2A protocol bridge
│   ├── agentCard.ts          # Agent Card definition
│   ├── tools.ts              # Agent's tools (Zod schemas)
│   └── llm.ts                # SAP GenAI Hub configuration
├── joule-capability/
│   ├── capability.sapdas.yaml
│   ├── capability_context.yaml
│   ├── da.sapdas.yaml
│   ├── functions/call_agent.yaml
│   └── scenarios/invoke_agent.yaml
├── package.json
├── tsconfig.json
├── manifest.yml
├── Procfile
├── .env.example
└── README.md

b) TypeScript + CAP

Run the scaffold script:

bash <skill-path>/scripts/scaffold-ts.sh \
  --name <agent-name> \
  --framework cap \
  --namespace <company-namespace> \
  --output <output-dir> \
  --description "<agent description>" \
  --landscape <cf-landscape>

Or manually using references/langgraph-a2a-agent-cap-typescript.md + references/cf-deployment-cap-typescript.md.

<agent-name>/
├── srv/
│   ├── server.ts              # CAP bootstrap + A2A Express endpoints
│   ├── agent-executor.ts      # LangGraph StateGraph agent orchestrator
│   ├── service.cds            # CDS service definition
│   ├── tools/
│   │   └── tools.ts           # Agent tools (Zod schemas)
│   └── utils/
│       ├── prompts.ts         # System prompt
│       ├── a2aToLangchain.ts  # A2A → LangChain converter
│       ├── a2a-operations.ts  # A2A event helpers
│       └── helpers.ts         # URL helpers
├── joule-capability/
│   ├── capability.sapdas.yaml
│   ├── capability_context.yaml
│   ├── da.sapdas.yaml
│   ├── functions/call_agent.yaml
│   └── scenarios/invoke_agent.yaml
├── package.json
├── tsconfig.json
├── mta.yaml
├── .cdsrc.sample.json
├── .env.example
└── README.md

c) Python + Express

Run the scaffold script:

python <skill-path>/scripts/scaffold.py \
  --name <agent-name> \
  --namespace <company-namespace> \
  --output <output-dir> \
  --description "<agent description>" \
  --landscape <cf-landscape>

Or manually using references/langgraph-a2a-agent.md + references/cf-deployment.md.

Key Differences

Aspect	TS Express	TS CAP	Python
Server	Express + jsonRpcHandler/agentCardHandler	CAP + cds.on("bootstrap")	Starlette + A2AStarletteApplication
A2A SDK	@a2a-js/sdk 0.3.10	@a2a-js/sdk 0.3.10	a2a-sdk >=0.2.7
LLM	OrchestrationClient	OrchestrationClient	langchain-openai ChatOpenAI (direct AI Core endpoint)
Deploy	cf push (manifest.yml)	mbt build && cf deploy (mta.yaml)	cf push (manifest.yml)
Auth	Manual	Built-in CAP auth	Manual
Agent pattern	createReactAgent	StateGraph (manual)	create_react_agent
Interrupt support	Basic (question heuristic)	Full (LangGraph interrupt())	Structured response format
Buildpack	nodejs_buildpack	nodejs_buildpack (via MTA)	python_buildpack
Reference	langgraph-a2a-agent-typescript.md	langgraph-a2a-agent-cap-typescript.md	langgraph-a2a-agent.md

The Joule capability YAML files are identical regardless of language/framework.

Step 3: Customize the Agent

Based on the user's requirements:

1. Define tools — In TypeScript: functions created with tool() and Zod schemas. In Python: functions decorated with @tool. Map each user requirement to a tool function.

   • Express TS: src/tools.ts
   • CAP TS: srv/tools/tools.ts
   • Python: app/tools.py

2. Configure the agent — Set the system instruction to match the agent's purpose.

   • Express TS: SYSTEM_PROMPT in src/agent.ts
   • CAP TS: srv/utils/prompts.ts
   • Python: SYSTEM_INSTRUCTION in app/agent.py

3. Set up the Agent Card — Name, skills, and supported modes.

   • Express TS: src/agentCard.ts
   • CAP TS: agentCard in srv/server.ts
   • Python: app/agent_card.py

4. Configure capability.sapdas.yaml — The scenario description in scenarios/invoke_agent.yaml must clearly describe when Joule should invoke this agent.

