selectools 0.5.2

Build AI agents that call your custom Python functions. Connect LLMs to tools, APIs, and databases with any provider (OpenAI, Anthropic, Gemini).

AI Tool Calling - Selectools

Build AI agents that can call your custom Python functions. Selectools lets you connect LLMs (OpenAI, Anthropic, Gemini) to your own tools and functions. Define what your agent can do—search databases, call APIs, process images, or anything else—and let the LLM decide when and how to use them. Works with any provider, handles errors gracefully, and includes streaming support.

Why This Library Stands Out

🎯 True Provider Agnosticism

Unlike other tool-calling libraries that lock you into a single provider's API, this library provides a unified interface across OpenAI, Anthropic, Gemini, and local providers. Switch providers with a single line change—no refactoring required. Your tool definitions remain identical regardless of the backend.

🛡️ Production-Ready Robustness

Built for real-world reliability:

• Hardened parser that handles malformed JSON, fenced code blocks, and mixed content (not just perfect API responses)
• Automatic retry logic with exponential backoff for rate limits and transient failures
• Per-tool execution timeouts to prevent runaway operations
• Request-level timeouts to avoid hanging on slow providers
• Iteration caps to control agent loop costs and prevent infinite loops

🔧 Developer-First Ergonomics

• @tool decorator with automatic schema inference from Python type hints
• ToolRegistry for organizing and discovering tools
• Injected kwargs for clean separation of user parameters and configuration (API keys, database connections, etc.)
• Zero boilerplate: Define a function, add a decorator, done

🎨 Vision + Streaming Support

• Native vision support for providers that offer it (OpenAI GPT-4o, etc.)
• Real-time streaming with callback handlers for responsive UIs
• Unified API for both streaming and one-shot responses

🧪 Testing-Friendly Architecture

• Local provider for offline development and testing (no API calls, no costs)
• Mock injection for deterministic testing (e.g., SELECTOOLS_BBOX_MOCK_JSON)
• Fake providers included for unit testing your agent logic
• Clean separation of concerns makes components easy to test in isolation

📦 Library-First Design

Not a framework that takes over your application—a library that integrates into your existing code. Use as much or as little as you need. No magic globals, no hidden state, no framework lock-in.

What's Included

• Core package at src/selectools/ with agent loop, parser, prompt builder, and provider adapters
• Providers: OpenAI plus Anthropic/Gemini/Local sharing the same interface
• Library-first examples (see below) and tests with fake providers for schemas, parsing, agent wiring
• PyPI-ready metadata (pyproject.toml) using a src-layout package

Install

From PyPI (Recommended)

pip install selectools

With optional provider dependencies:

pip install selectools[providers]  # Includes Anthropic and Gemini

From Source (Development)

git clone https://github.com/johnnichev/selectools.git
cd selectools
python3 -m venv .venv
source .venv/bin/activate
pip install -e .
# or: pip install -r requirements.txt

Set API Keys

export OPENAI_API_KEY="your-api-key-here"
export ANTHROPIC_API_KEY="your-api-key-here"  # Optional
export GEMINI_API_KEY="your-api-key-here"     # Optional

Usage (Library)

from selectools import Agent, AgentConfig, Message, Role, Tool, ToolParameter
from selectools.providers.openai_provider import OpenAIProvider

# Define a tool
search_tool = Tool(
    name="search",
    description="Search the web",
    parameters=[ToolParameter(name="query", param_type=str, description="query")],
    function=lambda query: f"Results for {query}",
)

provider = OpenAIProvider(default_model="gpt-4o")
agent = Agent(tools=[search_tool], provider=provider, config=AgentConfig(max_iterations=4))
response = agent.run([Message(role=Role.USER, content="Search for Backtrack")])
print(response.content)

Common ways to use it (library-first)

• Define tools (Tool or @tool/ToolRegistry), pick a provider, run Agent.run([...]).
• Add vision by supplying image_path on Message when the provider supports it.
• For offline/testing: use the Local provider and/or SELECTOOLS_BBOX_MOCK_JSON=tests/fixtures/bbox_mock.json.
• Optional dev helpers (not required for library use): scripts/smoke_cli.py for quick provider smokes; scripts/chat.py for the vision demo.

