Build a Production-Ready AI Trend Analyzer with FastAPI, Pydantic‑AI, and MCP

Tech Trends Agent is a FastAPI service that uses Pydantic‑AI agents and MCP servers to analyze technology trends and, optionally, GitHub repositories. It integrates Brave Search and Hacker News via active MCP servers, supports agent‑to‑agent handoffs, and exposes production‑ready endpoints, Docker deployment, and a transparent scoring system. GitHub intelligence works via API fallback unless its MCP is enabled, with future enhancements focused on expanding MCP coverage and scalability.

Key Points

• Combines FastAPI, Pydantic‑AI, and MCP servers to deliver a scalable, type‑safe AI service with pluggable tools.
• Implements two cooperating agents (EntryAgent for trend discovery and SpecialistAgent for repo intelligence) with A2A communication.
• Ships with active Brave Search and Hacker News MCP servers; GitHub/Filesystem MCPs are optional and disabled by default.
• Provides clear, production‑oriented deployment via Docker Compose, with comprehensive endpoints, tests, and code quality tooling.
• Uses an explicit scoring framework to rank web, Hacker News, and repository signals for transparent, reproducible analysis.

Sentiment

Cautiously positive: commenters are interested and supportive of the approach and tooling, but offer substantial constructive criticism on architecture, typing consistency, and documentation.

In Agreement

• Pydantic‑AI is a strong choice in the Python AI ecosystem and works well for structured outputs.
• Using MCP and A2A to coordinate multiple agents is a compelling, practical direction for building real applications.
• The repo is a useful learning resource and demo playground for multi‑agent orchestration.

Opposed

• API design is inconsistent (mix of dicts, JSONResponse, and Pydantic models), harming documentation and cohesion.
• Passing untyped dictionaries into agent logic is outdated; strongly typed schemas and interfaces should be enforced.
• Re‑wrapping agents with a custom BaseAgent leads to awkward abstractions; integrate Pydantic‑AI agents via a clear system interface instead.
• Project structure needs improvement; placing all view functions in main.py is undesirable—split routes into logical modules.
• Documentation is thin; slides/videos and a clearer top‑level summary are needed to make the repo graspable.
• Concern about Pydantic types leaking across layers; questions about what a well‑structured modern Python project should look like.