27 مستودعات
Functions invoked by agents to perform data transformations or external actions during a conversation.
Distinct from Local Function Execution: Distinct from generic serverless local execution: specifically focuses on agent-triggered code execution within a conversational loop.
Explore 27 awesome GitHub repositories matching development tools & productivity · Agent-Integrated Functions. Refine with filters or upvote what's useful.
This project is a Python framework for building autonomous, event-driven agent systems. It provides a unified runtime for orchestrating multi-agent workflows, managing persistent conversation state, and executing code within secure, isolated sandbox environments. The framework is designed to handle complex task delegation, allowing agents to invoke other agents as tools while maintaining context across multi-turn interactions. The framework distinguishes itself through its deep integration with the Model Context Protocol, enabling agents to connect to external data sources and remote services
Invokes custom functions during a live session, optionally requiring human approval before the model proceeds with the tool output.
Swarm is a framework for building conversational systems that coordinate multi-agent workflows. It functions as an orchestration engine that manages persistent, multi-turn dialogues by routing tasks between specialized agents and executing local functions. The system is designed to handle complex, multi-step processes by maintaining shared state and context across agent interactions. The framework distinguishes itself through its approach to dynamic task delegation and execution control. It enables agents to hand off tasks to one another by returning agent objects, allowing for modular, domai
Enables agents to invoke local code directly to perform data transformations or external actions.
LiveKit is a comprehensive framework for building and orchestrating real-time, multimodal AI agents that interact with users through voice, video, and text. It provides a centralized, event-driven architecture to manage the entire lifecycle of automated participants, from initialization and session state management to graceful shutdown. By utilizing a selective forwarding unit, the platform efficiently routes media streams between participants and agents, ensuring low-latency communication and secure, token-based authentication for all connections. The platform distinguishes itself through it
Triggers user-defined functions to perform side effects or interact with external systems during a conversation.
PydanticAI is a Python framework designed for building production-grade autonomous agents. It provides a unified interface for interacting with diverse language models, enabling developers to construct agents that perform complex tasks through structured data validation, tool execution, and multi-turn conversation management. The library centers on type-safe schema enforcement, ensuring that model inputs and outputs remain consistent and reliable throughout the agent's lifecycle. The framework distinguishes itself through a robust architecture that emphasizes modularity and testability. It ut
Executes specific logic immediately upon receiving model output to facilitate complex workflows.
The Gemini Cookbook is a comprehensive collection of implementation patterns, code samples, and development guides designed for building applications with Google Gemini models. It serves as a central resource for developers to integrate multimodal generative artificial intelligence into their software, providing the necessary frameworks to manage model interactions, stateful workflows, and structured data extraction. The repository distinguishes itself by offering specialized toolkits for autonomous agent orchestration, enabling the construction of agents that can execute code, browse the web
Invokes functions to perform data transformations or external actions during a conversational loop.
llmware is a Python framework for AI agent orchestration and model management, designed to coordinate multi-model workflows and autonomous agents. It provides a unified model catalog and standardized interface to execute specialized language models for complex research, analysis, and structured data generation. The project distinguishes itself through its heavy emphasis on local execution and quantized inference, allowing models to run on private infrastructure using CPU, GPU, and NPU acceleration via runtimes like ONNX and OpenVino. It features a specialized ability to translate natural lang
Implements functions triggered by agents to perform data transformations and external actions during conversational loops.
Pipecat is a framework and software development kit for building real-time multimodal AI agents and speech-to-speech systems. It utilizes a frame-based data pipeline to route audio, video, and text through a modular sequence of processors, enabling the orchestration of low-latency conversational AI. The project is distinguished by its ability to coordinate complex multimodal services, including speech-to-text, language models, and text-to-speech, within a single pipeline. It features semantic voice activity detection for natural turn-taking, state-machine conversation flows for dialogue manag
Implements a mechanism for AI agents to trigger external code execution within a conversational loop.
Shell GPT is an AI-powered command-line interface that generates shell commands and source code from natural language prompts. It serves as a terminal-based tool for automating technical tasks, producing executable commands, and generating code snippets directly within the shell. The tool distinguishes itself through a read-eval-print loop for interactive chatting and the ability to maintain stateful conversational history via named sessions. It supports flexible backend routing, allowing users to connect to cloud-based APIs or local language model hosts for offline operation and data privacy
Allows the AI to execute local system functions and analyze the resulting output.
Nango is an open-source platform that connects applications to external APIs by managing authentication, data synchronization, and custom function execution. It provides a managed runtime for TypeScript integration functions, handling OAuth flows, credential storage, and token refresh for hundreds of external APIs while keeping secrets isolated from application code. The platform distinguishes itself by exposing integration functions as discoverable tools for AI agents through an MCP server or API, with per-user credential isolation that keeps provider secrets out of the agent loop. It offers
Triggers TypeScript functions on demand from apps, backends, or agents.
This project is a comprehensive Node.js software development kit designed for integrating large language models into applications. It serves as a foundational client for interacting with REST and WebSocket services, enabling developers to implement chat functionality, multimodal content generation, and autonomous agent orchestration. The library provides a structured framework for defining executable tools and enforcing JSON schemas, ensuring that model outputs remain programmatically compatible with downstream systems. The SDK distinguishes itself through its robust request orchestration and
Registers custom functions with JSON schemas to enable model-triggered execution during conversations.
