Objective
Migrate your existing FastAPI services to the Model Context Protocol (MCP) using two different implementations:
mcp on PyPI)fastmcp on PyPI)Evaluate each on performance, LangChain integration, and production readiness.
Research Date: May 28, 2025
Status: Current as of major industry announcements
Note on LangChain/LangGraph's Industry Impact: While this analysis focuses on alternatives, it's important to acknowledge LangChain and LangGraph's foundational role in the agentic AI ecosystem. LangChain pioneered many of the abstractions and patterns we see across all frameworks today—from tool integration and memory management to agent orchestration concepts. LangGraph further advanced the field by demonstrating how graph-based architectures could provide precise control over agent workflows. These innovations helped establish industry standards and design patterns that influenced virtually every framework discussed below, creating a more mature and interoperable ecosystem for all developers.
Current Reality: Limited but Emerging
Based on the latest research, the Open Deep Research system's complexity makes direct drag-and-drop implementation challenging, but 2025 shows promising developments:
The Open Deep Research application currently uses configuration-driven search API selection:
# Current approach in utils.py
async def select_and_execute_search(search_api: str, query_list: list[str], params_to_pass: dict):
Developing complex graph-based systems like LangGraph's Open Deep Research requires a state-first architecture approach with incremental complexity layering. The key to success lies in proper planning, modular design, and systematic testing at each development phase.
This report is crafted for AI Makerspace Cohort 6 bootcamp graduates preparing to launch their careers in the dynamic US AI job market of 2025. Building on recent industry analyses and in-depth guidance, the report presents concrete strategies for leveraging LinkedIn, networking, and open source contributions; exposes common pitfalls new candidates face; provides real-world spotlights into key AI job types; and offers actionable advice for personal branding through resumes, LinkedIn, and cover letters. The emphasis is on actionable, market-tested recommendations and US-specific trends, tailored to help new graduates not only stand out in a competitive environment but also build strong, resilient, and authentic AI career foundations.
This document analyzes the warning message: "Anthropic structured output relies on forced tool calling, which is not supported when thinking is enabled" and provides evidence-based solutions for developers encountering this conflict.
This analysis examines the core Python files in the open_deep_research repository, with particular focus on state management architecture and human feedback mechanisms that enable interactive research workflows.
src/open_deep_research/
├── state.py # State object definitions and TypedDict schemas
├── configuration.py # Configuration management and model initialization