feat: migrate agent to LangGraph and bump version to 2.1.0

- Migrate agent implementation from LangChain's legacy `AgentExecutor` to LangGraph's `create_react_agent`.
- Update dependencies in `pyproject.toml` to include `langgraph` and newer versions of `langchain`.
- Refactor `create_commit_agent` to handle system prompt injection within the graph-based architecture.
- Set a default value for the `deep` flag in the CLI.
- Update unit tests to support the new agent execution flow and response structure.

Author: lguibr

commitai/__init__.py

@@ -3,7 +3,7 @@
 
 # This __version__ string is read by hatchling during the build process
 # Make sure to update it for new releases.
-__version__ = "2.0.0"
+__version__ = "2.1.0"
 
 # The importlib.metadata approach is generally for reading the version
 # of an *already installed* package at runtime. We don't need it here

commitai/agent.py

@@ -3,11 +3,11 @@
 import subprocess
 from typing import Any, Dict, Type
 
-from langchain.agents import AgentExecutor, create_tool_calling_agent
+# from langchain.agents import AgentExecutor, create_tool_calling_agent # Removed
 from langchain_core.language_models import BaseChatModel
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain_core.runnables import Runnable
 from langchain_core.tools import BaseTool
+from langgraph.prebuilt import create_react_agent
 from pydantic import BaseModel, Field
 
 # --- TOOLS ---
@@ -157,6 +157,9 @@ def process(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
 # --- AGENT ---
 
 
+# --- AGENT ---
+
+
 def create_commit_agent(llm: BaseChatModel) -> Runnable:
     # 1. Init Tools
     tools = [ReadOnlyShellTool(), FileSearchTool(), FileReadTool()]
@@ -186,18 +189,22 @@ def create_commit_agent(llm: BaseChatModel) -> Runnable:
 3. If clarification is needed, explore files.
 4. Final Answer MUST be ONLY the commit message.
 """
-    prompt = ChatPromptTemplate.from_messages(
-        [
-            ("system", system_prompt),
-            MessagesPlaceholder("chat_history", optional=True),
-            ("human", "Generate the commit message."),
-            MessagesPlaceholder("agent_scratchpad"),
-        ]
-    )
+    # Note: create_react_agent handles the prompt internally or via state_modifier.
+    # We can pass a system string or a function. Since our prompt depends on dynamic
+    # variables (diff, explanation, etc.), we need to inject them. LangGraph's
+    # prebuilt agent usually takes a static system message. However, we can use the
+    # 'messages' state. But to keep it simple and compatible with existing 'invoke'
+    # interface: We will format the system prompt in the wrapper and pass it as the
+    # first message.
+
+    # Actually, create_react_agent supports 'state_modifier'.
+    # If we pass a formatted string, it works as system prompt.
+
+    # 4. Construct Graph
+    # We don't construct the graph with ALL variables pre-bound if they change per run.
+    # Instead, we'll format the prompt in the pipeline and pass it to the agent.
 
-    # 4. Construct Agent
-    agent = create_tool_calling_agent(llm, tools, prompt)
-    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=False)
+    agent_graph = create_react_agent(llm, tools)
 
     # 5. Pipeline with Middleware
     def run_pipeline(inputs: Dict[str, Any]) -> str:
@@ -210,11 +217,34 @@ def run_pipeline(inputs: Dict[str, Any]) -> str:
         state.setdefault("explanation", "None")
         state.setdefault("summary", "None")
         state.setdefault("todo_str", "None")
-        state.setdefault("chat_history", [])
+
+        # Format System Prompt
+        formatted_system_prompt = system_prompt.format(
+            explanation=state["explanation"],
+            todo_str=state["todo_str"],
+            summary=state["summary"],
+            diff=state.get("diff", ""),
+        )
 
         # Run Agent
-        result = agent_executor.invoke(state)
-        return str(result["output"])
+        # LangGraph inputs: {"messages": [{"role": "user", "content": ...}]}
+        # We inject the system prompt as a SystemMessage or just update the state.
+        # create_react_agent primarily looks at 'messages'.
+
+        from langchain_core.messages import HumanMessage, SystemMessage
+
+        messages = [
+            SystemMessage(content=formatted_system_prompt),
+            HumanMessage(content="Generate the commit message."),
+        ]
+
+        # Invoke graph
+        # result is a dict with 'messages'
+        result = agent_graph.invoke({"messages": messages})
+
+        # Extract last message content
+        last_message = result["messages"][-1]
+        return str(last_message.content)
 