Step 4: Deploy to Cloud Foundry

Express (TypeScript or Python)

Read references/cf-deployment-typescript.md or references/cf-deployment.md.

cd <agent-name>
npm install
npm run build  # TypeScript only — must compile before push
cf push
curl https://<app-url>/.well-known/agent.json

CAP (TypeScript)

Read references/cf-deployment-cap-typescript.md.

cd <agent-name>
npm install
mbt build
cf deploy mta_archives/<agent-name>_1.0.0.mtar
curl https://<agent-name>-srv.cfapps.<landscape>.hana.ondemand.com/.well-known/agent.json

Step 5: Create the BTP Destination

After deployment, create a BTP Destination pointing to the agent's URL:

bash <skill-path>/scripts/create-destination.sh \
  --agent-name <agent-name> \
  --destination-name <DESTINATION_NAME> \
  --landscape <cf-landscape>

The destination name must match system_aliases.<AliasName>.destination in capability.sapdas.yaml.

Step 6: Deploy the Joule Capability

Read references/joule-capability.md for the complete reference.

# Install Joule CLI (if not installed)
npm install -g @sap/joule-cli

# Authenticate
joule login     # or joule login --use-env

# Navigate to the joule-capability directory
cd joule-capability

# Compile + deploy
joule deploy ./da.sapdas.yaml --compile -n "<assistant_name>"

Step 7: Test the Integration

1. Open Joule in your SAP system
2. Type a prompt that matches the capability's scenario description
3. Joule should route the request to your agent via A2A
4. Verify the response comes back correctly

If it doesn't work, check: destination configuration, agent card accessibility, capability scenario description matching, and CF app logs (cf logs <app-name> --recent).

Modifying an Existing Agent

When the user wants to change an existing agent:

1. Adding a new tool: Add the tool function, register it in the agent, add a skill to the agent card. Redeploy to CF.

2. Changing the model: Update MODEL_NAME env var. The model must be deployed on your AI Core instance.

3. Updating the Joule capability: Edit capability.sapdas.yaml and scenario files, recompile and redeploy with joule deploy ./da.sapdas.yaml --compile.

4. Scaling: Adjust instances and memory in manifest.yml (Express) or mta.yaml (CAP).

Reference Files

Read these before generating code:

LangGraph + A2A SDK

• references/langgraph-a2a-agent-typescript.md — TypeScript Express templates (jsonRpcHandler, agentCardHandler, createReactAgent, OrchestrationClient)
• references/langgraph-a2a-agent-cap-typescript.md — TypeScript CAP templates (cds.on bootstrap, StateGraph, interrupt support)
• references/langgraph-a2a-agent.md — Python templates (A2AStarletteApplication, create_react_agent, langchain-openai with direct AI Core credentials)

Cloud Foundry Deployment

• references/cf-deployment-typescript.md — TypeScript Express: manifest.yml, package.json, cf push
• references/cf-deployment-cap-typescript.md — TypeScript CAP: mta.yaml, mbt build, cf deploy
• references/cf-deployment.md — Python: manifest.yml, requirements.txt, Procfile

Joule Integration

• references/joule-capability.md — Multi-file DTA format: capability.sapdas.yaml (schema 3.28.0), da.sapdas.yaml (schema 1.4.0), functions/, scenarios/, multi-turn contextId/taskId pattern

Important Notes

• The A2A protocol v0.3.0 uses JSON-RPC 2.0 over HTTP(S). Your agent must handle message/send and optionally message/stream methods.
• Agent Cards must be served at /.well-known/agent.json for Joule's discovery.
• SAP GenAI Hub requires an AI Core service instance and a deployment of the model you want to use.
• TypeScript agents use OrchestrationClient from @sap-ai-sdk/langchain (auto-reads VCAP_SERVICES).
• Python agents use langchain-openai ChatOpenAI pointed at AI Core's OpenAI-compatible endpoint (do NOT use generative-ai-hub-sdk — pydantic version conflict with a2a-sdk).
• The Joule CLI (@sap/joule-cli) requires the capabilityadmin and extensibility_developer roles.
• Destinations in BTP must use NoAuthentication or OAuth2ClientCredentials.
• CAP is only available for TypeScript. Python uses Express (Starlette) only.
• The da.sapdas.yaml uses schema version 1.4.0. The capability.sapdas.yaml uses 3.28.0.
• Namespace must be joule.ext — the metadata.namespace in capability.sapdas.yaml must be set to joule.ext. Using any other namespace (e.g. mycompany) will cause joule deploy to fail with a namespace validation error. The scaffold scripts default to joule.ext.
• CAP agents require two extra tools: mbt (npm install -g mbt) for building and the MTA CF CLI plugin (cf install-plugin multiapps) for cf deploy to work with .mtar files. See CAP deploy to CF guide.