Open-source protein design agent for Claude Code

Commercial platforms wrap open-source tools behind paywalls and call it a revolution.
BY gives you direct access through Claude Code. No platform fees. Your tools, your compute, your designs.

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_{Source: trust us bro}

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Quick Start (5 minutes)

You don't need to be a developer. If you can open a terminal and paste commands, you can run BY.

1. Install prerequisites

Tool	Install	Check
Node.js 18+	nodejs.org	node --version
Python 3.11+	python.org	python3 --version
uv	curl -LsSf https://astral.sh/uv/install.sh | sh	uv --version
Claude Code	npm install -g @anthropic-ai/claude-code	claude --version

2. Create your project

mkdir my-campaign && cd my-campaign
npx blatant-why init

This scaffolds everything: 11 MCP servers, 16 agents, 14 skills, 11 slash commands, and a CLAUDE.md orchestration file. Takes about 30 seconds.

3. Add your API key

cp .env.example .env
# Open .env and add your Tamarind key (see Compute Setup below)

4. Start designing

claude

Then just tell it what you want:

> "Design VHH nanobodies against PD-L1"

Or use the guided workflow:

> /by:plan-campaign

Or if it's your first time:

> /by:welcome

That's it. Claude Code handles the rest -- research, design, screening, and ranking.

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What It Does

Give it a target protein. It researches across PDB, UniProt, and SAbDab. It plans a design campaign. It submits compute jobs to Tamarind Bio (free tier, no GPU required). It screens every design for structural quality, sequence liabilities, and developability. It ranks candidates by composite score. You get a table of lab-ready binders.

The whole pipeline runs inside Claude Code. No platform. No dashboard. No vendor lock-in.

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What's Inside

Component	Count	Description
MCP Servers	11	Biological databases, cloud compute, screening, campaign state, knowledge store
Agents	16	Research, design, screening, evaluation, lab integration, and more
Skills	14	BoltzGen, Protenix, PXDesign, scoring, epitope analysis, campaign management
Slash Commands	11	Campaign control from the Claude Code prompt

MCP Servers (11)

Server	Role
pdb	Protein Data Bank queries
uniprot	UniProt protein annotation
sabdab	Structural Antibody Database
screening	Screening battery orchestration
tamarind	Tamarind Bio cloud compute
cloud	Cloud compute abstraction
adaptyv	Adaptyv Bio lab submission (gated)
campaign	Campaign state management
research	Literature and target research
local_compute	Local GPU compute dispatch
knowledge	JSON-backed campaign knowledge store

Agents (16)

Agent	Role
by-research	Target analysis, literature review, prior art
by-design	Generate designs via cloud or local pipelines
by-screening	Score, filter, rank candidates
by-evaluator	Structural evaluation and quality assessment
by-visualization	Structure and results visualization
by-diversity	Sequence and structural diversity selection
by-campaign	Campaign lifecycle orchestration
by-knowledge	Learning system and campaign memory
by-verifier	Output verification and sanity checks
by-plan-checker	Campaign plan validation
by-environment	Environment setup and dependency checks
by-lab	Adaptyv Bio lab submission (triple-gated)
by-epitope	Epitope analysis and mapping
by-humanization	Antibody humanization engineering
by-liability-engineer	Sequence liability detection and fixes
by-formatter	Output formatting and reporting

Skills (14)

Skill	Description
boltzgen	BoltzGen antibody/nanobody generation
protenix	Protenix structure prediction
pxdesign	PXDesign de novo binder design
by-scoring	ipSAE + p_bind composite scoring
by-screening	Full screening battery
by-epitope-analysis	Epitope mapping and analysis
by-campaign-manager	Campaign state and lifecycle
by-campaign-optimizer	Active learning and iteration
by-design-workflow	End-to-end design pipeline
by-research	Target research and literature
by-knowledge	Campaign knowledge operations
by-database	Local results database
by-failure-diagnosis	Pipeline failure analysis
by-hypothesis-debate	Multi-agent hypothesis evaluation

Slash Commands (11)

