Add AG2 (formerly AutoGen) as a third AI engine (AI_ENGINE=ag2)

README.md

@@ -26,6 +26,7 @@ The Evo AI platform allows:
 - Custom tools management
 - **[Google Agent Development Kit (ADK)](https://google.github.io/adk-docs/)**: Base framework for agent development
 - **[CrewAI Support](https://github.com/crewAI/crewAI)**: Alternative framework for agent development (in development)
+- **[AG2 (formerly AutoGen)](https://github.com/ag2ai/ag2)**: Dynamic GroupChat, context-variable handoffs, and human-in-the-loop (`AI_ENGINE=ag2`)
 - JWT authentication with email verification
 - **[Agent 2 Agent (A2A) Protocol Support](https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/)**: Interoperability between AI agents
 - **[Workflow Agent with LangGraph](https://www.langchain.com/langgraph)**: Building complex agent workflows

pyproject.toml

@@ -55,6 +55,7 @@ dependencies = [
     "crewai==0.120.1",
     "crewai-tools==0.45.0",
     "a2a-sdk==0.2.4",
+    "ag2[openai]>=0.11.0",
 ]
 
 [project.optional-dependencies]

src/api/chat_routes.py

@@ -52,6 +52,7 @@
 from src.schemas.chat import ChatRequest, ChatResponse, ErrorResponse, FileData
 from src.services.adk.agent_runner import run_agent as run_agent_adk, run_agent_stream
 from src.services.crewai.agent_runner import run_agent as run_agent_crewai
+from src.services.ag2.agent_runner import run_agent as run_agent_ag2, run_agent_stream as run_agent_stream_ag2
 from src.core.exceptions import AgentNotFoundError
 from src.services.service_providers import (
     session_service,
@@ -221,16 +222,26 @@ async def websocket_chat(
                             logger.error(f"Error processing files: {str(e)}")
                             files = None
 
-                    async for chunk in run_agent_stream(
-                        agent_id=agent_id,
-                        external_id=external_id,
-                        message=message,
-                        session_service=session_service,
-                        artifacts_service=artifacts_service,
-                        memory_service=memory_service,
-                        db=db,
-                        files=files,
-                    ):
+                    if settings.AI_ENGINE == "ag2":
+                        stream_gen = run_agent_stream_ag2(
+                            agent_id=agent_id,
+                            external_id=external_id,
+                            message=message,
+                            db=db,
+                            files=files,
+                        )
+                    else:
+                        stream_gen = run_agent_stream(
+                            agent_id=agent_id,
+                            external_id=external_id,
+                            message=message,
+                            session_service=session_service,
+                            artifacts_service=artifacts_service,
+                            memory_service=memory_service,
+                            db=db,
+                            files=files,
+                        )
+                    async for chunk in stream_gen:
                         await websocket.send_json(
                             {"message": json.loads(chunk), "turn_complete": False}
                         )
@@ -300,6 +311,15 @@ async def chat(
                 db,
                 files=request.files,
             )
+        elif settings.AI_ENGINE == "ag2":
+            final_response = await run_agent_ag2(
+                agent_id,
+                external_id,
+                request.message,
+                session_service,
+                db,
+                files=request.files,
+            )
 
         return {
             "response": final_response["final_response"],

src/services/__init__.py

@@ -1 +1,5 @@
-from .adk.agent_runner import run_agent
+# google-adk is an optional dependency — guard so unit tests run without the full stack
+try:
+    from .adk.agent_runner import run_agent
+except ImportError:
+    pass

