feat: Workforce Workflow (#3180)

Co-authored-by: a7m-1st <Ahmed.jimi.awelkeir500@gmail.com>
Co-authored-by: Wendong-Fan <133094783+Wendong-Fan@users.noreply.github.com>

Author: 3 people

camel/agents/chat_agent.py

@@ -191,7 +191,7 @@ def _save_summary(self, summary: str) -> str:
             summary (str): The summary text to save.
 
         Returns:
-            str: Success message or error message.
+            str: "success" or error message starting with "Error:".
         """
         try:
             # prepare metadata for unified markdown saving
@@ -221,7 +221,7 @@ def _save_history(self, memory_records: List["MemoryRecord"]) -> str:
                 to save.
 
         Returns:
-            str: Success message or error message.
+            str: "success" or error message starting with "Error:".
         """
         try:
             # prepare metadata for markdown saving

camel/societies/workforce/single_agent_worker.py

@@ -30,7 +30,7 @@ class RoleType(Enum):
 
 
 class ModelType(UnifiedModelType, Enum):
-    DEFAULT = os.getenv("DEFAULT_MODEL_TYPE", "gpt-5-mini")
+    DEFAULT = os.getenv("DEFAULT_MODEL_TYPE", "gpt-4.1-mini-2025-04-14")
 
     GPT_3_5_TURBO = "gpt-3.5-turbo"
     GPT_4 = "gpt-4"

