Agent Frameworks

SQL-native memory infrastructure for AI agents and applications

Memori is an AI memory engine that provides persistent, queryable memory for agents and applications using SQL-native storage. It stores structured memories with semantic search, temporal awareness, and relationship tracking, enabling AI systems to remember user preferences, past interactions, and contextual facts across sessions. With 12,900 GitHub stars, it offers a database-native approach to the agent memory problem.

Multi-agent framework simulating a software company

MetaGPT is an open-source multi-agent framework with 56K+ GitHub stars that simulates a software company by assigning roles — product manager, architect, engineer, QA — to AI agents collaborating through structured SOPs. Given a one-line requirement, it outputs user stories, competitive analysis, data structures, APIs, and working code. Features incremental development, human feedback integration, and experience-based learning for continuous improvement across iterations.

Unified Python/.NET framework for multi-agent AI

Microsoft Agent Framework is Microsoft's official unified SDK for building multi-agent AI workflows in Python and .NET. It consolidates Semantic Kernel and AutoGen into a single framework with MCP tool integration, graph-based workflows, human-in-the-loop patterns, and multi-agent orchestration. The framework reached Release Candidate status in February 2026 and is Microsoft's recommended path for production agent development.

Open-source platform for managing AI coding agents as teammates

Multica is an open-source managed agents platform that lets you assign coding tasks to AI agents like Claude Code and Codex as if they were team members. It provides a unified dashboard for task assignment, execution monitoring, and skill reuse across local and cloud compute environments. With multi-workspace support, team-level isolation, and reusable skill compounding, Multica turns autonomous coding agents into organized, trackable development resources.

Secure sandboxed runtime for AI agent execution

NVIDIA OpenShell provides kernel-level isolation for AI agent workloads with Landlock, seccomp, and network namespace sandboxing. Announced at GTC 2026 with 17 enterprise partners including Adobe, Atlassian, SAP, and Salesforce, it offers declarative YAML policy enforcement, L7 HTTP inspection, and GPU passthrough — purpose-built to contain the blast radius when autonomous coding agents interact with filesystems and networks.

Framework for converting MCP servers into autonomous AI agents with UI

Nanobot transforms MCP servers into full autonomous agents by adding a planning layer, conversation memory, and web-based UI on top of MCP tool capabilities. It enables building agents that combine multiple MCP servers with LLM reasoning to complete multi-step tasks. Features MCP-UI for browser-based interaction and supports any MCP-compatible tools as agent capabilities.

Browser automation framework turning websites into action APIs

Notte is a browser automation framework for AI agents that converts any website into a structured action API. Instead of scraping pages for text, Notte lets agents interact with sites — clicking buttons, filling forms, and navigating flows. Built with hybrid AI-plus-deterministic scripting, it includes digital personas, CAPTCHA solving, and proxy management for reliable automation at scale.

Open-source async coding agent you can run in your own sandbox

Open-source framework from LangChain AI for building your organization's internal coding agent — the same pattern Stripe's Minions, Ramp's Inspect, and Coinbase's Cloudbot follow. Built on LangGraph and Deep Agents, Open SWE runs each task in an isolated cloud sandbox (Modal, Daytona, Runloop, or LangSmith), invokes from Slack, Linear, or GitHub, orchestrates subagents, and opens pull requests autonomously — customizable end-to-end for your codebase and conventions.

Lightweight multi-agent handoff framework by OpenAI

OpenAI Swarm is an experimental lightweight framework for building multi-agent systems with handoff patterns. Agents are defined as simple Python functions with instructions and tool lists, and can hand off conversations to other specialized agents. Designed to be minimal and educational rather than production-ready — demonstrates patterns for agent coordination without heavy abstractions. Runs on OpenAI's Chat Completions API with function calling for tool use and agent transitions.

