NAEOS local prototype: clone, configure, and run an AI coding agent example

TL;DR in plain English

• NAEOS is an open-source repository you can clone from GitHub: https://github.com/NAEOS-foundation/naeos. The public snapshot shows 441 commits, 11 stars, 6 forks, 5 issues, and 0 pull requests.
• Goal: run a local prototype to test agent wiring, prompts/config, and a human-in-the-loop flow.
• Quick plan in short steps:
  • Clone the repo (about 30 seconds).
  • Read the README and find an example (allow 90–150 minutes to get one example running end-to-end).
  • Wire an API key and run a single example (allow ~120 minutes for the first run).
  • Keep the first runs as dry-run and for manual review.

Concrete short scenario: a solo founder wants the system to auto-draft a pull request (PR) from an issue description. You run 10 example inputs in dry-run mode, review outputs, then enable a small canary for live runs.

One-minute checklist:

• [ ] git clone https://github.com/NAEOS-foundation/naeos
• [ ] open README and list files
• [ ] install dependencies
• [ ] set MODEL_API_KEY in environment (do not commit it)
• [ ] run an example in dry_run mode

Methodology: this note is based on the public repo snapshot at the link above.

What you will build and why it helps

You will build a local prototype from the NAEOS repository at https://github.com/NAEOS-foundation/naeos. The prototype should: clone the code, run an example, and confirm the agent and prompt wiring work with your model provider.

Immediate outputs you can produce:

• A reproducible run log from the cloned repo (one smoke test = one run).
• A minimal agent configuration file (YAML or JSON) that reads credentials from environment variables.

Why this helps for small teams and solo founders:

• It reduces repeated manual drafting work. Expect to save 10–30 minutes per task once stable.
• It gives you basic Service Level Indicators (SLIs). A SLI is a simple metric you track to judge behavior (for example, latency, error-rate, or human acceptance).
• It keeps early costs low. A prototype budget often fits in $5–$50 depending on the model and usage.

Suggested SLIs and initial thresholds you can track from day one:

• Latency: median local step < 500 ms; remote model call median < 2000 ms.
• Error-rate: development error-rate <= 2%.
• Human acceptance: initial target >= 70% for drafts.

Reference the repository snapshot: https://github.com/NAEOS-foundation/naeos (441 commits).

Plain-language explanation before advanced details

This project is experimental. Think of it as a staging setup to confirm the pieces work together. You will not deploy to production on the first run. The flow is: clone, inspect, configure, run one example, review outputs, then tighten controls before any automation.

Before you start (time, cost, prerequisites)

Estimated time to first successful run: 90–150 minutes (about 1.5–2.5 hours). See the repo at https://github.com/NAEOS-foundation/naeos.

Estimated cost: the code is open-source (no repo fee). Model or hosting costs depend on your provider. Plan $5–$50 for an initial prototype run. If you run many tokens, set a cap such as max_tokens = 2048 in config and a token budget so you do not exceed your intended spend.

Prerequisites:

• Git installed and network access to clone https://github.com/NAEOS-foundation/naeos.
• A terminal and basic command-line familiarity.
• A model provider API key or a local model endpoint.
• Basic Python or Node.js knowledge if the repo uses those runtimes.

Minimum verification steps before running:

• Confirm git clone completes and the repository folder is present.
• Confirm you can open README or a top-level file in the clone.
• Confirm environment variable MODEL_API_KEY is set locally and not committed to Git.

Pre-flight checklist:

• [ ] git installed
• [ ] cloned https://github.com/NAEOS-foundation/naeos
• [ ] terminal access
• [ ] MODEL_API_KEY available in your shell
• [ ] prototype budget set ($5–$50)

Step-by-step setup and implementation

1. Clone and inspect the repo (link: https://github.com/NAEOS-foundation/naeos).

# clone the repo
git clone https://github.com/NAEOS-foundation/naeos
cd naeos
# quick file list to find README or examples
ls -la | head -n 50

2. Read top-level docs and find an example or runner script. Note runtime hints (Python or Node) and where example files live.

3. Install dependencies according to the discovered runtime. Example commands for common runtimes:

# Python: create venv and install
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# Node (if package.json exists)
# npm install

4. Create a minimal config. Keep secrets in environment variables. Example YAML config you can adapt:

# example-agent-config.yml
agent:
  name: naeos-prototype
  max_retries: 3
  canary_percent: 1
model:
  provider: example
  api_key_env: MODEL_API_KEY
  max_tokens: 2048
  timeout_ms: 2000
integrations:
  github:
    dry_run: true

5. Run a smoke example. Replace the command below with the repository's script if present.

export MODEL_API_KEY="sk-xxxx"
python -m naeos.examples.run_example --config example-agent-config.yml

6. Iterate and gate rollout. Use explicit gates: canary at 1%, ramp to 10% (7 days), then 100% only if SLIs meet thresholds. Watch error-rate, latency, and human acceptance.

Rollout thresholds to enforce:

• Canary: 1% of runs.
• Ramp: 10% for 7 days.
• Rollback if error-rate > 5% or critical failures > 1 per hour.

Reference the repo snapshot: https://github.com/NAEOS-foundation/naeos.

Common problems and quick fixes

Problem: dependency or install errors

• Fix: confirm runtime version and reinstall in a clean virtual environment. Search the repo for requirements.txt or package.json.