     # Wrap in RunnableLambda to expose 'invoke'
     from langchain_core.runnables import RunnableLambda

commitai/cli.py

@@ -197,7 +197,7 @@ def generate_message(
     template: Optional[str],
     add: bool,
     model: str,
-    deep: bool,
+    deep: bool = False,
 ) -> None:
     explanation = " ".join(description)
 

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "hatchling.build"
 name = "commitai"
 # Make sure to update version in commitai/__init__.py as well
 
-version = "2.0.0"
+version = "2.1.0"
 
 description = "Commitai helps you generate git commit messages using AI"
 readme = "README.md"
@@ -30,10 +30,11 @@ classifiers = [
 ]
 dependencies = [
     "click>=8.1",
-    "langchain>=0.2",
+    "langchain>=1.0",
+    "langchain-community>=0.4.0",
     "langchain-core>=1.0",
-    "langchain-community>=0.2",
     "langchain-google-genai>=1.0",
+    "langgraph>=1.0.0",
     "pydantic>=2.0",
 ]
 

tests/test_agent_chains.py

@@ -62,17 +62,25 @@ def test_todo_scanner_middleware(mock_llm):
 
 def test_create_commit_agent(mock_llm):
     # This tests the factory function
-    agent_executor = create_commit_agent(mock_llm)
-    assert agent_executor is not None
-    # We can try to invoke it if we mock enough stuff
-    # But just creating it covers the definition lines.
+    # Mocking create_react_agent to avoid actual graph compilation
+    with patch("commitai.agent.create_react_agent") as mock_create_graph:
+        mock_graph = MagicMock()
+        mock_create_graph.return_value = mock_graph
+        agent_executor = create_commit_agent(mock_llm)
+        assert agent_executor is not None
 
 
 def test_agent_run(mock_llm):
     # E2E-ish test of the agent logic with mocks
-    with patch("commitai.agent.AgentExecutor") as MockExecutor:
-        mock_executor_instance = MockExecutor.return_value
-        mock_executor_instance.invoke.return_value = {"output": "Final Commit Message"}
+    with patch("commitai.agent.create_react_agent") as mock_create_graph:
+        mock_graph = MagicMock()
+        mock_create_graph.return_value = mock_graph
+
+        # Determine strict return structure for LangGraph invoke
+        # It yields a dict with "messages" list
+        last_message = MagicMock()
+        last_message.content = "Final Commit Message"
+        mock_graph.invoke.return_value = {"messages": [last_message]}
 
         agent_runnable = create_commit_agent(mock_llm)
         result = agent_runnable.invoke({"diff": "diff", "explanation": "expl"})

tests/test_cli.py

@@ -41,6 +41,7 @@ def mock_generate_deps(tmp_path):
             "commitai.cli.get_current_branch_name", return_value="main"
         ) as mock_branch,
         patch("commitai.cli.create_commit") as mock_commit,
+        # Update mock target for agent creation
         patch("commitai.cli.create_commit_agent") as mock_create_agent,
         patch("click.edit") as mock_edit,
         patch("click.clear"),
@@ -57,10 +58,10 @@ def mock_generate_deps(tmp_path):
 
         mock_google_instance = mock_google_class_in_cli.return_value
 
-        # Agent Mock
-        mock_agent_instance = MagicMock()
-        mock_agent_instance.invoke.return_value = "Generated commit message"
-        mock_create_agent.return_value = mock_agent_instance
+        # Agent Mock (RunnableLambda now)
+        mock_agent_runnable = MagicMock()
+        mock_agent_runnable.invoke.return_value = "Generated commit message"
+        mock_create_agent.return_value = mock_agent_runnable
 
         if mock_google_class_in_cli is not None:
             mock_google_instance.spec = ActualChatGoogleGenerativeAI
@@ -93,7 +94,7 @@ def getenv_side_effect(key, default=None):
             "path_exists": mock_path_exists,
             "commit_msg_path": fake_commit_msg_path,
             "create_agent": mock_create_agent,
-            "agent_instance": mock_agent_instance,
+            "agent_instance": mock_agent_runnable,  # Still useful alias for tests
             "confirm": mock_confirm,
         }