conductor-oss--conductor/ai/examples

Conductor AI Workflow Examples

This folder contains ready-to-use workflow examples demonstrating the AI capabilities of Conductor.

Prerequisites

1. Start Conductor Server

Ensure Conductor is running with AI integrations enabled:

# From the conductor root directory
./gradlew bootRun

2. Configure AI Providers

Set environment variables before starting the server:

# OpenAI (required for most examples)
export OPENAI_API_KEY=sk-your-openai-api-key

# Anthropic (optional, for RAG examples)
export ANTHROPIC_API_KEY=sk-ant-your-anthropic-key

# Google Gemini (optional, for Gemini/Veo examples)
# Option 1: API key (simplest)
export GEMINI_API_KEY=your-gemini-api-key
# Option 2: Vertex AI — set project and location in application.properties

For vector database examples, add to application.properties:

# PostgreSQL Vector DB (for RAG/embedding examples)
conductor.vectordb.instances[0].name=postgres-prod
conductor.vectordb.instances[0].type=postgres
conductor.vectordb.instances[0].postgres.datasourceURL=jdbc:postgresql://localhost:5432/vectors
conductor.vectordb.instances[0].postgres.user=conductor
conductor.vectordb.instances[0].postgres.password=secret
conductor.vectordb.instances[0].postgres.dimensions=1536

3. MCP Test Server (for MCP examples)

Install and start the MCP test server:

# Install mcp-testkit — a test MCP server with 65 deterministic tools
pip install mcp-testkit

# Start the server in HTTP mode
mcp-testkit --transport http

The server will be available at http://localhost:3001/mcp.

---

Available Examples

File	Description	Requirements
01-chat-completion.json	Basic chat with GPT-4o-mini	OpenAI
02-generate-embeddings.json	Generate text embeddings	OpenAI
03-image-generation.json	Generate images with DALL-E 3	OpenAI
04-audio-generation.json	Text-to-speech with OpenAI TTS	OpenAI
05-semantic-search.json	Index and search documents	OpenAI, PostgreSQL
06-rag-basic.json	Basic RAG with search + answer	OpenAI/Anthropic, PostgreSQL
07-rag-complete.json	Full RAG demo (index + search + answer)	OpenAI, PostgreSQL
08-mcp-list-tools.json	List tools from MCP server	MCP Server
09-mcp-call-tool.json	Call MCP tool (weather)	MCP Server
10-mcp-ai-agent.json	AI agent with MCP tools	OpenAI/Anthropic, MCP Server
11-video-openai-sora.json	Generate video with OpenAI Sora-2 (async)	OpenAI
12-video-gemini-veo.json	Generate video with Google Veo-3 (async)	Google Vertex AI
13-image-to-video-pipeline.json	Image + video generation pipeline	OpenAI
14-stabilityai-image.json	Image generation with Stability AI (SD3.5)	Stability AI
15-pdf-generation.json	Generate PDF from markdown content	None (built-in)
16-llm-to-pdf-pipeline.json	LLM generates report → convert to PDF	OpenAI
17-web-search.json	Chat with built-in web search for real-time info	OpenAI
18-code-execution.json	Chat with built-in code execution sandbox	Google Gemini
19-coding-agent.json	Coding agent: plan → write & run code → review	OpenAI
20-extended-thinking.json	Extended thinking with token budget for reasoning	Anthropic
21-web-search-research-agent.json	Research agent: web search → synthesize → PDF	OpenAI, Anthropic
22-multi-turn-chain.json	Multi-turn conversation chaining with previousResponseId	OpenAI
36-ai-workflow-routing.json	LLM selects an approved child workflow, then runs it as a dynamic sub-workflow	OpenAI; register the paired 36a–36c child workflows

A2A (Agent2Agent) examples

Conductor as an A2A client (calling remote agents) and server (exposing a workflow as an agent). The client tasks (AGENT, GET_AGENT_CARD, CANCEL_AGENT) need a reachable A2A agent — see ai/src/test/resources/a2a/ for a runnable test agent. The server examples are exposed by registering them with metadata.a2a.enabled=true and conductor.a2a.server.enabled=true.

