> ## Documentation Index > Fetch the complete documentation index at: https://metacognition-fdc534de-master.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Overview - What is Tex? > Long-term memory for assistants and agents. Store turns, recall the useful ones, and keep prompts small. New here? Start with the [Quickstart](/quickstart). Then read this overview and [Authentication](/authentication) before you ship. Most chat apps make you choose between two bad options. You either send the whole chat history to the model, or you lose memory when the page refreshes. Tex gives you a simpler path. Store each turn as it happens. When the user asks the next question, ask Tex for the few memories that matter. Then call your model with that smaller context. Your app still runs the model, routes, and UI. Tex handles storage, search, ranking, and usage tracking. ## API | Call | When | | -------------- | -------------------------------------------------------------- | | **`remember`** | Store new turns, plus optional metadata. | | **`recall`** | Before you call the model, ask for the most relevant memories. | Need access? Create an account in the [dashboard](https://app.getmetacognition.com/signup), copy the API key once, then follow the [Quickstart](/quickstart). Locally, set `TEX_API_KEY` or pass `api_key=` to the client. ## Start Install `tex-sdk`, store one turn, recall it, and print the score. LoCoMo and LongMemEval\_S results with splits, latency, and token counts. ## Benchmarks Full-system benchmark. Tex is ahead of EverMemOS (**92.3%**), MemMachine v0.2 (**91.7%**), Zep (**\~85%**), and Mem0 (**\~66%**). See [Benchmarks](/benchmarks) for splits and methodology. Active retrieval track. Tex is ahead of Emergence AI (**86.0%**), Supermemory (**81.6%**), and Zep (**71.2%**). See [Benchmarks](/benchmarks) for per-ability tables. ## Loop ```python theme={null} tex.conversations.remember(session_id="chat-1", turns=[ {"role": "user", "text": "I'm allergic to shellfish.", "timestamp": "..."}, ]) ``` ```python theme={null} hits = tex.recall(q=user_msg, session_id="chat-1") context = "\n".join(h.text for h in hits.hits.turns) ``` Put `context` where your model reads it. Answer the user. Store the new turns. Low `confidence` at the start usually means the session has very little memory. Store more real turns and the score becomes more useful. ## More ### Latency: active write vs background work The fast part of **`remember`** returns quickly. New turns are usually recallable within about **150 ms**. Tex then continues background work, such as observations, entities, and timeline updates. The diagrams and timing notes are in [How memory works](/concepts/memory-model). ### Isolation between customers Use **`org_id`**, **`user_id`**, and **`session_id`** to keep memory separated. [Scopes and multi-tenancy](/concepts/scopes) shows how to map those fields to your users and tenants. ### Python vs raw HTTP Use the [Python SDK](/sdk/installation) if you want token exchange and refresh handled for you. Use the [REST API](/api-reference/overview) from another language, or when your service already owns HTTP calls. ### Quotas and billing Tex meters `tokens_in` and `tokens_out` with daily caps. [Usage, quotas, and billing](/concepts/usage-billing) explains what counts and when limits reset. ## Docs What lands in storage after `remember`. Modes, `top_k`, confidence. Install and client setup. Apps, agents, production patterns.