Search results for "dynamic"
A Go implementation of the Model Context Protocol (MCP), enabling seamless integration between LLM applications and external data sources and tools.
eBPF-based GPU causal observability agent
Fastest enterprise AI gateway (50x faster than LiteLLM) with adaptive load balancer, cluster mode, guardrails, 1000+ models support & <100 µs overhead at 5k RPS.
A modular MCP server that provides commonly used developer tools for AI coding agents
Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search
Generate OpenAPI 3.1 specs from Go source code via static analysis — zero annotations, automatic framework detection
MCP Toolbox for Databases is an open source MCP server for databases.
Open-source Agentic AI framework in Go for building, orchestrating, and deploying intelligent agents. LLM-agnostic, event-driven, with multi-agent workflows, MCP tool discovery, and production-grade o
A powerful multi-database server implementing the Model Context Protocol (MCP) to provide AI assistants with structured access to databases.
trpc-agent-go is a powerful Go framework for building intelligent agent systems using large language models (LLMs) and tools.
LocalAI is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required.
Model Context Protocol (MCP) server for Kubernetes and OpenShift
MCP tool management and workflow proxy
Command-line tool for debugging MCP servers
Zero-dependency Web Application Firewall in Go. Single binary. Three deployment modes. Tokenizer-based detection.
OpenClaw. open claw. Alternative to openclaw. WhatsApp bot. Telegram bot. Slack bot. Desktop bot. Whatsappbot,telegrambot,slackbot, consolebot,clibot,mcpbot
Run AI coding agents in hardened container sandboxes.
A strongly-typed query builder and generic repository library
Autonomous local AI assistant in Go — 40+ tools, 20+ LLM providers, multi-agent orchestration, self-improving
Type-safe AI agents for Go. Suricata combines LLM intelligence with Go’s strong typing, declarative YAML specs, and code generation to build safe, maintainable, and production-ready AI agents.
🚀 Process JSON data in batches with `llm-batch`, leveraging sequential or parallel modes for efficient interaction with LLMs.