Rust-based agent OS with built-in security, WASM sandboxing, and multi-agent runtime

OpenFang is an open-source agent operating system built in Rust that provides a secure multi-agent runtime with WASM sandboxing, auditability layers, and multi-channel communication. It goes beyond typical orchestration SDKs by treating agent security and operational isolation as first-class concerns, making it suitable for teams deploying agents in environments where trust boundaries and audit trails matter.

Local-first personal AI agent with memory trees, desktop integrations, and private workspace context.

OpenHuman is an open-source, local-first personal AI agent from TinyHumans. It combines a desktop app, persistent memory trees, Obsidian-compatible storage, OAuth integrations, and local model support into a private assistant harness. It is most interesting for users who want agentic workflows and long-term memory without handing every context detail to a fully cloud-hosted assistant.

Open-source general AI agent framework from the MetaGPT team

OpenManus is an open-source framework for building general-purpose AI agents, developed by core contributors from the MetaGPT community. It provides a modular architecture with planning agents, reactive agents, and tool-calling agents that can execute code, browse the web, search for information, and handle files. Built as the open alternative to Manus AI, it gained over 55,000 GitHub stars and supports multi-agent collaboration with real-time execution feedback.

Enterprise-grade sandbox for AI agent code execution

OpenSandbox is an open-source sandbox platform from Alibaba providing secure, isolated execution environments for AI coding agents. It supports Python, Java, JavaScript, and C# SDKs with a unified Sandbox Protocol for custom runtimes. Integrates with Docker and Kubernetes, offering isolation through gVisor, Kata Containers, and Firecracker microVMs with per-sandbox network controls.

LangChain CLI for maintaining agent-friendly codebase documentation

OpenWiki is LangChain’s open-source CLI for generating and maintaining an agent-focused wiki inside a codebase. It can create an openwiki/ documentation folder, update it from repository changes, add guidance to AGENTS.md or CLAUDE.md, and run via an interactive CLI or daily GitHub Action so coding agents have durable context without stuffing every detail into prompts.

Vectorless, reasoning-based RAG that reads documents like a human expert — no vector DB, no chunking.

PageIndex is a vectorless, reasoning-based RAG system that builds hierarchical tree indexes from long documents and uses LLMs to navigate them like a human expert would. Instead of chunking text and comparing embeddings, it constructs a table-of-contents-style structure and reasons its way to the right sections — no vector database required. Available as an open-source Python package, cloud API, MCP server, and chat platform.

Behavioral control layer for reliable customer-facing AI agents

Parlant is an open-source framework that adds behavioral governance to conversational AI agents. Instead of relying on prompt engineering alone, it lets teams define explicit policies, conversation guidelines, and behavioral rules that agents follow predictably across multi-turn interactions. Parlant sits between the LLM and the user-facing interface, enforcing consistent agent behavior for customer support, sales, and service automation use cases.

Open-source framework for real-time voice and multimodal AI agents

Pipecat is an open-source framework with 11,000+ GitHub stars for building real-time voice and multimodal AI agents. Developed by Daily.co, it manages the STT to LLM to TTS pipeline with sub-second latency, integrating with AWS Bedrock, NVIDIA NIM, and AssemblyAI for production-grade voice agent deployment.

Low-code multi-agent framework with chat integrations

PraisonAI is an open-source low-code multi-agent framework with 6K+ GitHub stars for building AI agent teams through simple YAML configuration. Define agent roles, goals, and tools in YAML and PraisonAI handles orchestration. Features built-in integrations with WhatsApp, Telegram, Discord, and Slack for deploying conversational agents. Supports both CrewAI and AutoGen as backend orchestrators, RAG capabilities, and a web UI for monitoring agent interactions in real-time.

Build and evaluate LLM apps end-to-end

Prompt Flow is Microsoft's open-source development suite for building, testing, evaluating, and deploying LLM-based applications end-to-end. It links LLM calls, prompts, Python code, and other tools into executable flows defined in YAML, with a VS Code extension providing a visual flow designer. The tool supports tracing LLM interactions for debugging, running batch evaluations with quality metrics against larger datasets, and integrating tests into CI/CD pipelines before production deployment.