File	Description	Requirements
10-a2a-call-agent.json	Call a remote agent (poll mode)	A2A agent
11-a2a-get-agent-card.json	Discover an agent's skills/capabilities	A2A agent
12-a2a-server-workflow.json	Expose a workflow as an A2A agent (server)	conductor.a2a.server.enabled=true
23-a2a-streaming.json	Call an agent in streaming (SSE) mode	A2A agent (capabilities.streaming=true)
24-a2a-push.json	Call an agent in push-notification mode	A2A agent, conductor.a2a.callback.url
25-a2a-server-multi-turn.json	Multi-turn server agent (HUMAN task → input-required → resume)	conductor.a2a.server.enabled=true
26-a2a-cancel.json	Start then cancel a remote agent task	A2A agent
27-a2a-multi-agent.json	Call multiple agents in parallel (FORK_JOIN → JOIN)	A2A agents
28-a2a-llm-pick-skill.json	Discover an agent, let an LLM pick the prompt, then call it	A2A agent, OpenAI/Anthropic
29-a2a-client-multi-turn.json	Client multi-turn: branch on input-required, re-call with the same context	A2A agent

Conductor Agents workflow-integration recipes

These recipes integrate a deployed Conductor Agent into a larger workflow via AGENT with agentType: "conductor". The agent can be authored with any supported SDK bridge; these JSON files deliberately remain framework-agnostic. They require a running embedded Agent API with conductor.integrations.ai.enabled=true and at least one deployed agent (example 33 needs planner and researcher). The AGENT task is non-blocking — it starts the run and polls until it reaches a terminal (or WAITING) state.

File	Workflow name	Description	Requirements
31-conductor-agent-basic.json	conductor_agent_basic	Reusable deployed agent as a workflow step	conductor.integrations.ai.enabled=true, a deployed agent
32-conductor-agent-human-in-loop.json	conductor_agent_human_in_loop	Pause, collect human input, and resume via executionId	conductor.integrations.ai.enabled=true, a deployed agent
33-conductor-agent-multi-agent.json	conductor_agent_multi_agent	Parallel specialists via FORK_JOIN -> JOIN	conductor.integrations.ai.enabled=true, two deployed agents
34-conductor-agent-cancel.json	conductor_agent_cancel	Cancellation propagation to an in-flight agent	conductor.integrations.ai.enabled=true, a deployed agent
35-governed-adaptive-agent.json	governed_github_pr_reviewer	Four-pass GitHub PR reviewer: context, files, CI, then bounded adaptive deep dive; a human must approve the single comment write	Configured LLM provider and an authenticated GitHub MCP endpoint exposing pull_request_read and add_issue_comment

---

Quick Start

Step 1: Register a Workflow

# Register the chat completion workflow
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @01-chat-completion.json

Step 2: Execute the Workflow

# Run the workflow (no input needed for hardcoded examples)
curl -X POST 'http://localhost:8080/api/workflow/chat_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

Step 3: Check the Result

# Get workflow execution status (replace {workflowId} with the returned ID)
curl -X GET 'http://localhost:8080/api/workflow/{workflowId}'

---

Example Commands

1. Chat Completion

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @01-chat-completion.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/chat_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

2. Generate Embeddings

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @02-generate-embeddings.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/embedding_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

3. Image Generation

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @03-image-generation.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/image_gen_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

4. Audio Generation (TTS)

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @04-audio-generation.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/tts_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

5. Semantic Search

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @05-semantic-search.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/semantic_search_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

6. RAG (Basic)

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @06-rag-basic.json

# Execute with a question
curl -X POST 'http://localhost:8080/api/workflow/rag_workflow' \
  -H 'Content-Type: application/json' \
  -d '{"question": "What is Conductor?"}'

7. RAG (Complete Demo)

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @07-rag-complete.json

# Execute (no input needed - fully self-contained)
curl -X POST 'http://localhost:8080/api/workflow/complete_rag_demo' \
  -H 'Content-Type: application/json' \
  -d '{}'

