26 مستودعات
Capabilities for mapping AI model outputs to executable third-party API requests.
Distinct from Third-Party API Integrations: Focuses on AI-driven tool calling specifically, rather than general static API configuration.
Explore 26 awesome GitHub repositories matching web development · Model Tool Calls. Refine with filters or upvote what's useful.
Flow is an orchestration framework for designing and executing complex workflows using autonomous agents powered by large language models. It serves as a toolkit for constructing agentic pipelines and a runtime for managing agent lifecycles, session states, and tool execution. The project is distinguished by its support for hierarchical swarm management, where director agents decompose large projects into smaller tasks for specialized worker agents. It enables multiple coordination patterns, including sequential linear pipelines and concurrent execution where agents analyze tasks from differe
Provides event-driven hooks to execute side effects or validate operations during agent lifecycle events like tool usage.
Claude Code is a command-line interface and multi-agent orchestration framework designed for autonomous software engineering. It enables AI agents to perform codebase modifications, debugging, and Git workflow management while coordinating multiple specialized agents to decompose and execute complex engineering tasks in parallel. The system distinguishes itself through a high degree of isolation and safety, utilizing Git worktrees to create independent working directories for concurrent agents and implementing a tiered permission system that combines user rules, project policies, and OS-level
Provides event-driven hooks that trigger custom scripts during agent events such as tool usage and session changes.
Agentic is a tool marketplace and management platform designed for the Model Context Protocol. It provides a gateway and proxy that enables the discovery, publishing, and distribution of vetted tools for agentic AI frameworks. The platform specializes in Model Context Protocol monetization, allowing developers to transform services into paid products through integrated authentication, usage-based billing, and subscription management. It also includes a converter that transforms OpenAPI specifications into compatible protocol servers for use in AI workflows. The system covers a broad range of
Enables seamless tool calling across different SDKs through standardized integration protocols.
DocsGPT is a retrieval-augmented generation platform and private knowledge base used to build AI agents that perform grounded search and analysis. It functions as a multi-model AI orchestrator and enterprise agent builder, allowing for the integration of various local and cloud language models to customize reasoning and text generation. The project provides a visual environment for developing automated assistants using conditional logic and third-party API connectivity. It enables the creation of private AI agents capable of performing enterprise search and detailed document analysis using pr
Connects language models to external third-party services by mapping model outputs to executable API requests.
This project provides a dockerized AI workflow stack and orchestration templates for deploying a self-hosted AI environment. It establishes a localized infrastructure for building autonomous agents and model chains that process private data on-premises without external cloud dependencies. The environment is designed to support autonomous agent development, allowing models to dynamically select tools, execute shell commands, and interact with local file systems. It includes integrated vector database support to enable retrieval augmented generation and private document analysis. The stack cov
Maps AI model outputs to executable functions and third-party API requests to achieve specific goals.
LangChain4j is a framework and library for building applications powered by large language models on the JVM. It provides a unified API for developing AI agents, implementing retrieval augmented generation, and integrating generative AI capabilities into professional software built with frameworks like Spring Boot or Quarkus. The project enables the creation of autonomous agents that can reason through tasks, manage memory, and execute external tools to achieve specific goals. It differentiates itself through a unified model interface that allows developers to switch between multiple model pr
Connects models to external tools using standardized protocols to perform real-world actions.
Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, che
Maps AI model outputs to executable third-party tool calls and feeds the results back into the model.
MiniCPM is a collection of small language models designed for local, on-device deployment in resource-constrained environments. The project focuses on running dense Transformer models on consumer hardware, including GPUs, CPUs, and Apple Silicon, without requiring custom code forks. The project distinguishes itself through heavy optimization for edge hardware, utilizing quantized weight compression in GGUF and MLX formats to reduce memory overhead. It implements advanced inference techniques such as speculative sampling and radix-tree prefix caching to accelerate generation speed and throughp
Maps model outputs to executable fields to trigger automated external workflows.
Spring AI is an application framework for Java that provides a portable, fluent API for integrating AI models, tools, and vector stores into applications. It wraps multiple AI providers behind a common interface, allowing developers to switch between chat, embedding, image, and speech models without changing application code. The framework includes a chainable chat client API similar to WebClient or RestClient, supports both synchronous and streaming interactions, and offers structured output conversion that transforms unstructured AI responses into strongly-typed Java objects. The framework
Allows AI models to request execution of user-defined tools during conversations for real-time data access.
