Multi-tenant agent harness that makes enterprise knowledge retrievable, graph-reasonable, and deliverable by LLM-powered agents. Integrates RAG + a Milvus-based knowledge graph, LangGraph orchestration, and document parsing for citation-backed answers and graph reasoning; deployable via Docker (requires a compatible LLM API).
Connects an AI agent to a Supabase project over MCP to run SQL, manage tables and migrations, deploy Edge Functions, fetch keys and types, and read logs. Read-only mode and project scoping cap what the agent can touch.
Runs penetration tests autonomously: a multi-agent system (researcher, developer, executor) plans attacks, writes and runs exploit code, and chains 20+ tools like nmap, metasploit and sqlmap in isolated Docker containers — for authorized testing only.
Lets AI agents like Claude Desktop and Cursor explore schemas and run SQL across Postgres, MySQL, MariaDB, SQL Server, and SQLite through one MCP server. A read-only mode stops the agent mutating data; no per-database drivers to wire up.
Extracts and structures data from receipts, invoices and transaction documents using configurable LLM prompts for a self-hosted accounting workflow. Offers multi-currency (including crypto) historical conversion, custom fields/prompts, batch processing and Docker-based deployment for local data control.
Centralized enterprise platform to manage org-wide MCP servers with a private MCP registry, security guardrails, cost controls, and observability. Offers a Kubernetes-native orchestrator, built-in RAG knowledge base, security sub-agents, and tools for governed AI adoption.
An open-source memory layer that turns agent runs and conversations into structured, persistent state recallable across sessions. Captures facts, events, preferences, and relationships automatically; LLM-agnostic with SDK and MCP integration.
Compiles an agent's raw chat logs, documents, and tool traces into three persistent layers — index, learned skills, and user memory — so context survives sessions. Claims 92% Locomo-benchmark accuracy and up to 95% lower token cost than replaying history.
Lets AI coding agents provision and operate a full backend themselves — Postgres with pgvector, OAuth2 auth, S3-style storage, Deno edge functions, and hosting — through one interface, plus an OpenAI-compatible model gateway.
Agent memory that learns over time instead of just recalling past chats: retain/recall/reflect primitives turn interactions into facts, experiences, and mental models. Reports top LongMemEval scores; self-hostable with Python and Node SDKs.
Combines a vector store, Cypher-style graph queries, and on-device LLM inference in one Rust engine, with a graph neural network that reranks results and adapts to query patterns in under a millisecond. Services ship as self-contained .rvf containers.