A local-first web and desktop dashboard for Hermes Agent that runs streamed agent chats, manages profiles/providers/models/credentials, schedules cron jobs, and inspects files and terminals across local, Docker, SSH and Singularity backends.
Turns a codebase into a live structural knowledge graph that coding agents can query in milliseconds. Bi-temporal, replay-aware indexing of symbols and relationships performed locally with zero LLM API calls; Rust-native, MCP-native integrations and fast incremental updates.
Provides a local-first web-intelligence layer for AI agents: search, fetch, crawl, extract, cache, find-similar and agent-style research without API keys or per-query billing, running as an MCP server, REST daemon, or SDK.
Monitors and detects risky behavior in enterprise AI agents via high-fidelity telemetry, security benchmarking, and a two-tier detector. Comprises ADR Sensor, ADR-Bench, and ADR Detector; deployed in production at Uber and validated on public benchmarks.
Provides a single MCP endpoint that lets AI coding agents search AWS docs, run sandboxed Python scripts, and make authenticated AWS API calls with enterprise guardrails like IAM condition keys, CloudWatch metrics, and CloudTrail auditing.
Provides step-by-step guides to integrate DeepSeek V4 models (deepseek-v4-pro and deepseek-v4-flash) into 22 popular AI agents and coding-assistant tools. Each entry shows installation, configuration, and first-run steps for tools like Claude Code, Qwen Code, Codex, Cline, Deep Code, and more.
Self-hosted sales CRM that runs native AI agents (RAG per tenant) to handle WhatsApp conversations, qualify leads, trigger automations and move deals through configurable pipelines. Multi-tenant with LGPD-minded controls and a one-command VPS installer for full data ownership.
Lightweight cross-platform database client that exposes configured database connections to AI coding agents via an MCP server and includes a built-in AI SQL assistant. Ships as a single ~15MB binary, supports 60+ databases, and runs on desktop or Docker.
Ingests and normalizes security telemetry, runs multi-model AI agents to produce replayable investigations and automated triage/response; key features include a step-by-step Investigation Ledger, CI-gated eval harness, and self-hostable deployments.
Defines OpenTelemetry semantic conventions for generative AI telemetry — spans, metrics, and events for GenAI clients, the Model Context Protocol (MCP), and provider-specific integrations. Includes YAML models, human-readable docs, and reference implementations to standardize observability across GenAI deployments.
Collection of hands-on workshop materials and sample code from Anthropic's "Code with Claude" series, covering Claude Managed Agents, memory (Dreaming Service), eval-driven agent development, and multi-agent patterns. Not maintained and not accepting contributions.
Lets AI agents produce expressive, polished charts from compact, human-editable semantic specs; the compiler infers layout, scales, and labels and emits Vega-Lite, ECharts, or Chart.js outputs, with an MCP server for agent-driven chart creation and rendering.