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Software structures providing abstractions, runtimes, and configuration standards for building, managing, and executing language model-powered applications.
Explore 296 awesome GitHub repositories matching artificial intelligence & ml · Agent Frameworks. Refine with filters or upvote what's useful.
Openclaw एजेंट निष्पादन वातावरण को प्रबंधित करने के लिए एक प्लेटफ़ॉर्म है, जो एजेंट लाइफसाइकिल, सत्र स्थिति और वर्कस्पेस पर्सिस्टेंस को नियंत्रित करने के लिए इंफ्रास्ट्रक्चर प्रदान करता है। इसमें एक केंद्रीकृत गेटवे है जो मॉडल लूप, टूल इनवोकेशन और स्ट्रीमिंग इवेंट्स को संभालता है, साथ ही मल्टी-एजेंट रूटिंग और पर्सिस्टेंट मेमोरी प्रबंधन का समर्थन करता है। सिस्टम को टूल निष्पादन हस्ताक्षरों को सामान्य करने और क्रॉस-प्रदाता संगतता के लिए एक मानकीकृत इंटरफ़ेस प्रदान करने के लिए डिज़ाइन किया गया है। प्लेटफ़ॉर्म में व्यापक डेवलपर टूलिंग शामिल है, जैसे वर्कस्पेस प्रबंधन के लिए कमांड-लाइन इंटरफ़ेस, डायग्नोस्टिक लॉगिंग, और एक प्लगइन आर्किटेक्चर जो कस्टम टूल और क्षमताओं के पंजीकरण की अनुमति देता है। यह इवेंट-संचालित हुक, कार्य शेड्यूलिंग और बाहरी सेवाओं के साथ एकीकरण के माध्यम से स्वचालित वर्कफ़्लो का समर्थन करता है। सुरक्षा को निष्पादन नीतियों, क्रेडेंशियल पोर्टेबिलिटी और एजेंट कार्यों के लिए अनुमोदन वर्कफ़्लो के माध्यम से प्रबंधित किया जाता है। डिप्लॉयमेंट का समर्थन स्वचालित इंफ्रास्ट्रक्चर इंस्टॉलर और कंटेनरीकृत गेटवे हेल्पर्स के माध्यम से किया जाता है, जिसमें बैकअप और कॉन्फ़िगरेशन प्रबंधन के लिए अंतर्निहित यूटिलिटी शामिल हैं। सिस्टम मल्टी-स्टेप वर्कफ़्लो को ऑर्केस्ट्रेट करने के लिए एक संरचित प्रारूप प्रदान करता है और इसमें ब्राउज़र ऑटोमेशन और संरचित कोड पैचिंग के लिए विशेष टूल शामिल हैं।
Maintains conversation state through persistent storage, automatic history truncation, and intelligent context compaction.
ECC एक LLM एजेंट ऑर्केस्ट्रेशन फ्रेमवर्क और क्रॉस-प्लेटफ़ॉर्म AI टूलिंग सूट है जिसे मल्टी-मॉडल वर्कफ़्लो का समन्वय करने के लिए डिज़ाइन किया गया है। यह विभिन्न AI-संचालित कोड संपादकों में जटिल सॉफ्टवेयर विकास कार्यों को निष्पादित करने के लिए विशेष एजेंट भूमिकाओं, पुन: प्रयोज्य कौशल और संरचित नियोजन को प्रबंधित करने के लिए एक सिस्टम प्रदान करता है। प्रोजेक्ट खुद को एक मॉडल कॉन्टेक्स्ट प्रोटोकॉल मैनेजर के रूप में अलग करता है, जो बाहरी सर्वर को एकीकृत करने और टूल निष्पादन का ऑडिट करने के लिए एक कॉन्फ़िगरेशन परत प्रदान करता है। यह आगे एक एजेंटिक सुरक्षा सैंडबॉक्स लागू करता है जो संवेदनशील फ़ाइल एक्सेस को प्रतिबंधित करता है और स्वायत्त वर्कफ़्लो को सुरक्षित करने के लिए गुप्त रिसाव (secret leakage) के लिए स्कैन करता है। फ्रेमवर्क AI कोडिंग वर्कफ़्लो ऑटोमेशन, टेस्ट-ड्रिवन डेवलपमेंट गार्डरेल्स, इंटेलिजेंट रूटिंग के माध्यम से मॉडल लागत ऑप्टिमाइज़ेशन और स्टेट-आइसोलेटेड मेमोरी प्रबंधन सहित व्यापक क्षमता क्षेत्रों को कवर करता है। इसमें भाषा-विशिष्ट कोडिंग मानकों को लागू करने और विभिन्न एकीकृत विकास वातावरणों में एजेंट व्यवहारों को प्रबंधित करने के लिए टूल भी शामिल हैं। सिस्टम को एक कमांड-लाइन इंटरफ़ेस के माध्यम से प्रबंधित किया जाता है जो टूल इंस्टॉलेशन, कॉन्फ़िगरेशन मरम्मत और टूलिंग प्रीसेट की तैनाती को संभालता है।
Coordinates multiple agents by assigning specific roles and operating procedures to execute complex workflows.