Providers (incl. vision & limits)

• OpenAI: streaming; vision via Chat Completions image_url (e.g., gpt-5); request timeout default 30s; retries/backoff via AgentConfig.
• Anthropic: streaming; vision model-dependent; set ANTHROPIC_API_KEY.
• Gemini: streaming; vision model-dependent; set GEMINI_API_KEY.
• Local: no network; echoes latest user text; no vision.
• Rate limits: agent detects rate limit/429 and backs off + retries.
• Timeouts: AgentConfig.request_timeout (provider) and tool_timeout_seconds (per tool).

Agent config at a glance

• Core: model, temperature, max_tokens, max_iterations.
• Reliability: max_retries, retry_backoff_seconds, rate-limit backoff, request_timeout.
• Execution safety: tool_timeout_seconds to bound tool runtime.
• Streaming: stream=True to stream provider deltas; optional stream_handler callback.

Real-World Examples

1. Quick Start: Simple Echo Tool

The simplest possible tool—great for testing your setup:

from selectools import Agent, AgentConfig, Message, Role, tool
from selectools.providers.openai_provider import OpenAIProvider

@tool(description="Echo input back to user")
def echo(text: str) -> str:
    return text

agent = Agent(tools=[echo], provider=OpenAIProvider(), config=AgentConfig(max_iterations=3))
resp = agent.run([Message(role=Role.USER, content="Hello, world!")])
print(resp.content)

2. Customer Support: Multi-Tool Workflow

Build a customer support agent that can search a knowledge base, check order status, and create tickets:

from selectools import Agent, AgentConfig, Message, Role, ToolRegistry
from selectools.providers.openai_provider import OpenAIProvider
import json

registry = ToolRegistry()

@registry.tool(description="Search the knowledge base for help articles")
def search_kb(query: str, max_results: int = 5) -> str:
    # Your knowledge base search logic here
    results = [
        {"title": "How to reset password", "url": "https://help.example.com/reset"},
        {"title": "Shipping information", "url": "https://help.example.com/shipping"}
    ]
    return json.dumps(results)

@registry.tool(description="Look up order status by order ID")
def check_order(order_id: str) -> str:
    # Your order lookup logic here
    return json.dumps({
        "order_id": order_id,
        "status": "shipped",
        "tracking": "1Z999AA10123456784",
        "estimated_delivery": "2025-12-10"
    })

@registry.tool(description="Create a support ticket")
def create_ticket(customer_email: str, subject: str, description: str, priority: str = "normal") -> str:
    # Your ticketing system integration here
    ticket_id = "TKT-12345"
    return json.dumps({
        "ticket_id": ticket_id,
        "status": "created",
        "message": f"Ticket {ticket_id} created successfully"
    })

agent = Agent(
    tools=registry.all(),
    provider=OpenAIProvider(default_model="gpt-4o"),
    config=AgentConfig(max_iterations=5, temperature=0.7)
)

# Customer inquiry
response = agent.run([
    Message(role=Role.USER, content="Hi, I ordered something last week (order #12345) and haven't received it yet. Can you help?")
])
print(response.content)

3. Vision AI: Bounding Box Detection

Detect and annotate objects in images using OpenAI Vision:

from selectools import Agent, AgentConfig, Message, Role
from selectools.examples.bbox import create_bounding_box_tool
from selectools.providers.openai_provider import OpenAIProvider

bbox_tool = create_bounding_box_tool()
agent = Agent(
    tools=[bbox_tool],
    provider=OpenAIProvider(default_model="gpt-4o"),
    config=AgentConfig(max_iterations=5)
)

