Framework for building multi-channel AI assistants that autonomously plan tasks, invoke tools/skills, and keep long-term memory; supports many LLM providers and channels (WeChat, Feishu, QQ, web) for local or server 24/7 deployment.
Smart model router for personal AI agents that sends each request to the cheapest model capable of handling it — cutting API costs by up to ~70%. Uses a fast 23-dimension scorer, automatic fallbacks, per-tier controls, and supports local Docker self-hosting or a cloud app; ideal for cost-sensitive personal agents.
Build, run, and monitor LLM agents across one stack: an open framework for chaining models and tools, LangGraph for stateful agent orchestration, and LangSmith for tracing, evaluation, and deployment in production.
Build LLM-powered agents and applications from modular components: provider-agnostic model abstractions, tool integrations, retrievers for RAG, and agent orchestration primitives. Suited for prototyping and production agent workflows; requires developer wiring and dependency management.
Connects LLMs to private and domain-specific data with ingestion, indexing, and retrieval primitives for RAG and agentic apps. Centers on document parsing via LlamaParse for 90+ file formats, schema-based extraction, and composable queries.
Connect LLMs to major chat platforms so teams can build, deploy, and operate multi-platform AI chatbots and agents. Provides multi-platform adapters, a plugin marketplace, an MCP server and built-in RAG plus production features like access control, rate limiting and monitoring.
Connects one LLM agent to 15+ chat platforms — QQ, WeChat Work, Feishu, Telegram, Discord, Slack — from a single self-hosted backend. Routes to OpenAI, Anthropic, Gemini, DeepSeek or Ollama, and adds a WebUI, MCP tools, and a 1000+ plugin marketplace.
Builds no-code automations with TypeScript-based integrations, AI pieces, human-in-the-loop steps, and MCP exposure for community and product workflows.
Turns documents, web pages and audio into a private, searchable knowledge base and builder for AI assistants and agents — supports wide-format ingestion, multi-model (cloud or local) execution, retrieval-augmented responses, and enterprise deployment features like RBAC and SSO.
Runs an agentic RAG loop over scientific papers: searches literature, gathers and re-ranks evidence chunks, then answers with in-text citations. Adds metadata-aware embeddings, retraction checks, and contradiction detection across full PDFs.
Provides a minimal, Zig-written headless browser tailored for AI agents and automation — runs JavaScript, supports key Web APIs, exposes the Chrome DevTools Protocol for Puppeteer/Playwright, and targets low memory usage and fast startup for large-scale scraping and agent workflows.
Maps your existing C#, Python, or Java functions into a form AI models can invoke, then translates model requests into real function calls and feeds results back. Model-agnostic middleware: swap in newer models without rewriting your app.