Command	Action
/by:load	Load a campaign from file
/by:screen	Run screening battery on designs
/by:results	Display campaign results table
/by:watch	Live-watch running compute jobs
/by:status	Campaign status dashboard
/by:approve-lab	Approve Adaptyv Bio submission (gated)
/by:set-profile	Switch compute profile
/by:setup	Initialize environment and dependencies
/by:plan-campaign	Generate a detailed campaign plan
/by:welcome	Show welcome message and quick-start guide
/by:resume	Resume an interrupted or paused campaign

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Setup

API Keys

Key	Required?	Where to get it	What it enables
TAMARIND_API_KEY	Recommended	tamarind.bio (free account)	Cloud compute -- BoltzGen, Protenix, 200+ models. Free tier: 10 jobs/month
ADAPTYV_API_TOKEN	Optional	adaptyvbio.com	Lab testing submission (triple-gated)

Claude Code handles its own authentication. No separate Anthropic API key needed.

Configure your environment

After npx blatant-why init:

1. Copy .env.example to .env
2. Add your Tamarind key:

   TAMARIND_API_KEY=your_key_here
   

3. For local GPU (optional):

   PROTEUS_FOLD_DIR=/path/to/Protenix
   PROTEUS_PROT_DIR=/path/to/PXDesign
   PROTEUS_AB_DIR=/path/to/boltzgen
   

4. For SSH remotes (optional): Add host configs to .by/config.json

Compute Options

Provider	Cost	Setup	Best for
Tamarind Bio	Free tier: 10 jobs/month	Just add API key	Getting started, small campaigns
Tamarind Paid	Pay per job	Same API key	Production campaigns
Local GPU	Your hardware	Install tools + set env vars	Power users with GPUs
SSH Remote	Your infrastructure	Configure in .by/config.json	HPC clusters, cloud GPUs

Setting up Tamarind Bio (recommended -- no GPU needed)

1. Create a free account at tamarind.bio
2. Go to Settings → API Keys → Generate new key
3. Copy the key and add to .env:

   TAMARIND_API_KEY=your-key-here
   

4. That's it. BY will use Tamarind for all compute jobs.

Free tier: 10 jobs/month. Enough for a preview campaign (~5-10 designs). Paid tiers: Contact Tamarind for production pricing.

Tamarind provides access to 200+ structural biology tools including:

• BoltzGen -- antibody/nanobody design (Boltzmann generator diffusion)
• Protenix v1 -- AlphaFold3-class structure prediction (368M params)
• PXDesign -- de novo protein binder design (17-82% hit rates)
• TAP/TNP -- developability profiling
• AbLang2 -- humanness scoring

Setting up local GPU compute

Requires an NVIDIA GPU with CUDA support. Install the tools you need:

Protenix (structure prediction):

git clone https://github.com/bytedance/protenix
cd protenix && pip install -e .

Add to .env:

PROTEUS_FOLD_DIR=/path/to/protenix

PXDesign (de novo binder design):

# Follow PXDesign installation guide

Add to .env:

PROTEUS_PROT_DIR=/path/to/pxdesign

BoltzGen (antibody/nanobody design):

git clone https://github.com/jostorge/boltzgen
cd boltzgen && pip install -e .

Add to .env:

PROTEUS_AB_DIR=/path/to/boltzgen

BY will detect these automatically and offer local compute as an option.

Setting up SSH remote compute (Lambda.ai, RunPod, HPC)

For cloud GPU instances or HPC clusters:

1. Ensure SSH key-based authentication is set up
2. Add host configuration to .by/config.json:

   {
     "compute": {
       "ssh_hosts": [
         {
           "name": "lambda-gpu",
           "host": "your-instance.cloud.lambdalabs.com",
           "user": "ubuntu",
           "key": "~/.ssh/lambda_key",
           "gpu": "A100",
           "tools": ["protenix", "boltzgen"]
         }
       ]
     }
   }

3. BY will detect SSH hosts and offer them as compute options.

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Your First Campaign

After setup, here's what to expect when you run your first design campaign.

