feat(agent): add support for CrewAI agents and update related configurations

Author: Davidson Gomes

CHANGELOG.md

@@ -5,6 +5,12 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.0.10] - develop
+
+### Added
+
+- Add CrewAI agents
+
 ## [0.0.9] - 2025-05-13
 
 ### Added

README.md

@@ -11,9 +11,11 @@ The Evo AI platform allows:
 - Client management
 - MCP server configuration
 - Custom tools management
+- **[Google Agent Development Kit (ADK)](https://google.github.io/adk-docs/)**: Base framework for agent development, providing support for LLM Agents, Sequential Agents, Loop Agents, Parallel Agents and Custom Agents
 - JWT authentication with email verification
-- **Agent 2 Agent (A2A) Protocol Support**: Interoperability between AI agents following Google's A2A specification
-- **Workflow Agent with LangGraph**: Building complex agent workflows with LangGraph and ReactFlow
+- **[Agent 2 Agent (A2A) Protocol Support](https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/)**: Interoperability between AI agents following Google's A2A specification
+- **[Workflow Agent with LangGraph](https://www.langchain.com/langgraph)**: Building complex agent workflows with LangGraph and ReactFlow
+- **[CrewAI Agent Support](https://www.crewai.com/)**: Organizing agents into specialized crews with assigned tasks
 - **Secure API Key Management**: Encrypted storage of API keys with Fernet encryption
 - **Agent Organization**: Folder structure for organizing agents by categories
 
@@ -30,6 +32,8 @@ Agent based on language models like GPT-4, Claude, etc. Can be configured with t
   "client_id": "{{client_id}}",
   "name": "personal_assistant",
   "description": "Specialized personal assistant",
+  "role": "Personal Assistant",
+  "goal": "Help users with daily tasks and provide relevant information",
   "type": "llm",
   "model": "gpt-4",
   "api_key_id": "stored-api-key-uuid",
@@ -150,6 +154,39 @@ Executes sub-agents in a custom workflow defined by a graph structure. This agen
 
 The workflow structure is built using ReactFlow in the frontend, allowing visual creation and editing of complex agent workflows with nodes (representing agents or decision points) and edges (representing flow connections).
 
+### 7. CrewAI Agent
+
+Allows organizing agents into a "crew" with specific tasks assigned to each agent. Based on the CrewAI concept, where each agent has a specific responsibility to perform a more complex task collaboratively.
+
+```json
+{
+  "client_id": "{{client_id}}",
+  "name": "research_crew",
+  "type": "crew_ai",
+  "folder_id": "folder_id (optional)",
+  "config": {
+    "tasks": [
+      {
+        "agent_id": "agent-uuid-1",
+        "description": "Search for recent information on the topic",
+        "expected_output": "Search report in JSON format"
+      },
+      {
+        "agent_id": "agent-uuid-2",
+        "description": "Analyze data and create visualizations",
+        "expected_output": "Charts and analyses in HTML format"
+      },
+      {
+        "agent_id": "agent-uuid-3",
+        "description": "Write final report combining results",
+        "expected_output": "Markdown document with complete analysis"
+      }
+    ],
+    "sub_agents": ["agent-uuid-4", "agent-uuid-5"]
+  }
+}
+```
+
 ### Common Characteristics
 
 - All agent types can have sub-agents
@@ -355,7 +392,7 @@ Evo AI implements the Google's Agent 2 Agent (A2A) protocol, enabling seamless c
 - **Standardized Communication**: Agents can communicate using a common protocol regardless of their underlying implementation
 - **Interoperability**: Support for agents built with different frameworks and technologies
 - **Well-Known Endpoints**: Standardized endpoints for agent discovery and interaction
-- **Task Management**: Support for task-based interactions between agents
+- **Task Management**: Support for task creation, execution, and status tracking
 - **State Management**: Tracking of agent states and conversation history
 - **Authentication**: Secure API key-based authentication for agent interactions
 