Production RAG engine with hybrid search and knowledge graphs

R2R is a production-grade RAG engine from SciPhi AI that combines hybrid search with knowledge graph extraction and agentic retrieval capabilities. It provides a complete pipeline from document ingestion through retrieval and generation, supporting vector, keyword, and graph-based search strategies. The managed API and self-hosted options make it accessible for both rapid prototyping and production deployments requiring advanced retrieval beyond simple vector similarity.

All-in-one multimodal RAG framework

RAG-Anything is an all-in-one multimodal RAG framework from the University of Hong Kong that processes text, images, tables, and equations through a unified pipeline built on LightRAG. It constructs multi-modal knowledge graphs by extracting multimodal entities and establishing cross-modal relationships. The VLM-Enhanced Query mode integrates visual content into large language models for deeper document understanding beyond plain text retrieval.

Deep document understanding RAG engine

RAGFlow is an open-source RAG engine with 76K+ GitHub stars that provides deep document understanding for building knowledge-based AI applications. Optimizes chunking for 20+ document types including PDFs, Word docs, presentations, and images using layout-aware parsing. Features template-based chunking strategies, citation with source references, multi-recall retrieval combining keyword and semantic search, and a visual knowledge base management interface with drag-and-drop document upload.

Version control for AI coding-agent actions

Re_gent is an open-source version-control layer for AI coding-agent activity. Instead of only reviewing the final Git diff, it records what the agent attempted, changed, and executed along the way so teams can trace, undo, and govern autonomous coding work. It fits Claude Code, Codex, Cursor, and multi-agent teams that need an audit trail between prompt and pull request.

Durable execution engine for workflows and AI agents

Restate is a durable execution engine that provides reliable workflow orchestration for AI agents and backend services. It runs as a single binary with no external dependencies, delivering sub-50ms latency and 94K+ actions per second. Supports TypeScript, Python, Go, Java, and Kotlin SDKs with built-in retries, sagas, and virtual object state. MIT licensed with 3,700+ GitHub stars.

Build modular, scalable LLM applications in Rust

Open-source Rust library for building scalable, modular, and ergonomic LLM-powered applications. Rig unifies 20+ model providers (OpenAI, Anthropic, Mistral, DeepSeek, Ollama, and more) and 10+ vector stores behind one trait-based interface, supports completion and embedding workflows, multi-turn streaming, and transcription/audio/image generation, with full GenAI Semantic Convention compatibility and WASM-ready core library — production agentic infra for Rust teams.

Visual AI agent builder by Ironclad

Rivet is an open-source visual AI programming environment and TypeScript library by Ironclad for building, debugging, and deploying AI agents through a node-based graph editor. Complex LLM prompt chains become visual networks with real-time data flow inspection at every node, team collaboration on agent graphs, and a TypeScript library for embedding finished graphs in any application. Transforms agent development from text-editing into visual engineering.

Open-source AI coworker with persistent memory and tool use

Rowboat is an open-source AI coworker platform that provides persistent memory, tool use, and multi-agent orchestration in a chat-based interface. It enables teams to build AI assistants that remember context across sessions, access internal tools and databases, and coordinate specialized sub-agents for complex workflows. Over 9,300 GitHub stars.

Multi-agent model API that orchestrates frontier models behind one OpenAI-compatible endpoint

Sakana Fugu is a hosted model-provider API that exposes a learned multi-agent system as one OpenAI-compatible model. It dynamically routes coding, code review, research, and reasoning tasks across a frontier-model pool, with Fugu for lower-latency work and Fugu Ultra for harder workloads where answer quality matters more than cost or speed.

Microsoft's AI orchestration SDK for .NET, Python, and Java

Microsoft's open-source AI SDK that lets you combine AI models with conventional programming. Supports plugins, planners, memory, and function calling with availability for .NET, Python, and Java. Designed for enterprise developers building AI-powered applications within the Microsoft ecosystem, offering deep integration with Azure AI services and existing business logic.

