"Modified Dockerfile for HF Deployement, Made changes to main graph and intermediate graph states"

.gitignore

@@ -35,7 +35,14 @@ __pycache__/
 *.py[cod]
 *.pyc
 
-# Git backup
-src/.git_backup
+.vscode/*
+.cursor/*
 src/job_writing_agent.egg-info/*
-
+pyrightconfig.json
+app_env/*
+requirements.txt
+.langgraph_api/*
+.src/job_writing_agent/logs/*
+docker-compose.override.example.yml
+DOCKERFILE_EXPLANATION.md
+DEPLOYMENT_GUIDE.md

Dockerfile

@@ -1,41 +1,32 @@
+# syntax=docker/dockerfile:1.4
 FROM langchain/langgraph-api:3.12
 
+# HuggingFace Spaces requires port 7860
+ENV PORT=7860
+ENV LANGGRAPH_PORT=7860
 
+ENV LANGSERVE_GRAPHS='{"job_app_graph": "/deps/job_writer/src/job_writing_agent/workflow.py:job_app_graph", "research_workflow": "/deps/job_writer/src/job_writing_agent/nodes/research_workflow.py:research_workflow", "data_loading_workflow": "/deps/job_writer/src/job_writing_agent/nodes/data_loading_workflow.py:data_loading_workflow"}'
 
+COPY pyproject.toml langgraph.json /deps/job_writer/
+COPY src/ /deps/job_writer/src/
 
+RUN for dep in /deps/*; do \
+  if [ -d "$dep" ]; then \
+  echo "Installing $dep"; \
+  (cd "$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .); \
+  fi; \
+  done
 
+# Use cache mount for Playwright - browsers persist between builds!
+RUN --mount=type=cache,target=/root/.cache/ms-playwright \
+  playwright install chromium && \
+  playwright install-deps
 
+RUN mkdir -p /api/langgraph_api /api/langgraph_runtime /api/langgraph_license && \
+  touch /api/langgraph_api/__init__.py /api/langgraph_runtime/__init__.py /api/langgraph_license/__init__.py && \
+  PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir --no-deps -e /api
 
-# -- Adding local package . --
-ADD . /deps/job_writer
-# -- End of local package . --
+WORKDIR /deps/job_writer
 
-
-
-# -- Installing all local dependencies --
-
-RUN for dep in /deps/*; do             echo "Installing $dep";             if [ -d "$dep" ]; then                 echo "Installing $dep";                 (cd "$dep" && PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir -c /api/constraints.txt -e .);             fi;         done
-
-# -- End of local dependencies install --
-
-ENV LANGSERVE_GRAPHS='{"job_app_graph": "/deps/job_writer/src/job_writing_agent/workflow.py:job_app_graph", "research_workflow": "/deps/job_writer/src/job_writing_agent/nodes/research_workflow.py:research_workflow", "data_loading_workflow": "/deps/job_writer/src/job_writing_agent/nodes/initializing.py:data_loading_workflow"}'
-
-
-
-
-
-
-
-# -- Ensure user deps didn't inadvertently overwrite langgraph-api
-RUN mkdir -p /api/langgraph_api /api/langgraph_runtime /api/langgraph_license && touch /api/langgraph_api/__init__.py /api/langgraph_runtime/__init__.py /api/langgraph_license/__init__.py
-RUN PYTHONDONTWRITEBYTECODE=1 uv pip install --system --no-cache-dir --no-deps -e /api
-# -- End of ensuring user deps didn't inadvertently overwrite langgraph-api --
-# -- Removing build deps from the final image ~<:===~~~ --
-RUN pip uninstall -y pip setuptools wheel
-RUN rm -rf /usr/local/lib/python*/site-packages/pip* /usr/local/lib/python*/site-packages/setuptools* /usr/local/lib/python*/site-packages/wheel* && find /usr/local/bin -name "pip*" -delete || true
-RUN rm -rf /usr/lib/python*/site-packages/pip* /usr/lib/python*/site-packages/setuptools* /usr/lib/python*/site-packages/wheel* && find /usr/bin -name "pip*" -delete || true
-RUN uv pip uninstall --system pip setuptools wheel && rm /usr/bin/uv /usr/bin/uvx
-
-
-
-WORKDIR /deps/job_writer
+# Expose port for HuggingFace Spaces
+EXPOSE 7860