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
Integrates tool calling into the model sampling process by specifying tool choices and parameters.
This project is a Java-based framework integration that provides an AI agent runtime, a graph-based AI workflow engine, and an LLM orchestration framework for Spring applications. It enables the development of stateful autonomous agents and the implementation of retrieval-augmented generation systems using document processing and vector databases. The framework distinguishes itself through a graph-based workflow runtime for designing complex AI pipelines with conditional routing and persistent state. It supports multi-agent orchestration via service-discovery coordination and provides human-i
Maps AI model reasoning outputs to executable third-party API requests to perform real-world actions.
This project is a software development kit and framework for building AI agent orchestration, session management, and tool integration systems. It provides a backend infrastructure for hosting remote AI sessions and coordinating multi-agent workflows using large language models. The SDK enables the definition of specialized agents and the orchestration of complex tasks through parallel workstreams. It distinguishes itself by offering a multi-tenant backend capable of horizontal scaling and a headless server runtime that separates session execution from the client interface. The system covers
Provides interceptors to approve, deny, or modify AI tool calls before they are executed.
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
Attaches tool-calling capabilities to agents running on SageMaker models that support function calling.
Genkit is an open-source framework for building AI-powered applications. It provides a unified interface for connecting to hundreds of generative AI models from multiple providers, enabling text, image, audio, and video generation through a single API. The framework structures multi-step AI interactions—including chat, retrieval-augmented generation, tool use, and agentic workflows—as composable, traceable flows with built-in streaming and state management. The framework distinguishes itself through a comprehensive developer toolkit that includes a command-line interface and a local developer
Includes defined tools in a prompt so the LLM can request them to gather information or perform actions.
The free AI already on your Mac. CLI tool, OpenAI-compatible server, and interactive chat — all on-device via Apple Intelligence. No API keys, no cloud, no downloads.
Attaches MCP servers to give the on-device model access to calculators, APIs, and databases via tool calling.
AIOS is an LLM agent operating system and orchestration kernel designed to manage memory, resource scheduling, and tool execution for multiple autonomous AI agents. It serves as a comprehensive framework for developing and deploying agents, featuring a dedicated resource manager that coordinates model backends, GPU memory, and isolated kernel instances. The system distinguishes itself through a semantic memory engine that uses vector search and autonomous clustering for long-term knowledge management, and a semantic file system that allows users to control computer files and system operations
Provides a standardized protocol for executing external tools based on model decisions and receiving consistent results.
Claude Agent SDK is a Python library from Anthropic for building AI agents that use Claude’s tool‑calling, streaming, and session‑management capabilities. It provides a structured framework for intercepting and logging every tool call an agent makes, managing conversation sessions across multiple turns, and controlling which tools the agent is allowed to invoke through configurable permission rules. The SDK distinguishes itself with middleware‑driven tool interception that lets developers block, modify, or require approval for tool calls before or after execution. A permission policy engine e
Ships a library for intercepting and gating AI agent tool calls with pluggable middleware chains.
TablePro is a cross-platform database management client designed for browsing, querying, and administering both SQL and NoSQL databases. It functions as a unified workspace that integrates a code-centric SQL editor with schema visualization tools, allowing developers to manage complex data models and execute queries across diverse database engines. The application distinguishes itself through an agentic AI integration layer that connects language models directly to database tools, enabling automated query generation, optimization, and error fixing with configurable approval gates. It features
Implements a server that exposes database operations to external tools via the Model Context Protocol.
Cerbos is an open-source authorization service that provides a centralized, language-agnostic engine for managing access control. It functions as a policy-as-code platform, allowing teams to define, test, and distribute authorization rules using declarative YAML or JSON configurations. By decoupling access logic from application code, it enables consistent permission enforcement across diverse service stacks. The project distinguishes itself through its ability to translate high-level authorization policies into native database query filters. This capability allows applications to enforce sec
Intercepts agent tool requests to evaluate them against centralized access policies before execution.
gptme is a multi-agent orchestration platform designed for autonomous software engineering, terminal-based AI integration, and RAG-enhanced code navigation. It enables the deployment of persistent agents and specialized subagents to decompose complex tasks and execute parallel technical workflows. The system distinguishes itself through a combination of vision-based GUI automation for controlling desktop applications and surgical patching mechanisms for targeted source code modifications. It utilizes git-based memory management to maintain a versioned history of agent identities, lessons, and
Intercepts agent tool requests to require user confirmation, allow content editing, or automate approvals.