llm-agents-from-scratch: Instructions file for Claude Code

CLAUDE.md

llm-agents-from-scratch CLAUDE.md is an instructions file for Claude Code from nerdai/llm-agents-from-scratch. It costs 921 tokens per session, scanned A, original, Apache-2.0.

Project instructions for a Python library that teaches how to build language-model agents and connect them to tools through MCP.

In plain words
What is it for?
They are for installing dependencies, running tests, checking code quality, generating coverage reports, and creating architecture diagrams.
Why use it?
They give coding agents the project's setup, testing, formatting, and documentation conventions.

Instructions file for Claude Code

Written for Claude Code: the file is CLAUDE.md. Also seen: mentions CLAUDE.md; mentions Claude Code.

This is nerdai/llm-agents-from-scratch's own configuration. It tells Claude Code how to work on llm-agents-from-scratch itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything llm-agents-from-scratch configures →

Reuse

Borrowing it

Nothing to install: this file belongs to nerdai/llm-agents-from-scratch. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/nerdai/llm-agents-from-scratch/main/CLAUDE.md
Clone the repo
git clone --depth 1 https://github.com/nerdai/llm-agents-from-scratch

Made for: Claude Code.

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Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00921 $0.00921
Opus 5 $0.00461 $0.00461
Sonnet 5 $0.00184 $0.00184
Haiku 4.5 $0.00092 $0.00092

Measured 7d ago against content hash d3dd6fa0deed, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

llm-agents-from-scratch CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

CLAUDE.md · 102 lines

How it starts

The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

llm-agents-from-scratch is a Python library for building LLM agents from the ground up, designed to accompany a technical book on creating intelligent agents with Model Context Protocol (MCP) integration. It provides abstract base classes and concrete implementations for LLMs, tools, and agents.

Commands

# Install dependencies
uv sync --all-extras --dev

# Run all tests
make test                    # or: pytest tests -v --capture=no

# Run a single test file
pytest tests/test_file.py -v --capture=no

# Run a specific test
pytest tests/test_file.py::test_function_name -v

# Lint and format (runs pre-commit hooks: ruff, mypy)
make lint

# Format only (ruff)
make format

# Coverage
make coverage                # Generate XML report
make coverage-report         # Terminal summary
make coverage-html           # HTML report in htmlcov/

# Generate UML diagrams
make diagrams

Architecture

Core Abstractions

The library uses abstract base classes with type aliases for flexibility:

  • BaseLLM (base/llm.py) → Type alias LLM

    • Abstract methods: complete(), structured_output(), chat(), continue_chat_with_tool_results()
    • Implementation: OllamaLLM (primary), OpenAILLM (optional extra)
  • BaseTool / AsyncBaseTool (base/tool.py) → Type alias Tool = BaseTool | AsyncBaseTool

    • Required properties: name, description, parameters_json_schema
    • Implementations:
      • SimpleFunctionTool / AsyncSimpleFunctionTool - Direct function wrapping with JSON schema generation
      • PydanticFunctionTool / AsyncPydanticFunctionTool - Type-safe functions using Pydantic models
      • MCPTool / MCPToolProvider - Model Context Protocol integration

Agent Layer

  • LLMAgent (agent/llm_agent.py) - Main agent class managing LLM and tool registry
    • Contains nested TaskHandler class extending asyncio.Future for async task execution
    • Step-based execution with rollout tracking

Read the full file on GitHub · 102 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 102 lines · 921 tokens per session scan A d3dd6fa0deed

Subscribe to this mod's changes

llm-agents-from-scratch CLAUDE.md is an instructions file published in the GitHub repository nerdai/llm-agents-from-scratch (185 stars, last pushed yesterday), licensed Apache-2.0. It adds 921 tokens to every session, about $0.0046 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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