README.md

@@ -1,3 +1,13 @@
+---
+title: Job Application Writer
+emoji: 📝
+colorFrom: blue
+colorTo: purple
+sdk: docker
+app_port: 7860
+pinned: false
+---
+
 # Job Writer Module
 
 A modular, well-structured package for creating tailored job applications using LangChain and LangGraph with LangSmith observability.

docker-compose.yml

@@ -39,10 +39,14 @@ services:
     image: job-app-workflow:latest
     container_name: job-app-agent
     ports:
-      - "8000:8000"
+      - "7860:7860"
     environment:
-      - REDIS_URL=redis://redis:6379
-      - POSTGRES_URL=postgresql://postgres:postgres@postgres:5432/postgres
+      - PORT=7860
+      - LANGGRAPH_PORT=7860
+      - REDIS_URI=redis://redis:6379
+      - DATABASE_URI=postgresql://postgres:postgres@postgres:5432/postgres
+    env_file:
+      - .docker_env
     depends_on:
       redis:
         condition: service_healthy

langgraph.json

@@ -1,11 +1,9 @@
 {
-  "dependencies": [
-    "."
-  ],
+  "dependencies": ["."],
   "graphs": {
-    "job_app_graph": "./src/job_writing_agent/workflow.py:job_app_graph",
-    "research_workflow": "./src/job_writing_agent/nodes/research_workflow.py:research_workflow",
-    "data_loading_workflow": "./src/job_writing_agent/nodes/data_loading_workflow.py:data_loading_workflow"
+    "job_app_graph": "src/job_writing_agent/workflow.py:job_app_graph",
+    "research_workflow": "src/job_writing_agent/nodes/research_workflow.py:research_workflow",
+    "data_loading_workflow": "src/job_writing_agent/nodes/data_loading_workflow.py:data_loading_workflow"
   },
   "env": "./app_env",
   "python_version": "3.12"

src/job_writing_agent/__init__.py

@@ -8,7 +8,7 @@ using LangChain and LangGraph with LangSmith observability.
 __version__ = "0.1.0"
 
 import os
-from getpass import getpass
+import sys
 import logging
 from pathlib import Path
 from dotenv import load_dotenv
@@ -28,148 +28,20 @@ logger.info(
 env_path = Path(__file__).parent / ".env"
 