Model Context Protocol is a standardized framework for connecting large language models to external data sources and executable tools. It enables the creation of a universal interface where servers expose tools, resources, and prompts that can be discovered and utilized by various AI clients. The protocol utilizes a JSON-RPC message system that is transport-agnostic, supporting both standard input/output for local processes and HTTP with server-sent events for remote connections. It emphasizes security and control by delegating model sampling to the client to keep API keys secure from servers
Defines how executable functions are mapped to tools that a model can call with user approval.
Chainlink is a decentralized oracle network that connects smart contracts to off-chain data, computation, and real-world systems. It provides a secure and reliable infrastructure for blockchain applications to access external information, execute automated workflows, and interact with other blockchains. The network is secured by a staking-based model where node operators lock LINK tokens as collateral, which can be slashed for poor performance, incentivizing honest and accurate data delivery. The platform distinguishes itself through a comprehensive set of capabilities that extend beyond basi
Triggers core protocol functions like liquidations and interest accrual with reliable real-time automation.
The agent-framework is an LLM agent orchestration framework and multi-agent workflow engine designed for building autonomous AI agents. It provides a tool integration layer for binding external functions, APIs, and sandboxed code as executable tools for language models. The framework distinguishes itself through a graph-based system for designing sequential and parallel task flows, featuring state management and checkpointing for long-running processes. It implements comprehensive conversational state management and an observability suite that uses telemetry to trace execution flows and monit
Binds functions directly to an agent during creation or execution to extend capabilities without specialized attributes.
This is an open-source Python SDK for building and orchestrating production-grade AI agents. It provides a unified framework for creating conversational agents that can use tools, maintain state, and coordinate across multiple language model providers including OpenAI, Anthropic, Google, Amazon Bedrock, and locally-hosted models. The SDK supports multi-agent orchestration through graphs, teams, and swarms, allowing several specialized agents to collaborate on complex tasks. Agents can be composed as callable tools that other agents invoke, and the framework includes policy handlers that inspe
Wraps Python or TypeScript functions as callable tools with automatic schema inference and validation.
Attaches custom Python functions to the agent that the language model can invoke as tools.
1code is an AI-assisted development environment that provides a unified interface for switching between multiple AI coding agents. It toggles between a read-only analysis mode and a full execution mode, asking clarifying questions, building structured plans with previews, and requiring user approval before making code changes. The environment integrates with external services and tools through the Model Context Protocol (MCP), enabling connections to databases, project management systems, and code repositories. Agent sessions can run either locally or in persistent cloud sandboxes that stay al
Triggers AI agents on demand through issue comments, messages, and external integrations.
Plano is an AI agent orchestrator and LLM gateway proxy that unifies access to multiple AI providers through a single interoperable interface. It functions as a model routing engine that decouples applications from specific vendors using semantic aliases, allowing traffic to be shifted between providers without modifying application code. The system distinguishes itself with intent-based agent routing, which directs prompts to specialized agents based on semantic analysis. It features an interceptor-based filter chain system that acts as guardrail middleware to enforce safety policies, rewrit
Translates natural language prompts into structured calls to perform transactional operations via backend functions.
ACI هي منصة لاستدعاء الأدوات ونظام مركزي لإدارة وتنفيذ عمليات الخدمات الخارجية والنصوص البرمجية المخصصة لسير عمل الوكلاء (agentic workflows). تعمل كخادم بروتوكول سياق النموذج (Model Context Protocol) موحد يتيح لوكلاء الذكاء الاصطناعي وبيئات التطوير (IDEs) اكتشاف وتنفيذ مجموعات أدوات متنوعة ديناميكياً. تتميز المنصة بفهرس قدرات باللغة الطبيعية ومطابقة النوايا للبحث عن الأدوات المتاحة بناءً على متطلبات المهام. توفر المنصة مصادقة للخدمات الخارجية وربط الحسابات عبر إدارة بيانات الاعتماد المستندة إلى OAuth للسماح بتنفيذ الأدوات بشكل آمن نيابة عن المستخدمين. يغطي النظام مجموعة واسعة من القدرات بما في ذلك تعيين استدعاء الوظائف، وتنسيق الخدمات الخارجية، ووقت تشغيل قابل للتوصيل لدمج النصوص البرمجية المحلية كإضافات أدوات قابلة للتنفيذ. كما يتضمن إدارة تكوين التطبيقات للتحكم في كيفية تواصل الوكلاء مع تكاملات خارجية محددة.
Enables the execution of integration functions when triggered by AI agents or other application components.
Poml is a prompt management framework and templating engine designed for authoring, versioning, and rendering structured prompts for large language models. It uses a semantic markup language to organize prompts into reusable templates, combining them with dynamic context and data to generate formatted inputs. The system distinguishes itself by decoupling core prompt logic from final presentation through a stylesheet-based approach. It provides a dedicated JSON schema output generator to enforce strict, machine-parsable model responses and a configuration interface for managing function tool s
Defines tool schemas and manages the exchange of requests and responses between the prompt and the model.
This project is an LLM autonomous agent framework and orchestration tool designed to build goal-driven agents that automate complex workflows. It functions as a system for converting high-level objectives into a series of autonomous actions and managing the coordination of multiple specialized agents to solve multi-step problems. The framework features a tool integration layer that parses structured model outputs into executable functions and external API calls. It utilizes a non-blocking execution pipeline to manage task orchestration through recursive loops and asynchronous event handling.
Translates natural language intentions from model outputs into structured, executable function calls.