8. MCP List Tools

# Start MCP server first (see Prerequisites)

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @08-mcp-list-tools.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/mcp_list_tools_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

9. MCP Call Tool (Weather)

# Start MCP server first (see Prerequisites)

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @09-mcp-call-tool.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/mcp_weather_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

10. MCP AI Agent

# Start MCP server first (see Prerequisites)

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @10-mcp-ai-agent.json

# Execute with a task
curl -X POST 'http://localhost:8080/api/workflow/mcp_ai_agent_workflow' \
  -H 'Content-Type: application/json' \
  -d '{"task": "Get the current weather in San Francisco"}'

11. Video Generation (OpenAI Sora)

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @11-video-openai-sora.json

# Execute (async -- returns workflowId immediately, polls internally until video is ready)
curl -X POST 'http://localhost:8080/api/workflow/video_gen_openai_sora' \
  -H 'Content-Type: application/json' \
  -d '{}'

12. Video Generation (Google Gemini Veo)

# Requires Google Vertex AI credentials (see Prerequisites)

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @12-video-gemini-veo.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/video_gen_gemini_veo' \
  -H 'Content-Type: application/json' \
  -d '{}'

13. Image-to-Video Pipeline

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @13-image-to-video-pipeline.json

# Execute (generates a DALL-E image first, then a Sora video)
curl -X POST 'http://localhost:8080/api/workflow/image_to_video_pipeline' \
  -H 'Content-Type: application/json' \
  -d '{}'

14. Image Generation (Stability AI)

# Requires STABILITY_API_KEY environment variable

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @14-stabilityai-image.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/image_gen_stabilityai' \
  -H 'Content-Type: application/json' \
  -d '{}'

15. PDF Generation (Markdown to PDF)

# No external API keys required -- uses built-in PDFBox renderer

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @15-pdf-generation.json

# Execute
curl -X POST 'http://localhost:8080/api/workflow/pdf_generation_workflow' \
  -H 'Content-Type: application/json' \
  -d '{}'

16. LLM-to-PDF Pipeline (Report Generation)

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @16-llm-to-pdf-pipeline.json

# Execute with a topic and audience
curl -X POST 'http://localhost:8080/api/workflow/llm_to_pdf_pipeline' \
  -H 'Content-Type: application/json' \
  -d '{"topic": "Cloud Migration Best Practices", "audience": "CTO and engineering leadership"}'

17. Web Search

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @17-web-search.json

# Execute with a question about current events
curl -X POST 'http://localhost:8080/api/workflow/web_search_workflow' \
  -H 'Content-Type: application/json' \
  -d '{"question": "What are the latest developments in AI regulation?"}'

18. Code Execution

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @18-code-execution.json

# Execute with a data analysis task
curl -X POST 'http://localhost:8080/api/workflow/code_execution_workflow' \
  -H 'Content-Type: application/json' \
  -d '{"task": "Generate the first 50 Fibonacci numbers and calculate the golden ratio convergence"}'

19. Coding Agent

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @19-coding-agent.json

# Execute — the agent plans, writes code, executes, and reviews
curl -X POST 'http://localhost:8080/api/workflow/coding_agent' \
  -H 'Content-Type: application/json' \
  -d '{"task": "Write a Python function that converts Roman numerals to integers, with unit tests"}'

20. Extended Thinking

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @20-extended-thinking.json

# Execute with a complex reasoning problem
curl -X POST 'http://localhost:8080/api/workflow/extended_thinking_workflow' \
  -H 'Content-Type: application/json' \
  -d '{"problem": "Design a distributed consensus algorithm for a system with up to 3 Byzantine nodes out of 10 total. Explain the correctness proof."}'

21. Web Research Agent

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @21-web-search-research-agent.json

# Execute — researches the topic, writes a report, converts to PDF
curl -X POST 'http://localhost:8080/api/workflow/web_research_agent' \
  -H 'Content-Type: application/json' \
  -d '{"topic": "The state of WebAssembly in 2026"}'