 
-def _set_env(var: str):
-    if not os.environ.get(var):
-        os.environ[var] = getpass(f"{var}: ")
-        logger.info(f"{var} set to {os.environ[var]}")
-
-
-if env_path.exists():
-    logger.info("Loading environment variables from %s", env_path)
-    load_dotenv(dotenv_path=env_path, override=True)
-else:
-    logger.warning(
-        ".env file not found at %s. Using system environment variables.", env_path
-    )
-
-# Check for critical environment variables
-if not os.getenv("TAVILY_API_KEY"):
-    logger.warning(
-        "TAVILY_API_KEY environment variable is not set."
-        " Failed to get TAVILY_API_KEY at Path %s",
-        env_path,
-    )
-    _set_env("TAVILY_API_KEY")
-
-
-if not os.getenv("GEMINI_API_KEY"):
-    logger.warning(
-        "GEMINI_API_KEY environment variable is not set. "
-        "Failed to get GEMINI_API_KEY at Path %s",
-        env_path,
-    )
-    _set_env("GEMINI_API_KEY")
-
-
-if not os.getenv("PINECONE_API_KEY"):
-    logger.warning(
-        "PINECONE_API_KEY environment variable is not set."
-        " Failed to get PINECONE_API_KEY at Path %s",
-        env_path,
-    )
-    _set_env("PINECONE_API_KEY")
-
-if not os.getenv("LANGFUSE_PUBLIC_KEY"):
-    logger.warning(
-        "LANGFUSE_PUBLIC_KEY environment variable is not set."
-        " Failed to get LANGFUSE_PUBLIC_KEY at Path %s",
-        env_path,
-    )
-    _set_env("LANGFUSE_PUBLIC_KEY")
-
-if not os.getenv("LANGFUSE_SECRET_KEY"):
-    logger.warning(
-        "LANGFUSE_SECRET_KEY environment variable is not set."
-        " Failed to get LANGFUSE_SECRET_KEY at Path %s",
-        env_path,
-    )
-    _set_env("LANGFUSE_SECRET_KEY")
-
-if not os.getenv("LANGSMITH_API_KEY"):
-    logger.warning(
-        "LANGSMITH_API_KEY environment variable is not set."
-        " Failed to get LANGSMITH_API_KEY at Path %s",
-        env_path,
-    )
-    _set_env("LANGSMITH_API_KEY")
-
-if not os.getenv("OPENROUTER_API_KEY"):
-    logger.warning(
-        "OPENROUTER_API_KEY environment variable is not set."
-        " Failed to get OPENROUTER_API_KEY at Path %s",
-        env_path,
-    )
-    _set_env("OPENROUTER_API_KEY")
-
-if not os.getenv("LANGSMITH_PROJECT"):
-    logger.warning(
-        "LANGSMITH_PROJECT environment variable is not set."
-        " Failed to get LANGSMITH_PROJECT at Path %s",
-        env_path,
-    )
-    _set_env("LANGSMITH_PROJECT")
-
-if not os.getenv("LANGSMITH_ENDPOINT"):
-    logger.warning(
-        "LANGSMITH_ENDPOINT environment variable is not set."
-        " Failed to get LANGSMITH_ENDPOINT at Path %s",
-        env_path,
-    )
-    _set_env("LANGSMITH_ENDPOINT")
-
-if not os.getenv("CEREBRAS_API_KEY"):
-    logger.warning(
-        "CEREBRAS_API_KEY environment variable is not set."
-        " Failed to get CEREBRAS_API_KEY at Path %s",
-        env_path,
-    )
-    _set_env("CEREBRAS_API_KEY")
-
-os.environ["LANGSMITH_TRACING"] = "true"
-
-__all__: list[str] = ["job_app_graph", "workflows/research_workflow"]
-
-
-"""
-Job Application Writer Package
-
-A modular, well-structured package for creating tailored job applications
-using LangChain and LangGraph with LangSmith observability.
-"""
-
-__version__ = "0.1.0"
-
-import os
-import getpass
-import logging
-from pathlib import Path
-from dotenv import load_dotenv
-
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
-log_dir = Path(__file__).parent / "logs"
-log_dir.mkdir(exist_ok=True)
-logger.addHandler(logging.FileHandler(log_dir / "job_writer.log", mode="a"))
-logger.info(
-    "Logger initialized. Writing to %s", Path(__file__).parent / "job_writer.log"
-)
-
-env_path = Path(__file__).parent / ".env"
+def _is_interactive():
+    """Check if we're running in an interactive environment."""
+    return sys.stdin.isatty()
 
 
 def _set_env(var: str):
+    """Set environment variable - only prompt if running interactively."""
     if not os.environ.get(var):
-        os.environ[var] = getpass.getpass(f"{var}: ")
-        logger.info(f"{var} set to {os.environ[var]}")
-
-
-def load_environment_variables(key_array):
-    for key in key_array:
-        if not os.getenv(key):
-            logger.warning(
-                f"{key} environment variable is not set. Failed to get {key} at Path {env_path}"
-            )
-            _set_env(key)
+        if _is_interactive():
+            from getpass import getpass
+            os.environ[var] = getpass(f"{var}: ")
+            logger.info(f"{var} set interactively")
+        else:
+            logger.warning(f"{var} is not set and running non-interactively. Skipping.")
 
 
 if env_path.exists():
@@ -181,14 +53,39 @@ else:
     )
 
 
+# List of environment variables to check
 environment_key_array = [
     "TAVILY_API_KEY",
     "GEMINI_API_KEY",
     "PINECONE_API_KEY",
     "LANGFUSE_PUBLIC_KEY",
     "LANGFUSE_SECRET_KEY",
+    "LANGSMITH_API_KEY",
+    "OPENROUTER_API_KEY",
+    "LANGSMITH_PROJECT",
+    "LANGSMITH_ENDPOINT",
+    "CEREBRAS_API_KEY",
 ]
+
+
+def load_environment_variables(key_array):
+    """Load environment variables, warn if missing."""
+    missing_keys = []
+    for key in key_array:
+        if not os.getenv(key):
+            logger.warning(f"{key} environment variable is not set.")
+            missing_keys.append(key)
+            _set_env(key)
+    
+    if missing_keys and not _is_interactive():
+        logger.warning(f"Missing environment variables (non-interactive mode): {missing_keys}")
+
+
 # Check for critical environment variables
 load_environment_variables(environment_key_array)
 
