LLM Functions is a developer-friendly Python library that streamlines integrating function-calling into LLM-based applications. By defining JSON-schema function signatures and registering handler callbacks, it automates parsing LLM function call outputs and dispatching corresponding Python functions. With support for sync/async execution, schema validation, and extensible plugins, LLM Functions simplifies adding dynamic capabilities such as database queries, API integrations, and custom logic to conversational AI agents.
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LLM Functions
LLM Functions is a developer-friendly Python library that streamlines integrating function-calling into LLM-based applications. By defining JSON-schema function signatures and registering handler callbacks, it automates parsing LLM function call outputs and dispatching corresponding Python functions. With support for sync/async execution, schema validation, and extensible plugins, LLM Functions simplifies adding dynamic capabilities such as database queries, API integrations, and custom logic to conversational AI agents.
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What is LLM Functions?
LLM Functions provides a simple framework to bridge large language model responses with real code execution. You define functions via JSON schemas, register them with the library, and the LLM will return structured function calls when appropriate. The library parses those responses, validates the parameters, and invokes the correct handler. It supports synchronous and asynchronous callbacks, custom error handling, and plugin extensions, making it ideal for applications that require dynamic data lookup, external API calls, or complex business logic within AI-driven conversations.
Who will use LLM Functions?
- Python developers building AI agents
- AI researchers prototyping conversational tools
- Chatbot builders needing external integrations
- Backend engineers automating API workflows
How to use the LLM Functions?
- Step1: Install the library with `pip install llm-functions`
- Step2: Import LLMFunctions and configure your LLM client
- Step3: Define function schemas using JSON-schema in Python
- Step4: Register handler callbacks for each schema
- Step5: Pass schemas to your LLM call and await response
- Step6: Library parses the function call and invokes your handler
- Step7: Process the returned result and continue the conversation
Platform
- Linux
- Mac
- Windows
LLM Functions's Core Features & Benefits
The Core Features
- JSON-schema based function definition
- Automated function call parsing
- Sync and async callback support
- Parameter validation and error handling
- Extensible plugin architecture
The Benefits
- Reduces boilerplate code
- Ensures reliable schema validation
- Speeds up LLM integration
- Enables dynamic external calls
- Scalable for complex workflows
LLM Functions's Main Use Cases & Applications
- Building chatbots that query databases on demand
- Automating API calls within conversational interfaces
- Adding real-time business logic to AI assistants
- Integrating external tools like CRMs or analytics services
FAQs of LLM Functions
LLM Functions Company Information
- sigoden
LLM Functions Reviews
5/5
LLM Functions's Main Competitors and alternatives?
OpenAI Function Calling
LangChain FunctionRouter
MS Agentic SDK
Oracle func_spec
LLMSDK
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