migrations/versions/611d84e70bb2_add_crew_ai_coluns_agents_table.py

@@ -0,0 +1,34 @@
+"""add_crew_ai_coluns_agents_table
+
+Revision ID: 611d84e70bb2
+Revises: bdc5d363e2e1
+Create Date: 2025-05-14 07:31:08.741620
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '611d84e70bb2'
+down_revision: Union[str, None] = 'bdc5d363e2e1'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column('agents', sa.Column('role', sa.String(), nullable=True))
+    op.add_column('agents', sa.Column('goal', sa.Text(), nullable=True))
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_column('agents', 'goal')
+    op.drop_column('agents', 'role')
+    # ### end Alembic commands ###

migrations/versions/bdc5d363e2e1_add_crew_ai_agent_type_agents_table.py

@@ -0,0 +1,43 @@
+"""add_crew_ai_agent_type_agents_table
+
+Revision ID: bdc5d363e2e1
+Revises: 6db4a526335b
+Create Date: 2025-05-14 06:23:14.701878
+
+"""
+
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = "bdc5d363e2e1"
+down_revision: Union[str, None] = "6db4a526335b"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_constraint("check_agent_type", "agents", type_="check")
+    op.create_check_constraint(
+        "check_agent_type",
+        "agents",
+        "type IN ('llm', 'sequential', 'parallel', 'loop', 'a2a', 'workflow', 'crew_ai')",
+    )
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_constraint("check_agent_type", "agents", type_="check")
+    op.create_check_constraint(
+        "check_agent_type",
+        "agents",
+        "type IN ('llm', 'sequential', 'parallel', 'loop', 'a2a', 'workflow')",
+    )
+    # ### end Alembic commands ###

pyproject.toml

@@ -51,6 +51,7 @@ dependencies = [
     "langgraph==0.4.1",
     "opentelemetry-sdk==1.33.0",
     "opentelemetry-exporter-otlp==1.33.0",
+    "crewai==0.119.0",
 ]
 
 [project.optional-dependencies]

src/api/agent_routes.py

@@ -24,6 +24,8 @@
 │ For any future changes to the code in this file, it is recommended to        │
 │ include, together with the modification, the information of the developer    │
 │ who changed it and the date of modification.                                 │
+│                                                                              │
+│ @update: May 14, 2025 - Added support for crew_ai agent type                 │
 └──────────────────────────────────────────────────────────────────────────────┘
 """
 

src/models/models.py

@@ -100,6 +100,8 @@ class Agent(Base):
     id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
     client_id = Column(UUID(as_uuid=True), ForeignKey("clients.id", ondelete="CASCADE"))
     name = Column(String, nullable=False)
+    role = Column(String, nullable=True)
+    goal = Column(Text, nullable=True)
     description = Column(Text, nullable=True)
     type = Column(String, nullable=False)
     model = Column(String, nullable=True, default="")
@@ -121,7 +123,7 @@ class Agent(Base):
 
     __table_args__ = (
         CheckConstraint(
-            "type IN ('llm', 'sequential', 'parallel', 'loop', 'a2a', 'workflow')",
+            "type IN ('llm', 'sequential', 'parallel', 'loop', 'a2a', 'workflow', 'crew_ai')",
             name="check_agent_type",
         ),
     )

src/schemas/agent_config.py

@@ -32,6 +32,8 @@
 from uuid import UUID
 import secrets
 import string
+import uuid
+from pydantic import validator
 
 
 class ToolConfig(BaseModel):
@@ -234,3 +236,42 @@ class WorkflowConfig(BaseModel):
 
