Cognee vs Velos (formerly GradientJ)

Cognee turns documents, chats, tickets, and API data into graph memory language model agents can recall across sessions. It builds linked entity graphs from that context so agents stop restarting from scratch each run. You can connect Slack, GitHub, or Linear so coding agents and support bots share one permission-aware company brain.

Plain RAG returns similar text chunks. Cognee pairs vector search with knowledge graphs and auto-generated ontologies, so recall pulls connected entities and cited facts rather than isolated snippets. The SDK centers on four verbs, remember, recall, forget, and improve, and the same surface ships over HTTP and MCP for Claude Code, Codex, and OpenClaw.

Platform teams use it for coding agent memory, GraphRAG pipelines, deal intelligence, and customer-facing agents that need grounded answers. Run it with pip locally, self-host in Docker or on-prem, or move to Cognee Cloud when you want managed scale. The project reports 30.4k GitHub stars and 5M+ SDK runs per month, with production deployments at Bayer and Knowunity.

Velos is a managed automation platform for back-office teams that want to replace outsourced manual work with software. It targets insurance carriers, MGAs, and finance operations that still rely on BPOs or internal staff for document-heavy workflows like bordereaux, policy servicing, and month-end close.

The company learns your process rules, tests against your real data, and turns recurring work into auditable workflows that combine code with large language models. Velos handles design, deployment, and ongoing management so teams get faster turnaround without adding headcount every time volume spikes.

It is built for organizations handling sensitive, high-volume operations where accuracy matters. Customers include commercial insurance teams, private equity firms, and fractional CFO shops looking to automate multi-hour processes that used to require offshore teams or manual spreadsheets.