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UGC generation AI agent for D2C brands — script → render → publish pipelines. Open source (MIT).

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AI UGC Agent

License: MIT Part of Mesh Pilot Open on GitHub Mirrored on Codeberg

Part of the Mesh Pilot open-source 6-agent marketing stack. Short-form vertical (9:16) AI UGC video ad pipeline — built for cost-per-acquisition, not organic views.

Takes a structured brief (product, audience, hook_angles) and produces a finished MP4 with a synthesized actor, voiced script, b-roll, BGM, and TikTok-style captions. Designed to plug into the ads agent's approval flow — every variant queues for human review before it spends a dollar.

Quick start

git clone https://gitlab.com/nuraveda-lab/ai-ugc-agent.git
# or: git clone https://codeberg.org/Nuraveda_lab/ai-ugc-agent.git
cd ai-ugc-agent

uv pip install -e .          # or: pip install -e .
pnpm install                 # only if you want the Remotion fallback path
cp .env.example .env         # HeyGen / Pexels / ElevenLabs / etc. keys

cp src/ugc/briefs/example.yaml briefs/my_product.yaml
# edit briefs/my_product.yaml with your product, audience, hook angles
ugc render briefs/my_product.yaml

What it does

  1. Brief — you write a short YAML describing the product, audience pain, and the hook angles you want to A/B test.
  2. Scripts — the agent expands each hook into a full 15–30s ad body around a single CTA.
  3. Render — pluggable rendering pipeline:
    • HeyGen Video Agent CLI (recommended primary path)
    • Pexels + Remotion + ElevenLabs + ffmpeg (manual fallback)
    • fal.ai / Higgsfield / Omnihuman (experimental variants in scripts/)
  4. Output — vertical 9:16 MP4 with captions ready for Meta / TikTok ads.

The HITL pattern (shared across the stack)

Generated variants don't auto-publish. They're produced as draft assets that the ads agent (or any downstream uploader) queues through its approval gate. In the Mesh Pilot cockpit, those drafts show up in the same web inbox where every cross-agent proposal lands.

Why open source

This repo started as one of six agents in a closed digital-marketing stack. The engine is general-purpose; brand-specific prompt tuning, operational guardrails, and proprietary fine-tuning that turn it into a deployable product live elsewhere. What's open here is the pipeline — brief format, render adapters, caption tooling, BGM mixing, plus the v6→v18 iteration history showing what worked and what didn't.

Layout

src/ugc/        # core Python package — pipeline + brief loader
scripts/        # render iterations (v6 → v18) showing the path
                # from naive ffmpeg to HeyGen Video Agent
remotion/       # Remotion-based caption + b-roll composer (the fallback)
tests/          # smoke tests + caption / variant unit tests
assets/bgm/     # background music clips (only royalty-free clips ship)
pyproject.toml  # Python deps (uv / pip install -e .)
package.json    # Remotion / Node deps (pnpm install)

Companions in the stack

Agent Domain Repo
AI Ads Agent Meta / Google / TikTok / Amazon Ads mesh-pilot/ai-ads-agent
AI Sales Agent Outbound B2B sales mesh-pilot/ai-sales-agent
AI Social Agent Multi-platform posting + ORM mesh-pilot/ai-social-agent
AI UGC Agent This repo —
AI Voice Agent LiveKit-based phone agent mesh-pilot/ai-voice-agent
AI SEO Agent Shopify SEO autopilot mesh-pilot/ai-seo-agent

In production they're orchestrated by Mesh Pilot — the closed-source cockpit that runs all six in concert with shared brand context, a single web approval inbox, and cross-agent handoffs.

Mirrors

Contributing

Bug reports + PRs welcome. See CONTRIBUTING.md for the contribution shape (issue-first for non-trivial changes, preserve the HITL gate, conventional commits).

Security

Security reports go to help.nuraveda@gmail.com — see SECURITY.md. Please do not open public issues for vulnerabilities.

Code of conduct

Be kind, stay on scope — see CODE_OF_CONDUCT.md.

License

MIT — fork it, ship products with it, no attribution required.


Built by Mesh Pilot.

About

UGC generation AI agent for D2C brands — script → render → publish pipelines. Open source (MIT).

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