diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000000..1571a0d38e
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,5194 @@
+# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
+
+[[package]]
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+version = "2.6.1"
+description = "Happy Eyeballs for asyncio"
+optional = false
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+    {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_armv7l.whl", hash = "sha256:57edc88517d7fc62b174fcfb2e939fbc486a68315d648d7e74d07fac42cec240"},
+    {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:dab096ce479d5894d62c26ff4f699ec9072269d514b4edd630a393223f45a0ee"},
+    {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:14a85f3bd2d7bb255be7183e5d7d6e70add151a98edf56a770d6140f5d5f4010"},
+    {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:2c89b5c792685dd9cd3fa9761c1b9f46fc240c2a3265483acc1565769996a3f8"},
+    {file = "yarl-1.20.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:69e9b141de5511021942a6866990aea6d111c9042235de90e08f94cf972ca03d"},
+    {file = "yarl-1.20.1-cp39-cp39-win32.whl", hash = "sha256:b5f307337819cdfdbb40193cad84978a029f847b0a357fbe49f712063cfc4f06"},
+    {file = "yarl-1.20.1-cp39-cp39-win_amd64.whl", hash = "sha256:eae7bfe2069f9c1c5b05fc7fe5d612e5bbc089a39309904ee8b829e322dcad00"},
+    {file = "yarl-1.20.1-py3-none-any.whl", hash = "sha256:83b8eb083fe4683c6115795d9fc1cfaf2cbbefb19b3a1cb68f6527460f483a77"},
+    {file = "yarl-1.20.1.tar.gz", hash = "sha256:d017a4997ee50c91fd5466cef416231bb82177b93b029906cefc542ce14c35ac"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+propcache = ">=0.2.1"
+
+[[package]]
+name = "zipp"
+version = "3.23.0"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e"},
+    {file = "zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166"},
+]
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more_itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
+type = ["pytest-mypy"]
+
+[extras]
+agentops = []
+aisuite = []
+docling = []
+embeddings = []
+mem0 = []
+openpyxl = ["openpyxl"]
+pandas = []
+pdfplumber = ["pdfplumber"]
+tools = []
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.10,<3.14"
+content-hash = "ca98410d9b097eab6a799323bf0ae2a9cf50deef08ea3ba7d85154db398f1b2f"
diff --git a/pyproject.toml b/pyproject.toml
index 43eb7c99cf..b2193a982b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,127 +1,71 @@
-[project]
+[tool.poetry]
 name = "crewai"
 version = "0.126.0"
-description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
+description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents."
+authors = ["Joao Moura <joao@crewai.com>"]
 readme = "README.md"
-requires-python = ">=3.10,<3.14"
-authors = [
-    { name = "Joao Moura", email = "joao@crewai.com" }
-]
-dependencies = [
-    # Core Dependencies
-    "pydantic>=2.4.2",
-    "openai>=1.13.3",
-    "litellm==1.72.0",
-    "instructor>=1.3.3",
-    # Text Processing
-    "pdfplumber>=0.11.4",
-    "regex>=2024.9.11",
-    # Telemetry and Monitoring
-    "opentelemetry-api>=1.30.0",
-    "opentelemetry-sdk>=1.30.0",
-    "opentelemetry-exporter-otlp-proto-http>=1.30.0",
-    # Data Handling
-    "chromadb>=0.5.23",
-    "tokenizers>=0.20.3",
-    "onnxruntime==1.22.0",
-    "openpyxl>=3.1.5",
-    "pyvis>=0.3.2",
-    # Authentication and Security