Auto-GPT is an autonomous agent framework that uses large language models to decompose complex goals and execute multi-step tasks without human intervention. It functions as a workflow automation tool that chains language model tasks and manages memory to achieve specific objectives. The project features a visual agent designer that allows users to define behaviors and goals by connecting functional blocks through a graphical interface. It employs a vector database memory system to recall information across different sessions and a sliding-window buffer for immediate short-term context. The
Integrates core agent logic with external functional plugins for web and file system interactions.
AutoGPT is an orchestration platform designed for building, managing, and deploying autonomous agents. It provides a visual canvas-based environment where users can assemble agents by connecting modular blocks that represent actions, data flows, and conditional logic. The platform supports the entire agent lifecycle, including task scheduling, execution monitoring, and configuration management, while offering a marketplace for discovering and sharing community-built workflows. The project includes a legacy framework for command-line agent execution and an extensible component system for devel
Standardizes message injection interfaces to maintain context within agent prompts.
This project provides a standardized framework for extending the functional range of artificial intelligence agents through a registry of modular, declarative instructions. It enables agentic workflow automation by allowing developers to define task-specific behaviors and operational constraints that guide how agents interact with external tools and execute multi-step processes. The system distinguishes itself through a directory-based discovery model and a plugin-registry architecture that facilitates the distribution of specialized workflows. By utilizing a schema-driven specification that
Establishes a uniform schema for defining agent behaviors and operational constraints when interacting with external tools.
LangChain is an orchestration framework designed for building, managing, and deploying applications powered by large language models. It provides a unified integration layer that normalizes disparate model provider APIs into a consistent set of primitives, enabling developers to build complex, multi-step AI workflows that manage state, memory, and tool execution. The project distinguishes itself through a durable execution runtime that maintains persistent state across long-running processes by checkpointing progress to external storage. It models agent workflows as directed graphs, allowing
Maintains persistent state across long-running processes by automatically checkpointing execution progress to external storage.
This project is an AI agent workflow framework and development toolkit designed for AI-driven software engineering. It provides a system of modular instructions, prompt libraries, and standardized routines to orchestrate complex engineering sequences and automate the decomposition of plans into technical tasks. The system differentiates itself through advanced context management and prompt engineering, using state compression and handoff documents to preserve conversation history between different AI sessions. It employs a structured library of prompt skills and high-signal trigger words to e
Creates modular and standardized system prompt structures to ensure predictable agent behaviors.
gstack is an AI agent framework and development workflow system designed to automate the software development lifecycle. It coordinates specialized AI personas to manage tasks across product design, engineering management, and quality assurance, transforming product intent into technical specifications and final releases. The project is distinguished by its deep integration of headless browser automation and semantic code memory. It utilizes a persistent Chromium daemon for web scraping and visual auditing, and implements a searchable knowledge base that logs architectural decisions and repos
Coordinates multiple agents by assigning specialized roles for design, engineering management, and quality assurance.
This project provides a command-line interface for managing autonomous agent workflows, task orchestration, and system-level automation. It includes a comprehensive framework for defining agent skills, managing persistent memory, and delegating tasks to specialized subagents. Users can configure complex planning modes, execute shell commands with safety constraints, and integrate external tools through standardized protocols. The platform supports non-interactive execution via a headless mode and provides an event-driven hook framework for custom lifecycle automation. It features centralized
Session management maintains persistent state and interaction history, allowing users to resume workflows or rewind to specific points in time.
Browser-use is a framework for building autonomous agents that navigate, interact with, and extract data from web interfaces using natural language instructions. By acting as an orchestration layer between large language models and browser automation protocols, it enables the execution of complex, multi-step workflows without relying on brittle selectors. The system functions as a headless browser controller, providing a programmatic interface to manage browser instances and execute granular interactions. The project distinguishes itself through its ability to translate high-level intent into
Orchestrates iterative task execution by processing visual page context and generating actionable commands through language models.
App-ideas is a development platform that integrates autonomous AI agents into local environments to orchestrate code review, automated fix application, and workflow management. It functions as a command-line interface that connects external AI assistants to your codebase, enabling iterative development cycles through plugin-based integration and natural language triggers. The platform distinguishes itself through a robust static analysis engine that traverses syntax trees to enforce structural coding standards and identify violations. Users can define custom review rules, architectural prefer
Standardizes interfaces that connect external AI assistants to local development environments for automated remediation.
