High-frequency in-browser behavior tree execution engine for autonomous AI agents — 60Hz tick loops, reactive triggers, telemetry recordings, and safety guardrails.

Local-first, self-hosted AI agent runtime and MCP bridge with sandboxed sessions, memory, credentials, audit/replay, and a local Console.

Manage your Hevy workouts, routines, folders, and exercise templates. Create and update sessions faster, organize plans, and search exercises to build workouts quickly. Stay synced with changes so your training log is always up to date.

Local deterministic memory and safety kernel for AI agents. Replaces vector RAG with STRATA, a cryptographically signed, append-only ledger, so every agent state carries proof. Uses causal_walk engine to trace execution failures back to their root cause, and Operator Lite to block destructive terminal terminal commands before they ever run

Enables agents to stream browser HAR captures into an indexed, redacted session and query endpoints, forms, authentication flows, and resource types through paginated MCP tools, with optional headless or interactive browser capture and export to OpenAPI or client SDKs.

Own your AI. The native macOS harness for AI agents -- any model, persistent memory, autonomous execution, cryptographic identity. Built in Swift. Fully offline. Open source.

Enables AI agents to debug Commodore software in a locally running VICE emulator through its text remote monitor, inspecting registers, memory and screens, disassembling code, setting breakpoints and watchpoints, controlling execution, and working with disk images, snapshots and exported graphics. Runs locally over stdio and includes safety guidance for state-changing operations.

Enables agents to execute shell commands and upload/download files on remote Linux hosts over SSH/SFTP with just two tools, returning structured, verifiable results such as exit codes, separated stdout/stderr, timeout and truncation flags, and the current working directory. Supports connection reuse, host alias resolution, host-key verification, and cross-call session state so multi-step work needs fewer calls.

Enables local MCP clients to expose and execute typed tools through a bounded native runtime with Ed25519 admission, per-session budgets, rate limits, schemas, and cached outputs.

Enables developers to register custom tool-calling functions, execute path-safe file operations within an isolated workspace, and keep in-memory memory and audit state for AI agent workflows.

Enables live inspection and debugging of running C++ processes (development builds with PDBs) by controlling execution through cdb, evaluating expressions with natvis, reading and writing memory, and rapidly querying symbols and types via DIA, with special support for Unreal Engine processes.

Enables Alexa+ to turn natural-language household goals into context-aware, confirmation-gated plans, execute them, and verify that devices actually reached the intended state.

Enables MCP clients to search job listings, read complete job descriptions, manage local search presets and candidate pools, and prepare application or message drafts. Optional session-backed chat reading and human-confirmed single-action execution are also supported.

Enables deterministic execution, cryptographic security, and game-theoretic optimization through the Model Context Protocol, returning deterministic JSON tool results for agent telemetry and task inputs.

Enables creating, inspecting, and stopping Google Colab sessions, executing Python code or files, retrieving session URLs, and reading logs through the local Colab CLI.

Overture is an open-source, locally running web interface delivered as an MCP (Model Context Protocol) server that visually maps out the execution plan of any AI coding agent as an interactive flowchart/graph before the agent begins writing code.

A Model Context Protocol server that executes commands in the current iTerm session - useful for REPL and CLI assistance