SciLink

AI-Powered Scientific Research Automation Platform

SciLink Logo

SciLink employs a system of intelligent agents to automate experimental design, data analysis, and iterative optimization workflows. Built around large language models with domain-specific tools, these agents act as AI research partners that can plan experiments, analyze results across multiple modalities, and suggest optimal next steps.

Overview

SciLink provides three complementary agent systems that cover the full scientific research cycle:

| System | Purpose | Key Capabilities | |--------|---------|------------------| | Planning Agents | Experimental design & optimization | Hypothesis generation, Bayesian optimization, literature-aware planning | | Analysis Agents | Multi-modal data analysis | Image analysis, spectroscopy, hyperspectral datacubes, curve fitting | | Simulation Agents | Computational modeling | DFT calculations, classical MD (LAMMPS), structure recommendations |

All systems support three autonomy levels:

Co-Pilot (default) — Human leads, AI assists. Reviews every step.
Supervised — AI leads, human reviews major decisions.
Autonomous — Full autonomy, no human review.

Installation

pip install scilink

# With web UI
pip install scilink[ui]

# With simulation dependencies (ASE, atomate2, etc.)
pip install scilink[sim]

The analysis agents work without additional dependencies, but installing Meta's Segment Anything Model (SAM) enables more advanced particle and grain segmentation. SAM is not available on PyPI and must be installed from source:

pip install git+https://github.com/facebookresearch/segment-anything.git

Environment Variables

Set API keys for your preferred LLM provider:

# Google Gemini (default)
export GEMINI_API_KEY="your-key"

# OpenAI
export OPENAI_API_KEY="your-key"

# Anthropic
export ANTHROPIC_API_KEY="your-key"

# OpenAI-compatible proxy (if applicable)
export SCILINK_API_KEY="your-key"

When using SCILINK_API_KEY, also provide a --base-url pointing to your OpenAI-compatible endpoint.

Quick Start

SciLink can be used via the CLI, web UI, MCP server, or Python API.

CLI

# Planning session
scilink plan
scilink plan --autonomy supervised --data-dir ./results --knowledge-dir ./papers

# Analysis session
scilink analyze
scilink analyze --data ./sample.tif --metadata ./metadata.json

Web UI

scilink ui

Requires pip install scilink[ui].

MCP Server

scilink serve --model gemini-3.1-pro-preview

See MCP Integration for details.

Python API

from scilink.agents.planning_agents import PlanningAgent
from scilink.agents.exp_agents import AnalysisOrchestratorAgent, AnalysisMode

# Generate an experimental plan
planner = PlanningAgent(model_name="gemini-3.1-pro-preview")
plan = planner.propose_experiments(
    objective="Optimize lithium extraction yield",
    knowledge_paths=["./literature/"],
    primary_data_set={"file_path": "./composition_data.xlsx"}
)

# Analyze image data
analyzer = AnalysisOrchestratorAgent(analysis_mode=AnalysisMode.SUPERVISED)
result = analyzer.chat("Analyze ./stem_image.tif and generate scientific claims")

SciLink Reports

MCP Integration

SciLink supports the Model Context Protocol (MCP) as both a server (exposing its tools/agents to external clients like Claude Code) and a client (connecting to external MCP servers for additional capabilities).

As an MCP Server

Expose SciLink's analysis and planning tools to any MCP-compatible client:

# Default (stdio transport, autonomous mode)
scilink serve --model gemini-3.1-pro-preview

# Analysis only, with human approval for major actions
scilink serve --mode analyze --autonomy co-pilot

# HTTP transport (SSE)
scilink serve --transport sse --host 127.0.0.1 --port 8000

The server exposes all orchestrator tools (prefixed scilink_ for analysis, scilink_plan_ for planning), plus job management tools for long-running operations. Autonomy modes control which tools require human approval before execution. See docs/claude_code_integration.md for the full MCP server guide.

As an MCP Client

Connect external MCP servers to extend SciLink with additional tools:

# Python MCP server (e.g., arXiv paper search)
scilink analyze --mcp stdio:arxiv:python,-m,arxiv_mcp_server,--storage-path,/tmp/papers

Programmatically:

orchestrator = AnalysisOrchestratorAgent()
tool_count = orchestrator.connect_mcp_server(
    server_name="arxiv",
    command=["python", "-m", "arxiv_mcp_server", "--storage-path", "/tmp/papers"]
)

In the web UI, go to the Tools tab > MCP Servers section, select a transport (stdio/SSE), enter the server name and command, and click Connect.

See docs/mcp_client_integration.md for the full MCP guide.

Extensibility

SciLink supports custom tools, skills, and agents that can be added via CLI flags, the web UI, or programmatically.

Custom Tools

Provide a Python file with tool_schemas (list of OpenAI-format tool dicts) and a create_tool_functions(data) factory:

scilink analyze --tools ./my_image_tools.py

See docs/custom_tools_integration.md for the full guide, including how custom tool outputs flow into built-in agents and how to feed a preprocessed file back into the analysis pipeline.