# Analyze an image and find specific objects
response = agent.run([
    Message(
        role=Role.USER,
        content="Find the laptop in this image and draw a bounding box around it",
        image_path="assets/office_desk.jpg"
    )
])
print(response.content)
# Output: Detected laptop; output saved to assets/office_desk_with_bbox.png

4. Data Pipeline: Research Assistant

Chain multiple tools to research a topic, summarize findings, and save results:

from selectools import Agent, AgentConfig, Message, Role, tool
from selectools.providers.openai_provider import OpenAIProvider
import json

@tool(description="Search academic papers and articles")
def search_papers(query: str, year_from: int = 2020) -> str:
    # Your search API integration (e.g., Semantic Scholar, arXiv)
    papers = [
        {"title": "Attention Is All You Need", "authors": "Vaswani et al.", "year": 2017},
        {"title": "BERT: Pre-training of Deep Bidirectional Transformers", "authors": "Devlin et al.", "year": 2018}
    ]
    return json.dumps(papers)

@tool(description="Extract key insights from text")
def extract_insights(text: str, num_insights: int = 5) -> str:
    # Your summarization logic
    insights = [
        "Transformers use self-attention mechanisms",
        "BERT uses bidirectional training",
        "Pre-training on large corpora improves performance"
    ]
    return json.dumps(insights)

@tool(description="Save research findings to a file")
def save_findings(filename: str, content: str) -> str:
    with open(filename, 'w') as f:
        f.write(content)
    return f"Saved findings to {filename}"

agent = Agent(
    tools=[search_papers, extract_insights, save_findings],
    provider=OpenAIProvider(default_model="gpt-4o"),
    config=AgentConfig(max_iterations=8, temperature=0.3)
)

response = agent.run([
    Message(role=Role.USER, content="Research transformer architectures, extract key insights, and save to research_notes.txt")
])
print(response.content)

5. Streaming UI: Real-Time Chat

Build responsive UIs with streaming responses:

from selectools import Agent, AgentConfig, Message, Role, tool
from selectools.providers.openai_provider import OpenAIProvider
import sys

@tool(description="Get current time in a timezone")
def get_time(timezone: str = "UTC") -> str:
    from datetime import datetime
    import pytz
    tz = pytz.timezone(timezone)
    return datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S %Z")

def stream_to_console(chunk: str):
    """Print chunks as they arrive for responsive UX"""
    print(chunk, end='', flush=True)

agent = Agent(
    tools=[get_time],
    provider=OpenAIProvider(default_model="gpt-4o"),
    config=AgentConfig(stream=True, max_iterations=3)
)

response = agent.run(
    [Message(role=Role.USER, content="What time is it in Tokyo and New York?")],
    stream_handler=stream_to_console
)
print("\n")  # Newline after streaming completes

6. Secure Tool Injection: Database Access

Keep sensitive credentials out of tool signatures using injected_kwargs:

from selectools import Agent, AgentConfig, Message, Role, Tool
from selectools.tools import ToolParameter
from selectools.providers.openai_provider import OpenAIProvider
import psycopg2

def query_database(sql: str, db_connection) -> str:
    """
    Execute a SQL query. The db_connection is injected, not exposed to the LLM.
    """
    with db_connection.cursor() as cursor:
        cursor.execute(sql)
        results = cursor.fetchall()
    return str(results)

# Create connection (not exposed to LLM)
db_conn = psycopg2.connect(
    host="localhost",
    database="myapp",
    user="readonly_user",
    password="secret"
)

# Tool only exposes 'sql' parameter to LLM
db_tool = Tool(
    name="query_db",
    description="Execute a read-only SQL query against the database",
    parameters=[
        ToolParameter(name="sql", param_type=str, description="SQL SELECT query to execute")
    ],
    function=query_database,
    injected_kwargs={"db_connection": db_conn}  # Injected at runtime
)

agent = Agent(
    tools=[db_tool],
    provider=OpenAIProvider(),
    config=AgentConfig(max_iterations=3)
)

response = agent.run([
    Message(role=Role.USER, content="How many users signed up last week?")
])
print(response.content)