-    "auth0-python>=4.7.1",
-    "python-dotenv>=1.0.0",
-    # Configuration and Utils
-    "click>=8.1.7",
-    "appdirs>=1.4.4",
-    "jsonref>=1.1.0",
-    "json-repair>=0.25.2",
-    "uv>=0.4.25",
-    "tomli-w>=1.1.0",
-    "tomli>=2.0.2",
-    "blinker>=1.9.0",
-    "json5>=0.10.0",
-]
 
-[project.urls]
-Homepage = "https://crewai.com"
-Documentation = "https://docs.crewai.com"
-Repository = "https://github.com/crewAIInc/crewAI"
+[tool.poetry.dependencies]
+python = ">=3.10,<3.14"
+pydantic = ">=2.4.2"
+openai = ">=1.13.3"
+litellm = "==1.72.0"
+instructor = ">=1.3.3"
+pdfplumber = ">=0.11.4"
+regex = ">=2024.9.11"
+opentelemetry-api = ">=1.30.0"
+opentelemetry-sdk = ">=1.30.0"
+opentelemetry-exporter-otlp-proto-http = ">=1.30.0"
+chromadb = ">=0.5.23"
+tokenizers = ">=0.20.3"
+onnxruntime = "==1.22.0"
+openpyxl = ">=3.1.5"
+pyvis = ">=0.3.2"
+auth0-python = ">=4.7.1"
+python-dotenv = ">=1.0.0"
+click = ">=8.1.7"
+appdirs = ">=1.4.4"
+jsonref = ">=1.1.0"
+json-repair = ">=0.25.2"
+uv = ">=0.4.25"
+tomli-w = ">=1.1.0"
+tomli = ">=2.0.2"
+blinker = ">=1.9.0"
+json5 = ">=0.10.0"
 
-[project.optional-dependencies]
-tools = ["crewai-tools~=0.46.0"]
-embeddings = [
-    "tiktoken~=0.8.0"
-]
-agentops = ["agentops>=0.3.0"]
-pdfplumber = [
-    "pdfplumber>=0.11.4",
-]
-pandas = [
-    "pandas>=2.2.3",
-]
-openpyxl = [
-    "openpyxl>=3.1.5",
-]
-mem0 = ["mem0ai>=0.1.94"]
-docling = [
-    "docling>=2.12.0",
-]
-aisuite = [
-    "aisuite>=0.1.10",
-]
+[tool.poetry.extras]
+tools = ["crewai-tools"]
+embeddings = ["tiktoken"]
+agentops = ["agentops"]
+pdfplumber = ["pdfplumber"]
+pandas = ["pandas"]
+openpyxl = ["openpyxl"]
+mem0 = ["mem0ai"]
+docling = ["docling"]
+aisuite = ["aisuite"]
 
-[tool.uv]
-dev-dependencies = [
-    "ruff>=0.8.2",
-    "mypy>=1.10.0",
-    "pre-commit>=3.6.0",
-    "mkdocs>=1.4.3",
-    "mkdocstrings>=0.22.0",
-    "mkdocstrings-python>=1.1.2",
-    "mkdocs-material>=9.5.7",
-    "mkdocs-material-extensions>=1.3.1",
-    "pillow>=10.2.0",
-    "cairosvg>=2.7.1",
-    "pytest>=8.0.0",
-    "python-dotenv>=1.0.0",
-    "pytest-asyncio>=0.23.7",
-    "pytest-subprocess>=1.5.2",
-    "pytest-recording>=0.13.2",
-    "pytest-randomly>=3.16.0",
-    "pytest-timeout>=2.3.1",
-]
 
-[project.scripts]
-crewai = "crewai.cli.cli:crewai"
-
-[tool.mypy]
-ignore_missing_imports = true
-disable_error_code = 'import-untyped'
-exclude = ["cli/templates"]
-
-[tool.bandit]
-exclude_dirs = ["src/crewai/cli/templates"]
+[tool.poetry.dev-dependencies]
+ruff = ">=0.8.2"
+mypy = ">=1.10.0"
+pre-commit = ">=3.6.0"
+mkdocs = ">=1.4.3"
+mkdocstrings = ">=0.22.0"
+mkdocstrings-python = ">=1.1.2"
+mkdocs-material = ">=9.5.7"
+mkdocs-material-extensions = ">=1.3.1"
+pillow = ">=10.2.0"
+cairosvg = ">=2.7.1"
+pytest = ">=8.0.0"
+pytest-asyncio = ">=0.23.7"
+pytest-subprocess = ">=1.5.2"
+pytest-recording = ">=0.13.2"
+pytest-randomly = ">=3.16.0"
+pytest-timeout = ">=2.3.1"
 
-# PyTorch index configuration, since torch 2.5.0 is not compatible with python 3.13
-[[tool.uv.index]]
-name = "pytorch-nightly"
-url = "https://download.pytorch.org/whl/nightly/cpu"
-explicit = true
-
-[[tool.uv.index]]
-name = "pytorch"
-url = "https://download.pytorch.org/whl/cpu"
-explicit = true
-
-[tool.uv.sources]
-torch = [
-  { index = "pytorch-nightly", marker = "python_version >= '3.13'" },
-  { index = "pytorch", marker = "python_version < '3.13'" },
