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LLM orchestration

Conductor provides native system tasks for LLM orchestration and integration. No external frameworks or custom workers required — configure a provider and use it in any workflow. Each provider supports function calling via MCP tool integration.

Supported LLM providers

Provider Chat Completion Text Completion Embeddings
Anthropic (Claude) ✓ ✓ —
OpenAI (GPT) ✓ ✓ ✓
Azure OpenAI ✓ ✓ ✓
Google Gemini ✓ ✓ ✓
AWS Bedrock ✓ ✓ ✓
Mistral ✓ ✓ ✓
Cohere ✓ ✓ ✓
HuggingFace ✓ ✓ ✓
Ollama ✓ ✓ ✓
Perplexity ✓ — —
Grok (xAI) ✓ ✓ —
StabilityAI — — —

No other open source workflow engine provides native LLM orchestration at this breadth. Each provider is a configuration — switch models by changing a parameter, not your code.

Built-in tools & advanced capabilities

Conductor supports provider-native tools that run on the provider's infrastructure — no MCP server or custom worker needed. Enable them with a single parameter in the LLM_CHAT_COMPLETE task.

Capability Parameter OpenAI Anthropic Google Gemini
Web Search webSearch: true ✓ ✓ ✓
Code Execution codeInterpreter: true ✓ (code_interpreter) ✓ (code_execution) ✓ (code_execution)
File Search fileSearchVectorStoreIds: [...] ✓ — —
Extended Thinking thinkingTokenLimit: N — ✓ ✓
Reasoning Effort reasoningEffort: "high" ✓ — —
Google Search googleSearchRetrieval: true — — ✓
Custom Functions tools: [...] ✓ ✓ ✓

The LLM can search the web for real-time information during chat completion. Enable it with "webSearch": true:

{
  "type": "LLM_CHAT_COMPLETE",
  "inputParameters": {
    "llmProvider": "openai",
    "model": "gpt-4o-mini",
    "messages": [{"role": "user", "message": "What happened in tech news today?"}],
    "webSearch": true
  }
}

Works with OpenAI, Anthropic, and Google Gemini. Each provider uses its own native web search implementation.

Code execution

The LLM can write and execute code in a sandboxed environment. Enable it with "codeInterpreter": true:

{
  "type": "LLM_CHAT_COMPLETE",
  "inputParameters": {
    "llmProvider": "google_gemini",
    "model": "gemini-2.5-flash",
    "messages": [{"role": "user", "message": "Calculate the first 100 prime numbers and plot them"}],
    "codeInterpreter": true
  }
}

Use this for data analysis, chart generation, mathematical computation, or any task that benefits from running code.

Extended thinking

Give the LLM a token budget for step-by-step reasoning before it responds. Useful for complex problems that benefit from chain-of-thought reasoning:

{
  "type": "LLM_CHAT_COMPLETE",
  "inputParameters": {
    "llmProvider": "anthropic",
    "model": "claude-sonnet-4-20250514",
    "messages": [{"role": "user", "message": "Prove that there are infinitely many primes"}],
    "thinkingTokenLimit": 10000,
    "maxTokens": 16000
  }
}

Supported by Anthropic and Google Gemini.

Vector database workflows

Built-in vector database integration enables RAG (retrieval-augmented generation) pipelines as standard vector database workflows.

Vector Database Store Embeddings Index Text Semantic Search
Pinecone ✓ ✓ ✓
pgvector (PostgreSQL) ✓ ✓ ✓
MongoDB Atlas Vector Search ✓ ✓ ✓

Example: RAG pipeline

A complete RAG workflow using native system tasks — index documents, search, and generate an answer. No custom workers required.