+# Enable LangSmith tracing if API key is set
+if os.getenv("LANGSMITH_API_KEY"):
+    os.environ["LANGSMITH_TRACING"] = "true"
+
 __all__ = ["job_app_graph", "workflows/research_workflow"]

src/job_writing_agent/agents/nodes.py

@@ -8,13 +8,14 @@ writer workflow graph, each handling a specific step in the process.
 # Standard library imports
 import logging
 from datetime import datetime
+from langgraph.types import interrupt
 
 # Third-party imports
 from langchain_core.messages import SystemMessage
 from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
 
 # Local imports
-from ..classes.classes import AppState, DataLoadState, ResearchState, ResultState
+from ..classes.classes import AppState, ResearchState, ResultState
 from ..prompts.templates import (
     BULLET_POINTS_PROMPT,
     COVER_LETTER_PROMPT,
@@ -40,7 +41,7 @@ def create_draft(state: ResearchState) -> ResultState:
     # Create LLM inside function (lazy initialization)
     llm_provider = LLMFactory()
     llm = llm_provider.create_langchain(
-        "cognitivecomputations/dolphin-mistral-24b-venice-edition:free",
+        "mistralai/devstral-2512:free",
         provider="openrouter",
         temperature=0.3,
     )
@@ -97,7 +98,6 @@ def create_draft(state: ResearchState) -> ResultState:
         feedback="",
         critique_feedback="",
         current_node="create_draft",
-        company_research_data=company_background_information,
         output_data={},
     )
 
@@ -118,6 +118,7 @@ def critique_draft(state: ResultState) -> ResultState:
         draft_content = str(state.get("draft", ""))
         feedback = state.get("feedback", "")
         output_data = state.get("output_data", "")
+        current_node = state.get("current_node", "")
 
         # Debug logging to verify values
         logger.debug(f"Job description length: {len(job_description)}")
@@ -126,19 +127,12 @@ def critique_draft(state: ResultState) -> ResultState:
         # Early return if required fields are missing
         if not job_description or not draft_content:
             logger.warning("Missing job_description or draft in state")
-            return ResultState(
-                draft=draft_content,
-                feedback=feedback,
-                critique_feedback="",
-                current_node="critique",
-                company_research_data=company_research_data,
-                output_data=output_data,
-            )
+            return ResultState(**state, current_node=current_node)
 
         # Create LLM inside function (lazy initialization)
         llm_provider = LLMFactory()
         llm = llm_provider.create_langchain(
-            "cognitivecomputations/dolphin-mistral-24b-venice-edition:free",
+            "mistralai/devstral-2512:free",
             provider="openrouter",
             temperature=0.3,
         )
@@ -160,20 +154,20 @@ def critique_draft(state: ResultState) -> ResultState:
         # Append HumanMessagePromptTemplate with variables (like line 97-124 in create_draft)
         critique_context_message = HumanMessagePromptTemplate.from_template(
             """
-    # Job Description
-    {job_description}
+            # Job Description
+            {job_description}
 
-    # Current Draft
-    {draft}
+            # Current Draft
+            {draft}
 
-    Critique this draft and suggest specific improvements. Focus on:
-    1. How well it targets the job requirements
-    2. Professional tone and language
-    3. Clarity and impact
-    4. Grammar and style
+            Critique this draft and suggest specific improvements. Focus on:
+            1. How well it targets the job requirements
+            2. Professional tone and language
+            3. Clarity and impact
+            4. Grammar and style
 
-    Return your critique in a constructive, actionable format.
-    """,
+            Return your critique in a constructive, actionable format.
+            """,
             input_variables=["job_description", "draft"],
         )
 
@@ -204,12 +198,7 @@ def critique_draft(state: ResultState) -> ResultState:
 
         # Store the critique - using validated variables from top of function
         return ResultState(
-            draft=draft_content,
-            feedback=feedback,
-            critique_feedback=critique_content,
-            current_node="critique",
-            company_research_data=company_research_data,
-            output_data=output_data,
+            **state, critique_feedback=critique_content, current_node=current_node
         )
 
     except Exception as e:
@@ -223,8 +212,6 @@ def human_approval(state: ResultState) -> ResultState:
     # Validate and extract all required state fields once
     draft_content = state.get("draft", "")
     critique_feedback_content = state.get("critique_feedback", "No critique available")
-    company_research_data = state.get("company_research_data", {})
-    output_data = state.get("output_data", "")
 
