diff --git a/docs/swarms/structs/forest_swarm.md b/docs/swarms/structs/forest_swarm.md
index 3e1f0de9..3aab358a 100644
--- a/docs/swarms/structs/forest_swarm.md
+++ b/docs/swarms/structs/forest_swarm.md
@@ -71,6 +71,55 @@ The architecture allows for efficient task assignment by selecting the most rele
 
 ## Full Code Example
 
+```python
+from swarms.structs.tree_swarm import TreeAgent, Tree, ForestSwarm
+# Example Usage:
+
+# Create agents with varying system prompts and dynamically generated distances/keywords
+agents_tree1 = [
+    TreeAgent(
+        system_prompt="Stock Analysis Agent",
+        agent_name="Stock Analysis Agent",
+    ),
+    TreeAgent(
+        system_prompt="Financial Planning Agent",
+        agent_name="Financial Planning Agent",
+    ),
+    TreeAgent(
+        agent_name="Retirement Strategy Agent",
+        system_prompt="Retirement Strategy Agent",
+    ),
+]
+
+agents_tree2 = [
+    TreeAgent(
+        system_prompt="Tax Filing Agent",
+        agent_name="Tax Filing Agent",
+    ),
+    TreeAgent(
+        system_prompt="Investment Strategy Agent",
+        agent_name="Investment Strategy Agent",
+    ),
+    TreeAgent(
+        system_prompt="ROTH IRA Agent", agent_name="ROTH IRA Agent"
+    ),
+]
+
+# Create trees
+tree1 = Tree(tree_name="Financial Tree", agents=agents_tree1)
+tree2 = Tree(tree_name="Investment Tree", agents=agents_tree2)
+
+# Create the ForestSwarm
+multi_agent_structure = ForestSwarm(trees=[tree1, tree2])
+
+# Run a task
+task = "Our company is incorporated in delaware, how do we do our taxes for free?"
+output = multi_agent_structure.run(task)
+print(output)
+```
+
+
+
 ---
 
 ## Example Workflow