This project is a comprehensive retrieval-augmented generation platform designed for building, managing, and deploying knowledge-based AI applications. It provides a unified environment for organizing datasets, configuring conversational chat assistants, and developing autonomous agents that execute multi-step reasoning workflows. By integrating document intelligence with advanced retrieval pipelines, the platform enables the creation of grounded, verifiable responses supported by traceable citations. The platform distinguishes itself through deep document understanding and sophisticated know
Handles the lifecycle of autonomous agents through dedicated API endpoints for listing, managing, and interacting with system entities.
OpenHands is an autonomous agent framework designed for software engineering workflows. It provides a modular platform for orchestrating AI agents that reason, plan, and execute tasks within isolated, containerized development environments. By integrating with standard version control and development tools, the system enables agents to autonomously navigate codebases, implement features, and resolve issues through iterative reasoning and tool execution. The platform distinguishes itself through a model-agnostic orchestrator that connects diverse language models to a unified tool registry. It
Defines custom tools by extending base classes with specific action and observation schemas for agent execution.
Paperclip is an LLM agent orchestration platform and governance suite designed to coordinate teams of autonomous AI agents. It provides a management plane for defining organizational hierarchies, assigning roles, and aligning individual agent tasks with a structured mission tree to ensure work maps to business objectives. The project distinguishes itself through a specialized agent skill registry and workspace manager. It allows for the discovery and injection of reusable workflows into agent runtimes without retraining and provides isolated, sandboxed execution environments with persistent s
Records all agent conversations and tool calls in an immutable log for complete action traceability.
MetaGPT is an agentic workflow engine and multi-agent orchestration framework designed to automate complex software engineering and data analysis tasks. It functions as an automated software factory that transforms high-level natural language requirements into functional web applications, technical documentation, and production-ready code. By utilizing a runtime environment that manages the lifecycle of specialized agents, the platform bridges the gap between user intent and finished software components. The system distinguishes itself through role-based agent orchestration and dynamic task d
Assigns specialized roles and standard operating procedures to agents to streamline their collaboration on software development and data analysis.
MetaGPT is an agentic workflow orchestrator and multi-agent framework designed to transform natural language requirements into complete software deliverables. It functions as an AI software engineering suite that automates the creation of technical documentation, data structures, and source code by treating natural language as a programming environment. The system distinguishes itself by assigning professional roles to large language models, creating specialized agent teams that collaborate through a shared communication structure. It utilizes standard operating procedures to convert organiza
Coordinates specialized agent teams by assigning professional personas and operating procedures to partition complex engineering tasks.
This project provides a modular framework for building and orchestrating autonomous AI agents. It functions as an agentic workflow engine that manages the full lifecycle of task execution, including model reasoning, tool invocation, and the integration of results. By utilizing a centralized orchestration platform, the system enables the creation of multi-agent teams that collaborate on complex objectives through structured communication and shared task graphs. The framework distinguishes itself through its focus on persistent, stateful operations and multi-agent coordination. It employs file-
Provides a middleware layer for registering and dispatching external services as executable agent tools.
This project is a structured educational resource and technical guide for designing and implementing autonomous systems using large language models. It provides a comprehensive curriculum and code samples focused on agentic design patterns, autonomous development, and the creation of systems capable of planning and executing multi-step tasks. The resource details the implementation of agentic retrieval-augmented generation, where models autonomously plan and refine data searches. It covers a wide array of orchestrators and design patterns, including metacognitive reflection for self-correctin
Integrates user feedback gates for real-time approval or termination to steer autonomous agent actions.
Cline is an extensible agent runtime and multi-agent orchestration engine designed to automate complex software engineering workflows. It functions as an integrated development environment extension that bridges strategic task planning with autonomous execution, allowing users to manage multi-step projects through human-in-the-loop oversight or independent agent operation. The platform distinguishes itself by enabling the creation of specialized agent teams that share a common state and coordinate through a centralized task manager. It enforces project-specific architectural guidelines and co
Decomposes complex engineering requirements into manageable sub-processes to streamline coordination across multiple specialized agents.
Pi-mono is an autonomous coding agent orchestrator designed to coordinate multiple intelligent agents for complex software development tasks. It functions as a framework that integrates directly with local file systems and terminal environments to automate development workflows. The system distinguishes itself through a stateful session manager that serializes the entire context of a coding interaction to disk, allowing agents to maintain project awareness across separate sessions. It utilizes a plugin architecture for tool registration and prompt-template injection, enabling the integration
Connects autonomous assistants to custom tools and external providers to handle specialized software engineering requirements.