From c5eab56eff523db146bc8e0c0ffa5fdb04aac615 Mon Sep 17 00:00:00 2001
From: Your Name <you@example.com>
Date: Tue, 1 Oct 2024 14:00:00 -0400
Subject: [PATCH] [CLEANUP]

---
 agents_config_example.yaml                    | 36 +++++++++----------
 docs/swarms/structs/forest_swarm.md           |  3 ++
 .../structs/swarms/experimental/sap.py        |  0
 3 files changed, 21 insertions(+), 18 deletions(-)
 rename sap.py => examples/structs/swarms/experimental/sap.py (100%)

diff --git a/agents_config_example.yaml b/agents_config_example.yaml
index 2af8c6912..7db030e7d 100644
--- a/agents_config_example.yaml
+++ b/agents_config_example.yaml
@@ -18,21 +18,21 @@ agents:
     return_step_meta: false
     output_type: "str"  # Can be "json" or any other format
 
-  # - agent_name: "Stock-Analysis-Agent"
-  #   model:
-  #     openai_api_key: "your_openai_api_key"
-  #     model_name: "gpt-4o-mini"
-  #     temperature: 0.2
-  #     max_tokens: 1500
-  #   system_prompt: "stock_agent_sys_prompt"  # Reference to system prompt file
-  #   max_loops: 2
-  #   autosave: true
-  #   dashboard: false
-  #   verbose: true
-  #   dynamic_temperature_enabled: false
-  #   saved_state_path: "stock_agent.json"
-  #   user_name: "stock_user"
-  #   retry_attempts: 3
-  #   context_length: 150000
-  #   return_step_meta: true
-  #   output_type: "str"
+  - agent_name: "Stock-Analysis-Agent"
+    model:
+      openai_api_key: "your_openai_api_key"
+      model_name: "gpt-4o-mini"
+      temperature: 0.2
+      max_tokens: 1500
+    system_prompt: "stock_agent_sys_prompt"  # Reference to system prompt file
+    max_loops: 2
+    autosave: true
+    dashboard: false
+    verbose: true
+    dynamic_temperature_enabled: false
+    saved_state_path: "stock_agent.json"
+    user_name: "stock_user"
+    retry_attempts: 3
+    context_length: 150000
+    return_step_meta: true
+    output_type: "str"
diff --git a/docs/swarms/structs/forest_swarm.md b/docs/swarms/structs/forest_swarm.md
index 3aab358af..6d838b359 100644
--- a/docs/swarms/structs/forest_swarm.md
+++ b/docs/swarms/structs/forest_swarm.md
@@ -4,6 +4,9 @@ This documentation describes the **ForestSwarm** that organizes agents into tree
 
 The architecture allows for efficient task assignment by selecting the most relevant agent from a set of trees. Tasks are processed asynchronously, with agents selected based on task relevance, calculated by the similarity of system prompts and task keywords.
 
+
+## Module Path: `swarms.structs.tree_swarm`
+
 ---
 
 ### Class: `TreeAgent`
diff --git a/sap.py b/examples/structs/swarms/experimental/sap.py
similarity index 100%
rename from sap.py
rename to examples/structs/swarms/experimental/sap.py