Interleaves chain-of-thought reasoning with tool-using actions in one LLM loop: the model plans, queries a source like Wikipedia, then revises from results. Cuts hallucination versus reasoning-only prompting and beats trained agents on interactive tasks.
Gives developers direct REST access to Claude models for chat, coding, agents, batch jobs, token counting, and structured outputs. It is best for teams that want Anthropic's newest features without routing through a cloud marketplace.
Treats the interface between an LM agent and a computer as a design variable. A custom agent-computer interface (ACI) with concise file-edit, repo-navigation, and test commands plus compact feedback reaches 12.5% pass@1 on SWE-bench, 87.7% on HumanEvalFix.
A graph-based RAG framework pairing a knowledge graph with vector retrieval and a dual-level (low/high) query mode. New documents merge into the graph via set operations instead of triggering a rebuild, cutting the cost of keeping the index current.
A free, lesson-based curriculum for building AI agents in Python from first principles: agentic frameworks, design patterns, tool use, RAG, planning, multi-agent systems, memory, and protocols like MCP and A2A. Hands-on, with Azure AI and Semantic Kernel.
Headless AI coding agent that runs a local HTTP server (OpenAPI 3.1) any client can drive — TUI, desktop, IDE plugins. Provider-agnostic: bring keys for any LLM, no vendor lock-in. Ships LSP-aware editing, plan/build agents, and shareable session links.
Lets developers build AI features with hosted frontier models for text, code, vision, audio, images, and agents. The platform pairs model APIs with tools, SDKs, safety controls, and enterprise options.
Coordinates coding agents across triage, implementation, review, and verification for large engineering teams. Its context engine and enterprise controls focus on shared workflows rather than one developer's local assistant.
Runnable starter projects for the Claude API you fork and adapt: a knowledge-base customer support agent, a financial analyst that charts results in chat, plus computer-use, browser-use, and autonomous-coding-agent reference implementations.
Lets developers call Gemini, Nano Banana, Veo, and other Google AI models from apps through SDKs or REST. It is fastest for teams that want hosted multimodal generation without running model infrastructure.
Provides a collaborative API development platform for designing, testing, documenting, and monitoring APIs — with sharable Collections, mock servers, CLI, and AI-driven features (Agent Mode, AI Agent Builder, MCP Server) to automate API workflows.