fastfold-agent-cli 0.0.51

Fastfold CLI — An autonomous agent for drug discovery research

Fastfold Agent CLI

Fastfold Agent CLI is an agentic research environment for scientists in search of great tools, combining open-source skills and tools with Fastfold workflows, cloud services, and local or hosted LLMs.

It is built on CellType,1 an open-source agent for computational biology that achieves a state-of-the-art 90% on BixBench-Verified-50.2

Why Fastfold CLI

You get the full open-source agent1 with 190+ tools, 30+ database APIs, and multi-step planning, plus what Fastfold adds for composability and extensibility:

• GPU compute models & workflows: run heavy scientific workflows like folding, protein design, and MD simulation on Fastfold Cloud, your own compute, or providers like Modal, Nebius, and more.
• Installable skills: discover, add, and share workflows natively (fastfold skills find, fastfold skills add <github url>).
• Any model: Anthropic, OpenAI, or local/open models like Gemma, Qwen, and DeepSeek through endpoints like Ollama and Unsloth via /model or fastfold setup.

Quick install

Install uv (for native CLI install)

• Python 3.10+ (recommended: let uv install managed interpreters).
• uv — Installing uv. Quick options:

Linux / macOS:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows (cmd/PowerShell):

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Alternatively: winget install --id=astral-sh.uv -e (see Astral docs for other methods).

After installing uv, close and reopen your terminal or PowerShell so PATH picks up the uv executable.

Linux / macOS:

uv tool install "fastfold-agent-cli[all]" --python 3.10

Windows (cmd/PowerShell):

uv tool install "fastfold-agent-cli[win_build]" --python 3.10

Install via WSL2 + Ubuntu (full [all] stack, recommended)

tiledbsoma does not publish usable native Windows wheels, so [all] on cmd/PowerShell usually fails. Use WSL instead:

1. Install WSL (Ubuntu recommended).
2. Open an Ubuntu terminal and install uv + Python (see Prerequisites above).
3. Run the same install command inside WSL:

uv tool install "fastfold-agent-cli[all]" --python 3.10

Docker

docker run -it --rm \
  -v fastfold-cli:/root/.fastfold-cli \
  fastfold/fastfold-agent-cli:latest

Authentication

The interactive setup wizard is the easiest way to get started. It lets you pick provider(s) from a toggle list, then enter keys:

fastfold setup

To skip the toggle list, pass the provider(s) explicitly (comma-separated):

fastfold setup --provider anthropic
fastfold setup --provider openai
fastfold setup --provider openai_compatible
fastfold setup --provider anthropic,openai

Prefer environment variables? Set keys directly:

export ANTHROPIC_API_KEY="sk-ant-..."
export OPENAI_API_KEY="sk-..."
export FASTFOLD_API_KEY="sk-..."

For CI or scripting, pass keys non-interactively:

fastfold setup --api-key sk-ant-... --fastfold-api-key sk-...
fastfold setup --provider openai --openai-api-key sk-... --fastfold-api-key sk-...

Local/compatible endpoints can also be configured in one line:

fastfold setup --provider openai_compatible --openai-compatible-backend ollama --openai-base-url http://localhost:11434/v1
fastfold setup --provider openai_compatible --openai-compatible-backend unsloth --openai-base-url http://localhost:8888/v1

Local and OpenAI-compatible models (Ollama, Unsloth, other gateways)

Fastfold supports local/self-hosted OpenAI-compatible endpoints in both fastfold setup and interactive /model.

Recommended (interactive):

fastfold setup --provider openai_compatible

The setup wizard will guide you through:

1. Endpoint type selection:

• Ollama (/api/tags)
• Unsloth (/v1/models, auth)
• Other OpenAI-compatible (/v1/models then /api/tags)

2. Endpoint base URL:

• Ollama default: http://localhost:11434/v1
• Unsloth default: http://localhost:8888/v1

3. API key prompt (backend-aware):

• Ollama commonly uses ollama placeholder key
• Unsloth uses your Unsloth Studio key

4. Model discovery + selection from endpoint models (or manual model ID entry)

You can also configure directly:

# Ollama
fastfold config set llm.provider openai
fastfold config set llm.openai_base_url http://localhost:11434/v1
fastfold config set llm.openai_compatible_backend ollama
fastfold config set llm.openai_compatible_api_key ollama
fastfold config set llm.model llama3.1