camel/societies/workforce/workforce.py

@@ -0,0 +1,317 @@
+#!/usr/bin/env python3
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+
+
+import os
+
+from dotenv import load_dotenv
+
+from camel.agents import ChatAgent
+from camel.configs import ChatGPTConfig
+from camel.logger import get_logger
+from camel.messages import BaseMessage
+from camel.models import ModelFactory
+from camel.societies.workforce import Workforce
+from camel.tasks.task import Task
+from camel.toolkits import MathToolkit
+from camel.types import ModelPlatformType, ModelType
+
+load_dotenv()
+
+logger = get_logger(__name__)
+
+
+def create_math_agent() -> ChatAgent:
+    r"""Create a math agent with math tools.
+
+    Returns:
+        ChatAgent: A configured math expert agent with MathToolkit tools.
+    """
+    math_msg = BaseMessage.make_assistant_message(
+        role_name="Math Expert",
+        content=(
+            "You are a math expert specialized in solving "
+            "mathematical problems.You can perform calculations, "
+            "solve equations, and work with various "
+            "mathematical concepts. Use the math tools available to you."
+        ),
+    )
+    model = ModelFactory.create(
+        model_platform=ModelPlatformType.OPENAI,
+        model_type=ModelType.DEFAULT,
+        model_config_dict=ChatGPTConfig().as_dict(),
+    )
+    return ChatAgent(
+        system_message=math_msg,
+        model=model,
+        tools=MathToolkit().get_tools(),
+    )
+
+
+def create_writer_agent() -> ChatAgent:
+    r"""Create a writer agent without tools.
+
+    Returns:
+        ChatAgent: A configured content writer agent without additional tools.
+    """
+    writer_msg = BaseMessage.make_assistant_message(
+        role_name="Content Writer",
+        content=(
+            "You are a skilled content writer who specializes in creating "
+            "clear, engaging, and well-structured written content. You excel "
+            "at storytelling, technical writing, and adapting your tone to "
+            "different audiences."
+        ),
+    )
+    model = ModelFactory.create(
+        model_platform=ModelPlatformType.DEFAULT,
+        model_type=ModelType.DEFAULT,
+        model_config_dict=ChatGPTConfig().as_dict(),
+    )
+    return ChatAgent(system_message=writer_msg, model=model)
+
+
+def demonstrate_first_session():
+    r"""Demonstrate first workforce session with workflow saving.
+
+    Creates a workforce with math and writer agents, processes tasks,
+    and saves the resulting workflows for future use.
+
+    Returns:
+        Dict[str, str]: Results of the workflow saving operation.
+    """
+    # Create workforce with two specialized agents
+    workforce = Workforce("Simple Demo Team")
+
+    # Add math agent with math tools
+    math_agent = create_math_agent()
+    workforce.add_single_agent_worker(
+        description="math_expert",
+        worker=math_agent,
+        enable_workflow_memory=True,  # Enable to save workflows later
+    )
+
+    # Add writer agent without tools
+    writer_agent = create_writer_agent()
+    workforce.add_single_agent_worker(
+        description="content_writer",
+        worker=writer_agent,
+        enable_workflow_memory=True,  # Enable to save workflows later
+    )
+
+    # Assign one task to each agent
+    tasks = [
+        Task(
+            content="Calculate the compound interest on $1000 invested at 5% "
+            "annual rate for 3 years",
+            id="math_task",
+        ),
+        Task(
+            content="Write a one-paragraph creative story about a robot "
+            "learning to paint",
+            id="writing_task",
+        ),
+    ]
+
+    for task in tasks:
+        try:
+            workforce.process_task(task)
+        except Exception as e:
+            logger.warning(f"Failed to process task {task.id}: {e}")
+
+    # Save workflows after completing tasks
+    saved_workflows = workforce.save_workflow_memories()
+
+    return saved_workflows
+
+
+def demonstrate_second_session():
+    r"""Demonstrate second workforce session with workflow loading.
+
+    Creates a new workforce instance, loads previously saved workflows,
+    and processes new tasks with the loaded workflow context.
+
+    Returns:
+        Dict[str, bool]: Results of the workflow loading operation.
+    """
+    # Create new workforce (simulating new session/process)
+    workforce = Workforce("Simple Demo Team - Session 2")
+
+    # Add workers with same descriptive names as before
+    math_agent = create_math_agent()
+    workforce.add_single_agent_worker(
+        description="math_expert",  # Same description = loads matching
+        # workflow
+        worker=math_agent,
+        enable_workflow_memory=False,  # Not saving in this session
+    )
+
+    writer_agent = create_writer_agent()
+    workforce.add_single_agent_worker(
+        description="content_writer",  # Same description = loads
+        # matching workflow
+        worker=writer_agent,
+        enable_workflow_memory=False,  # Not saving in this session
+    )
+
+    # Load previous workflows
+    loaded_workflows = workforce.load_workflow_memories()
+
+    # Process new tasks with loaded workflow context
+    new_tasks = [
+        Task(
+            content="Calculate the area of a circle with radius 7.5 meters",
+            id="new_math_task",
+        ),
+        Task(
+            content="Write a brief technical explanation of machine "
+            "learning for beginners",
+            id="new_writing_task",
+        ),
+    ]
+
