SuperAPI AI Directory

All AI tools

35,738 results · Page 501 of 993

AgentDock

AI developer platforms

AgentDock is a TypeScript-based AI agent development framework for building AI agents capable of delivering complex tasks with configurable determinism. It consists of AgentDock Core (an open-source, backend-first, framework-agnostic, vendor-independent agent core framework) and an open-source Next.js-based client reference implementation. The framework emphasizes simplicity, extensibility, and configurable determinism, making it suitable for building reliable and predictable AI systems that can operate with minimal supervision. It requires Node.js ≥ 20.11.0, pnpm ≥ 9.15.0, and an API key from at least one LLM provider. The framework repository has been retired; the prompt and documentation generator has been migrated to AgentDock/prompt-library, and the book has been migrated to AgentDock/ai-agents-book.

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AgentFlow

AI developer platforms

AgentFlow is a trainable, tool-integrated agent framework developed by lupantech, designed to overcome the scalability and generalization limitations of tool-augmented reasoning methods. It adopts a modular design comprising four specialized modules: Planner, Executor, Verifier, and Generator, which work collaboratively across multiple rounds of interaction through evolving memory and integrated tools. The framework uses Flow-based Group Refined Policy Optimization (Flow-GRPO) to optimize the Planner online, performing excellently on 10 benchmarks spanning search, agentic reasoning, mathematics, and science, with a 7B model even surpassing GPT-4o with around 200B parameters. The framework supports multi-tool integration (such as Python coding, Google search, Wikipedia search, etc.) and provides inference and training pipelines. The project has been accepted by ICLR 2026 and the NeurIPS 2025 workshop, and ranked second in HuggingFace Daily Paper.

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AgentForge

AI agents

AgentForge is a low-code framework for rapidly developing, testing, and iterating AI-driven autonomous agents and cognitive architectures. Its core concepts include flexible Agents, declarative Cogs, and optional Memory, supporting everything from simple first runs to complex multi-agent orchestration. It is compatible with a variety of LLM models, including OpenAI, Google Gemini, Anthropic Claude, as well as local models running via Ollama or LMStudio, and allows different models to be run for different agents.

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Agentic-RAG-R1

AI developer platforms

Agentic-RAG-R1 (repository name Harness-RL) is a reinforcement learning-based agentic RAG framework, and the official implementation of the paper accepted at PCC 2026. It proposes training language model agents within a central-agent multi-agent execution framework (harness), without flattening the execution process into a single token sequence, but instead constructing black-box trajectories from interface calls and session prefix trees, and employing Conflict-Aware Policy Optimization (CAPO) to route the gradients of action tokens and argument tokens separately to their corresponding parameter subspaces. The framework supports central-only agent training and joint multi-agent training, integrates the production-grade agent framework StackPlanner and the slime distributed training stack, and targets Linux x86_64 + NVIDIA GPU environments.

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