diff --git a/playground/agents/multion_examples/ multion_example.py b/ multion_example.py
similarity index 82%
rename from playground/agents/multion_examples/ multion_example.py
rename to  multion_example.py
index 43ca622f..6a3c22ed 100644
--- a/playground/agents/multion_examples/ multion_example.py	
+++ b/ multion_example.py	
@@ -8,10 +8,7 @@ def run_model(api_key):
         api_key=api_key, max_steps=500, url="https://x.com"
     )
     out = model.run(
-        """
-        click on the 'Tweet' button to start a new tweet and post it saying: $pip3 install swarms
-
-        """
+        ""
     )
     print(out)
 
diff --git a/README.md b/README.md
index d071f5d6..ac338dbb 100644
--- a/README.md
+++ b/README.md
@@ -1127,122 +1127,6 @@ mv = MajorityVoting(
 # Start the majority voting
 mv.run("What is your stance on healthcare?")
 ```
-
-### Real-World Deployment
-
-### Multi-Agent Swarm for Logistics
-Here's a production grade swarm ready for real-world deployment in a factory and logistics settings like warehouses. This swarm can automate 3 costly and inefficient workflows, safety checks, productivity checks, and warehouse security.
-
-
-```python
-import os
-
-from dotenv import load_dotenv
-
-from swarms.models import GPT4VisionAPI
-from swarms.prompts.logistics import (
-    Efficiency_Agent_Prompt,
-    Health_Security_Agent_Prompt,
-    Productivity_Agent_Prompt,
-    Quality_Control_Agent_Prompt,
-    Safety_Agent_Prompt,
-    Security_Agent_Prompt,
-    Sustainability_Agent_Prompt,
-)
-from swarms.structs import Agent
-
-# Load ENV
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-
-# GPT4VisionAPI
-llm = GPT4VisionAPI(openai_api_key=api_key)
-
-# Image for analysis
-factory_image = "factory_image1.jpg"
-
-# Initialize agents with respective prompts
-health_security_agent = Agent(
-    llm=llm,
-    sop=Health_Security_Agent_Prompt,
-    max_loops=1,
-    multi_modal=True,
-)
-
-# Quality control agent
-quality_control_agent = Agent(
-    llm=llm,
-    sop=Quality_Control_Agent_Prompt,
-    max_loops=1,
-    multi_modal=True,
-)
-
-
-# Productivity Agent
-productivity_agent = Agent(
-    llm=llm,
-    sop=Productivity_Agent_Prompt,
-    max_loops=1,
-    multi_modal=True,
-)
-
-# Initiailize safety agent
-safety_agent = Agent(llm=llm, sop=Safety_Agent_Prompt, max_loops=1, multi_modal=True)
-
-# Init the security agent
-security_agent = Agent(
-    llm=llm, sop=Security_Agent_Prompt, max_loops=1, multi_modal=True
-)
-
-
-# Initialize sustainability agent
-sustainability_agent = Agent(
-    llm=llm,
-    sop=Sustainability_Agent_Prompt,
-    max_loops=1,
-    multi_modal=True,
-)
-
-
-# Initialize efficincy agent
-efficiency_agent = Agent(
-    llm=llm,
-    sop=Efficiency_Agent_Prompt,
-    max_loops=1,
-    multi_modal=True,
-)
-
-# Run agents with respective tasks on the same image
-health_analysis = health_security_agent.run(
-    "Analyze the safety of this factory", factory_image
-)
-quality_analysis = quality_control_agent.run(
-    "Examine product quality in the factory", factory_image
-)
-productivity_analysis = productivity_agent.run(
-    "Evaluate factory productivity", factory_image
-)
-safety_analysis = safety_agent.run(
-    "Inspect the factory's adherence to safety standards",
-    factory_image,
-)
-security_analysis = security_agent.run(
-    "Assess the factory's security measures and systems",
-    factory_image,
-)
-sustainability_analysis = sustainability_agent.run(
-    "Examine the factory's sustainability practices", factory_image
-)
-efficiency_analysis = efficiency_agent.run(
-    "Analyze the efficiency of the factory's manufacturing process",
-    factory_image,
-)