+    for task in new_tasks:
+        try:
+            workforce.process_task(task)
+        except Exception as e:
+            logger.warning(f"Failed to process task {task.id}: {e}")
+
+    return loaded_workflows
+
+
+def demonstrate_workflow_file_management() -> None:
+    r"""Demonstrate workflow file management and inspection.
+
+    Shows information about where workflow files are stored and how
+    to inspect the saved workflow data.
+    """
+
+    # Get the base directory for workforce workflows
+    camel_workdir = os.environ.get("CAMEL_WORKDIR")
+    if camel_workdir:
+        base_dir = os.path.join(camel_workdir, "workforce_workflows")
+    else:
+        base_dir = "workforce_workflows"
+
+    logger.info(f"Workforce workflows are stored in: {base_dir}")
+
+    # Show how to check for existing workflow files
+    if os.path.exists(base_dir):
+        session_dirs = [
+            d
+            for d in os.listdir(base_dir)
+            if os.path.isdir(os.path.join(base_dir, d))
+        ]
+        logger.info(f"Found {len(session_dirs)} session directories")
+
+        for session_dir in session_dirs[:3]:  # Show first 3 sessions
+            session_path = os.path.join(base_dir, session_dir)
+            workflow_files = [
+                f for f in os.listdir(session_path) if f.endswith('.md')
+            ]
+            logger.info(
+                f"Session {session_dir}: {len(workflow_files)} workflow files"
+            )
+    else:
+        logger.info("No workflow directory found yet")
+
+
+def main() -> None:
+    try:
+        # First session to execute tasks and save workflows
+        saved_results = demonstrate_first_session()
+        print(f"Workflow save results: {saved_results}")
+
+        # Second session to load workflows and execute new tasks
+        loaded_results = demonstrate_second_session()
+        print(f"Workflow load results: {loaded_results}")
+
+    except Exception as e:
+        logger.error(f"Demonstration failed: {e}")
+        raise
+
+
+if __name__ == "__main__":
+    main()
+
+"""
+===============================================================================
+Workflows saved: 
+workforce_workflows/session_20250925_150330_302341/content_writer_workflow.md
+workforce_workflows/session_20250925_150330_302341/math_expert_workflow.md
+
+Saved workflow in math_expert_workflow.md
+<MARKDOWN CONTENT>
+## Metadata
+
+- session_id: session_20250925_150330_302341
+- working_directory: .../camel/workforce_workflows/
+  session_20250925_150330_302341
+- created_at: ...
+- base_directory: .../Codebase/camel/workforce_workflows
+- agent_id: 687b2e57-11ce-4138-9990-4d5ab58d2acc
+- message_count: 3
+
+## WorkflowSummary
+
+### Task Title
+Calculate compound interest
+
+### Task Description
+Calculate the future value of $1000 at 5% annual interest compounded
+once per year for 3 years, and produce a plain-text result containing:
+the compound interest formula with symbol definitions and numeric
+substitution, balances at end of Years 1-3, the final amount,
+total interest earned (all rounded to 2 decimals), and a final
+one-line JSON object with numeric values to two decimals.
+
+
+### Tools
+[Bullet point list of tools used]
+
+### Steps
+1. Identify the required formula: A = P*(1 + r)^n.
+2. Substitute given values into the formula: P = 1000, r = 0.05, n = 3.
+3. Compute the sum inside parentheses: calculate 1 + r = 1.05.
+4. Compute the exponentiation: calculate 1.05^3 = 1.157625.
+5. Multiply by principal: compute A = 1000 * 1.157625 = 1157.625 (unrounded).
+6. Compute total interest unrounded: Interest = A - P = 
+   1157.625 - 1000 = 157.625.
+7. Round results to 2 decimals and format as USD: A → $1157.63;
+   Interest → $157.63.
+8. Return deliverables: formula, each arithmetic step with numbers substituted,
+   unrounded A, rounded A in USD, and rounded interest in USD.
+
+### Failure And Recovery Strategies
+(No failure and recovery strategies recorded)
+
+### Notes And Observations
+No errors encountered.
+</MARKDOWN CONTENT>
+
+System message of math agent after loading workflow:
+You are a math expert specialized in solving mathematical problems. 
+You can perform calculations, solve equations, and work with various
+mathematical concepts. 
+Use the math tools available to you.
+
+--- Workflow Memory ---
+The following is the context from a previous session or workflow which might be
+useful for to the current task. This information might help you understand the
+background, choose which tools to use, and plan your next steps.
+
+## WorkflowSummary
+[The Workflow Summary from the above]
+"""

camel/toolkits/context_summarizer_toolkit.py

@@ -83,7 +83,7 @@
     [
         ModelFactory.create(
             model_platform=ModelPlatformType.OPENAI,
-            model_type=ModelType.GPT_5_MINI,
+            model_type=ModelType.DEFAULT,
         ),
         pytest.param(None, marks=pytest.mark.model_backend),
     ],
@@ -103,11 +103,11 @@ def test_chat_agent(model, step_call_count=3):
     assistant_without_sys_msg = ChatAgent(model=model)
 
     assert str(assistant_with_sys_msg) == (
-        "ChatAgent(doctor, " f"RoleType.ASSISTANT, {ModelType.GPT_5_MINI})"
+        "ChatAgent(doctor, " f"RoleType.ASSISTANT, {ModelType.DEFAULT})"
     )
     assert str(assistant_without_sys_msg) == (
         "ChatAgent(assistant, "
-        f"RoleType.ASSISTANT, {UnifiedModelType(ModelType.GPT_5_MINI)})"
+        f"RoleType.ASSISTANT, {UnifiedModelType(ModelType.DEFAULT)})"
     )
 
     for assistant in [assistant_with_sys_msg, assistant_without_sys_msg]: