Cognee vs Galactica

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.

Five open-weight checkpoints from 125M to 120B parameters give researchers a science-trained language model built from 106 billion curated tokens across 48 million papers, textbooks, encyclopedias, and knowledge bases. Galactica stores, combines, and reasons across modalities including LaTeX, Python code, SMILES formulas, and amino acid sequences inside one decoder-only Transformer architecture. Meta's Papers with Code team open sourced the weights for researchers who want to study how language models organize scientific knowledge.

General-purpose LLMs train on broad web crawls where social chatter can dominate the token budget. Galactica used only curated open-access science sources, which the paper argues lets it train for multiple epochs without overfitting. Meta removed the public web demo three days after launch in November 2022 when critics showed confident but fabricated citations and equations, so today you download checkpoints from Hugging Face rather than chat through a hosted interface.

Machine learning researchers can reproduce benchmark numbers from the arXiv paper, including 77.6% on PubMedQA and 52.9% on MedMCQA dev. Computational biologists and chemists can test protein annotation and molecule tasks through Galactica's specialized tokens for amino sequences and SMILES strings. Graduate students exploring scientific QA, citation prediction, or math word problems can experiment with sizes up to 120 billion parameters without training from scratch.