Reflect hitl_basic being removed

docs/humanInTheLoop_example.md

@@ -1,76 +1,46 @@
 # URSA Human-in-the-Loop Agent Interface Documentation
 
-## Overview
+## Previous Human-in-the-Loop Example has been Depreciated
 
-This module implements a human-in-the-loop (HITL) interface for the URSA agent framework, allowing users to directly interact with different specialized AI agents through a command-line interface. The system maintains context between agent interactions and provides persistent storage for agent states.
+The HITL interface can now be accessed via the URSA CLI, launch with:
 
-The file can be run with:
+`$ ursa`
 
-`python /path/to/hitl_basic.py`
+and help on commands can he accessed with 
 
-### Setup and Initialization
+`$ ursa --help`
 
-1. Creates a workspace directory for storing agent data and checkpoints
-2. Initializes SQLite databases and checkpointers for various agents:
-   - Executor agent
-   - Planner agent
-   - WebSearcher agent
-3. Configures the language model and embedding model
-4. Instantiates the following agents:
-   - ArxivAgent
-   - ExecutionAgent
-   - PlanningAgent
-   - WebSearchAgent
-   - RecallAgent
 
-### User Interaction Loop
+## Basic Usage
 
-The function runs a continuous interaction loop until the user enters "[USER DONE]":
+To prompt an URSA agent through the CLI, first select an agent, then issue a prompt to the agent:
 
-1. Displays a header explaining how to use the interface
-2. Prompts the user for input
-3. Parses the input to determine which agent to invoke:
-   - `[Arxiver]`: Searches ArXiv for academic papers
-   - `[Executor]`: Executes code or commands
-   - `[Planner]`: Creates plans or strategies
-   - `[WebSearcher]`: Performs web searches
-   - `[Rememberer]`: Retrieves information from memory
-   - `[Chatter]`: Has a general conversation using the language model
-4. **Importantly, the output from the previous agent interaction is automatically included in the prompt to the next agent.** This creates a continuous context flow where each agent has access to what the previous agent produced.
-5. Invokes the appropriate agent with the user's query and context from previous interactions
-6. Displays the agent's response
-7. Continues the loop until the user indicates they're done
-
-### Agent-Specific Behavior
-
-- **ArxivAgent**: Converts user query into a search query, retrieves relevant papers, and summarizes results
-- **ExecutionAgent**: Processes user instructions in the context of previous outputs, can execute code
-- **PlanningAgent**: Creates plans based on user requirements and previous context
-- **WebSearchAgent**: Performs web searches based on user queries
-- **RecallAgent**: Retrieves relevant information from persistent memory
-- **Chatter**: Provides direct access to the language model for general conversation
-
-## Usage
-
-Run the script and interact with agents by prefixing your queries with the appropriate agent tag:
+```
+ursa> execute
+execute: Make me a histogram of the first 10000 prime number spacings
+```
 
+You can also issue the prompt in one line by prepending the agent name:
+```
+ursa> execute Make me a histogram of the first 10000 prime number spacings`
 ```
-[Arxiver] Find recent papers on transformer architecture improvements
-[Executor] Write a Python function to calculate Fibonacci numbers
-[Planner] Create a research plan for analyzing climate data
-[WebSearcher] What are the latest developments in quantum computing?
-[Rememberer] What did we discuss about neural networks earlier?
-[Chatter] Explain the concept of attention mechanisms in simple terms
+
+to see the names of available agents, prompt the CLI with `help`:
 ```
+ursa> help
 
-### Context Continuity
+Documented commands (type help <topic>):
+========================================
+EOF  agents  arxiv  chat  clear  execute  exit  help  models  web
 
-A key feature of this interface is that each agent receives both your current query AND the output from the previous agent interaction. This allows for natural follow-up queries and building on previous results. For example:
+Undocumented commands:
+======================
+hypothesize  plan
 
-1. `[WebSearcher] Find information about large language models`
-2. `[Planner] Create a research plan based on this information`
+```
 
-In this sequence, the Planner would receive both your planning request AND the search results about large language models from the WebSearcher, enabling it to create a more informed and contextually relevant plan.
 
-Use "[USER DONE]" to exit the interface.
+Some additional documentation on the URSA github repo: [LINK](https://github.com/lanl/ursa)
+with more to come.
 
+We should have an in-depth documentation for it, but right now it's documented a bit on the main README and through the help flags with the CLI call.