Lets developers delegate codebase work from terminal, IDE, desktop, web, Slack, or mobile. It reads projects, edits files, runs commands, opens PRs, and asks for permission before changing files or executing commands.
Turns natural-language tasks into edits, reviews, and agent runs inside a developer workspace. It pairs codebase indexing with model choice across editor, CLI, cloud agents, and PR review.
Manages provider configs for seven coding CLIs (Claude Code, Codex, Gemini CLI, OpenCode and more) from one desktop app, so switching API endpoints no longer means hand-editing JSON, TOML, or .env files. Adds tray quick-switch and cloud sync.
Turns editor, CLI, and repository context into code suggestions, chat help, reviews, and autonomous coding tasks. Its edge is native workflow integration; teams still need review, policy, and security controls.
Cross-platform API client for debugging, designing, testing and mocking GraphQL, REST, WebSockets, SSE and gRPC. Provides selectable storage backends (Local Vault, Git Sync, Cloud Sync with optional E2EE), a native OpenAPI editor, built-in test suites and a plugin ecosystem — useful for reproducible API development and pre-production validation.
Self-hosted, MIT-licensed interface that puts OpenAI, Anthropic, Google, Azure, AWS, and local models behind one chat UI. Adds agents, a code interpreter, MCP tool connections, persistent memory, web search, artifacts, and multi-user SSO auth on top.
Runs open-weight LLMs (Llama, Gemma, Qwen, GGUF) offline on your machine, with an optional bridge to OpenAI/Anthropic/Mistral. Exposes an OpenAI-compatible API at localhost:1337, so SDK code built for OpenAI switches by changing one base URL.
Orchestrates low-code multi-agent teams that plan, research, code and deliver results to Telegram, Discord, and WhatsApp. Includes handoffs, guardrails, memory and RAG, and integrates 100+ LLM providers via MCP for production-ready agent workflows.
BYOK desktop app working as a universal MCP client: run any MCP server against OpenAI, Anthropic, Gemini, Grok, Ollama and 10+ providers. Also offers prompt-anywhere, AI text commands, local-file RAG, media generation and voice input.
Official Python implementation of the Model Context Protocol. Build servers that expose tools, resources, and prompts to any MCP host, or clients that connect to any server; type hints and docstrings become the schemas, so a server fits in ~15 lines.
Expose Python functions as MCP‑compliant servers and clients so LLMs can call tools and resources directly; includes automatic schema generation, input validation, transport negotiation, authentication, and in‑conversation interactive UIs.