brand-gen

A memory-backed brand generation runtime for agent-led creative iteration.

brand-gen helps an agent plan, generate, review, score, and improve brand materials without forgetting product truth, visual rules, exact-copy requirements, or past user feedback. It is built for teams that have moved past one-off image prompting and need a repeatable brand workflow that learns across sessions.

Quick start • How it works • Examples • Pi setup • Docs

Why brand-gen?

Image models are good at producing a single artifact. Brand work needs a system: briefs, source knowledge, approved copy, references, design tokens, critiques, rejections, and iteration memory. brand-gen gives agent hosts a typed runtime for that loop.

• Ground every run in brand memory — profiles, design tokens, scratchpads, examples, bans, review history, and source knowledge all live in the workspace.

• Plan before spending generation tokens — the six-phase pipeline validates a material plan before image/video generation starts.

• Protect exact text — visible copy can be routed to deterministic HTML, SVG, composite, or overlay flows instead of asking an image model to spell.

• Use brand-specific knowledge bases — connect Obsidian vaults or docs folders per brand and query them through brand_source_knowledge.

• Capture optimizer-ready review data — reviews preserve axis scores, rationales, disqualifiers, before/after diffs, and disagreement records for GEPA/DSPy-style prompt improvement.

• Run from multiple agent hosts — Claude Code, Pi, OpenClaw, Codex, Cursor, and plain MCP hosts can call the same Python runtime.

Related MCP server: Muninn

Quick start

Requirements

Install

git clone https://github.com/velinussage/brand-gen.git
cd brand-gen

python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install -e .

cp .env.example .env
# Edit .env and add REPLICATE_API_TOKEN=...

python3 scripts/validate_setup.py

Create a brand

bgen create-brand \
  --name "Orbit Ops" \
  --description "Operational intelligence software for distributed teams." \
  --tone "calm,technical,trustworthy" \
  --palette "#1A6B6B,#C85A2A" \
  --keywords "operations,distributed systems" \
  --value-prop "Clear operational visibility"

This creates a brand workspace:

.brand-gen/brands/orbit-ops/
├── brand-profile.json
├── brand-identity.json
├── prompts/pi-full-pipeline.md
├── custom-scratchpad.md
├── custom-scratchpad.json
├── runs/
└── reviews/

Run the generated Pi prompt

Every brand gets its own Pi prompt. Use that generated prompt instead of copying a prompt from another brand.

cat .brand-gen/brands/orbit-ops/prompts/pi-full-pipeline.md

Paste the prompt into Pi, then ask the orchestrator for a material:

/run brand-orchestrator "Create a 1:1 social launch card for Orbit Ops. Make the product truth clear in under three seconds."

After reviewing the image, record feedback and iterate:

Use brand_feedback on the generated version. Score it 4/5. Notes: strong trust tone; simplify the proof inset.

/run brand-orchestrator "Iterate from v001. Keep the trust tone, simplify proof, and make the headline deterministic."

How it works

The orchestrator runs a six-phase quality gate. The CLI exists for scripting and debugging, but agents should call typed brand_* tools whenever the host supports them.

flowchart TD
    User([Brief or iteration request]) --> Orch[brand-orchestrator]
    Orch --> Prep[1. Prepare<br/>context, learnings, source knowledge]
    Prep --> Plan[2. Plan<br/>material type, surface, product truth, references]
    Plan --> Validate[3. Validate<br/>critic blocks weak plans]
    Validate --> Execute[4. Execute<br/>scratchpad + image/video generation]
    Execute --> Review[5. Review<br/>rubric, DSPy/VLM, before/after diffs]
    Review --> Evolve[6. Evolve<br/>promote learnings + next actions]
    Evolve -. feedback / source-version .-> Orch

Core concepts

Examples

Generate a social proof card

Agent prompt:

/run brand-orchestrator "Create a 1:1 social proof card for Orbit Ops. Use one real product/proof anchor, keep proof secondary, and render exact copy deterministically."

Manual CLI flow for debugging:

bgen context-snapshot --format json
bgen source-knowledge --query "proof customer operations workflow" --format json
bgen orchestrate-material \
  --material-type social \
  --mode hybrid \
  --purpose "social proof" \
  --target-surface "1:1 social promo" \
  --prompt-seed "Show operational trust with one concrete product proof moment." \
  --format json

Start a temporary testing session

Use this when you are still discovering a brand direction and do not want to commit to a saved brand yet.

bgen start-testing \
  --working-name "Field Notes" \
  --goal "Explore a documentary-style identity for a field operations tool"

cat .brand-gen/sessions/field-notes/brand-materials/prompts/pi-full-pipeline.md

Testing sessions keep their own prompt, memory, scratchpad, reviews, and assets.