Visual agent builder with 1000+ integrations

Sim is an open-source platform for building, deploying, and orchestrating AI agents with a visual workflow editor. Connects 1,000+ integrations and LLMs with drag-and-drop canvas design, AI-assisted Copilot for generating nodes from natural language, and built-in knowledge base for RAG. Trusted by 100K+ builders. Includes 11 pre-built workflow templates for quick deployment.

Hugging Face's lightweight agent framework

smolagents is Hugging Face's lightweight agent framework for building AI agents that can use tools, write and execute code, and collaborate in multi-agent setups. Designed for simplicity with minimal abstractions — agents are just LLMs that write Python code to orchestrate tool calls rather than using JSON-based function calling. Supports any LLM provider, integrates with Hugging Face Hub for sharing tools and agents, and runs with as few as 1,000 lines of core library code.

Alibaba's Spring framework for building AI applications in Java

Spring AI Alibaba is Alibaba's open-source framework that brings AI capabilities to Java Spring Boot applications. It provides auto-configuration for AI model providers, RAG pipeline components, agent frameworks, and tool integration following Spring conventions. With 9,100 GitHub stars and 220+ contributors, it is the most mature AI framework for Java enterprise developers building production AI features.

AWS open-source SDK for building model-driven AI agents

Strands Agents is an open-source SDK from AWS that takes a model-driven approach to building AI agents. Developers define a prompt, model, and tools, and the LLM handles planning and orchestration autonomously. Supports Amazon Bedrock, Anthropic, OpenAI, Gemini, Ollama, and more. Powers Amazon Q Developer and AWS Glue in production. Available in Python and TypeScript with native MCP support.

Open-source generalist AI agent for browser and code tasks

Suna is an open-source generalist AI agent that can autonomously browse the web, write and execute code, manage files, and interact with external services. It features a real-time browser automation engine, an isolated code execution sandbox, and integrations with popular APIs. Designed as an open-source alternative to commercial AI agent platforms. Over 9,000 GitHub stars with rapid community growth.

Open-source Firecracker sandboxes for long-running AI agents

Superserve is an open-source sandbox infrastructure layer for AI agents that need durable computers instead of short-lived shells. It runs isolated Firecracker microVMs, supports pause, resume, snapshot, fork, preview URLs, MCP connectivity, SDK/API control, Docker workloads, and self-hosting, while the hosted service adds pay-as-you-go agent sandboxes for teams.

Multi-agent orchestration with 10+ swarm patterns

Swarms is an enterprise-grade multi-agent orchestration framework with 6,100+ GitHub stars that provides 10+ swarm patterns including sequential, concurrent, hierarchical, mixture-of-agents, and graph-based workflows. The SwarmRouter lets teams switch between orchestration strategies by changing a single parameter. It supports MCP for tool integration, multi-model providers via Anthropic, OpenAI, and local models, and includes an AutoSwarmBuilder that generates agents from task descriptions.

Code-first agent framework for data analytics tasks

TaskWeaver is Microsoft's open-source code-first agent framework that converts natural language requests into executable Python code for data analytics and workflow automation. Unlike text-based agent frameworks, it preserves rich in-memory data structures like DataFrames across conversation turns, supports custom algorithm plugins as callable functions, and verifies generated code before execution. It includes a Planner for task decomposition and a Code Interpreter for generation and execution.

Open-source observability and self-healing layer for AI agents

TraceRoot is a YC S25-backed open-source observability platform purpose-built for AI agents and LLM apps. It combines OpenTelemetry-compatible tracing with an agentic debugging runtime that reads your source code, correlates failures with recent commits, and proposes fix PRs automatically. BYOK support spans seven LLM providers; the entire stack runs self-hosted via Docker Compose, with TraceRoot Cloud available for managed deployments.

Agentic AI security posture management

Trent AI is a specialized security platform for agentic AI applications providing AI Security Posture Management that compounds with every development cycle. Scans, judges, mitigates, and evaluates AI agent security detecting threats traditional tools miss including prompt injection attacks, tool misuse, unintended autonomous actions, data exfiltration through agent chains, and privilege escalation. Offers continuous assessment with remediation plan execution through Claude Code.

