Search results for "capability"
eBPF-based GPU causal observability agent
GoClaw - GoClaw is OpenClaw rebuilt in Go — with multi-tenant isolation, 5-layer security, and native concurrency. Deploy AI agent teams at scale without compromising on safety.
Nornicdb is a low-latency, Graph + Vector, Temporal MVCC with all sub-ms HNSW search, graph traversal, and writes. Uses Neo4j Bolt/Cypher and qdrant's gRPC drivers so you can switch with no changes. T
The open agent control plane. Govern autonomous AI agents with pre-execution policy enforcement, approval gates, and audit trails. Works with LangChain, CrewAI, MCP, and any framework.
LLM-powered framework for deep document understanding, semantic retrieval, and context-aware answers using RAG paradigm.
The cognitive database. A new class of data storage. Not a vector store, not a graph DB, not a RAG wrapper. Ebbinghaus decay, Hebbian learning, and Bayesian confidence are engine-native primitives.
Model Context Protocol (MCP) server for Kubernetes and OpenShift
A selective learning and memory substrate for agentic systems — typed, revisable, decayable memory with competence learning and trust-aware retrieval.
The Maestro App Factory: a highly-opinionated multi-agent orchestration tool for app development that emulates the workflow of high-functioning human development teams using AI agents
A Slack bot and MCP client acts as a bridge between Slack and Model Context Protocol (MCP) servers. Using Slack as the interface, it enables large language models (LLMs) to connect and interact with v
Official Scrapfly MCP server for Cursor, Claude Desktop, and any MCP-compatible client. Enterprise-grade web scraping, AI extraction, and anti-bot–aware data access as first-class tools.
Run AI coding agents in hardened container sandboxes.
ZimaOS Blue - A Local-First Agent Runtime for Bold Builders. Out-of-the-Box, Open-Source, Universal, Vendor-Neutral
Multi-LLM agent orchestration TUI — parallel Claude/Gemini/Codex sessions, 126 MCP tools
Autonomous local AI assistant in Go — 40+ tools, 20+ LLM providers, multi-agent orchestration, self-improving
🚀 Process JSON data in batches with `llm-batch`, leveraging sequential or parallel modes for efficient interaction with LLMs.