Build an AI Agent From Scratch in Python - Tutorial for Beginners

Short tutorial summary — Build an AI agent from scratch in Python (LangChain + LLMs)

What the video teaches (high level)

• Create a simple research-assistant agent in Python that uses LLMs plus external “tools” (web search, Wikipedia, custom file-save).
• Enforce structured, typed outputs from the LLM so results can be consumed predictably by code (using Pydantic schemas and a parser).
• Hook multiple LLM providers (OpenAI or Anthropic/Claude) and swap models via configuration (.env).

Key technologies and libraries

• Python (recommended 3.10+), VS Code (suggested editor)
• LangChain core APIs:
  • ChatOpenAI / ChatAnthropic wrappers
  • ChatPromptTemplate
  • create_tool_calling_agent
  • AgentExecutor
• Pydantic (BaseModel) + PydanticOutputParser
• LangChain community tools / wrappers (Wikipedia API wrapper, DuckDuckGo search run)
• duckduckgo_search package (installed separately)
• Any LLM provider via API keys: OpenAI (OPENAI_API_KEY) or Anthropic/Claude (ANTHROPIC_API_KEY)
• Optional: GitHub Copilot (editor autocomplete; sponsor mention)

Project structure and files to create

• requirements.txt — Python dependencies (LangChain, Pydantic, duckduckgo_search, etc.)
• .env — environment variables with API keys (OPENAI_API_KEY or ANTHROPIC_API_KEY)
• main.py — main logic: set up LLM, prompt template, parser, agent, and execution
• tools.py — define and wrap external tools (search, Wikipedia, custom save-to-file)

Key implementation steps / code components

1. Environment

   • Create and activate a virtual environment:
     • python -m venv venv
     • Activate the venv and run: pip install -r requirements.txt
   • Place API keys in .env and load them in code (e.g., load_dotenv()).

2. LLM selection

   • Create an llm instance using ChatOpenAI or ChatAnthropic and specify the model name.
   • Use the appropriate API key from .env.

3. Structured output model

   • Define a Pydantic class inheriting from BaseModel with the fields you want, for example:
     • topic: str
     • summary: str
     • sources: list[str]
     • tools_used: list[str]
   • Create a PydanticOutputParser to convert LLM text output into the typed model.

4. Prompt template

   • Use ChatPromptTemplate with a system message instructing the model to produce output in the provided format.
   • Include the parser’s format instructions in the system message.
   • Provide variables for: chat history, query, agent scratchpad.

5. Agent creation & execution

   • Use create_tool_calling_agent(...) to make an agent able to call tools.
   • Wrap the agent in an AgentExecutor:
     • AgentExecutor(agent=..., tools=[...], verbose=True)
   • Invoke the agent, for example:
     • agent_executor.invoke({"query": user_query, ...})
   • Parse the returned raw output using the parser, e.g.:
     • parser.parse(raw_output["output"][0]["text"])
   • Add try/except around parsing to handle malformed outputs.

6. Tools

   • Use built-in/community tools: DuckDuckGo search run, Wikipedia API wrapper.
   • Create custom tools by writing Python functions (e.g., save_to_txt(data, filename)) and wrapping with:
     • langchain.tools.Tool(name=..., func=..., description=...)
   • Pass all tools to the agent; it will choose which to call based on prompt and tool descriptions.

Runtime / examples

• Demo flow:
  • Ask the agent: “Tell me about LangChain and its applications / save to file.”
  • Agent searches web/Wikipedia, composes topic, summary, sources, and then calls the custom save-to-file tool to write results to a timestamped text file.
• Verbose mode shows internal chain-of-thought and tool usage; disable verbose if undesired.
• Note: installing duckduckgo_search separately was required during the walkthrough.

Example prompt used in the demo: “Tell me about LangChain and its applications / save to file.”

Practical notes & tips

• You need API keys (OpenAI/Anthropic); keep keys secret and be aware of possible charges or free-trial limits.
• Watch for rate limits on public wrappers (DuckDuckGo, Wikipedia).
• Use structured outputs (Pydantic) to make downstream usage predictable and typed.
• You can add many more tools or integrate additional community tool packages to expand capabilities.
• Code and a full example are provided in the video’s GitHub repo (link shown in the video description).

Miscellaneous

• Sponsor: Microsoft / GitHub Copilot — presenter demonstrates using Copilot for editor autocomplete and encourages sharing Copilot stories.
• The presenter emphasizes that this tutorial covers the core components powering roughly 80% of LangChain agent applications.

Main speakers / sources

• Video presenter / tutorial author (YouTuber delivering the walkthrough)
• Libraries & platforms referenced:
  • LangChain (docs & APIs)
  • OpenAI (platform.openai.com API keys)
  • Anthropic / Claude (console.anthropic.com keys)
• Sponsor: Microsoft / GitHub Copilot
• LangChain community tools (Wikipedia wrapper, DuckDuckGo search run) and the GitHub repo containing the example code