Discover free AI agents built for Research: Comprehensive directory of solutions verified by AI agency professionals focused on the Research sector.

AI Agents for Research, free pricing | AI Agent Store

Wan2.2 is an open-weights family of large-scale video generative models released by the Wan-Video team as the successor to Wan2.1. It introduces expanded training data and architecture upgrades, offering multiple variants for text-to-video, image-to-video, video-to-video, and even speech-to-video generation. The models produce cinematic, higher-resolution clips with stronger temporal consistency and motion control, and are available under an Apache-2.0 license with runnable repos and Hugging Face checkpoints.

Wan2.2

wan2-2

Open-source video generation model family (5B/14B) for text/image/video-to-video, delivering 1080p clips with improved motion and control.

Cell2Sentence (C2S) is an open-source framework from the van Dijk Lab that represents single-cell RNA-seq profiles as textual “cell sentences” (genes ordered by expression), enabling large language models to natively model biology. The project includes code, docs, and open models (C2S-Scale) released with Google Research/DeepMind for state-of-the-art single-cell tasks like cell typing, perturbation prediction, and virtual cell generation.

Cell2Sentence

cell2sentence

Open-source framework that turns single-cell gene expression into 'cell sentences' so LLMs can analyze and generate biology insights.

GLM‑4.5 is an open‑source, Mixture‑of‑Experts (MoE) large‑language model from Z.ai, designed for intelligent agent applications. It features 355 B parameters (32 B active) with an efficient ’thinking/non‑thinking’ hybrid reasoning mode and 128 K context length. A smaller variant, GLM‑4.5‑Air, offers 106 B parameters (12 B active) for more efficient deployments. GLM‑4.5 excels at reasoning, coding, and tool‑calling tasks and ranks among the top performers on benchmarks compared with models like o3, Grok‑4, Claude Sonnet, and GPT‑4.1.

GLM‑4.5

glm-4-5

Open-source hybrid‑reasoning MoE foundation model optimized for intelligent agent tasks with 128K context and tool use.

Amazon SageMaker Studio Lab is a free, browser‑based machine learning development environment powered by open‑source JupyterLab. It gives users access to T3.xlarge CPU and G4dn.xlarge GPU runtimes (with session and daily limits), along with 15 GB of persistent storage, Git integration, preinstalled ML frameworks, and the SageMaker Distribution environment for easy migration to SageMaker Studio. No AWS account or credit card is required. Ideal for students, educators, and developers looking to learn, prototype, or teach ML workflows without setup overhead.

Amazon SageMaker Studio Lab

amazon-sagemaker-studio-lab

Free browser‑based JupyterLab environment for ML experiments with CPU/GPU and persistent storage.

TEN Framework is an open-source modular system for building autonomous AI agents capable of reasoning, memory management, and tool usage. Designed to reflect real-world constraints, it incorporates a Thought-Emission-Norm (TEN) architecture, encouraging agents to think before acting. The framework supports open-ended planning, persistent memory storage, and tool augmentation. Developers can use TEN to experiment with agent autonomy and deploy agents that simulate complex decision-making workflows.

TEN Framework

ten-framework

An open-source framework for building autonomous AI agents that interact with the real world through tools and memory.

Trae Agent is an open-source, LLM-powered command-line assistant developed by ByteDance for software development workflows. It supports multi-LLM providers, file editing, bash execution, tool integration, trajectory logging, and a modular architecture—ideal for debugging, patching, documentation, and research.

Trae Agent

trae-agent

Open-source CLI LLM agent for automating software engineering tasks via natural-language commands.

AIWaves Agents (repo name “agents”) is an open‑source framework for autonomous language agents that supports planning, memory (short‑ and long‑term), tool usage, web navigation, multi‑agent communication, human‑agent interaction, symbolic control via SOPs, and symbolic learning with backprop‑style updates.

AIWaves Agents

aiwaves-agents

Open‑source adaptive LLM framework for building, training & deploying autonomous language agents.

Swarm is an open‑source, educational multi‑agent orchestration framework from OpenAI. It allows developers to define agents with instructions and tools, and supports handoffs—agent transfers via function calls—for dynamic workflows. Swarm is client‑side, stateless between runs, MIT‑licensed, and ideal for exploring multi‑agent patterns, though not intended for production.

OpenAI Swarm

openai-swarm

Open‑source experimental SDK for lightweight multi‑agent orchestration via instructions and handoffs.

AutoAgent is an open‑source, fully automated LLM agent framework from HKUDS that allows users to build, customize, and deploy multi-agent workflows using only natural language. It features an agent editor and workflow builder for zero-code design, a self‑managing vector DB, support for multiple LLMs, and strong performance on GAIA benchmark compared to research-grade agents.

AutoAgent

autoagent

Open-source zero-code LLM framework to create and deploy multi-agent workflows via natural language.

ScreenAgent is an open‑source Vision Language Model agent that interacts with real computer screens via screenshot observation and mouse/keyboard actions, following a planning‑execution‑reflection loop. It supports multi‑step GUI tasks, dataset collection, and achieves positioning accuracy comparable to GPT‑4V.

ScreenAgent

screenagent

Open‑source VLM agent to control computer GUIs via mouse/keyboard planning and execution.

AutoML-Agent is an open-source multi-agent framework that uses LLMs to automate the full ML pipeline—from data ingestion to model deployment. Designed for diverse data modalities, it decomposes tasks across specialized agents (e.g. prompt, data, model, operation), with retrieval‑augmented planning and multi-stage verification to ensure robust and deployable model solutions.

AutoML-Agent

automl-agent

Open-source multi-agent LLM framework for end-to-end automated machine learning pipelines.

AgentScope is an open‑source, developer‑centric multi‑agent platform for building, orchestrating, and deploying LLM‑powered agent systems. It features explicit message passing, zero‑code drag‑and‑drop UI, built‑in tool and MCP support, distributed execution, monitoring, automatic prompt tuning, and fault tolerance.

AgentScope

agentscope

Open‑source multi‑agent framework with drag‑and‑drop UI, distributed execution, tool calls, and fault tolerance.

AI Agents for "Research" Industry, free pricing