7. Testing: Offline Development

Develop and test without API calls using the Local provider:

from selectools import Agent, AgentConfig, Message, Role, tool
from selectools.providers.stubs import LocalProvider

@tool(description="Format a todo item with priority")
def create_todo(task: str, priority: str = "medium", due_date: str = None) -> str:
    result = f"[{priority.upper()}] {task}"
    if due_date:
        result += f" (due: {due_date})"
    return result

agent = Agent(
    tools=[create_todo],
    provider=LocalProvider(),  # No network calls, no API costs
    config=AgentConfig(max_iterations=2, model="local")
)

response = agent.run([
    Message(role=Role.USER, content="Add 'finish project report' to my todos with high priority")
])
print(response.content)

8. Provider Switching: Zero Refactoring

Switch between providers without changing your tool definitions:

from selectools import Agent, AgentConfig, Message, Role, tool
from selectools.providers.openai_provider import OpenAIProvider
from selectools.providers.anthropic_provider import AnthropicProvider
from selectools.providers.gemini_provider import GeminiProvider
import os

@tool(description="Calculate compound interest")
def calculate_interest(principal: float, rate: float, years: int) -> str:
    amount = principal * (1 + rate/100) ** years
    return f"After {years} years: ${amount:.2f}"

# Choose provider based on environment or preference
provider_name = os.getenv("LLM_PROVIDER", "openai")
providers = {
    "openai": OpenAIProvider(default_model="gpt-4o"),
    "anthropic": AnthropicProvider(default_model="claude-3-5-sonnet-20241022"),
    "gemini": GeminiProvider(default_model="gemini-2.0-flash-exp")
}

agent = Agent(
    tools=[calculate_interest],
    provider=providers[provider_name],
    config=AgentConfig(max_iterations=3)
)

response = agent.run([
    Message(role=Role.USER, content="If I invest $10,000 at 7% annual interest for 10 years, how much will I have?")
])
print(response.content)

9. Cost Tracking: Monitor Token Usage & Costs (v0.5.0)

Track token usage and estimated costs automatically:

from selectools import Agent, AgentConfig, Message, Role, tool
from selectools.providers.openai_provider import OpenAIProvider

@tool(description="Search for information")
def search(query: str) -> str:
    return f"Results for: {query}"

@tool(description="Summarize text")
def summarize(text: str) -> str:
    return f"Summary: {text[:50]}..."

# Enable cost tracking with optional warning threshold
agent = Agent(
    tools=[search, summarize],
    provider=OpenAIProvider(default_model="gpt-4o"),
    config=AgentConfig(
        max_iterations=5,
        cost_warning_threshold=0.10  # Warn if cost exceeds $0.10
    )
)

response = agent.run([
    Message(role=Role.USER, content="Search for Python tutorials and summarize the top result")
])

# Access usage statistics
print(f"Total tokens: {agent.total_tokens:,}")
print(f"Total cost: ${agent.total_cost:.6f}")
print("\nDetailed breakdown:")
print(agent.get_usage_summary())

# Output:
# 📊 Usage Summary
# Total Tokens: 1,234
# Total Cost: $0.012345
#
# Tool Usage:
#   - search: 1 calls, 567 tokens
#   - summarize: 1 calls, 667 tokens

Key Features:

• Automatic token counting for all providers
• Cost estimation for 15+ models (OpenAI, Anthropic, Gemini)
• Per-tool usage breakdown
• Configurable cost warnings
• Reset usage: agent.reset_usage()