Problem: missing API key or auth errors

• Fix: set MODEL_API_KEY as an environment variable. Do not commit .env. Rotate keys regularly.

Problem: rate limits from hosted models

• Fix: add retries (max_retries: 3) with exponential backoff (base 200 ms). Use a smaller model or a local inference endpoint.

Problem: unexpected agent output

• Fix: enable debug logs, re-run the provided example, and add small unit tests (5–10 unit tests) plus a few integration tests.

Keep three core observability metrics from day one: latency (ms), error-rate (%), and human-acceptance (%). Suggested thresholds: median latency < 500 ms, p95 < 2000 ms, error-rate <= 2%, human-acceptance >= 70%.

All diagnostic steps assume you have the repository cloned at https://github.com/NAEOS-foundation/naeos.

First use case for a small team

Reference repo: https://github.com/NAEOS-foundation/naeos.

Use case: a solo founder or a 2–3 person team wants to auto-draft PRs from issue descriptions while keeping humans in the loop.

Concrete steps for a very small team:

1. Start in dry-run mode and capture outputs to a review queue.
   • Set integrations.github.dry_run: true in your config.
   • Run 10–20 example inputs locally and save each run log.
2. Limit model spend and tokens during iteration.
   • Use max_tokens: 512–2048 depending on content. Set a daily cap of $5–$10 while iterating.
   • Limit concurrent calls to 1–2 to keep latency predictable (target median < 500 ms locally).
3. Automate a manual review step and sample for quality.
   • Require manual approval for the first 100 drafts.
   • Track acceptance rate; aim for >= 70% before wider automation.
4. Use a simple canary progression you control.
   • Canary: enable automation for 1% of real issues.
   • Ramp: 10% for 7 days with monitoring.
5. Keep config under version control but remove secret values; use MODEL_API_KEY env variable and a secrets vault when available.

Decision table (example):

| Issue type | Automation action | Human gate | Acceptance threshold | |---|---:|---|---:| | docs | auto-draft PR | optional review | 95% | | minor bugfix | draft + human edit | required review | 70% | | core logic change | suggestion only | required review | 100% |

Checklist for small teams:

• [ ] Run 10–20 local examples and save logs
• [ ] Keep dry_run = true for first 100 drafts
• [ ] Set token cap (e.g., max_tokens: 2048) and daily spend cap ($5–$10)
• [ ] Monitor: latency median < 500 ms; error-rate < 2%; human-acceptance >= 70%

See the repo snapshot: https://github.com/NAEOS-foundation/naeos (441 commits).

Technical notes (optional)

Repository metadata: the public snapshot shows standard GitHub UI and repository data for https://github.com/NAEOS-foundation/naeos (441 commits, 11 stars, 6 forks, 5 issues, 0 PRs). Use the repository tree to locate examples, tests, or README files.

Secrets handling example (.env local only):

# never commit this file
MODEL_API_KEY=sk-...

Testing guidance: add 5–10 unit tests for prompt/output transforms and 3 integration tests for external calls. Track p95 latency and median latency in ms; set alert thresholds at median > 2000 ms or error-rate > 2%.

Observability: start with three metrics — latency (ms), error-rate (%), and human-acceptance (%). Suggested thresholds: median latency < 500 ms, error-rate <= 2%, human-acceptance >= 70%.

What to do next (production checklist)

Assumptions / Hypotheses

• The repository at https://github.com/NAEOS-foundation/naeos is accessible and matches the public snapshot (441 commits, basic metadata).
• The repo contains runnable code or scripts you can discover; if not, you will adapt repo patterns to your agents.
• Your model provider accepts API keys via environment variables and supports token limits such as max_tokens: 2048.

Risks / Mitigations

• Risk: leaking credentials. Mitigation: use environment variables, a secrets vault, and rotate keys regularly.
• Risk: runaway model spend. Mitigation: set daily caps ($5–$50), set max_tokens (512–2048), and restrict concurrency to 1–2 calls during early tests.
• Risk: poor output quality. Mitigation: keep dry_run true for the first 100 drafts; require human approval until acceptance >= 70%.

Next steps

• Harden secrets: move MODEL_API_KEY into a secret store and remove local .env from the repo.
• Add tests: 5 unit tests and 3 integration tests. Require CI to pass before enabling automation.
• Instrument dashboards: monitor median latency (ms), p95 latency (ms), error-rate (%), and human-acceptance (%). Alert if error-rate > 2% or median latency > 2000 ms.
• Rollout plan: Canary at 1% → Ramp to 10% for 7 days → Full 100% if SLIs pass. Rollback if error-rate > 5% or critical failures > 1 per hour.

Quick commands recap:

git clone https://github.com/NAEOS-foundation/naeos
cd naeos
ls -la

Config example (YAML):

agent:
  name: naeos-prototype
  max_retries: 3
  canary_percent: 1
model:
  provider: example
  api_key_env: MODEL_API_KEY
  max_tokens: 2048
  timeout_ms: 2000
integrations:
  github:
    dry_run: true

For repository details and to verify the snapshot metadata, see https://github.com/NAEOS-foundation/naeos.

NAEOS local prototype: clone, configure, and run an AI coding agent example

Step-by-step guide to clone NAEOS, wire a model API key, run an example in dry-run mode, and produce a reproducible agent prototype—ideal for solo founders and…

https://aisignals.dev/posts/2026-08-22-naeos-local-prototype-clone-configure-and-run-an-ai-coding-agent-example

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