src/services/ag2/agent_builder.py

@@ -0,0 +1,199 @@
+import uuid
+from typing import Tuple, Optional
+from autogen import ConversableAgent, LLMConfig
+from autogen.agentchat import initiate_group_chat
+from autogen.agentchat.group.patterns import DefaultPattern, AutoPattern
+from autogen.agentchat.group import (
+    ContextVariables,
+    RevertToUserTarget,
+    TerminateTarget,
+    AgentTarget,
+    OnCondition,
+    StringLLMCondition,
+    OnContextCondition,
+    ExpressionContextCondition,
+    ContextExpression,
+)
+from sqlalchemy.orm import Session
+from src.services.agent_service import get_agent
+from src.services.apikey_service import get_decrypted_api_key
+from src.utils.logger import setup_logger
+
+logger = setup_logger(__name__)
+
+
+class AG2AgentBuilder:
+    def __init__(self, db: Session):
+        self.db = db
+
+    async def _get_api_key(self, agent) -> str:
+        """Reuse the same key resolution logic as ADK and CrewAI builders."""
+        if hasattr(agent, "api_key_id") and agent.api_key_id:
+            key = get_decrypted_api_key(self.db, agent.api_key_id)
+            if key:
+                return key
+            raise ValueError(f"API key {agent.api_key_id} not found or inactive")
+        config_key = agent.config.get("api_key") if agent.config else None
+        if config_key:
+            try:
+                key = get_decrypted_api_key(self.db, uuid.UUID(config_key))
+                return key or config_key
+            except (ValueError, TypeError):
+                return config_key
+        raise ValueError(f"No API key configured for agent {agent.name}")
+
+    def _build_llm_config(self, agent, api_key: str) -> LLMConfig:
+        return LLMConfig({"model": agent.model, "api_key": api_key})
+
+    def _build_system_message(self, agent) -> str:
+        parts = []
+        if agent.role:
+            parts.append(f"Role: {agent.role}")
+        if agent.goal:
+            parts.append(f"Goal: {agent.goal}")
+        if agent.instruction:
+            parts.append(agent.instruction)
+        return "\n\n".join(parts)
+
+    async def build_conversable_agent(self, agent) -> ConversableAgent:
+        api_key = await self._get_api_key(agent)
+        # AG2 0.11+ rejects names containing whitespace for OpenAI models
+        safe_name = agent.name.replace(" ", "_")
+        return ConversableAgent(
+            name=safe_name,
+            system_message=self._build_system_message(agent),
+            description=agent.description or "",
+            llm_config=self._build_llm_config(agent, api_key),
+        )
+
+    def _apply_handoffs(self, ca: ConversableAgent, config: dict, all_agents: dict):
+        """
+        Apply AG2 handoff conditions from the agent config's optional 'handoffs' field.
+
+        Config format:
+        {
+          "handoffs": [
+            {
+              "type": "llm",
+              "target_agent_id": "<uuid>",
+              "condition": "Route when the user asks about billing"
+            },
+            {
+              "type": "context",
+              "target_agent_id": "<uuid>",
+              "expression": "${is_vip} == True"
+            }
+          ],
+          "after_work": "revert_to_user"  // or "terminate"
+        }
+        """
+        handoffs_config = config.get("handoffs", [])
+        llm_conditions = []
+        context_conditions = []
+
+        for h in handoffs_config:
+            target_id = h.get("target_agent_id")
+            target_agent = all_agents.get(str(target_id))
+            if not target_agent:
+                logger.warning(f"Handoff target {target_id} not found, skipping")
+                continue
+
+            if h["type"] == "llm":
+                llm_conditions.append(
+                    OnCondition(
+                        target=AgentTarget(target_agent),
+                        condition=StringLLMCondition(prompt=h["condition"]),
+                    )
+                )
+            elif h["type"] == "context":
+                context_conditions.append(
+                    OnContextCondition(
+                        target=AgentTarget(target_agent),
+                        condition=ExpressionContextCondition(
+                            expression=ContextExpression(h["expression"])
+                        ),
+                    )
+                )
+
+        if llm_conditions:
+            ca.handoffs.add_llm_conditions(llm_conditions)
+        if context_conditions:
+            ca.handoffs.add_context_conditions(context_conditions)
+
+        after_work = config.get("after_work", "revert_to_user")
+        if after_work == "terminate":
+            ca.handoffs.set_after_work(TerminateTarget())
+        else:
+            ca.handoffs.set_after_work(RevertToUserTarget())
+
+    async def build_group_chat_setup(self, root_agent) -> dict:
+        """
+        Build a GroupChat pattern from an agent record with sub_agents.
+        Returns a dict consumed by the runner's initiate_group_chat call.
+        """
+        config = root_agent.config or {}
+        sub_agent_ids = config.get("sub_agents", [])
+        if not sub_agent_ids:
+            raise ValueError("group_chat agent requires at least one sub_agent")
+
+        # Build all sub-agents first so handoff resolution can reference them
+        all_agents = {}
+        agents = []
+        for aid in sub_agent_ids:
+            db_agent = get_agent(self.db, str(aid))
+            if db_agent is None:
+                raise ValueError(f"Sub-agent {aid} not found")
+            ca = await self.build_conversable_agent(db_agent)
+            all_agents[str(aid)] = ca
+            agents.append(ca)
+
+        root_ca = await self.build_conversable_agent(root_agent)
+        all_agents[str(root_agent.id)] = root_ca
+
+        # Apply handoffs to each agent if configured
+        for aid in sub_agent_ids:
+            db_agent = get_agent(self.db, str(aid))
+            if db_agent and db_agent.config:
+                self._apply_handoffs(all_agents[str(aid)], db_agent.config, all_agents)
+
+        api_key = await self._get_api_key(root_agent)
+        manager_llm = self._build_llm_config(root_agent, api_key)
+
+        pattern_type = config.get("pattern", "auto")
+        if pattern_type == "auto":
+            pattern = AutoPattern(
+                initial_agent=root_ca,
+                agents=[root_ca] + agents,
+                group_manager_args={"llm_config": manager_llm},
+            )
+        else:
+            pattern = DefaultPattern(
+                initial_agent=root_ca,
+                agents=[root_ca] + agents,
+                group_after_work=RevertToUserTarget(),
+            )
+
+        return {
+            "pattern": pattern,
+            "agents": [root_ca] + agents,
+            "max_rounds": config.get("max_rounds", 10),
+            "context_variables": ContextVariables(
+                data=config.get("context_variables", {})
+            ),
+        }
+
+    async def build_agent(self, root_agent) -> Tuple[object, None]:
+        """
+        Entry point matching the ADK/CrewAI AgentBuilder interface.
+        Returns (agent_or_setup_dict, exit_stack).
+
+        Orchestration mode is read from config["ag2_mode"]:
+          "group_chat" → GroupChat with sub-agents from config["sub_agents"]
+          "single" / absent → single ConversableAgent (default)
+        No new agent type is required in the DB; all AG2 agents use type="llm".
+        """
+        ag2_mode = (root_agent.config or {}).get("ag2_mode", "single")
+        if ag2_mode == "group_chat":
+            return await self.build_group_chat_setup(root_agent), None
+        else:
+            return await self.build_conversable_agent(root_agent), None