CLAUDE.md@cli · diff
git:20260712.a2157b8 to git:20260713.890fd11
20 added, 12 removed. Audit A to A.
- # fastfold-agent-cli — Project Instructions
+ # sandwalk — Project Instructions
## What This Is
- fastfold-agent-cli is an autonomous agent for drug discovery research — like Claude Code, but for biology.
- It takes natural language questions about compounds/targets/indications and executes
- multi-step research workflows using computational biology tools.
+ Sandwalk is an autonomous agent for open science and drug discovery research —
+ like Claude Code, but for biology. It takes natural language questions about
+ compounds/targets/indications and executes multi-step research workflows using
+ computational biology tools.
## Architecture
**Deep Agents (LangGraph) agentic loop**: Query → model (plans, calls tools, self-corrects, synthesizes) → Report
Built on `deepagents`/LangGraph. A single agentic session orchestrates all domain tools, with native
progressive skill discovery and Programmatic Tool Calling (tools injected as Python callables in the
sandbox). Supports Anthropic and OpenAI (including OpenAI-compatible endpoints via `llm.openai_base_url`).
### Key directories:
```
src/
├── agent/ # Runner, MCP server, system prompt, config
+ ├── agent_server/ # Local FastAPI server for the web UI / Mac app
├── tools/ # All research tools (190+), registered via @registry.register()
├── data/ # Data loaders (DepMap, PRISM, L1000, proteomics)
├── models/ # LLM client abstraction
└── ui/ # Interactive terminal
```
## Tool Pattern
Every tool follows this exact pattern:
```python
@registry.register(
name="category.tool_name",
description="What this tool does",
category="category",
parameters={"param": "description"},
requires_data=["proteomics"], # optional
)
def tool_name(param: str = "default", **kwargs) -> dict:
"""Docstring."""
# ... implementation ...
return {
"summary": "Human-readable result summary",
# ... additional data fields ...
}
```
Rules:
- Name prefix MUST match category
- Always accept `**kwargs`
- Always return a dict with a `"summary"` key
- Use lazy imports for data loaders inside the function body
- Use `from data.loaders import load_X` pattern
## Commands
```bash
- fastfold --version # Check version
- fastfold "your question" # Single query
- fastfold # Interactive mode
- fastfold tool list # List all tools
- fastfold config set key value # Set config
- fastfold data pull depmap # Download dataset
- pytest tests/ -v # Run tests
- pip install -e ".[dev]" # Install for development
+ sandwalk --version # Check version
+ sandwalk "your question" # Single query
+ sandwalk # Interactive mode
+ sandwalk tool list # List all tools
+ sandwalk config set key value # Set config
+ sandwalk data pull depmap # Download dataset
+ pytest tests/ -v # Run tests
+ ruff check src # Lint
+ mypy # Typecheck (scoped packages in pyproject.toml)
+ pip install -e ".[dev]" # Install for development
```
## Testing
Tests use mocked data loaders — never require real datasets.
Mock pattern: `@patch("tools.module.load_X")` to inject test DataFrames.
+
+ ## Linting
+
+ Matches fastfold-cloud-backend: **ruff** (`E,W,F,I,B,C4,UP`) + **mypy** (strict, expanding package by package).