Autonomous coding agents that ship while you sleep

Twill is an autonomous coding agent platform that implements features, fixes bugs, and ships pull requests without manual intervention. Uses structured workflow of research, planning, human review, implementation in isolated sandbox, AI code review, then merge. Supports custom agent configurations with multiple LLM providers, isolated dev environments for verification, and integrations with GitHub, Linear, Sentry, Notion, and cloud platforms for end-to-end engineering automation.

MCP-native personal context vault for keeping AI agents aligned with your work, voice, and projects.

Unabyss is a personal context headquarters for AI agents. It syncs sources such as email, Slack, Notion, Drive, meetings, and professional profiles into structured context files that can be served to MCP-capable clients. The strongest angle is not generic note taking; it is permissioned, reusable context for Claude, Cursor, custom agents, and other tools that otherwise need the same background explained repeatedly.

Agent framework with native MCP server support

Upsonic is an agent framework designed for building reliable AI agents with native Model Context Protocol (MCP) server support. It provides structured task execution, tool calling orchestration, and built-in reliability patterns for production agent deployments. Features include automatic error recovery, structured output validation, and seamless integration with MCP-compatible tools and data sources for enterprise agentic workflows.

TypeScript-first AI agent framework with built-in observability

VoltAgent is an open-source TypeScript AI agent framework with built-in observability, RAG support, memory management, and MCP integration. It provides a structured approach to building production AI agents in the Node.js ecosystem with agent debugging tools, sub-agent orchestration, and tool management. Over 7,000 GitHub stars and 150K+ weekly npm downloads.

Enterprise RAG framework by Tencent

WeKnora is a Tencent-developed LLM-powered knowledge management and Q&A framework for enterprise document understanding and semantic retrieval. Supports 10+ document formats including PDF, Word, Excel, and images with seamless IM platform integration for WeCom, Feishu, Slack, and Telegram. Offers Quick Q&A mode using RAG pipelines and Intelligent Reasoning mode with ReACT agents for complex multi-step reasoning tasks across organizational knowledge bases.

Context engineering platform for AI agents with temporal knowledge graphs

Zep is a context engineering platform that assembles relationship-aware context for AI agents from conversations, business data, documents, and events. It maintains a temporal knowledge graph that automatically extracts entities and relationships, tracking how context evolves over time. Zep delivers formatted context blocks optimized for LLMs with sub-200ms latency, integrating with LangChain, LlamaIndex, AutoGen, and Google ADK through Python, TypeScript, and Go SDKs.

Google's official Agent Development Kit for building AI agents in Go

adk-go is Google's official Agent Development Kit for the Go programming language, providing the tools and abstractions needed to build production AI agents. It supports tool calling, multi-turn conversations, structured outputs, and integration with Google's Gemini models. With 7,300 GitHub stars and Apache 2.0 license, it brings first-class AI agent development capabilities to the Go ecosystem.

Lightweight OS for running AI agents in-process

agentOS is a portable open-source operating system for AI agents that delivers ~6ms cold starts at 32x lower cost than traditional sandboxes. Powered by WebAssembly and V8 isolates, it runs agents like Claude Code and Codex directly inside your process with granular permissions and host-managed tool access for S3, GitHub, and databases. Available as a simple npm package with no special infrastructure or vendor lock-in required.

Persistent memory layer for AI coding agents — keeps Claude Code, Codex, Cursor, and any MCP agent in context across sessions

agentmemory is an open-source MCP server that gives AI coding agents persistent, cross-session memory. Built on hybrid vector-graph search, it achieves 95.2% recall on the LongMemEval-S benchmark while using up to 92% fewer context tokens than naive context injection. Works out of the box with Claude Code, Codex, Cursor, Windsurf, Cline, OpenCode, Kilo Code, Hermes, and any MCP client through 51 MCP tools plus 12 hooks and 4 skills.