-```
----
-
-
-
-
 ## Build your own LLMs, Agents, and Swarms!
 
 ### Swarms Compliant Model Interface
diff --git a/playground/demos/plant_biologist_swarm/bad_tomato.jpg b/bad_tomato.jpg
similarity index 100%
rename from playground/demos/plant_biologist_swarm/bad_tomato.jpg
rename to bad_tomato.jpg
diff --git a/json_log_cleanup.py b/json_log_cleanup.py
index 463f582e..6daf1620 100644
--- a/json_log_cleanup.py
+++ b/json_log_cleanup.py
@@ -31,4 +31,4 @@ def cleanup_json_logs(name: str = None):
 
 
 # Call the function
-cleanup_json_logs("arifacts_swarmm_o1")
+cleanup_json_logs("agriculture_swarm")
diff --git a/jamba_swarm/api.py b/playground/demos/jamba_swarm/api.py
similarity index 100%
rename from jamba_swarm/api.py
rename to playground/demos/jamba_swarm/api.py
diff --git a/jamba_swarm/api_schemas.py b/playground/demos/jamba_swarm/api_schemas.py
similarity index 100%
rename from jamba_swarm/api_schemas.py
rename to playground/demos/jamba_swarm/api_schemas.py
diff --git a/jamba_swarm/jamba_llm.py b/playground/demos/jamba_swarm/jamba_llm.py
similarity index 100%
rename from jamba_swarm/jamba_llm.py
rename to playground/demos/jamba_swarm/jamba_llm.py
diff --git a/jamba_swarm/main.ipynb b/playground/demos/jamba_swarm/main.ipynb
similarity index 100%
rename from jamba_swarm/main.ipynb
rename to playground/demos/jamba_swarm/main.ipynb
diff --git a/jamba_swarm/main.py b/playground/demos/jamba_swarm/main.py
similarity index 100%
rename from jamba_swarm/main.py
rename to playground/demos/jamba_swarm/main.py
diff --git a/jamba_swarm/prompts.py b/playground/demos/jamba_swarm/prompts.py
similarity index 100%
rename from jamba_swarm/prompts.py
rename to playground/demos/jamba_swarm/prompts.py
diff --git a/jamba_swarm/simple_jamba_swarm.py b/playground/demos/jamba_swarm/simple_jamba_swarm.py
similarity index 100%
rename from jamba_swarm/simple_jamba_swarm.py
rename to playground/demos/jamba_swarm/simple_jamba_swarm.py
diff --git a/playground/swarms_example.ipynb b/playground/swarms_example.ipynb
index 734687f1..ece6101d 100644
--- a/playground/swarms_example.ipynb
+++ b/playground/swarms_example.ipynb
@@ -8,7 +8,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip3 install -U swarms"
+    "!pip3 install -U swarms python-dotenv"
    ]
   },
   {
@@ -49,7 +49,7 @@
     "\n",
     "# Initialize the language model\n",
     "llm = OpenAIChat(\n",
-    "    temperature=0.5, model_name=\"gpt-4\", openai_api_key=api_key, max_tokens=4000\n",
+    "    temperature=0.5, openai_api_key=api_key, max_tokens=4000\n",
     ")\n",
     "\n",
     "\n",
@@ -95,7 +95,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from swarms import Agent, ChromaDB, OpenAIChat\n",
+    "from swarms import Agent, OpenAIChat\n",
+    "from playground.memory.chromadb_example import ChromaDB\n",
     "\n",
     "# Making an instance of the ChromaDB class\n",
     "memory = ChromaDB(\n",
diff --git a/pyproject.toml b/pyproject.toml
index 46551071..ac7207ed 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "5.2.3"
+version = "5.2.5"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]
diff --git a/playground/demos/plant_biologist_swarm/swarm_workers_agents.py b/swarm_workers_agents.py
similarity index 93%
rename from playground/demos/plant_biologist_swarm/swarm_workers_agents.py
rename to swarm_workers_agents.py
index 2df24758..e35b0048 100644
--- a/playground/demos/plant_biologist_swarm/swarm_workers_agents.py
+++ b/swarm_workers_agents.py
@@ -28,7 +28,7 @@ api_key = os.environ.get("OPENAI_API_KEY")
 
 
 # llm = llm,
-llm = GPT4o(max_tokens=200, openai_api_key=os.getenv("OPENAI_API_KEY"))
+llm = GPT4o(max_tokens=3000, openai_api_key=os.getenv("OPENAI_API_KEY"))
 
 # Initialize Diagnoser Agent
 diagnoser_agent = Agent(
@@ -42,6 +42,7 @@ diagnoser_agent = Agent(
     # saved_state_path="diagnoser.json",
     multi_modal=True,
     autosave=True,
+    streaming_on=True,
 )
 
 # Initialize Harvester Agent
@@ -56,6 +57,7 @@ harvester_agent = Agent(
     # saved_state_path="harvester.json",
     multi_modal=True,
     autosave=True,
+    streaming_on=True,
 )
 
 # Initialize Growth Predictor Agent
@@ -70,6 +72,7 @@ growth_predictor_agent = Agent(
     # saved_state_path="growth_predictor.json",
     multi_modal=True,
     autosave=True,
+    streaming_on=True,
 )
 
 # Initialize Treatment Recommender Agent
@@ -84,6 +87,7 @@ treatment_recommender_agent = Agent(
     # saved_state_path="treatment_recommender.json",
     multi_modal=True,
     autosave=True,
+    streaming_on=True,
 )
 
 # Initialize Disease Detector Agent
@@ -98,6 +102,7 @@ disease_detector_agent = Agent(
     # saved_state_path="disease_detector.json",
     multi_modal=True,
     autosave=True,
+    streaming_on=True,
 )
 agents = [
     diagnoser_agent,
@@ -108,7 +113,7 @@ agents = [
 ]
 
 task = "Conduct a diagnosis on the plants's symptoms, this wasn't grown in dirt, it grew from hydroponics"
-img = "tomato.jpg"
+img = "bad_tomato.jpg"
 
 loop = 0
 for i in range(len(agents)):