     # Display draft and critique for review
     print("\n" + "=" * 80)
@@ -236,25 +223,24 @@ def human_approval(state: ResultState) -> ResultState:
     print("\nPlease provide your feedback (press Enter to continue with no changes):")
 
     # In a real implementation, this would be handled by the UI
-    human_feedback = input()
-
-    return ResultState(
-        draft=draft_content,
-        feedback=human_feedback,
-        critique_feedback=critique_feedback_content,
-        current_node="human_approval",
-        company_research_data=company_research_data,
-        output_data=output_data,
+    human_feedback = interrupt(
+        {
+            "draft": draft_content,
+            "message": "Please review the draft and provide feedback (empty string to approve as-is)",
+        }
     )
 
+    print(f"Human feedback: {human_feedback}")
 
-def finalize_document(state: ResultState) -> DataLoadState:
+    return ResultState(**state, feedback=human_feedback, current_node="human_approval")
+
+
+def finalize_document(state: ResultState) -> ResultState:
     """Incorporate feedback and finalize the document."""
     # Validate and extract all required state fields once
     draft_content = state.get("draft", "")
     feedback_content = state.get("feedback", "")
     critique_feedback_content = state.get("critique_feedback", "")
-    company_research_data = state.get("company_research_data", {})
 
     if not draft_content:
         logger.warning("Missing draft in state for finalization")
@@ -262,7 +248,7 @@ def finalize_document(state: ResultState) -> DataLoadState:
     # Create LLM inside function (lazy initialization)
     llm_provider = LLMFactory()
     llm = llm_provider.create_langchain(
-        "cognitivecomputations/dolphin-mistral-24b-venice-edition:free",
+        "mistralai/devstral-2512:free",
         provider="openrouter",
         temperature=0.3,
     )
@@ -287,16 +273,21 @@ def finalize_document(state: ResultState) -> DataLoadState:
         }
     )
 
+    print(
+        f"Final content: {final_content.content if hasattr(final_content, 'content') else final_content}"
+    )
+
     # Return final state using validated variables
-    return DataLoadState(
+    return ResultState(
         draft=draft_content,
         feedback=feedback_content,
         critique_feedback=critique_feedback_content,
-        company_research_data=company_research_data,
         current_node="finalize",
-        output_data=final_content.content
-        if hasattr(final_content, "content")
-        else str(final_content),
+        output_data=(
+            final_content.content
+            if hasattr(final_content, "content")
+            else final_content
+        ),
     )
 
 

src/job_writing_agent/classes/__init__.py

@@ -1,3 +1,17 @@
-from .classes import AppState, ResearchState, DataLoadState, ResultState
+from .classes import (
+    AppState,
+    ResearchState,
+    DataLoadState,
+    ResultState,
+    FormField,
+    FormFieldsExtraction,
+)
 
-__all__ = ["AppState", "ResearchState", "DataLoadState", "ResultState"]
+__all__ = [
+    "AppState",
+    "ResearchState",
+    "DataLoadState",
+    "ResultState",
+    "FormField",
+    "FormFieldsExtraction",
+]

src/job_writing_agent/nodes/research_workflow.py

@@ -150,7 +150,7 @@ def company_research_data_summary(state: ResearchState) -> ResearchState:
 
         llm_provider = LLMFactory()
         llm = llm_provider.create_dspy(
-            model="cognitivecomputations/dolphin-mistral-24b-venice-edition:free",
+            model="mistralai/devstral-2512:free",
             provider="openrouter",
             temperature=0.3,
         )

src/job_writing_agent/tools/SearchTool.py

@@ -37,7 +37,7 @@ class TavilyResearchTool:
         job_description,
         company_name,
         max_results=5,
-        model_name="cognitivecomputations/dolphin-mistral-24b-venice-edition:free",
+        model_name="mistralai/devstral-2512:free",
     ):
         # Create LLM inside __init__ (lazy initialization)
         llm_provider = LLMFactory()

src/job_writing_agent/workflow.py

@@ -143,7 +143,7 @@ class JobWorkflow:
         )
 
     @cached_property
-    def job_app_graph(self) -> StateGraph:
+    def job_app_graph(self) -> CompiledStateGraph:
         """
         Build and configure the job application workflow graph.
 
