Human‑in‑the‑Loop Middleware Demo

This repository contains a minimal LangChain agent that demonstrates how to pause execution at every tool call and ask the user for approval or rejection before continuing.
The core of this behaviour is provided by HumanInTheLoopMiddleware, which automatically injects a confirmation step into the agent’s workflow.

⚠️ The example uses the open‑source Ollama LLM backend.
If you don’t have Ollama installed, follow the instructions in the Prerequisites section.

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Table of Contents

• Description
• Prerequisites
• Installation
• Running the Demo
• Example Interaction

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Description

solution.py implements a LangChain agent that:

1. Creates an LLM chain using ChatOllama.
2. Wraps it with a MemorySaver checkpoint for state persistence.
3. Adds the HumanInTheLoopMiddleware, which:
   • Intercepts every tool call,
   • Prints the intended action to the terminal (via rich),
   • Prompts the user for "approve" or "reject",
   • Resumes execution based on the response.

This pattern is useful when you want a human operator to supervise or validate each step of an automated workflow, without having to manually interrupt the agent.

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Prerequisites

Item	Version	Notes
Python	≥ 3.10	Tested with 3.11
pip	–	Standard package installer
Ollama	Latest	Install from https://ollama.ai/ and run ollama serve before starting the demo.
OpenAI‑compatible model	e.g., llama2:7b	Pull via ollama pull llama2:7b.

Tip: If you prefer a different LLM, replace ChatOllama with any LangChain-compatible LLM (e.g., OpenAI, Anthropic).

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Installation

# 1. Clone the repo
git clone https://github.com/your-username/human-in-the-loop-demo.git
cd human-in-the-loop-demo

# 2. Create a virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate   # On Windows: .\.venv\Scripts\activate

# 3. Install dependencies
pip install --upgrade pip
pip install langchain langgraph langchain-ollama rich

Note:
langgraph is required for the checkpointing and middleware support.

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Running the Demo

# Make sure Ollama server is running:
#   ollama serve

# Run the agent script
python solution.py

You will be prompted to enter a question. The agent will then:

1. Generate a plan.
2. For each tool call, pause and display the intended action.
3. Await your "approve" or "reject" input before proceeding.

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Example Interaction

$ python solution.py
Enter your question: What is the capital of France?

[Agent] Thinking...
[HumanInTheLoopMiddleware] Tool call planned: "search" with query "capital of France"
Approve? (yes/no): yes

[Agent] Executing tool...
Result: Paris

[Agent] Continuing...

Final answer: The capital of France is Paris.

If you type no instead of yes, the agent will skip that tool call and attempt to continue with its plan, potentially leading to a different outcome.

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Customization

• Different tools – Add more tools via @tool decorators or by passing a list to create_agent.
• Custom prompt – Modify the middleware’s confirmation message by subclassing HumanInTheLoopMiddleware.
• Persist state – The MemorySaver checkpoint can be swapped for a database-backed store if you need long‑term persistence.

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Happy experimenting! 🚀