-]
-torchvision = [
-  { index = "pytorch-nightly", marker = "python_version >= '3.13'" },
-  { index = "pytorch", marker = "python_version < '3.13'" },
-]
+[tool.poetry.scripts]
+crewai = "crewai.cli.cli:crewai"
 
 [build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/src/crewai/tools/__init__.py b/src/crewai/tools/__init__.py
index 2467fa906d..435838b479 100644
--- a/src/crewai/tools/__init__.py
+++ b/src/crewai/tools/__init__.py
@@ -4,4 +4,5 @@
     "BaseTool",
     "tool",
     "EnvVar",
-]
\ No newline at end of file
+    "CollaborationOptimizerTool"
+]
diff --git a/src/crewai/tools/agent_scheduler.py b/src/crewai/tools/agent_scheduler.py
new file mode 100644
index 0000000000..a2235489dd
--- /dev/null
+++ b/src/crewai/tools/agent_scheduler.py
@@ -0,0 +1,53 @@
+import random
+from typing import Dict, List
+from crewai.tools import BaseTool
+from pydantic import Field
+
+class AgentScheduler:
+    """
+    Tracks agent performance and suggests dynamic retraining intervals.
+    """
+
+    def __init__(self, agent_ids: List[str]):
+        self.performance_log: Dict[str, List[float]] = {
+            agent_id: [] for agent_id in agent_ids
+        }
+
+    def track_performance(self, agent_id: str, success: bool):
+        self.performance_log[agent_id].append(1.0 if success else 0.0)
+
+    def adjust_training_schedule(self, agent_id: str) -> int:
+        log = self.performance_log.get(agent_id, [])
+        if not log:
+            return 3  # Default if no data
+
+        avg_score = sum(log[-10:]) / min(len(log), 10)
+        if avg_score < 0.5:
+            return 1  # Frequent retraining
+        elif avg_score > 0.8:
+            return 5  # Rare retraining
+        return 3  # Moderate
+
+
+class AgentSchedulerTool(BaseTool):
+    name: str = "agent_scheduler"
+    description: str = (
+        "Tracks agent performance and suggests dynamic retraining intervals. "
+        "Takes agent_id (e.g., 'agent_alpha') and performance (comma-separated values like 'True,False,True')"
+    )
+    agent_ids: List[str]
+    scheduler: AgentScheduler = Field(default=None)
+
+    def __init__(self, agent_ids: List[str]):
+        super().__init__(agent_ids=agent_ids)
+        object.__setattr__(self, 'scheduler', AgentScheduler(agent_ids))
+
+    def _run(self, agent_id: str, performance: str) -> str:
+        try:
+            performance_list = [x.strip() == "True" for x in performance.split(",")]
+            for result in performance_list:
+                self.scheduler.track_performance(agent_id, result)
+            interval = self.scheduler.adjust_training_schedule(agent_id)
+            return f"Recommended retraining interval for {agent_id}: {interval} days"
+        except Exception as e:
+            return f"Error processing input: {e}"
diff --git a/src/crewai/tools/collaboration_optimizer.py b/src/crewai/tools/collaboration_optimizer.py
new file mode 100644
index 0000000000..1696d96952
--- /dev/null
+++ b/src/crewai/tools/collaboration_optimizer.py
@@ -0,0 +1,53 @@
+# src/crewai/tools/collaboration_optimizer.py
+
+from crewai.tools import BaseTool
+from stable_baselines3 import PPO
+from stable_baselines3.common.env_checker import check_env
+import numpy as np
+import gymnasium as gym
+from gymnasium.spaces import Discrete, Box
+
+class AgentCollaborationEnv(gym.
