[docs]

README.md

@@ -1025,6 +1025,58 @@ swarm.run(
 
 ```
 
+
+## `ForestSwarm`
+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. [Learn More with the documentation](https://docs.swarms.world/en/latest/swarms/structs/forest_swarm/)
+
+
+```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)
+```
+
 ----------
 
 ## Onboarding Session