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ToolUniverse Python SDK

ToolUniverse provides programmatic access to 1000++ scientific tools through a unified interface. It implements the AI-Tool Interaction Protocol for building AI scientist systems that integrate ML models, databases, APIs, and scientific packages.

IMPORTANT - Language Handling : Most tools accept English terms only. When building workflows, always translate non-English input to English before passing to tool parameters. Only try original-language terms as a fallback if English returns no results.

Installation

# Standard installation
pip install tooluniverse

# With optional features
pip install tooluniverse[embedding]  # Embedding search (GPU)
pip install tooluniverse[ml]         # ML model tools
pip install tooluniverse[all]        # All features

Environment Setup

# Required for LLM-based tool search and hooks
export OPENAI_API_KEY="sk-..."

# Optional for higher rate limits
export NCBI_API_KEY="..."

Or use .env file:

from dotenv import load_dotenv
load_dotenv()

Quick Start

from tooluniverse import ToolUniverse

# 1. Initialize and load tools
tu = ToolUniverse()
tu.load_tools()  # Loads 1000++ tools (~5-10 seconds first time)

# 2. Find tools (three methods)
# Method A: Keyword (fast, no API key)
tools = tu.run({
    "name": "Tool_Finder_Keyword",
    "arguments": {"description": "protein structure", "limit": 10}
})

# Method B: LLM (intelligent, requires OPENAI_API_KEY)
tools = tu.run({
    "name": "Tool_Finder_LLM",
    "arguments": {"description": "predict drug toxicity", "limit": 5}
})

# Method C: Embedding (semantic, requires GPU)
tools = tu.run({
    "name": "Tool_Finder",
    "arguments": {"description": "protein interactions", "limit": 10}
})

# 3. Execute tools (two ways)
# Dictionary API
result = tu.run({
    "name": "UniProt_get_entry_by_accession",
    "arguments": {"accession": "P05067"}
})

# Function API (recommended)
result = tu.tools.UniProt_get_entry_by_accession(accession="P05067")

Core Patterns

Pattern 1: Discovery → Execute

# Find tools
tools = tu.run({
    "name": "Tool_Finder_Keyword",
    "arguments": {"description": "ADMET prediction", "limit": 3}
})

# Check results structure
if isinstance(tools, dict) and 'tools' in tools:
    for tool in tools['tools']:
        print(f"{tool['name']}: {tool['description']}")

# Execute tool
result = tu.tools.ADMETAI_predict_admet(
    smiles="CC(C)Cc1ccc(cc1)C(C)C(O)=O"
)

Pattern 2: Batch Execution

# Define calls
calls = [
    {"name": "UniProt_get_entry_by_accession", "arguments": {"accession": "P05067"}},
    {"name": "UniProt_get_entry_by_accession", "arguments": {"accession": "P12345"}},
    {"name": "RCSB_PDB_get_structure_by_id", "arguments": {"pdb_id": "1ABC"}}
]

# Execute in parallel
results = tu.run_batch(calls)

Pattern 3: Scientific Workflow

def drug_discovery_pipeline(disease_id):
    tu = ToolUniverse(use_cache=True)
    tu.load_tools()
    
    try:
        # Get targets
        targets = tu.tools.OpenTargets_get_associated_targets_by_disease_efoId(
            efoId=disease_id
        )
        
        # Get compounds (batch)
        compound_calls = [
            {"name": "ChEMBL_search_molecule_by_target", 
             "arguments": {"target_id": t['id'], "limit": 10}}
            for t in targets['data'][:5]
        ]
        compounds = tu.run_batch(compound_calls)
        
        # Predict ADMET
        admet_results = []
        for comp_list in compounds:
            if comp_list and 'molecules' in comp_list:
                for mol in comp_list['molecules'][:3]:
                    admet = tu.tools.ADMETAI_predict_admet(
                        smiles=mol['smiles'],
                        use_cache=True
                    )
                    admet_results.append(admet)
        
        return {"targets": targets, "compounds": compounds, "admet": admet_results}
    finally:
        tu.close()

Configuration

Caching

# Enable globally
tu = ToolUniverse(use_cache=True)
tu.load_tools()

# Or per-call
result = tu.tools.ADMETAI_predict_admet(
    smiles="...",
    use_cache=True  # Cache expensive predictions
)

# Manage cache
stats = tu.get_cache_stats()
tu.clear_cache()

Hooks (Auto-summarization)

