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+# Language Agent Tree Search (LATS) for Python Code Generation
+
+A lightweight, local-first Python implementation of the **Language Agent Tree Search (LATS)** framework. This script integrates a Large Language Model (LLM) with Monte Carlo Tree Search (MCTS) to generate, execute, evaluate, and refine Python code iteratively to solve complex programming tasks.
+
+---
+
+## 🛠️ How It Works
+
+LATS enhances standard LLM generation by framing code synthesis as a tree-search problem. Rather than relying on a single-shot generation, it explores different paths using an iterative MCTS process:
+
+```
+                  [ Root Node ] (Empty State)
+                 /             \
+       [Option 1: Code]     [Option 2: Code]
+         /         \            /         \
+   (Execute)    (Score)    (Execute)    (Score)
+
+```
+
+### The 6-Step Search Loop
+
+1. **Selection (`UCT`):** Traverses existing paths to find the most promising code candidate using the Upper Confidence bound applied to Trees (UCT).
+2. **Expansion:** Generates multiple alternative code modifications or fixes (`EXPANSION_N`) using the LLM.
+* If at the root, it generates a first draft.
+* If deep in the tree, it acts as a debugging agent using previous execution logs and reflections.
+
+
+3. **Simulation (Execution):** Executes the generated Python code in an isolated dictionary-based sandboxed environment, capturing stdout and runtime errors as observations.
+4. **Evaluation (Scoring):** Employs a critic LLM with a structured JSON schema to score the execution result (`0.0` to `1.0`) and provide step-by-step reasoning.
+5. **Reflection:** Stores the critic's feedback directly on the node to act as debug hints for future iterations.
+6. **Backpropagation:** Propagates the highest reward score back up the selection path to update the value metrics of parent nodes.
+
+---
+
+## ⚙️ Configuration
+
+You can easily configure the script parameters at the top of `lats.py`:
+
+```python
+# --- CONFIGURATION ---
+LLM_API_URL = "http://localhost:8090/v1/chat/completions"  # Endpoint of your LLM provider
+MAX_ITERATIONS = 3                                        # Number of complete MCTS cycles
+EXPANSION_N = 2                                           # Children nodes to generate per expansion
+UCT_CONSTANT = 1.41                                       # Exploration constant for MCTS selection
+
+```
+
+> **Note:** The LLM client expects an OpenAI-compatible API endpoint (e.g., LocalAI, vLLM, Ollama, or LM Studio).
+
+---
+
+## 🚀 Getting Started
+
+### Prerequisites
+
+* Python 3.8+
+* An OpenAI-compatible LLM server running locally (or pointing to a cloud provider)
+* Required Python libraries:
+
+```bash
+pip install requests rich
+
+```
+
+### Running the Search
+
+To test the script, execute it directly:
+
+```bash
+python lats.py
+
+```
+
+By default, the script runs a sample algorithm-heavy task:
+
+> *Given strings S and T, find the shortest substring of S which has T as a subsequence. Return the substring or empty string if none.*
+
+---
+
+## 📦 Key Functions
+
+### `LATS_Search(task_description)`
+
+The main entry point. Executes the entire MCTS loop to search for the most optimal, bug-free Python code snippet matching the `task_description`.
+
+### `execute_python(code, supplied_input="")`
+
+Safely runs the generated code string dynamically using Python's built-in `exec()`. It redirects stdout and traps standard runtime errors, returning them to the LLM agent as feedback.
+
+### `clean_code(text)`
+
+Extracts raw Python scripts out of the LLM's markdown formatting block (````python ... ````) and normalizes code indentation before execution.
+
+---
+
+## 📊 Evaluation Output Format
+
+The evaluation step relies on structured JSON outputs from the LLM, ensuring deterministic grading:
+
+```json
+{
+  "reward": 1.0,
+  "reasoning": "The code runs successfully, passes the test case logic, and correctly outputs 'bcde' as the shortest substring."
+}
+
+```