10. Pre-built Toolbox: Ready-to-Use Tools (v0.5.1)

Skip the boilerplate and use production-ready tools from the toolbox:

from selectools import Agent, AgentConfig, Message, Role
from selectools.providers.openai_provider import OpenAIProvider
from selectools.toolbox import get_all_tools, get_tools_by_category

# Use all 22 tools from the toolbox
all_tools = get_all_tools()
agent = Agent(
    tools=all_tools,
    provider=OpenAIProvider(),
    config=AgentConfig(max_iterations=8)
)

# Or get specific categories
file_tools = get_tools_by_category("file")  # read_file, write_file, list_files, file_exists
data_tools = get_tools_by_category("data")  # parse_json, json_to_csv, csv_to_json, etc.
text_tools = get_tools_by_category("text")  # count_text, search_text, extract_emails, etc.
datetime_tools = get_tools_by_category("datetime")  # get_current_time, parse_datetime, etc.
web_tools = get_tools_by_category("web")  # http_get, http_post

# Complex multi-step task using multiple tool categories
response = agent.run([
    Message(
        role=Role.USER,
        content="""
        1. Get the current time in UTC
        2. Parse this JSON: {"users": [{"name": "Alice", "email": "[e-mail]"}]}
        3. Extract all email addresses from the JSON
        4. Write the results to a file called results.txt
        """
    )
])
print(response.content)

Available Tool Categories:

Category	Tools	Description
File (4)	read_file, write_file, list_files, file_exists	File system operations
Data (5)	parse_json, json_to_csv, csv_to_json, extract_json_field, format_table	Data parsing and formatting
Text (7)	count_text, search_text, replace_text, extract_emails, extract_urls, convert_case, truncate_text	Text processing
DateTime (4)	get_current_time, parse_datetime, time_difference, date_arithmetic	Date/time utilities
Web (2)	http_get, http_post	HTTP requests

See examples/toolbox_demo.py for a complete demonstration.

11. Async Agent: Modern Python with asyncio

Build high-performance async applications with native async support:

import asyncio
from selectools import Agent, AgentConfig, Message, Role, tool, ConversationMemory
from selectools.providers.openai_provider import OpenAIProvider

# Async tools for I/O-bound operations
@tool(description="Fetch weather data")
async def fetch_weather(city: str) -> str:
    await asyncio.sleep(0.1)  # Simulate async API call
    return f"Weather in {city}: Sunny, 72°F"

# Sync tools work seamlessly alongside async tools
@tool(description="Calculate")
def calculate(a: int, b: int) -> str:
    return f"{a} + {b} = {a + b}"

async def main():
    # Conversation memory works with async
    memory = ConversationMemory(max_messages=20)

    agent = Agent(
        tools=[fetch_weather, calculate],
        provider=OpenAIProvider(),
        config=AgentConfig(max_iterations=5),
        memory=memory
    )

    # Use arun() instead of run()
    response = await agent.arun([
        Message(role=Role.USER, content="What's the weather in Seattle?")
    ])
    print(response.content)

asyncio.run(main())

FastAPI Integration:

from fastapi import FastAPI
from selectools import Agent, Message, Role, tool, OpenAIProvider

app = FastAPI()

@tool(description="Fetch data")
async def fetch_data(query: str) -> str:
    return f"Data for {query}"

@app.post("/chat")
async def chat(message: str):
    agent = Agent(tools=[fetch_data], provider=OpenAIProvider())
    response = await agent.arun([Message(role=Role.USER, content=message)])
    return {"response": response.content}

Key Async Features:

• Agent.arun() for non-blocking execution
• Async tools with async def
• All providers support async (OpenAI, Anthropic, Gemini)
• Concurrent execution with asyncio.gather()
• Works with FastAPI, aiohttp, and async frameworks

Tool ergonomics

• Use ToolRegistry or the @tool decorator to infer schemas from function signatures and register tools.
• Inject per-tool config or auth using injected_kwargs or config_injector when constructing a Tool.
• Type hints map to JSON schema; defaults make parameters optional.