Custom Skills

Add domain-specific analysis guidance via Markdown skill files:

scilink analyze --skills ./raman_skill.md ./ftir_skill.md

Built-in skills are available for image analysis (atomic-resolution STEM, etc.), curve fitting (XPS, Raman, etc.), and hyperspectral analysis (EELS, etc.).

Custom Agents

scilink analyze --agents ./my_xrd_agent.py

Planning Agents

The Planning Agents module automates experimental design, data analysis, and iterative optimization workflows.

Architecture

PlanningOrchestratorAgent (main coordinator)
├── PlanningAgent (scientific strategy)
│   ├── Dual KnowledgeBase (Docs KB + Code KB)
│   ├── RAG Engine (retrieval-augmented generation)
│   └── Literature Agent (external search)
├── ScalarizerAgent (raw data → scalar metrics)
└── BOAgent (Bayesian optimization)

| Agent | Purpose | |-------|---------| | PlanningOrchestratorAgent | Coordinates the full experimental workflow via natural language | | PlanningAgent | Generates experimental strategies using dual knowledge bases | | ScalarizerAgent | Converts raw data (CSV, Excel) into optimization-ready metrics | | BOAgent | Suggests optimal parameters via Bayesian Optimization |

CLI Usage

scilink plan
scilink plan --autonomy supervised --data-dir ./results --knowledge-dir ./papers
scilink plan --model claude-opus-4-5

Interactive Session Example

$ scilink plan

📋 What's your research objective?
Your objective: Optimize lithium extraction from brine

👤 You: Generate a plan using papers in ./literature/

🤖 Agent: ⚡ Generating Initial Plan...
    📚 Retrieved 8 document chunks.

🔬 EXPERIMENT 1: pH-Controlled Selective Precipitation
> 🎯 Hypothesis: Adjusting pH to 10-11 will selectively precipitate Mg(OH)₂ while retaining Li⁺

👤 You: Analyze ./results/batch_001.csv and run optimization

🤖 Agent: [calls analyze_file → {"metrics": {"yield": 78.5}}]
  [calls run_optimization → {"recommended_parameters": {"temp": 85.2, "pH": 6.8}}]

CLI Commands

| Command | Description | |---------|-------------| | /help | Show available commands | | /tools | List all available agent tools | | /files | List files in workspace | | /state | Show current agent state | | /autonomy [level] | Show or change autonomy level | | /checkpoint | Save session checkpoint | | /quit | Exit session |

Python API

from scilink.agents.planning_agents.planning_orchestrator import (
    PlanningOrchestratorAgent, AutonomyLevel
)
from scilink.agents.planning_agents import PlanningAgent, ScalarizerAgent, BOAgent

# Using the orchestrator
orchestrator = PlanningOrchestratorAgent(
    objective="Optimize reaction yield",
    autonomy_level=AutonomyLevel.SUPERVISED,
    data_dir="./experimental_results",
    knowledge_dir="./papers"
)
response = orchestrator.chat("Generate initial plan and analyze batch_001.csv")

# Direct agent usage
agent = PlanningAgent(model_name="gemini-3.1-pro-preview")
plan = agent.propose_experiments(
    objective="Screen precipitation conditions",
    knowledge_paths=["./literature/"],
    primary_data_set={"file_path": "./composition_data.xlsx"}
)

# Bayesian optimization
bo = BOAgent(model_name="gemini-3.1-pro-preview")
result = bo.run_optimization_loop(
    data_path="./optimization_data.csv",
    objective_text="Maximize yield while minimizing cost",
    input_cols=["Temperature", "pH", "Concentration"],
    input_bounds=[[20, 80], [6, 10], [0.1, 2.0]],
    target_cols=["Yield"],
    batch_size=1
)

Experimental Analysis Agents

The Analysis Agents module provides automated scientific data analysis across multiple modalities.

Architecture

AnalysisOrchestratorAgent (main coordinator)
├── CurveFittingAgent (ID: 0)
├── ImageAnalysisAgent (ID: 1)
└── HyperspectralAnalysisAgent (ID: 2)

| ID | Agent | Use Case | |----|-------|----------| | 0 | CurveFittingAgent | 1D fitting — XRD, UV-Vis, PL, DSC, TGA, kinetics | | 1 | ImageAnalysisAgent | All

SciLink

Install / Use

README

SciLink

Overview

Installation

Environment Variables

Quick Start

CLI

Web UI

MCP Server

Python API

MCP Integration

As an MCP Server

As an MCP Client

Extensibility

Custom Tools

Custom Skills

Custom Agents

Planning Agents

Architecture

CLI Usage

Interactive Session Example

CLI Commands

Python API

Experimental Analysis Agents

Architecture