Remove ensemble_agent - not relocated

Author: shuds13

AGENTIC_SETUP.md

@@ -1,8 +1,11 @@
 # Running libEnsemble agentic workflows
 
+> **Note:** The agentic directory in this repo is experimental scripts and
+has been superceeded by the libEnsemble agent at https://github.com/Libensemble/ensemble_agent
+
 For all workflows, you will need a key to access an LLM.
 
-For example, you can set an OpenAPI key. 
+For example, you can set an OpenAPI key.
 Requires an [OpenAI account](https://platform.openai.com).
 Make sure to check MODEL at top of agentic script and usage rates.
 
@@ -63,7 +66,7 @@ cd script-creator/agentic/
 
 To run:
 
-Provide path to directory with your libEnsemble scripts. These will be copied 
+Provide path to directory with your libEnsemble scripts. These will be copied
 to a working directory `generated_scripts/`.
 
 ```bash
@@ -99,7 +102,7 @@ and update paths to `sim_app` and `input_file` in `tests/scripts_with_errors/run
 
 To run:
 
-Provide path to directory with your libEnsemble scripts. These will be copied 
+Provide path to directory with your libEnsemble scripts. These will be copied
 to a working directory `generated_scripts/`.
 
 ```bash
@@ -156,7 +159,7 @@ python libe_agent_with_script_generator.py --prompt-file my_prompt.txt
 # Use existing scripts (skip MCP generation/tweaking)
 python libe_agent_with_script_generator.py --scripts example_scripts/
 
-# Run from anywhere 
+# Run from anywhere
 python libe_agent_with_script_generator.py --mcp-server <path/to/mcp_server.mjs>
 ```
 Scripts saved to `generated_scripts/` directory.

CLAUDE.md

@@ -4,11 +4,10 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 ## Project Overview
 
-This is a **libEnsemble Script Creator** — a system for generating, running, and iteratively fixing [libEnsemble](https://libensemble.readthedocs.io/) scripts (used for parallel optimization at HPC centers). It has three deployment modes:
+This is a **libEnsemble Script Creator** — a system for generating [libEnsemble](https://libensemble.readthedocs.io/) scripts (used for parallel optimization at HPC centers). It has two deployment modes:
 
 1. **MCP Server** (`mcp_server.mjs`) — Node.js server exposing script generation as an MCP tool (used from Cursor IDE or AI agents)
-2. **Agentic Python scripts** (`agentic/`) — LangChain agents that generate scripts via MCP, run them, and auto-fix errors
-3. **Web UI** (`web/`) — Flask backend + HTML/JS frontend for interactive script refinement
+2. **Web UI** (`web/`) — Flask backend + HTML/JS frontend for interactive script refinement
 
 ## Key Commands
 
@@ -18,37 +17,6 @@ npm install          # Install dependencies
 node mcp_server.mjs  # Run MCP server
 ```
 
-### Agentic Agents (Python)
-
-Install dependencies:
-```bash
-pip install langchain langchain-openai mcp openai libensemble scipy mpmath
-```
-
-Run the primary agent (generate → run → fix):
-```bash
-cd agentic/
-python libe_agent_with_script_generator.py             # Uses DEFAULT_PROMPT
-python libe_agent_with_script_generator.py "Create APOSMM scripts..."
-python libe_agent_with_script_generator.py --prompt-file my_prompt.txt
-python libe_agent_with_script_generator.py --scripts example_scripts/  # Skip generation
-python libe_agent_with_script_generator.py --show-prompts               # Debug prompts
-```
-
-Run the interactive agent (with user chat loop + RAG doc lookup):
-```bash
-# Extra deps for RAG:
-pip install llama-index llama-index-embeddings-huggingface sentence-transformers
-
-export GENERATOR_MCP_SERVER=/path/to/mcp_server.mjs
-export RAG_MCP_SERVER=/path/to/rag/mcp_server.py
-export OPENAI_API_KEY=your_key
-
-python libe_agent_interactive_with_rag.py --interactive
-python libe_agent_interactive_with_rag.py --generate-only  # No execution
-python libe_agent_interactive_with_rag.py --scripts dir/   # Review existing
-```
-
 ### Web Interface
 ```bash
 cd web/
@@ -57,15 +25,6 @@ export OPENAI_API_KEY=your_key
 python server.py   # Serves on http://localhost:5000
 ```
 
-### RAG System
-```bash
-cd rag/
-pip install llama-index llama-index-embeddings-huggingface sentence-transformers mcp
-python build_index.py   # Build vector index from libEnsemble docs (run once)
-python rag_query.py "What generators are available?"
-python test_mcp_server.py  # Test the RAG MCP server
-```
-
 ## Architecture
 
 ### Script Generation Pipeline
@@ -76,41 +35,8 @@ The MCP server (`mcp_server.mjs`) uses **Mustache templates** (`templates/*.j2`
 
 Generator configurations are data-driven via `data/generators.json` and `data/generator_specs.json`.
 
-### Agent Workflow
-```
-User prompt
-    → LangChain agent calls MCP tool CreateLibEnsembleScripts
-    → Agent refines scripts (second LLM pass)
-    → Agent runs scripts via subprocess
-    → On failure: optionally queries RAG for doc context, then fixes and reruns
-    → All versions saved to generated_scripts/versions/
-```
-
-Key constants in agents:
-- `MAX_RETRIES = 2` — retry attempts on script failure
-- `LLM_MODEL` env var (default: `gpt-4o-mini`) — overrides model for all agents
-
-### MCP Protocol Usage
-Agents connect to MCP servers via `stdio_client` (the MCP Python SDK). There are two MCP servers:
-1. `mcp_server.mjs` — exposes `CreateLibEnsembleScripts` tool
-2. `rag/mcp_server.py` — exposes `query_libe_docs`, `query_generator_docs`, etc.
-
-### Environment Variables
-```
-OPENAI_API_KEY        # Required for agents
-OPENAI_BASE_URL       # Optional: alternative LLM endpoint (e.g., ALCF inference)
-LLM_MODEL             # Optional: model name (default: gpt-4o-mini)
-MCP_SERVER            # Path to mcp_server.mjs (used by agents)
-GENERATOR_MCP_SERVER  # Path to generator MCP server
-RAG_MCP_SERVER        # Path to rag/mcp_server.py
-```
+### MCP Tool
+The MCP server exposes the `CreateLibEnsembleScripts` tool.
 
 ### Testing
-No automated test suite. Manual testing uses:
-- `agentic/tests/` — test scripts with intentional errors (filename errors, wrong paths, missing try/except, etc.)
-- The six_hump_camel benchmark problem in `six_hump_camel/` as a reference optimization case
-
-To test an agent against a known-error script:
-```bash
-python libe_agent_with_script_generator.py --scripts agentic/tests/scripts_with_errors/
-```
+No automated test suite. The six_hump_camel benchmark problem in `six_hump_camel/` serves as a reference optimization case.