@@ -197,7 +197,9 @@ class JobWorkflow:
         agent_workflow_graph.add_edge("critique", "human_approval")
         agent_workflow_graph.add_edge("human_approval", "finalize")
 
-        return agent_workflow_graph
+        job_app_graph = agent_workflow_graph.compile()
+
+        return job_app_graph
 
     def _get_callbacks(self) -> list:
         """
@@ -258,7 +260,7 @@ class JobWorkflow:
             in the "output_data" field, or None if execution fails.
         """
         try:
-            compiled_graph = self.compile()
+            compiled_graph = self.job_app_graph
         except Exception as e:
             logger.error("Error compiling graph: %s", e, exc_info=True)
             return None
@@ -308,24 +310,72 @@ class JobWorkflow:
             logger.error("Error running graph: %s", e, exc_info=True)
             return None
 
-    @log_execution
-    @log_errors
-    def compile(self) -> CompiledStateGraph:
-        """
-        Compile the workflow graph into an executable state machine.
 
-        Returns
-        -------
-        CompiledStateGraph
-            Compiled LangGraph state machine ready for execution.
+# At the bottom of workflow.py, after the JobWorkflow class definition
 
-        Raises
-        ------
-        Exception
-            If graph compilation fails (e.g., invalid edges, missing nodes).
-        """
-        compiled_graph = self.job_app_graph.compile()
-        return compiled_graph
+
+def build_job_app_graph() -> CompiledStateGraph:
+    """
+    Build and compile the job application workflow graph.
+
+    This function creates the graph structure independent of runtime inputs.
+    Actual runtime values (resume, job description) come from the state
+    passed during invocation.
+    """
+
+    # Helper function for the adapter (since we can't use instance methods)
+    def dataload_to_research_adapter(state: DataLoadState) -> ResearchState:
+        logger.info("Adapter for converting DataLoadState to ResearchState")
+        return ResearchState(
+            company_research_data=state.get("company_research_data", {}),
+            attempted_search_queries=[],
+            current_node="",
+            content_category=state.get("content_category", ""),
+            messages=state.get("messages", []),
+        )
+
+    # Helper function for routing
+    def route_after_load(state: DataLoadState) -> str:
+        next_node = state.get("next_node", "research")
+        logger.info(f"Routing after load: {next_node}")
+        return next_node
+
+    # Build the graph
+    agent_workflow_graph = StateGraph(DataLoadState)
+
+    # Add nodes
+    agent_workflow_graph.add_node("load", data_loading_workflow)
+    agent_workflow_graph.add_node("to_research_adapter", dataload_to_research_adapter)
+    agent_workflow_graph.add_node("research", research_workflow)
+    agent_workflow_graph.add_node("create_draft", create_draft)
+    agent_workflow_graph.add_node("critique", critique_draft)
+    agent_workflow_graph.add_node("human_approval", human_approval)
+    agent_workflow_graph.add_node("finalize", finalize_document)
+
+    # Set entry and exit
+    agent_workflow_graph.set_entry_point("load")
+    agent_workflow_graph.set_finish_point("finalize")
+
+    # Add edges
+    agent_workflow_graph.add_conditional_edges(
+        "load",
+        route_after_load,
+        {
+            "load": "load",
+            "research": "to_research_adapter",
+        },
+    )
+    agent_workflow_graph.add_edge("to_research_adapter", "research")
+    agent_workflow_graph.add_edge("research", "create_draft")
+    agent_workflow_graph.add_edge("create_draft", "critique")
+    agent_workflow_graph.add_edge("critique", "human_approval")
+    agent_workflow_graph.add_edge("human_approval", "finalize")
+
+    return agent_workflow_graph.compile()
+
+
+# Export at module level for LangGraph deployment
+job_app_graph = build_job_app_graph()
 
 
 def main():
@@ -336,12 +386,12 @@ def main():
         content=args.content_type,
     )
     result = asyncio.run(workflow.run())
-    if result:
-        print_result(args.content_type, result["output_data"])
-        save_result(args.content_type, result["output_data"])
+    if result and hasattr(result, "output_data"):
+        print_result(args.content_type, result.get("output_data", ""))
+        save_result(args.content_type, result.get("output_data", ""))
         print("Workflow completed successfully.")
     else:
-        print("Error running workflow.")
+        print("Error running workflow. No output data available.")
         sys.exit(1)
 