+                            Env):
+    def __init__(self, num_agents: int = 3):
+        super(AgentCollaborationEnv, self).__init__()
+        self.num_agents = num_agents
+        self.observation_space = Box(low=0, high=1, shape=(self.num_agents,), dtype=np.float32)
+        self.action_space = Discrete(self.num_agents * 2)
+        self.state = np.zeros(self.num_agents, dtype=np.float32)
+
+    def reset(self, seed=None, options=None):
+        self.state = np.random.rand(self.num_agents).astype(np.float32)
+        return self.state, {}
+
+    def step(self, action):
+        self.state = np.random.rand(self.num_agents).astype(np.float32)
+        reward = float(np.mean(self.state))
+        terminated = np.random.rand() > 0.95
+        truncated = False
+        return self.state, reward, terminated, truncated, {}
+
+
+class CollaborationOptimizerTool(BaseTool):
+    name: str = "collaboration_optimizer"
+    description: str = "Optimizes collaboration strategies among agents using reinforcement learning."
+
+    def _run(self, num_agents: int = 3, timesteps: int = 2000):
+        env = AgentCollaborationEnv(num_agents)
+        check_env(env, warn=True)
+
+        model = PPO("MlpPolicy", env, verbose=0)
+        model.learn(total_timesteps=timesteps)
+
+        # Evaluation phase (returns average reward over 5 steps)
+        obs, _ = env.reset()
+        total_reward = 0
+        for _ in range(5):
+            action, _ = model.predict(obs)
+            obs, reward, terminated, truncated, _ = env.step(action)
+            total_reward += reward
+            if terminated or truncated:
+              break
+        avg_reward = total_reward / 5.0
+
+        return f"Average collaboration reward for {num_agents} agents: {avg_reward:.4f}"
diff --git a/tests/tools/test_agent_scheduler.py b/tests/tools/test_agent_scheduler.py
new file mode 100644
index 0000000000..cd94d2aa9d
--- /dev/null
+++ b/tests/tools/test_agent_scheduler.py
@@ -0,0 +1,57 @@
+import os
+import unittest
+from crewai import Agent, Task, Crew, LLM
+from crewAI.src.crewai.tools.agent_scheduler import AgentSchedulerTool
+from langchain_openai import AzureChatOpenAI
+
+
+os.environ["AZURE_API_TYPE"] = "azure"
+os.environ["AZURE_API_KEY"] = os.getenv("AZURE_OPENAI_API_KEY")
+os.environ["AZURE_API_BASE"] = os.getenv("AZURE_OPENAI_ENDPOINT")
+os.environ["AZURE_API_VERSION"] = os.getenv("AZURE_OPENAI_API_VERSION")
+os.environ["AZURE_DEPLOYMENT_NAME"] = os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME")
+
+
+class TestAgentSchedulerTool(unittest.TestCase):
+    def setup(self):
+        self.tool = AgentSchedulerTool(agent_ids=["agent_alpha", "agent_beta", "agent_gamma"])
+        self.llm = LLM(model="azure/gpt-4o", api_version="2023-05-15")
+
+        self.agent = Agent(
+            name="Scheduler Agent",
+            role="Agent Performance Monitor",
+            goal="Optimize agent retraining schedules based on recent outcomes",
+            backstory="This agent reviews logs and adjusts how frequently agents should be retrained.",
+            tools=[self.tool],
+            llm=self.llm
+        )
+
+        self.task = Task(
+            description="Use the agent_scheduler tool to analyze agent_alpha performance with 'True,False,True,True,False,False,True' and suggest a retraining interval.",
+            expected_output="Suggest how often agent_alpha should be retrained",
+            agent=self.agent
+        )
+
+    def test_tool_schema_structure(self):
+        schema = self.tool.args_schema.schema()
+        self.assertIn("agent_id", schema["properties"])
+        self.assertIn("performance", schema["properties"])
+
+    def test_agent_and_task_integration(self):
+        self.assertEqual(self.agent.name, "Scheduler Agent")
+        self.assertEqual(self.task.agent.name, "Scheduler Agent")
+        self.assertTrue(any(isinstance(t, AgentSchedulerTool) for t in self.agent.tools))
+
+    def test_crew_execution(self):
+        crew = Crew(agents=[self.agent], tasks=[self.task], verbose=False)
+        # This line will actually trigger execution. Comment if avoiding LLM calls.