# Unsloth
fastfold config set llm.provider openai
fastfold config set llm.openai_base_url http://localhost:8888/v1
fastfold config set llm.openai_compatible_backend unsloth
fastfold config set llm.openai_compatible_api_key sk-unsloth-...
fastfold config set llm.model <unsloth-model-id>

Environment variables for compatible endpoints:

export OPENAI_BASE_URL="http://localhost:11434/v1"
export OPENAI_COMPATIBLE_API_KEY="ollama"

Inside interactive mode, run /model to switch providers/models and re-run endpoint/model discovery at any time.

Provider selection:

fastfold config set llm.provider anthropic
fastfold config set llm.model claude-sonnet-4-5-20250929
fastfold config set llm.anthropic_api_key sk-ant-...

fastfold config set llm.provider openai
fastfold config set llm.model gpt-4o
fastfold config set llm.openai_api_key sk-...

# Legacy fallback (Anthropic only, still supported)
fastfold config set llm.api_key sk-ant-...

Getting Started

# Start interactive session
fastfold

# Single query
fastfold "What are the top degradation targets for this compound?"

# Validate setup
fastfold doctor

# List available tools
fastfold tool list

# List loaded skills
fastfold skills list

Interactive commands

Inside fastfold interactive mode (run /help for the full reference):

Discover

• /help: show command reference with examples
• /tools: list all tools with status (stable/experimental)
• /skills: list currently loaded skills
• /skills-find [query]: discover installable skills from the catalog
• /skills-add <source>: install a skill from GitHub/local path/name
• /skills-remove <name>: remove a globally-installed skill
• /case-study: run/list curated case studies (/case-study list)

Models & configuration

• /model: switch LLM model/provider interactively
• /settings: configure UI and agent preferences
• /config: show active runtime configuration
• /keys: show API key setup status by service

Run control

• /agents N <query>: run a query with N parallel research agents
• /plan: toggle plan mode (preview & approve before executing)
• /interrupt: interrupt the active generation (add ! to force)
• /compact: compress session context for longer runs
• /tasks: show background task watcher status (/tasks refresh for live probe)

Sessions & output

• /new: start a new empty session
• /sessions: list saved sessions (or delete: /sessions delete <id>)
• /resume: resume a previous session by id/index
• /usage: show session token/cost usage
• /copy: copy the last answer to clipboard
• /export: export current session transcript to markdown
• /export-share: export session, send to Slack, and save to library
• /notebook: export current session as Jupyter notebook (.ipynb)

Maintenance

• /upgrade: upgrade fastfold-agent-cli via uv
• /doctor: run readiness diagnostics and fix hints
• /autofix: apply automatic local fixes for common runtime issues
• /clear: clear the screen
• /exit: exit the terminal

Quick examples

Target prioritization

fastfold "I have a CRBN molecular glue. Proteomics shows it degrades
          IKZF1, GSPT1, and CK1α. Which target should I prioritize?"

Protein folding

fastfold "Fold this sequence with boltz-2 and find the binding pockets: MALWMRLLPLL..."

Combination strategy

fastfold "My lead compound is immune-cold. What combination strategy should I use?"

Key Features

190+ Domain Tools

Category	Examples
Target	Neosubstrate scoring, degron prediction, co-essentiality networks
Chemistry	SAR analysis, fingerprint similarity, scaffold clustering
Expression	L1000 signatures, pathway enrichment, TF activity, immune scoring
Viability	Dose-response modeling, PRISM screening, therapeutic windows
Biomarker	Mutation sensitivity, resistance profiling, dependency validation
Clinical	Indication mapping, population sizing, TCGA stratification
Safety	Anti-target flagging, multi-modal profiling, SALL4 risk
Structure	AlphaFold fetch, docking, binding sites, MD simulation
Folding	Fastfold AI Cloud: boltz-2, monomer, multimer, simplefold_*
Literature	PubMed, OpenAlex, ChEMBL search
DNA	ORF finding, codon optimization, primer design, Gibson/Golden Gate assembly

Agent Skills

Fastfold ships with a bundled skill catalog and lets you discover, add, and manage skills natively. Installed skills live in ~/.fastfold-cli/skills/ and are picked up automatically.

