agentic_workflows

Agentic Workflow Examples

AI/LLM workflow examples using Conductor's built-in system tasks (LLM_CHAT_COMPLETE, LLM_INDEX_TEXT, LLM_SEARCH_INDEX, MCP tools) combined with Python workers.

All examples use inline ChatMessage objects for system prompts -- no named prompt templates or AIOrchestrator required. They work with OSS Conductor with AI/LLM support.

Prerequisites

• Conductor server with AI/LLM support running (e.g., http://localhost:7001/api)
• LLM provider named openai configured with a valid API key
• export CONDUCTOR_SERVER_URL=http://localhost:7001/api

Examples

Example	Description	Interactive?	Pattern
llm_chat.py	Automated multi-turn science Q&A between two LLMs	No	LoopTask + LLM_CHAT_COMPLETE + worker for history
llm_chat_human_in_loop.py	Interactive chat with WAIT task pauses for user input	Yes	LoopTask + WaitTask + LLM_CHAT_COMPLETE
multiagent_chat.py	Multi-agent debate with moderator routing between panelists	No	LoopTask + SwitchTask + SetVariableTask + JavaScript routing
function_calling_example.py	LLM picks which Python function to call based on user query	Yes	LoopTask + WaitTask + LLM_CHAT_COMPLETE (json_output) + dispatch worker
mcp_weather_agent.py	AI agent using MCP tools to answer weather questions	No	ListMcpTools + CallMcpTool + LLM_CHAT_COMPLETE

Quick Start

# Automated multi-turn chat (no interaction needed)
python examples/agentic_workflows/llm_chat.py

# Multi-agent debate
python examples/agentic_workflows/multiagent_chat.py --topic "renewable energy"

# Interactive chat
python examples/agentic_workflows/llm_chat_human_in_loop.py

# Function calling agent
python examples/agentic_workflows/function_calling_example.py

Key Patterns

Passing dynamic messages to LLM_CHAT_COMPLETE

When passing a workflow reference as messages, set it via input_parameters AFTER construction:

chat = LlmChatComplete(task_ref_name="ref", llm_provider="openai", model="gpt-4o-mini")
chat.input_parameters["messages"] = "${some_task.output.result}"  # CORRECT

Do NOT pass a string reference to the constructor messages= parameter -- it iterates the string as a list of characters.

Worker parameter type annotations

Use object or dict for parameters that receive dynamic data from workflow references (lists, dicts, etc.). Avoid List[dict] -- it triggers conversion bugs in the worker framework on Python 3.12+.

Single-parameter workers with object annotation

If a worker has exactly one parameter annotated as object, the framework treats it as a raw Task handler. Use dict instead, or add a second parameter.