     class Config:
         from_attributes = True
+
+
+class CrewAITask(BaseModel):
+    """Task configuration for Crew AI agents"""
+
+    agent_id: Union[UUID, str] = Field(
+        ..., description="ID of the agent assigned to this task"
+    )
+    description: str = Field(..., description="Description of the task to be performed")
+    expected_output: str = Field(..., description="Expected output from this task")
+
+    @validator("agent_id")
+    def validate_agent_id(cls, v):
+        if isinstance(v, str):
+            try:
+                return uuid.UUID(v)
+            except ValueError:
+                raise ValueError(f"Invalid UUID format for agent_id: {v}")
+        return v
+
+    class Config:
+        from_attributes = True
+
+
+class CrewAIConfig(BaseModel):
+    """Configuration for Crew AI agents"""
+
+    tasks: List[CrewAITask] = Field(
+        ..., description="List of tasks to be performed by the crew"
+    )
+    api_key: Optional[str] = Field(
+        default_factory=generate_api_key, description="API key for the Crew AI agent"
+    )
+    sub_agents: Optional[List[UUID]] = Field(
+        default_factory=list, description="List of IDs of sub-agents used in crew"
+    )
+
+    class Config:
+        from_attributes = True

src/schemas/schemas.py

@@ -33,7 +33,7 @@
 from uuid import UUID
 import uuid
 import re
-from src.schemas.agent_config import LLMConfig
+from src.schemas.agent_config import LLMConfig, CrewAIConfig
 
 
 class ClientBase(BaseModel):
@@ -94,8 +94,11 @@ class AgentBase(BaseModel):
         None, description="Agent name (no spaces or special characters)"
     )
     description: Optional[str] = Field(None, description="Agent description")
+    role: Optional[str] = Field(None, description="Agent role in the system")
+    goal: Optional[str] = Field(None, description="Agent goal or objective")
     type: str = Field(
-        ..., description="Agent type (llm, sequential, parallel, loop, a2a, workflow)"
+        ...,
+        description="Agent type (llm, sequential, parallel, loop, a2a, workflow, crew_ai)",
     )
     model: Optional[str] = Field(
         None, description="Agent model (required only for llm type)"
@@ -126,9 +129,17 @@ def validate_name(cls, v, values):
 
     @validator("type")
     def validate_type(cls, v):
-        if v not in ["llm", "sequential", "parallel", "loop", "a2a", "workflow"]:
+        if v not in [
+            "llm",
+            "sequential",
+            "parallel",
+            "loop",
+            "a2a",
+            "workflow",
+            "crew_ai",
+        ]:
             raise ValueError(
-                "Invalid agent type. Must be: llm, sequential, parallel, loop, a2a or workflow"
+                "Invalid agent type. Must be: llm, sequential, parallel, loop, a2a, workflow or crew_ai"
             )
         return v
 
@@ -188,6 +199,33 @@ def validate_config(cls, v, values):
                 raise ValueError(
                     f'Agent {values["type"]} must have at least one sub-agent'
                 )
+        elif values["type"] == "crew_ai":
+            if not isinstance(v, dict):
+                raise ValueError(f'Invalid configuration for agent {values["type"]}')
+            if "tasks" not in v:
+                raise ValueError(f'Agent {values["type"]} must have tasks')
+            if not isinstance(v["tasks"], list):
+                raise ValueError("tasks must be a list")
+            if not v["tasks"]:
+                raise ValueError(f'Agent {values["type"]} must have at least one task')
+            for task in v["tasks"]:
+                if not isinstance(task, dict):
+                    raise ValueError("Each task must be a dictionary")
+                required_fields = ["agent_id", "description", "expected_output"]
+                for field in required_fields:
+                    if field not in task:
+                        raise ValueError(f"Task missing required field: {field}")
+
+            # Validar sub_agents, se existir
+            if "sub_agents" in v and v["sub_agents"] is not None:
+                if not isinstance(v["sub_agents"], list):
+                    raise ValueError("sub_agents must be a list")
+
+            try:
+                # Convert the dictionary to CrewAIConfig
+                v = CrewAIConfig(**v)
+            except Exception as e:
+                raise ValueError(f"Invalid Crew AI configuration for agent: {str(e)}")
 
         return v