List what's loaded:

fastfold skills list

Discover skills from the catalog:

fastfold skills find                 # browse all
fastfold skills find "protein design"  # filter by query

Add a skill from a GitHub URL, owner/repo@subpath, a local path, or a catalog name:

fastfold skills add https://github.com/fastfold-ai/skills/tree/main/skills/fold
fastfold skills add fastfold-ai/skills@skills/fold
fastfold add skills ./my-skill        # alias for `skills add`

Keep skills current (sync the Fastfold catalog — adds new + updates existing — and re-install other tracked skills):

fastfold skills upgrade                 # sync catalog + update installed
fastfold skills upgrade --catalog-only  # only refresh the Fastfold catalog
fastfold skills upgrade --no-catalog    # only update already-installed skills

Inspect or remove:

fastfold skills info fold
fastfold skills remove fold
fastfold skills delete --all   # remove ALL user-installed skills (asks to confirm)

Inside the interactive session you can use the slash commands /skills, /skills-find [query], /skills-add <source>, and /skills-remove <name>. Install uses a native git clone and falls back to npx skills add (skills.sh) when needed.

fastfold setup also offers to install skills interactively — it live-fetches the current Fastfold catalog, lets you multi-select, suggests the community collections below, and accepts custom sources. It prefers npx skills add when Node is available, otherwise uses git. Non-interactive: fastfold setup --skills "fastfold-ai/skills@skills/fold,..." or --skip-skills.

Community skill collections

You can also install skills from other providers. Each command installs the whole collection (use npx skills add ... instead if you prefer Node):

• K-Dense-AI — scientific agent skills (repo)

fastfold skills add K-Dense-AI/scientific-agent-skills

• Anthropic — life-sciences skills (repo)

fastfold skills add anthropics/life-sciences

• DeepMind — science skills (repo)

fastfold skills add google-deepmind/science-skills

Create your own skill with the bundled skill-creator (scaffold, validate, package), and let the agent discover skills via the find-skills skill. To let the agent install skills itself, enable fastfold config set skills.allow_agent_install true.

Data Management

fastfold data pull depmap    # DepMap CRISPR, mutations, expression
fastfold data pull prism     # PRISM cell viability
fastfold data pull msigdb    # Gene sets
fastfold data pull alphafold     # Protein structures (on-demand)

# Or point to existing data
fastfold config set data.depmap /path/to/depmap/

Reports

fastfold report list         # list reports
fastfold report publish      # convert latest .md to .html
fastfold report show         # open in browser

Explore Fastfold Apps

Browse the Fastfold Apps catalog at https://cloud.fastfold.ai/apps

• Fold model options include: ESM-1b, IntelliFold, OpenFold 3, AlphaFold2, Boltz-1, Boltz-2, Chai-1, and SimpleFold.
• MD workflow options include: OpenMM Calvados and OpenMMDL.
• Protein Design workflows coming soon: Boltzgen and Bindcraft.

Contributing

Contributions are welcome, from bug reports and docs fixes to new tools and skills.

Clone the repo and set up a development environment:

git clone https://github.com/fastfold-ai/fastfold-agent-cli.git
cd fastfold-agent-cli
uv venv --python 3.12 && uv sync
fastfold setup

Run the test suite before opening a PR:

pytest tests/          # full suite
pytest tests/ -v       # verbose
pytest tests/test_cli.py::test_name   # a single test

Adding a new tool? Tools live in src/tools/ and register with the @registry.register(...) decorator. Each tool's name prefix must match its category, it should accept **kwargs, and it must return a dict with a "summary" key. Use lazy imports for data loaders inside the function body. See CLAUDE.md for the full tool pattern and conventions.

A few guidelines:

• Keep changes focused and add tests for new behavior (tests mock data loaders, so they don't require real datasets).
• Match the existing code style and run pytest locally until green.
• Open a PR with a clear description of the change and why it's needed.

License

MIT

Notes

1. Agent foundation and capabilities ("Why ct"): github.com/celltype/celltype-agent#why-ct
2. Benchmark — 90% on BixBench-Verified-50, as reported upstream: github.com/celltype/celltype-agent#benchmark