lobster-dev

name

lobster-dev

description

Develop, extend, and contribute to Lobster AI — the multi-agent bioinformatics engine. Use when working on Lobster codebase, creating agents/services, understanding architecture, fixing bugs, adding features, or contributing to the open-source project. Trigger phrases: "add agent", "create service", "extend lobster", "contribute", "understand architecture", "how does X work in lobster", "fix bug", "add feature", "write tests", "lobster development", "agent development", "bioinformatics code"

Lobster AI Development Guide

Lobster AI is a multi-agent bioinformatics platform using LangGraph for orchestration. This skill teaches you how to work with, extend, and contribute to the codebase.

Quick Navigation

Task	Documentation
Architecture overview	references/architecture.md
Creating new agents	references/creating-agents.md
Creating new services	references/creating-services.md
Code layout & finding files	references/code-layout.md
Testing patterns	references/testing.md
CLI reference	references/cli.md

Critical Rules

1. ComponentRegistry is truth — Agents discovered via entry points, NOT hardcoded
2. AGENT_CONFIG at module top — Define before heavy imports for <50ms discovery
3. Services return 3-tuple — (AnnData, Dict, AnalysisStep) always
4. Always pass ir — log_tool_usage(..., ir=ir) for reproducibility
5. No lobster/__init__.py — PEP 420 namespace package

Package Structure

lobster/
├── packages/                    # Agent packages (PEP 420)
│   ├── lobster-transcriptomics/ # transcriptomics_expert, annotation_expert, de_analysis_expert
│   ├── lobster-research/        # research_agent, data_expert_agent
│   ├── lobster-visualization/   # visualization_expert
│   ├── lobster-metadata/        # metadata_assistant
│   ├── lobster-structural-viz/  # protein_structure_visualization_expert
│   ├── lobster-genomics/        # genomics_expert
│   ├── lobster-proteomics/      # proteomics_expert
│   └── lobster-ml/              # machine_learning_expert
└── lobster/                     # Core SDK
    ├── agents/supervisor.py     # Supervisor (stays in core)
    ├── agents/graph.py          # LangGraph builder
    ├── core/                    # Infrastructure (registry, data_manager, provenance)
    ├── services/                # Analysis services
    └── tools/                   # Agent tools

Quick Commands

# Setup (development)
make dev-install              # Full dev setup with editable install
make test                     # Run all tests
make format                   # black + isort

# Setup (end-user testing via uv tool)
uv tool install 'lobster-ai[full,anthropic]'   # Install as users see it
uv tool upgrade lobster-ai                      # Upgrade to latest

# Running
lobster chat                  # Interactive mode
lobster query "your request"  # Single-turn

# Testing
pytest tests/unit/            # Fast unit tests
pytest tests/integration/     # Integration tests

Service Pattern (Essential)

All services return a 3-tuple:

def analyze(self, adata, **params) -> Tuple[AnnData, Dict, AnalysisStep]:
    # Your analysis logic
    stats = {"n_cells": adata.n_obs, "status": "complete"}
    ir = AnalysisStep(
        activity_type="analyze",
        inputs={"n_obs": adata.n_obs},
        outputs=stats,
        params=params
    )
    return processed_adata, stats, ir

Tools wrap services:

@tool
def analyze_modality(modality_name: str, **params) -> str:
    result, stats, ir = service.analyze(adata, **params)
    data_manager.log_tool_usage("analyze", params, stats, ir=ir)  # IR mandatory!
    return f"Complete: {stats}"

Agent Registration (Entry Points)

Agents register via pyproject.toml:

[project.entry-points."lobster.agents"]
my_agent = "lobster.agents.my_domain.my_agent:AGENT_CONFIG"

AGENT_CONFIG must be defined at module top (before imports):

# lobster/agents/mydomain/my_agent.py
from lobster.config.agent_registry import AgentRegistryConfig

AGENT_CONFIG = AgentRegistryConfig(
    name="my_agent",
    display_name="My Expert Agent",
    description="What this agent does",
    factory_function="lobster.agents.mydomain.my_agent.my_agent",
    handoff_tool_name="handoff_to_my_agent",
    handoff_tool_description="Assign tasks for my domain analysis",
    tier_requirement="free",  # All official agents are free
)

# Heavy imports AFTER config
from lobster.core.data_manager_v2 import DataManagerV2
# ... rest of implementation

Key Files

File	Purpose
lobster/agents/graph.py	LangGraph orchestration
lobster/core/component_registry.py	Agent discovery
lobster/core/data_manager_v2.py	Data/workspace management
lobster/core/provenance.py	W3C-PROV tracking
lobster/cli.py	CLI implementation

Online Documentation

Full documentation at docs.omics-os.com (or local docs-site/):

• Getting Started: docs/getting-started/
• Core SDK: docs/core/
• Agents: docs/agents/
• Developer Guide: docs/developer/
• API Reference: docs/api-reference/

Common Tasks

Adding a New Agent

1. Create package: packages/lobster-mydomain/
2. Define AGENT_CONFIG at top of agent file
3. Register entry point in pyproject.toml
4. Implement agent with tools
5. Add tests in tests/unit/agents/

See references/creating-agents.md for full guide.

Adding a New Service

1. Create service class in appropriate package
2. Implement 3-tuple return pattern
3. Wrap in tool with log_tool_usage
4. Add unit tests

See references/creating-services.md for full guide.

Understanding Data Flow

User Query → CLI → LobsterClientAdapter → AgentClient
                                              ↓
                            LangGraph (supervisor → agents)
                                              ↓
                               Services → DataManagerV2
                                              ↓
                                    Results + Provenance

Testing

# Unit tests (fast, no external deps)
pytest tests/unit/ -v

# Integration tests (may need env vars)
pytest tests/integration/ -v

# Specific test
pytest tests/unit/test_my_feature.py -v

# With coverage
pytest --cov=lobster tests/

Contributing

1. Fork the repository
2. Create feature branch: git checkout -b feature/my-feature
3. Make changes following patterns above
4. Run tests: make test
5. Format code: make format
6. Submit PR with clear description