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David Van Assche (S.L)
David Van Assche (S.L)

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Running AI Coding Agents for Free: The Open Source & Local Setup Guide (2026)

#beginners #ai #tutorial #opensource

Part 2 of the AI Coding Tools Deep Dive. Part 1 mapped every tool. This one shows you how to run them for free — or close to it.

You don't need a subscription to get serious AI coding assistance. Between open-source tools, free APIs, and local models, you can build a professional-grade AI coding stack for $0-15/month. Here's exactly how.

Strategy 1: The Free Cloud Stack ($0/month)

Tools: Gemini CLI + Qwen Code

# Install Gemini CLI
npm install -g @anthropic-ai/gemini-cli
gemini login  # uses your Google account

# 1,000 requests/day with Gemini 2.5 Pro
# That's enough for a full day of coding
gemini "Refactor the auth module to use middleware pattern"
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For a second opinion or when you hit Gemini's style limits:

# Qwen Code — completely free API from Alibaba
pip install qwen-code
qwen-code init
# Uses Qwen Coder models, no cost
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Cost: $0. Literally.

Limitation: You're dependent on Google's and Alibaba's continued generosity. Free tiers can change without notice.

Strategy 2: The BYOK Power Stack ($5-15/month)

Tools: Aider + OpenRouter (or direct API keys)

# Install Aider
pip install aider-chat

# Option A: Use OpenRouter for model shopping
export OPENROUTER_API_KEY=your-key
aider --model openrouter/anthropic/claude-sonnet-4.6

# Option B: Direct API key (cheaper, fewer models)
export ANTHROPIC_API_KEY=your-key
aider --model claude-sonnet-4.6-latest
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Aider's git-native workflow:

cd your-project
aider

# Inside aider:
> Fix the race condition in session_store.py
# Aider reads the file, makes changes, auto-commits with a descriptive message
# You review the diff, accept or reject
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Cost: $5-15/month depending on usage. Claude Sonnet 4.6 at $3/$15 per million tokens. Moderate use = ~$10/month.

Why this works: Aider is the most mature CLI coding tool (39K stars, 4.1M installs, 15B tokens processed per week). It handles git, multi-file edits, and test running natively. OpenRouter lets you compare models by switching one flag.

The CLIProxyAPI Hack

If you want to use Gemini's free tier through Aider or any OpenAI-compatible tool:

# CLIProxyAPI wraps Gemini CLI as an OpenAI-compatible endpoint
git clone https://github.com/router-for-me/CLIProxyAPI
cd CLIProxyAPI && pip install -r requirements.txt
python proxy.py  # Starts an OpenAI-compatible server

# Now point Aider at it
export OPENAI_API_BASE=http://localhost:8080/v1
export OPENAI_API_KEY=dummy
aider --model gemini-2.5-pro
# Free Gemini 2.5 Pro through Aider's interface
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Strategy 3: The Fully Local Stack ($0/month, offline-capable)

Tools: Ollama + Aider (or Continue.dev)

Step 1: Install Ollama 

curl -fsSL https://ollama.ai/install.sh | sh

# Pull a coding model
ollama pull qwen2.5-coder:7b     # 4.5GB, laptop-friendly
ollama pull qwen2.5-coder:32b    # 18GB, desktop with GPU
ollama pull devstral2:24b         # Mistral's coding model
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Step 2: Wire It Into Your Tool

With Aider:

aider --model ollama/qwen2.5-coder:32b
# That's it. Fully local, fully private, zero cost.
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With Continue.dev (VS Code):

  1. Install the Continue extension
  2. Configure ~/.continue/config.json: 
{
  "models": [{
    "title": "Qwen Coder 32B",
    "provider": "ollama",
    "model": "qwen2.5-coder:32b"
  }]
}
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With OpenCode:

# OpenCode auto-detects Ollama
opencode --provider ollama --model qwen2.5-coder:32b
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Step 3: Model Selection Guide

Your Hardware Model Quality Speed
Laptop (16GB RAM, no GPU) qwen2.5-coder:7b Good for completions, basic refactoring ~15 tok/s
Desktop (32GB RAM, RTX 3060) qwen2.5-coder:32b Excellent — rivals cloud models for most tasks ~20 tok/s
Desktop (64GB RAM, RTX 4090) devstral2:24b or deepseek-coder-v2:33b Near-frontier quality ~40 tok/s
Server (80GB+ VRAM) glm-5 via vLLM 77.8% SWE-bench — competes with Claude Production speed

When Local Beats Cloud

Local wins when:

  • Privacy matters — code never leaves your machine
  • Latency matters — no network round-trip, instant responses
  • Cost matters — zero marginal cost per request
  • Offline works — airplane, air-gapped environments, spotty internet

Cloud wins when:

  • Quality ceiling matters — Claude/GPT-5 still beat local models on the hardest tasks
  • Context window matters — local 7B models max at 32K; Claude Code has 1M
  • Multi-file reasoning matters — large models handle cross-file dependencies better
  • You value your time — setup is one pip install, not GPU driver debugging

The Honest Take on Local Quality

Local models are genuinely good for:

  • Code completions and inline suggestions
  • Single-file refactoring
  • Writing tests for existing code
  • Explaining code
  • Documentation generation

Local models still struggle with:

  • Multi-file architectural changes (context window limits)
  • Complex debugging chains (reasoning depth)
  • Understanding project-wide patterns (needs more context than 32K)

The sweet spot: Use local for the 80% of tasks that are routine, cloud for the 20% that are hard. Your average cost drops from $20/month to $3-5/month.

Strategy 4: IDE + BYOK (Best of Both Worlds)

Tools: Cursor or Zed or Continue.dev + your preferred model

All three support BYOK:

Cursor ($16/mo or BYOK):

Settings → Models → Add Custom Model → Your API key
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Zed (free, BYOK):

Settings → AI → Provider → Ollama / Anthropic / OpenAI
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Continue.dev (free, any IDE):

  • VS Code + JetBrains support
  • Configure any model provider in config.json
  • Autocomplete, chat, edit, and agent modes
  • Only tool that works in both IDEs

The $0 Starter Kit

If you're just getting started today and want to spend nothing:

# 1. Gemini CLI for cloud (1000 req/day free)
npm install -g @anthropic-ai/gemini-cli
gemini login

# 2. Ollama for local (zero cost)
curl -fsSL https://ollama.ai/install.sh | sh
ollama pull qwen2.5-coder:7b

# 3. Aider to tie them together
pip install aider-chat

# Cloud mode (Gemini):
export GEMINI_API_KEY=your-free-key
aider --model gemini/gemini-2.5-pro

# Local mode (Ollama):
aider --model ollama/qwen2.5-coder:7b

# Done. Professional AI coding setup. $0.
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Next: *Part 3 — What Every AI Coding Tool Gets Wrong** — the measurement gap. None of these tools track whether the AI is actually getting better at helping you.*

Previous: *Part 1 — Every AI Coding CLI in 2026: The Complete Map***

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