fix types in agent completer + version bump

Author: evalstate

.gitignore

@@ -190,3 +190,4 @@ tests/integration/workflow/orchestrator/fastagent.jsonl
 fastagent.jsonl
 tests/integration/resources/fastagent.jsonl
 fastagent.jsonl
+tests/e2e/workflow/weather_location.txt

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "fast-agent-mcp"
-version = "0.2.5"
+version = "0.2.6"
 description = "Define, Prompt and Test MCP enabled Agents and Workflows"
 readme = "README.md"
 license = { file = "LICENSE" }

src/mcp_agent/agents/agent.py

@@ -66,7 +66,7 @@ async def prompt(self, default_prompt: str = "", agent_name: Optional[str] = Non
         agent_name_str = str(self.name)
 
         # Create agent_types dictionary with just this agent
-        agent_types = {agent_name_str: getattr(self.config, "agent_type", "Agent")}
+        agent_types = {agent_name_str: self.agent_type}
 
         # Create the interactive prompt
         prompt = InteractivePrompt(agent_types=agent_types)

src/mcp_agent/agents/base_agent.py

@@ -622,6 +622,18 @@ async def apply_prompt_messages(
         response = await self.generate(prompts, request_params)
         return response.first_text()
 
+    @property
+    def agent_type(self) -> str:
+        """
+        Return the type of this agent. 
+        
+        This is used for display purposes in the interactive prompt and other UI elements.
+        
+        Returns:
+            String representing the agent type
+        """
+        return self.config.agent_type
+        
     @property
     def message_history(self) -> List[PromptMessageMultipart]:
         """

src/mcp_agent/agents/workflow/chain_agent.py

@@ -9,8 +9,9 @@
 
 from mcp.types import TextContent
 
-from mcp_agent.agents.agent import Agent, AgentConfig
+from mcp_agent.agents.agent import Agent
 from mcp_agent.agents.base_agent import BaseAgent
+from mcp_agent.core.agent_types import AgentConfig, AgentType
 from mcp_agent.core.prompt import Prompt
 from mcp_agent.core.request_params import RequestParams
 from mcp_agent.mcp.interfaces import ModelT
@@ -22,6 +23,11 @@ class ChainAgent(BaseAgent):
     A chain agent that processes requests through a series of specialized agents in sequence.
     Passes the output of each agent to the next agent in the chain.
     """
+    
+    @property
+    def agent_type(self) -> str:
+        """Return the type of this agent."""
+        return AgentType.CHAIN.value
 
     def __init__(
         self,

src/mcp_agent/agents/workflow/evaluator_optimizer.py

@@ -14,6 +14,7 @@
 
 from mcp_agent.agents.agent import Agent
 from mcp_agent.agents.base_agent import BaseAgent
+from mcp_agent.core.agent_types import AgentType
 from mcp_agent.core.exceptions import AgentConfigError
 from mcp_agent.core.prompt import Prompt
 from mcp_agent.core.request_params import RequestParams
@@ -63,6 +64,11 @@ class EvaluatorOptimizerAgent(BaseAgent):
     for refinement, continuing until a quality threshold is reached or a maximum
     number of refinement cycles is completed.
     """
+    
+    @property
+    def agent_type(self) -> str:
+        """Return the type of this agent."""
+        return AgentType.EVALUATOR_OPTIMIZER.value
 
     def __init__(
         self,

src/mcp_agent/agents/workflow/orchestrator_agent.py

@@ -27,7 +27,7 @@
     SYNTHESIZE_PLAN_PROMPT_TEMPLATE,
     TASK_PROMPT_TEMPLATE,
 )
-from mcp_agent.core.agent_types import AgentConfig
+from mcp_agent.core.agent_types import AgentConfig, AgentType
 from mcp_agent.core.exceptions import AgentConfigError
 from mcp_agent.core.prompt import Prompt
 from mcp_agent.core.request_params import RequestParams
@@ -46,6 +46,11 @@ class OrchestratorAgent(BaseAgent):
     to specialized worker agents, synthesizing their results into a cohesive output.
     Supports both full planning and iterative planning modes.
     """
+    
+    @property
+    def agent_type(self) -> str:
+        """Return the type of this agent."""
+        return AgentType.ORCHESTRATOR.value
 
     def __init__(
         self,

src/mcp_agent/agents/workflow/parallel_agent.py

@@ -3,8 +3,9 @@
 
 from mcp.types import TextContent
 
-from mcp_agent.agents.agent import Agent, AgentConfig
+from mcp_agent.agents.agent import Agent
 from mcp_agent.agents.base_agent import BaseAgent
+from mcp_agent.core.agent_types import AgentConfig, AgentType
 from mcp_agent.core.request_params import RequestParams
 from mcp_agent.mcp.interfaces import ModelT
 from mcp_agent.mcp.prompt_message_multipart import PromptMessageMultipart
@@ -17,6 +18,11 @@ class ParallelAgent(BaseAgent):
     This workflow performs both the fan-out and fan-in operations using LLMs.
     From the user's perspective, an input is specified and the output is returned.
     """
+    
+    @property
+    def agent_type(self) -> str:
+        """Return the type of this agent."""
+        return AgentType.PARALLEL.value
 
     def __init__(
         self,

src/mcp_agent/agents/workflow/router_agent.py

@@ -12,7 +12,7 @@
 
 from mcp_agent.agents.agent import Agent
 from mcp_agent.agents.base_agent import BaseAgent
-from mcp_agent.core.agent_types import AgentConfig
+from mcp_agent.core.agent_types import AgentConfig, AgentType
 from mcp_agent.core.exceptions import AgentConfigError
 from mcp_agent.core.prompt import Prompt
 from mcp_agent.core.request_params import RequestParams
@@ -87,6 +87,11 @@ class RouterAgent(BaseAgent):
     A simplified router that uses an LLM to determine the best agent for a request,
     then dispatches the request to that agent and returns the response.
     """
+    
+    @property
+    def agent_type(self) -> str:
+        """Return the type of this agent."""
+        return AgentType.ROUTER.value
 
     def __init__(
         self,

src/mcp_agent/core/agent_app.py

@@ -248,18 +248,7 @@ async def interactive(self, agent: str | None = None, default_prompt: str = "")
         # The agent's prompt method doesn't fully support switching between agents
 
         # Create agent_types dictionary mapping agent names to their types
-        agent_types = {}
-        for name, agent in self._agents.items():
-            # Determine agent type if possible
-            agent_type = "Agent"  # Default type
-
-            # Try to get the type from the agent directly
-            if hasattr(agent, "agent_type"):
-                agent_type = agent.agent_type
-            elif hasattr(agent, "config") and hasattr(agent.config, "agent_type"):
-                agent_type = agent.config.agent_type
-
-            agent_types[name] = agent_type
+        agent_types = {name: agent.agent_type for name, agent in self._agents.items()}
 
         # Create the interactive prompt
         prompt = InteractivePrompt(agent_types=agent_types)