Kit for AI

LiveKit is an open-source framework and developer platform for building, testing, deploying, scaling, and observing real-time voice, video, and physical AI agents. Its agent pipeline streams user speech from an app, browser, or phone call to an agent, which applies custom business logic and returns a response; the platform supports automatic turn detection and interruption handling, speech-to-text, language-model, and text-to-speech providers, web and mobile applications, and telephony through phone numbers and SIP integrations. LiveKit Cloud provides deployment and scaling on LiveKit's real-time infrastructure, alongside an inference gateway and full-stack observability for agent sessions.

LiveKit Agents is an open-source Python framework for building programmable, real-time multimodal voice agents that run as server-side participants. It combines speech-to-text, large language models, text-to-speech, and realtime APIs with LiveKit's WebRTC clients, telephony stack, RPC and data APIs, and MCP tool support. The framework provides agent sessions, server-side job scheduling and dispatch, semantic turn detection based on a transformer model, multi-agent handoffs, and tools for voice, text-only, transcription, vision, and video-avatar applications. Agents can be tested with native test integration, including event assertions and LLM-based judges, and can run locally in console mode, in development mode with hot reloading, or in production mode. The framework can use LiveKit Cloud or a self-hosted LiveKit server and is distributed as a Python package with plugins for model providers. The Agents framework is licensed under Apache-2.0; LiveKit turn-detection models use the LiveKit Model License.

OKF Agent Memory is a Git-native persistent memory layer for AI coding agents, developed as a pure Go CLI and library. It stores project knowledge as plain Markdown files with YAML front matter in a repository, following the vendor-neutral Open Knowledge Format (OKF) v0.2 rather than relying on an external vector database. The tooling parses and validates OKF bundles, builds an in-memory BM25 index for local lexical search, and provides progressive disclosure through hierarchical index files and link graphs. Its search-before-write workflow queries existing concepts before creating new ones; concepts can include provenance, trust tiers, lifecycle metadata, and code references for linking knowledge to source files. The standalone executable also supports creating and updating concepts, bootstrapping the memory structure into a project, and running an embedded Model Context Protocol (MCP) server over stdio for agent platforms. The repository reports sub-300-microsecond concept search and approximately 4-millisecond graph validation for its benchmark cases, with no external databases or API costs for retrieval. It is distributed under the MIT License.

ai-memory is an open-source local server for long-term memory and session handoff among AI coding agents. It captures prompts, tool calls, and agent context through MCP integrations and lifecycle hooks, storing the material in a searchable, Git-backed Markdown wiki. When a session ends or is explicitly finalized, it generates handoff context for a subsequent session, including the project architecture, failed approaches, and open questions, so work can continue across agent vendors without restating the context; clients without a true session-end hook use explicit finalization commands. The project documents integrations for Claude Code, Codex, Command Code, Devin CLI, OpenCode, Cursor, Gemini CLI, Oh My Pi, Pi, and Crush, with agent-specific configuration, generated plugins or extensions, and capture exclusions. It runs on Linux, macOS, and Windows through WSL2, with experimental native Windows support, and is distributed through Docker images and native release binaries.