Start Claude Code:

claude

Tell it what you want:

> "Design VHH nanobodies against PD-L1"

What happens next:

1. Research (~1-2 min) -- BY searches PDB, UniProt, and SAbDab for your target. It pulls crystal structures, known binders, epitope data, and literature context.
2. Campaign plan (~30 sec) -- You get a plan showing how many designs will be generated, which models will be used, and estimated compute cost. You approve before anything runs.
3. Design generation (~5-15 min) -- Compute jobs run on Tamarind Bio (or your local GPU). BY monitors progress and reports back.
4. Screening (~2-5 min) -- Every design is scored for structural quality (ipSAE), binding confidence (ipTM), and sequence liabilities. Problem candidates are flagged.
5. Ranking -- You get a ranked table of candidates with composite scores, ready for lab ordering.

Typical first campaign: 5-10 nanobody designs, ~20 minutes end-to-end, zero GPU required (Tamarind free tier).

You can also use slash commands for more control:

Command	What it does
/by:welcome	Guided walkthrough for first-time users
/by:plan-campaign	Generate and review a campaign plan before running
/by:status	Check progress on a running campaign
/by:results	View ranked results table

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Architecture

flowchart TB
    User([User]) -->|prompt| Claude[Claude Code + CLAUDE.md]

    Claude -->|delegates| Agents[16 Agents]
    Claude -->|invokes| Skills[14 Skills]
    Claude -->|slash cmds| Commands[11 Commands]

    Agents --> MCP[11 MCP Servers]
    Skills --> MCP

    subgraph Data["Biological Databases"]
        PDB[(PDB)]
        UniProt[(UniProt)]
        SAbDab[(SAbDab)]
    end

    subgraph Compute["Compute"]
        Tamarind["Tamarind Bio -- Free Cloud"]
        LocalGPU["Local GPU -- Optional"]
    end

    subgraph Models["Models"]
        BoltzGen["BoltzGen -- Ab/Nb Design"]
        Protenix["Protenix v1 -- Structure Prediction"]
        PXDesign["PXDesign -- Binder Design"]
    end

    subgraph Screening["Screening"]
        ipSAE["ipSAE Scoring"]
        Liabilities["Liability Scan"]
        Developability["Developability"]
        Diversity["Diversity Selection"]
    end

    subgraph Lab["Lab Integration"]
        Adaptyv["Adaptyv Bio -- Triple-Gated"]
    end

    MCP --> Data
    MCP --> Compute
    Compute --> Models
    MCP --> Screening
    MCP --> Lab

    Knowledge[("Knowledge Store")] <--> MCP

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Model Profiles

Model	Type	What It Does
Protenix v1	Structure prediction (368M params)	AlphaFold3-class folding -- protein, nucleic acid, ligand
PXDesign	De novo binder design	17-82% hit rates on published benchmarks
BoltzGen	Antibody/nanobody design	Boltzmann generator + Protenix confidence scoring

Learning System

Every campaign writes results to a JSON knowledge store. The knowledge MCP server provides keyword search over past campaigns, so the system learns which design strategies work for which target classes.

Stored per campaign:

• Target metadata and research context
• Design parameters and compute profiles
• Screening results and composite scores
• Success/failure annotations

Over time, the agent develops institutional memory about what works.

Repository Structure

blatant-why/
├── assets/                  # Banner and diagrams
├── src/
│   ├── init-cli/            # npx blatant-why init CLI
│   └── proteus_cli/         # Python CLI (scoring, screening, campaign)
├── templates/               # Deployed by init CLI
│   └── .claude/
│       ├── agents/          # 16 specialized agents
│       ├── commands/by/     # 11 slash commands
│       ├── skills/          # 14 skills
│       └── mcp_servers/     # 11 MCP server implementations
├── tests/                   # Test suite
├── CLAUDE.md                # Agent orchestration rules
├── package.json             # npm package
├── pyproject.toml           # Python package (uv)
└── README.md

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Credits

Built by Tristan Farmer

• Hannes Stark and the MIT team for BoltzGen
• Deniz Kavi and Sherry Liu at Tamarind Bio
• Julian Englert at Adaptyv Bio
• The Claude Code team

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License

MIT