 

uv.lock

@@ -1909,7 +1909,6 @@ dependencies = [
     { name = "langfuse" },
     { name = "langgraph" },
     { name = "langgraph-api" },
-    { name = "langgraph-checkpoint" },
     { name = "langgraph-cli" },
     { name = "langgraph-prebuilt" },
     { name = "langgraph-runtime-inmem" },
@@ -2149,20 +2148,19 @@ requires-dist = [
     { name = "jsonschema-specifications", specifier = "==2025.9.1" },
     { name = "justext", specifier = "==3.0.2" },
     { name = "kiwisolver", specifier = "==1.4.9" },
-    { name = "langchain", specifier = "==0.3.27" },
-    { name = "langchain-cerebras", specifier = "==0.5.0" },
-    { name = "langchain-community", specifier = "==0.3.30" },
-    { name = "langchain-core", specifier = "==0.3.78" },
-    { name = "langchain-ollama", specifier = "==0.3.10" },
-    { name = "langchain-openai", specifier = "==0.3.34" },
-    { name = "langchain-tavily", specifier = "==0.2.12" },
-    { name = "langchain-text-splitters", specifier = "==0.3.11" },
+    { name = "langchain" },
+    { name = "langchain-cerebras" },
+    { name = "langchain-community" },
+    { name = "langchain-core", specifier = ">=1.0.0" },
+    { name = "langchain-ollama" },
+    { name = "langchain-openai" },
+    { name = "langchain-tavily" },
+    { name = "langchain-text-splitters" },
     { name = "langfuse", specifier = "==3.6.1" },
-    { name = "langgraph", specifier = "==0.6.8" },
-    { name = "langgraph-api", specifier = "==0.4.46" },
-    { name = "langgraph-checkpoint", specifier = "==2.1.1" },
-    { name = "langgraph-cli", specifier = "==0.4.4" },
-    { name = "langgraph-prebuilt", specifier = "==0.6.4" },
+    { name = "langgraph" },
+    { name = "langgraph-api" },
+    { name = "langgraph-cli" },
+    { name = "langgraph-prebuilt" },
     { name = "langgraph-runtime-inmem", specifier = "==0.14.1" },
     { name = "langgraph-sdk", specifier = "==0.2.9" },
     { name = "langsmith", specifier = "==0.4.32" },
@@ -2214,8 +2212,8 @@ requires-dist = [
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     { name = "opentelemetry-semantic-conventions", specifier = "==0.58b0" },
     { name = "optuna", specifier = "==4.5.0" },
-    { name = "orjson", specifier = "==3.11.3" },
-    { name = "ormsgpack", specifier = "==1.10.0" },
+    { name = "orjson", specifier = ">=3.9.7,<3.10.17" },
+    { name = "ormsgpack", specifier = ">=1.12.0" },
     { name = "packaging", specifier = "==25.0" },
     { name = "pandas", specifier = "==2.3.3" },
     { name = "parse", specifier = "==1.20.2" },
@@ -2535,33 +2533,29 @@ wheels = [
 
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-version = "0.3.27"
+version = "1.2.0"
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 dependencies = [
     { name = "langchain-core" },
-    { name = "langchain-text-splitters" },
-    { name = "langsmith" },
+    { name = "langgraph" },
     { name = "pydantic" },
-    { name = "pyyaml" },
-    { name = "requests" },
-    { name = "sqlalchemy" },
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+version = "0.8.2"
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@@ -2589,7 +2583,7 @@ wheels = [
 
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-version = "0.3.78"
+version = "1.2.6"
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 dependencies = [
     { name = "jsonpatch" },
@@ -2599,10 +2593,11 @@ dependencies = [
     { name = "pyyaml" },
     { name = "tenacity" },
     { name = "typing-extensions" },
+    { name = "uuid-utils" },
 ]
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 [[package]]
@@ -2620,16 +2615,16 @@ wheels = [
 
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-version = "0.3.34"
+version = "1.1.6"
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@@ -2681,7 +2676,7 @@ wheels = [
 
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-version = "0.6.8"
+version = "1.0.4"
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@@ -2691,9 +2686,9 @@ dependencies = [
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