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Coding Agents

Nanocode

Nanocode is a Python terminal coding agent and companion learning repository whose chapter snapshots progressively show how to build an agent that reads and edits files, runs shell commands, searches code and the web, and retains context across sessions.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Nanocode — OpenViST project profile in Coding Agents.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Nanocode when learning or modifying a coding agent from its Python implementation is more important than adopting a prebuilt editor assistant.

Who it is for

  • Developers learning how tool-using coding agents are implemented.
  • Python programmers who want to inspect or adapt a terminal agent.
  • Readers choosing between Claude, DeepSeek and Ollama-backed local models.

Documented uses

  • Study a runnable progression from an agent loop to a coding assistant with tools.
  • Build an agent that reads and edits project files and searches a codebase.
  • Add command execution and an error-feedback loop to an agent.
  • Explore persistent context, web search and different model providers.
Before you choose

Requirements & limits

The repository is public and MIT-licensed. The documented API setup requires an Anthropic API key; a DeepSeek key is listed as optional, and Ollama is an option for local models.

  • The reference implementation is Python and requires Python 3.10 or newer.
  • Capabilities are introduced chapter by chapter rather than being present in every snapshot.
  • Chapter 1 is an event-loop skeleton with no AI; chapter 3 onward requires an API key for the documented hosted setup.
  • The repository favors inspectable code using requests and subprocess over orchestration frameworks.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the Python repository

Prepare the chapter examples for use with the documented Anthropic API configuration.

Before you start

  • Python 3.10 or newer
  • Git
  • An Anthropic API key

Steps

  1. Clone the repository and change into its directory.

  2. Create and activate a Python virtual environment.

  3. Install dependencies with the command below.

    Documented command
    pip install -r requirements.txt

  4. Copy .env.example to .env and add the ANTHROPIC_API_KEY value.

Expected outcome

The repository and its Python dependencies are set up, with the Anthropic key configured for the documented API-based examples.

Guide limitations

  • The setup steps alone do not run an agent chapter.
  • The README specifies that chapter 3 and later need an API key.
Documentation 1 ↗
Source-based practical guide

Run a chapter with an Ollama model

Use the documented local-model configuration for the chapter 10 example.

Before you start

  • Python 3.10 or newer
  • The repository cloned and dependencies installed
  • Ollama

Steps

  1. Change into the repository directory.

  2. Set NANOCODE_BRAIN=ollama.

  3. Run the command below.

    Documented command
    python ch10/nanocode.py

Expected outcome

The chapter 10 example is launched using the documented Ollama provider configuration.

Guide limitations

  • The README documents local-model operation from chapter 10 onward.
  • It does not establish that every feature behaves identically across models.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for AI coding assistance

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
NanocodeStudy a runnable progression from an agent loop to a coding assistant with tools.CLI, Linux, macOS, Self-hostedThe repository is public and MIT-licensed. The documented API setup requires an Anthropic API key; a DeepSeek key is listed as optional, and Ollama is an option for local models.
SpectraClarify requirements and compare approaches before proposing a change.CLI, DesktopThe README lists a macOS and Windows desktop app, and a Homebrew installation for macOS. The CLI is published through npm and requires Node.js 22.14 or newer.
Skills — michaelshimelesInstall reusable task-specific skills into a coding-agent setup.CLIThe repository README provides an npx installation command for most coding agents and describes automatic or explicit skill invocation in Claude Code.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
12 October 2026
Original website title
optimalone/build-your-own-coding-agent · Official homepage title · retained in its source language
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