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Provides ready-to-use sample agents for Google’s Agent Development Kit across Python, TypeScript, Go, Java, Kotlin, and Android, from simple assistants to multi-agent workflows.
Combines static code analysis with LLM reasoning to produce interactive architecture diagrams, component-level documentation, and navigable outputs for IDEs, CI, and docs. Emits Mermaid diagrams and incremental updates with CLI and editor integrations.
Autonomously proposes hypotheses, runs experiments, analyzes results, and drafts workshop-level papers via an agentic tree-search pipeline. Unlike template-driven predecessors, it explores open-ended ML research paths but requires GPU/PyTorch and careful sandboxing due to execution of LLM-written code.
Turns natural-language requirements into a dependency-aware graph of atomic, testable dev tasks for AI coding agents. Adds cross-session memory and a plan-reflect loop that forces the agent to think through each step before writing code.
Builds production-grade AI agents and multi-agent workflows in .NET and Python, with graph-based orchestration for sequential, concurrent, and handoff patterns. Unifies Microsoft's Semantic Kernel and AutoGen lineages, adding durable, checkpointed runs.
Orchestrates a lead agent, isolated parallel sub-agents, long-term memory, and sandboxes for long-horizon tasks — minutes to hours of deep research, coding, and content creation. LangChain/LangGraph-based with extensible skills; v2 is a full rewrite.
Turns Chromium into a local-first AI browser with an embedded assistant that can summarise pages, extract structured data, automate web tasks, and run scheduled agents. Built as an open-source Chromium fork with 53+ built-in browser tools, 40+ app integrations, and support for BYO AI keys or fully local models (Ollama / LM Studio).
Wraps Claude Code and Codex with an execution harness that turns one coding agent into coordinated swarms. A single init command adds ~98 agents, an MCP tool server, cross-session vector memory, and cross-machine federation.
Demonstrates orchestration of specialist customer-service agents built with the OpenAI Agents SDK, pairing a Python backend for agent logic with a Next.js UI (ChatKit) to visualize routing, guardrails, and demo flows. Useful for prototyping multi-agent customer-service workflows; uses mock flight data and requires an OpenAI API key.
Trains and optimizes AI agents with reinforcement learning using almost zero code change. Works with any agent framework (LangChain, OpenAI Agents SDK, AutoGen, CrewAI) or none, and can selectively optimize a single agent inside a multi-agent system.
Provides a terminal REPL that gives AI coding agents a persistent, structured context memory (a versionable context tree) which can be synced across machines. Distinguishes itself with local-first TUI workflows, Git-like versioning for knowledge, and broad multi-LLM and agent tool integrations; source-available under Elastic License 2.0.
A Tauri desktop GUI for Claude Code: browse and resume past sessions, build reusable agents with scoped permissions, and track token spend per project. Adds checkpoint branching and visual MCP server management, with all data kept locally and no telemetry.