Tests

python tests/test_framework.py

• Covers parsing (mixed/fenced), agent loop (retries/streaming), provider mocks (Anthropic/Gemini), CLI streaming, bbox mock path, and tool schema basics.

Packaging

The project ships a pyproject.toml with console scripts and a src layout. Adjust version/metadata before publishing to PyPI. CI workflow (.github/workflows/ci.yml) runs tests, build, and twine check. Tags matching v* attempt TestPyPI/PyPI publishes when tokens are provided.

License

This project is licensed under the GNU Lesser General Public License v3.0 or later (LGPL-3.0-or-later).

What This Means for You

✅ You CAN:

• Use this library in commercial applications
• Profit from applications that use this library
• Import and use the library without sharing your application code
• Distribute applications that use this library

✅ You MUST:

• Preserve copyright notices and license information
• Share any modifications you make to the library itself under LGPL-3.0
• Provide attribution to the original authors

❌ You CANNOT:

• Relicense this library under different terms
• Claim this code as your own
• Create proprietary forks (modifications must remain open source)

In Practice: You can build and sell proprietary applications using this library via normal import/usage. Only if you modify the library's source code itself must you share those modifications. This is the same license used by popular projects like Qt and GTK.

For the full license text, see the LICENSE file.

More docs

• Single source of truth is this README.
• Examples:
  • python examples/search_weather.py - Simple tool with local mock provider
  • python examples/async_agent_demo.py - Async/await usage with FastAPI patterns
  • python examples/conversation_memory_demo.py - Multi-turn conversation with memory
  • python examples/cost_tracking_demo.py - Token counting and cost tracking (v0.5.0)
  • python examples/toolbox_demo.py - Using pre-built tools from toolbox (v0.5.1)
  • python examples/v0_5_2_demo.py - Tool validation & observability hooks (v0.5.2)
  • python examples/customer_support_bot.py - Multi-tool customer support workflow
  • python examples/data_analysis_agent.py - Data exploration and analysis tools
• Dev helpers:
  • python scripts/smoke_cli.py - Quick provider smoke tests (skips missing keys)
  • python scripts/test_memory_with_openai.py - Test memory with real OpenAI API

---

Roadmap

We're actively developing new features to make Selectools the most production-ready tool-calling library. See ROADMAP.md for the complete development roadmap, including:

✅ Completed in v0.4.0:

• Conversation Memory - Multi-turn context management
• Async Support - Agent.arun(), async tools, async providers
• Real Provider Implementations - Full Anthropic & Gemini SDK integration

✅ Completed in v0.5.0:

• Better Error Messages - Custom exceptions with helpful context and suggestions
• Cost Tracking - Automatic token counting and cost estimation with warnings
• Gemini SDK Migration - Updated to new google-genai SDK (v1.0+)

✅ Completed in v0.5.1:

• Pre-built Tool Library - 22 production-ready tools in 5 categories (file, web, data, datetime, text)

✅ Completed in v0.5.2:

• Tool Validation at Registration - Catch tool definition errors during development, not production
• Observability Hooks - 10 lifecycle hooks for monitoring, debugging, and tracking agent behavior

🟡 Coming in v0.6.0:

• Parallel Tool Execution - Run multiple tools concurrently
• Streaming Tool Results - Stream tool output as generated
• Tool Composition - Chain tools together with @compose decorator

🚀 Future (v0.7.0+):

• Tool composition and chaining
• Advanced context management
• Local model support (Ollama)
• And much more...

See ROADMAP.md for detailed feature descriptions, status tracking, and implementation notes.

🤝 Contributing

Want to help build these features? See CONTRIBUTING.md for guidelines. We'd love contributions for:

• Priority 1 features (quick wins)
• Tool implementations for the toolbox
• Examples and tutorials
• Documentation improvements

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See the full comparison with LangChain in docs/LANGCHAIN_COMPARISON.md.