# Enable hooks for large outputs
tu = ToolUniverse(hooks_enabled=True)
tu.load_tools()

result = tu.tools.OpenTargets_get_target_gene_ontology_by_ensemblID(
    ensemblId="ENSG00000012048"
)

# Check if summarized
if isinstance(result, dict) and "summary" in result:
    print(f"Summarized: {result['summary']}")

Load Specific Categories

# Faster loading
tu = ToolUniverse()
tu.load_tools(categories=["proteins", "drugs"])

Critical Things to Know

⚠️ Always Call load_tools()

# ❌ Wrong - will fail
tu = ToolUniverse()
result = tu.tools.some_tool()  # Error!

# ✅ Correct
tu = ToolUniverse()
tu.load_tools()
result = tu.tools.some_tool()

⚠️ Tool Finder Returns Nested Structure

# ❌ Wrong
tools = tu.run({"name": "Tool_Finder_Keyword", "arguments": {"description": "protein"}})
for tool in tools:  # Error: tools is dict
    print(tool['name'])

# ✅ Correct
if isinstance(tools, dict) and 'tools' in tools:
    for tool in tools['tools']:
        print(tool['name'])

⚠️ Check Required Parameters

# Check tool schema first
tool_info = tu.all_tool_dict["UniProt_get_entry_by_accession"]
required = tool_info['parameter'].get('required', [])
print(f"Required: {required}")

# Then call
result = tu.tools.UniProt_get_entry_by_accession(accession="P05067")

⚠️ Cache Strategy

# ✅ Cache: ML predictions, database queries (deterministic)
result = tu.tools.ADMETAI_predict_admet(smiles="...", use_cache=True)

# ❌ Don't cache: real-time data, time-sensitive results
result = tu.tools.get_latest_publications()  # No cache

⚠️ Error Handling

from tooluniverse.exceptions import ToolError, ToolUnavailableError

try:
    result = tu.tools.UniProt_get_entry_by_accession(accession="P05067")
except ToolUnavailableError as e:
    print(f"Tool unavailable: {e}")
except ToolError as e:
    print(f"Execution failed: {e}")

⚠️ Tool Names Are Case-Sensitive

# ❌ Wrong
result = tu.tools.uniprot_get_entry_by_accession(accession="P05067")

# ✅ Correct
result = tu.tools.UniProt_get_entry_by_accession(accession="P05067")

Execution Options

result = tu.tools.tool_name(
    param="value",
    use_cache=True,      # Cache this call
    validate=True,       # Validate parameters (default)
    stream_callback=None # Streaming output
)

Performance Tips

# 1. Load specific categories
tu.load_tools(categories=["proteins"])

# 2. Use batch execution
results = tu.run_batch(calls)

# 3. Enable caching
tu = ToolUniverse(use_cache=True)

# 4. Disable validation (after testing)
result = tu.tools.tool_name(param="value", validate=False)

Troubleshooting

Tool Not Found

# Search for tool
tools = tu.run({
    "name": "Tool_Finder_Keyword",
    "arguments": {"description": "partial_name", "limit": 10}
})

# Check if exists
if "Tool_Name" in tu.all_tool_dict:
    print("Found!")

API Key Issues

import os
if not os.environ.get("OPENAI_API_KEY"):
    print("⚠️ OPENAI_API_KEY not set")
    print("Set: export OPENAI_API_KEY='sk-...'")

Validation Errors

from tooluniverse.exceptions import ToolValidationError

try:
    result = tu.tools.some_tool(param="value")
except ToolValidationError as e:
    # Check schema
    tool_info = tu.all_tool_dict["some_tool"]
    print(f"Required: {tool_info['parameter'].get('required', [])}")
    print(f"Properties: {tool_info['parameter']['properties'].keys()}")

Enable Debug Logging

from tooluniverse.logging_config import set_log_level
set_log_level("DEBUG")

Tool Categories

Resources

• Documentation : https://zitniklab.hms.harvard.edu/ToolUniverse/
• Tool List : https://zitniklab.hms.harvard.edu/ToolUniverse/tools/tools_config_index.html
• GitHub : https://github.com/mims-harvard/ToolUniverse
• Examples : See examples/ directory in repository
• Slack : https://join.slack.com/t/tooluniversehq/shared_invite/zt-3dic3eoio-5xxoJch7TLNibNQn5_AREQ

For detailed guides, see REFERENCE.md.

Weekly Installs

162

Repository

mims-harvard/to…universe

GitHub Stars

1.2K

First Seen

Feb 4, 2026

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