Cognee vs Respan (formerly Keywords AI)

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.

Respan is an LLM engineering platform for teams shipping production AI agents and applications. Route model traffic through one gateway, trace every call in detail, run evals on offline datasets and live traffic, and monitor cost, latency, and errors from a single dashboard.

The product grew out of Keywords AI, a Y Combinator Winter 2024 company founded by engineers from the University of Illinois. What started as LLM routing expanded into full observability after customers asked for visibility into how agents behaved in production. Respan now positions itself around the loop from tracing to evaluation to iteration.

It fits ML engineers, platform teams, and AI product builders who need gateway failover, prompt version testing, production quality scoring, and spend controls without stitching together separate tools. Customers cited on the site include teams at Retell AI, Mem0, Lovable, and Gumloop.