{
  "name": "rag_pipeline",
  "description": "Index documents, search, and generate RAG answer",
  "version": 1,
  "schemaVersion": 2,
  "tasks": [
    {
      "name": "index_document",
      "taskReferenceName": "index_ref",
      "type": "LLM_INDEX_TEXT",
      "inputParameters": {
        "vectorDB": "postgres-prod",
        "index": "knowledge_base",
        "namespace": "docs",
        "docId": "${workflow.input.docId}",
        "text": "${workflow.input.text}",
        "embeddingModelProvider": "openai",
        "embeddingModel": "text-embedding-3-small",
        "dimensions": 1536,
        "metadata": "${workflow.input.metadata}"
      }
    },
    {
      "name": "search_index",
      "taskReferenceName": "search_ref",
      "type": "LLM_SEARCH_INDEX",
      "inputParameters": {
        "vectorDB": "postgres-prod",
        "index": "knowledge_base",
        "namespace": "docs",
        "query": "${workflow.input.question}",
        "embeddingModelProvider": "openai",
        "embeddingModel": "text-embedding-3-small",
        "dimensions": 1536,
        "maxResults": 3
      }
    },
    {
      "name": "generate_answer",
      "taskReferenceName": "answer_ref",
      "type": "LLM_CHAT_COMPLETE",
      "inputParameters": {
        "llmProvider": "openai",
        "model": "gpt-4o-mini",
        "messages": [
          {
            "role": "system",
            "message": "Answer the question using only the provided context."
          },
          {
            "role": "user",
            "message": "Context:\n${search_ref.output.result}\n\nQuestion: ${workflow.input.question}"
          }
        ],
        "temperature": 0.2
      }
    }
  ],
  "outputParameters": {
    "searchResults": "${search_ref.output.result}",
    "answer": "${answer_ref.output.result}"
  }
}

Every task type — LLM_INDEX_TEXT, LLM_SEARCH_INDEX, LLM_CHAT_COMPLETE — is a native Conductor system task. The vector database, embedding model, and LLM provider are all configuration parameters. Switch from pgvector to Pinecone or from OpenAI to Anthropic by changing a parameter value.

Content generation

Native system tasks for multimodal content generation:

Task Type Description
Generate Image GENERATE_IMAGE Text-to-image generation via AI models
Generate Audio GENERATE_AUDIO Text-to-speech synthesis
Generate Video GENERATE_VIDEO Text/image-to-video generation (async)
Generate PDF GENERATE_PDF Markdown-to-PDF document conversion

Examples

Ready-to-use workflow definitions for every AI task type. Each example is a complete JSON workflow you can register and run directly.

Example Task types used
Chat Completion LLM_CHAT_COMPLETE
Generate Embeddings LLM_GENERATE_EMBEDDINGS
Image Generation GENERATE_IMAGE
Audio Generation GENERATE_AUDIO
Semantic Search LLM_SEARCH_INDEX
RAG Basic LLM_SEARCH_INDEX, LLM_CHAT_COMPLETE
RAG Complete LLM_INDEX_TEXT, LLM_SEARCH_INDEX, LLM_CHAT_COMPLETE
MCP List Tools LIST_MCP_TOOLS
MCP Call Tool CALL_MCP_TOOL
MCP AI Agent LIST_MCP_TOOLS, LLM_CHAT_COMPLETE, CALL_MCP_TOOL
Video — OpenAI Sora GENERATE_VIDEO
Video — Gemini Veo GENERATE_VIDEO
Image-to-Video Pipeline GENERATE_IMAGE, GENERATE_VIDEO
StabilityAI Image GENERATE_IMAGE
PDF Generation GENERATE_PDF
LLM-to-PDF Pipeline LLM_CHAT_COMPLETE, GENERATE_PDF
Web Search LLM_CHAT_COMPLETE (web search)
Code Execution LLM_CHAT_COMPLETE (code execution)
Coding Agent LLM_CHAT_COMPLETE (code_interpreter)
Extended Thinking LLM_CHAT_COMPLETE (thinking)
Web Research Agent LLM_CHAT_COMPLETE (web search + thinking), GENERATE_PDF
Multi-Turn Chain LLM_CHAT_COMPLETE (previousResponseId)

Browse all examples: ai/examples/

Next steps