+        # print(agent.tools[0].args_schema.schema_json(indent=2))
+        crew.kickoff()
+        # self.assertIn("retrain", result.lower())
+        # Instead, print schema for debug
+        print(self.agent.tools[0].args_schema.schema_json(indent=2))
+
+
+# ag = TestAgentSchedulerTool()
+# ag.setup()
+# ag.test_crew_execution()
diff --git a/tests/tools/test_collaboration_optimizer.py b/tests/tools/test_collaboration_optimizer.py
new file mode 100644
index 0000000000..8dce430a0c
--- /dev/null
+++ b/tests/tools/test_collaboration_optimizer.py
@@ -0,0 +1,50 @@
+import os
+import unittest
+from crewai import Agent, Task, Crew, LLM
+from crewAI.src.crewai.tools.collaboration_optimizer import CollaborationOptimizerTool
+
+# Set Azure OpenAI credentials
+os.environ["AZURE_API_TYPE"] = "azure"
+os.environ["AZURE_API_KEY"] = os.getenv("AZURE_OPENAI_API_KEY")
+os.environ["AZURE_API_BASE"] = os.getenv("AZURE_OPENAI_ENDPOINT")
+os.environ["AZURE_API_VERSION"] = "2025-01-01-preview"
+os.environ["AZURE_DEPLOYMENT_NAME"] = "gpt-4o"
+
+
+class TestCollaborationOptimizerTool(unittest.TestCase):
+    def setup(self):
+        self.llm = LLM(model="azure/gpt-4o", api_version="2023-05-15")
+        self.agent = Agent(
+            name="Optimizer Agent",
+            role="Collaboration Strategist",
+            backstory="An AI agent specialized in optimizing teamwork among multiple agents through reinforcement learning strategies.",
+            goal="Maximize team collaboration efficiency",
+            tools=[CollaborationOptimizerTool()],
+            llm=self.llm,
+            verbose=True
+        )
+
+        self.task = Task(
+            description="Run a simulation to optimize collaboration among 4 agents.",
+            expected_output="Optimal reward score and strategy feedback",
+            agent=self.agent
+        )
+
+        self.crew = Crew(agents=[self.agent], tasks=[self.task], verbose=True)
+
+      def test_collaboration_optimizer_tool_attached_to_agent(self):
+        # Ensure the tool is properly attached
+        tool_names = [tool.name for tool in self.agent.tools]
+        self.assertIn("Collaboration Optimizer", tool_names)
+
+      def test_crew_kickoff_returns_result(self):
+          # Run the crew and assert the result format
+          result = self.crew.kickoff()
+          self.assertIsInstance(result, str)  # Or dict, depending on what the tool returns
+          self.assertIn("Optimal", result)  # You can refine this based on expected output
+
+      def test_tool_description_contains_expected_keywords(self):
+          tool = self.agent.tools[0]
+          self.assertIn("optimiz", tool.description.lower())  # fuzzy match for "optimize", etc.
+          self.assertTrue(tool.description)  # Ensure description is not empty
+