22. Multi-Turn Conversation Chain

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @22-multi-turn-chain.json

# Execute — second turn uses previousResponseId to continue the conversation without resending history
curl -X POST 'http://localhost:8080/api/workflow/multi_turn_chain' \
  -H 'Content-Type: application/json' \
  -d '{"topic": "Real-time collaborative document editor"}'

31. Conductor Agents: Basic workflow integration

# Requires conductor.integrations.ai.enabled=true and a deployed 'planner' Conductor Agent

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @31-conductor-agent-basic.json

# Execute — runs the agent to completion and surfaces text/output/state
curl -X POST 'http://localhost:8080/api/workflow/conductor_agent_basic' \
  -H 'Content-Type: application/json' \
  -d '{"prompt": "Draft a project plan for a mobile app launch"}'

Tune the run with the optional pollIntervalSeconds (poll cadence, default 5), maxDurationSeconds (absolute deadline, default 86400), and maxPollFailures (consecutive transient poll-failure cap, default 30) input parameters on the AGENT task.

32. Conductor Agents: Human-in-the-loop workflow integration

# Requires conductor.integrations.ai.enabled=true and a deployed 'planner' Conductor Agent

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @32-conductor-agent-human-in-loop.json

# Execute — when the run pauses (waiting=true), a HUMAN task collects the answer and a second
# AGENT call resumes the run using its executionId
curl -X POST 'http://localhost:8080/api/workflow/conductor_agent_human_in_loop' \
  -H 'Content-Type: application/json' \
  -d '{"prompt": "Book a meeting; ask me for the preferred time if unclear"}'

33. Conductor Agents: Parallel workflow integration

# Requires conductor.integrations.ai.enabled=true and two deployed Conductor Agents: 'planner' and 'researcher'

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @33-conductor-agent-multi-agent.json

# Execute — fans out to both agents in parallel (FORK_JOIN), then JOINs their results
curl -X POST 'http://localhost:8080/api/workflow/conductor_agent_multi_agent' \
  -H 'Content-Type: application/json' \
  -d '{"prompt": "Assess the market for an AI note-taking app"}'

34. Conductor Agents: Cancellation workflow integration

# Requires conductor.integrations.ai.enabled=true and a deployed 'planner' Conductor Agent

# Register
curl -X POST 'http://localhost:8080/api/metadata/workflow' \
  -H 'Content-Type: application/json' \
  -d @34-conductor-agent-cancel.json

# Execute — starts a long-running agent run, then TERMINATEs the workflow, cancelling the
# in-flight agent task (CANCELED mapping)
curl -X POST 'http://localhost:8080/api/workflow/conductor_agent_cancel' \
  -H 'Content-Type: application/json' \
  -d '{"prompt": "Begin a long-running research job"}'

---

Register All Workflows at Once

# Register all example workflows
for f in *.json; do
  echo "Registering $f..."
  curl -s -X POST 'http://localhost:8080/api/metadata/workflow' \
    -H 'Content-Type: application/json' \
    -d @"$f"
  echo ""
done

---

Troubleshooting

"VectorDB not found: postgres-prod"

Ensure you have configured the PostgreSQL vector database in your application.properties:

conductor.vectordb.instances[0].name=postgres-prod
conductor.vectordb.instances[0].type=postgres
conductor.vectordb.instances[0].postgres.datasourceURL=jdbc:postgresql://localhost:5432/vectors
conductor.vectordb.instances[0].postgres.user=conductor
conductor.vectordb.instances[0].postgres.password=secret
conductor.vectordb.instances[0].postgres.dimensions=1536

"No configuration found for: openai"

Ensure you have set the OpenAI API key environment variable:

export OPENAI_API_KEY=sk-your-openai-api-key

MCP Server Connection Refused

1. Verify the MCP server is running:

   curl http://localhost:3001/mcp
   

2. Check the server logs for errors

3. Ensure you're using the correct port in the workflow (default: 3001)

PostgreSQL Vector Extension Not Found

Ensure the pgvector extension is installed in your PostgreSQL database:

CREATE EXTENSION IF NOT EXISTS vector;

---

License

Copyright 2026 Conductor Authors. Licensed under the Apache License 2.0.