# Your users shouldn't have to repeat themselves > Long-term memory for AI agents. Remember what users already shared, retrieve it with its source, and keep context across sessions. Canonical URL: https://mnemohq.com Mnemo remembers the conversations, decisions, and details your users already shared. When your agent needs them, it gets the right context with the source attached. Mnemo is long-term memory infrastructure for AI agents: hybrid retrieval across seven signals (vector, BM25, atomic fact, temporal, entity graph, concept boost, semantic bridge) fused with intent-aware Reciprocal Rank Fusion, atomic fact extraction, and prompt-ready profiles. Model-agnostic and US-hosted. Free tier: 1K writes and 10K searches each month, no credit card. ## Measured, not asserted - ~150ms — warm search p50 on the production API. - 99.2% — answer-session found in the top 5 on LongMemEval-S (496 of 500, Recall@5, top-50 candidate chunks, no LLM reranker). - 85.2% — end-to-end answers judged correct on LongMemEval-S under the official LongMemEval judge. ## Store it once. Find it when it matters. Add a detail when it happens. Search for it later in the language your user actually uses. ## Find the right memory. Not just a plausible one. 98.0% recall with only five chunks. Increasing the candidate pool tenfold moves recall by 1.2 points — the result saturates early instead of depending on an oversized search pool. ## A larger context window is still not a memory. Your agent does not need every old message. It needs the few past details that change what it should say or do next. ### Chat history Useful at first. Then the prompt grows, old decisions get compressed, and every request carries messages the agent does not need. ### Document RAG Great for manuals and knowledge bases. Less useful when a user changes their mind or a fact only makes sense in time. ### Mnemo Keeps the details worth remembering, retrieves only what matters now, and returns the source that supports each result. ## From a conversation to context your agent can use ### I. Send the history Send a conversation, document, or transcript through the SDK or API. ### II. Keep the details Mnemo pulls out the facts, dates, people, and decisions that may matter later. ### III. Ask naturally Search the way a user speaks. Mnemo looks across meaning, wording, entities, and time. ### IV. Check the source Each result includes confidence and a reference to the conversation or document behind it. ## Your users' memory, already filled in Memory shouldn't start empty. Connect the tools your users already live in and Mnemo keeps their context continuously in sync — 20 sources, backfilled, updated, and deletion-aware, landing in the same search as everything else. ## Built for the moments users notice Good memory is quiet. The conversation simply continues from the right place. - Customer support — pick up with the customer's setup, the unresolved issue, and the next step your team already promised. - Personal assistants — remember preferences, plans, and changing circumstances without asking the same questions every session. - Workflow agents — carry decisions and outcomes from one run into the next, with enough history to explain what changed. ## The unglamorous details that make memory useful - Find the detail, not just a similar paragraph — Mnemo searches meaning, wording, facts, entities, and time together, then combines the evidence into one ranked result. - Keep small facts from disappearing — long conversations become short, reusable facts without losing the people, dates, or source they came from. - Try again when the first search is weak — low-confidence searches can be rewritten and run again instead of quietly returning a poor match. - Keep dates and changes straight — ask what happened last week, before a migration, or after a user changed their preference. - Keep the model and framework you already use — Mnemo handles memory beside your existing agent. - Know whose memory you are reading — workspace isolation, scoped API keys, audit history, and US data residency are part of the service, not application TODOs. ## Use the tools already in your stack. Seven packages published across npm and PyPI: getmnemo, getmnemo-cli, getmnemo-mcp, getmnemo-anthropic, getmnemo-vercel-ai, getmnemo-mastra, and the Python getmnemo on PyPI. Install the SDK with `npm install getmnemo`, the CLI with `npm install -g getmnemo-cli`, or run the MCP server with `npx -y getmnemo-mcp`. ## Start the next conversation with context Your users have already told you what matters. Give your agent a way to use it. Free tier: 1K writes and 10K searches each month, no card. ## Explore Mnemo - [Mnemo API documentation](https://mnemohq.com/docs) - [Pricing](https://mnemohq.com/pricing) - [Benchmarks](https://mnemohq.com/benchmarks) - [Company Brain for Slack](https://mnemohq.com/brain) - [Ask Mnemo widget](https://mnemohq.com/widget) - [Data-source connectors](https://mnemohq.com/connectors) - [Framework integrations](https://mnemohq.com/integrations) - [Trust and security](https://mnemohq.com/trust) - [Contact](https://mnemohq.com/contact) - [llms.txt](https://mnemohq.com/llms.txt) - [OpenAPI 3.1 specification](https://mnemohq.com/openapi.json) --- This is the Markdown representation of https://mnemohq.com, served by content negotiation on `Accept: text/markdown`. Machine-readable index: https://mnemohq.com/llms.txt · https://mnemohq.com/openapi.json · https://mnemohq.com/sitemap.xml