Record a recurring failure as a durable ban

bgen append-forbidden-pattern \
  --pattern "generic four-node capability diagram without real product truth" \
  --reason "User rejected prior social direction as too generic" \
  --format json

Add a one-run push for the next iteration

bgen orchestrate-material \
  --material-type social \
  --source-version v012 \
  --push "Show the dispatch review workflow, not abstract network nodes." \
  --ban "generic AI network decoration" \
  --format json

Score and iterate

bgen feedback v012 \
  --score 2 \
  --status rejected \
  --notes "Too generic; source knowledge was not visible; exact text was weak."

bgen orchestrate-material \
  --material-type social \
  --source-version v012 \
  --push "Use source_knowledge facts and deterministic text." \
  --format json

Generate video after motion grammar exists

bgen set-motion-grammar \
  --director "restrained editorial product film" \
  --favored "slow dolly, soft parallax, clean reveal" \
  --banned "spin, whip pan, chaotic zoom, fake UI typing" \
  --intensity medium \
  --format json

bgen orchestrate-material \
  --material-type short-video \
  --mode hybrid \
  --purpose "launch teaser" \
  --target-surface "8 second social video" \
  --format json

Example outputs

The README intentionally keeps visuals out of the hero so the first screen stays usable. Generated examples live here instead:

Source knowledge

Use .brand-gen-local.json for machine-local docs or Obsidian vault paths. This file is intentionally untracked.

{
  "repo_root": "/path/to/brand-gen",
  "vault_paths": [],
  "brand_vault_paths": {
    "orbit-ops": ["/path/to/orbit-ops-vault"]
  },
  "brand_knowledge_base_paths": {
    "orbit-ops": ["/path/to/orbit-ops/docs"]
  }
}

Agents can discover and query the configured knowledge bases:

bgen context-snapshot --format json
bgen source-knowledge --query "customer proof workflow trust" --format json

Recommended agent instruction:

Before planning the social asset, call brand_source_knowledge with the query
"customer proof workflow trust". Use only Orbit Ops source facts, not facts from
another brand. Convert useful details into prompt seeds or scratchpad notes.

Typed runtime

Every host exposes the same canonical brand_* tools from packages/brand-gen-core/src/tool-registry.ts. The Python bridge maps those verbs to the bgen runtime.

Agent rules:

1. Prefer brand_orchestrate_material for normal generation.

2. Follow each tool's next_action pointer.

3. Never manually edit brand-identity.json, learnings.json, iteration-memory.json, custom-scratchpad.md, or custom-scratchpad.json.

4. Never bypass blocking findings unless the user explicitly authorizes it.

5. Treat decision: pending or empty axis_scores as not reviewed.

See docs/architecture/runtime-agent-contract.md for the full contract.

Agent and host setup

Pi agent setup

Install or load the local Pi extension from packages/pi-brand-gen, then use the generated prompt for the active brand:

cat .brand-gen/brands/<brand-key>/prompts/pi-full-pipeline.md

Inside Pi:

/run brand-orchestrator "Create the requested brand material using typed brand_* tools only."

The Pi plugin registers all 45 canonical tools plus compatibility shims. Subagent frontmatter in .pi/agents/brand-*.md should resolve against that registry.

Claude Code

Use the brand-orchestrator subagent. Agent definitions live in .claude/agents/brand-*.md and are mirrored under skills/brand-gen/claude-agents/.

Use the Agent tool with subagent_type="brand-orchestrator".
Task: Create a social launch card for Orbit Ops using the full brand-gen pipeline.

OpenClaw or any MCP host

Run the MCP server from the repo root:

python3 -m brand_gen.brand_iterate_mcp

Use module syntax (-m) so Python package imports resolve correctly.

Agent reference

Nine specialist agents share the same typed runtime:

Manual chain for hosts without subagents:

brand-explorer -> brand-philosopher -> brand-router -> brand-planner -> brand-critic -> brand-generator -> brand-critic

For video, insert brand-cinematographer before brand-generator.

File layout

.brand-gen/
├── config.json
├── brands/
│   └── <brand-key>/
│       ├── brand-profile.json
│       ├── brand-identity.json
│       ├── prompts/pi-full-pipeline.md
│       ├── custom-scratchpad.md
│       ├── custom-scratchpad.json
│       ├── inspiration-memory.json
│       ├── inspiration-board.json
│       ├── runs/*.jsonl
│       ├── reviews/*
│       └── v###-*.png|jpg|mp4
└── sessions/
    └── <session-key>/brand-materials/

Untracked local config:

.brand-gen-local.json     # machine paths such as Obsidian vaults; do not commit
.env                      # API keys; do not commit

Prompt types

Do not reuse the Sage prompt for unrelated brands. It contains Sage product truth and Sage vocabulary. New brands get generated prompts from their own profile, approved copy, creative context, and source-knowledge configuration.

Documentation

• skills/brand-gen/SKILL.md — main workflow for agents

• skills/brand-gen-setup/SKILL.md — installation and host setup

• docs/architecture/runtime-agent-contract.md — typed runtime rules

• docs/architecture/source-knowledge.md — brand-scoped vault/docs ingestion

• docs/architecture/gepa-dspy-optimization.md — review/disagreement records for optimizers

• docs/prompts/pi-sage-brand-gen-full-pipeline.md — Sage-only Pi prompt

• docs/host-setup.md — host adapter architecture

When not to use brand-gen

Use a direct image prompt instead when you need one disposable concept, do not care about preserving feedback, and have no product truth or exact-copy requirements. Use brand-gen when the work needs memory, review gates, source knowledge, deterministic text handling, or cross-material consistency.

Contributing

See CONTRIBUTING.md. Run the normal checks before opening a PR:

.venv/bin/python -m pytest
npm --prefix packages/pi-brand-gen test
npm --prefix packages/brand-gen-core run typecheck
make lint

License

MIT © brand-gen maintainers