Memory that survives the context window
Long-term context persistence across LLM sessions with automatic semantic compression and auto-distillation, built for agents that run for days, not one chat at a time.
Infinite context retention
Agents keep what matters across sessions instead of losing state every time a context window resets.
Automatic episode summarization
Long sessions get distilled into compact, queryable memory instead of raw transcript dumps nobody re-reads.
Sub-millisecond semantic search
Recall relevant memory fast enough to sit in the hot path of every agent turn, not just offline analysis.
Works across multiple agents
A support bot, a research agent, and a coding assistant can all read from the same memory, if you want them to.
The problem with stateless agents
Every new session starts from zero unless you build your own memory layer, usually a pile of ad-hoc summarization prompts and a vector store bolted on after the fact. ClawDB gives an agent a single place to write what it learns and query it back, so "remember this" is a function call, not a project.
What this looks like day to day
A support agent that remembers the account
Instead of asking a customer to re-explain their setup every ticket, the agent recalls prior issues, plan details, and past resolutions the moment a new conversation starts.
A research agent that builds on its own work
A multi-day research task keeps a running memory of what's been checked, what conflicted, and what's still open, instead of restarting its train of thought each session.
A coding assistant that knows your conventions
Naming patterns, architectural decisions, and "we tried that already" notes persist across sessions instead of being re-explained in every new chat.
Common questions
There's no hard cap built into the design. The Free tier includes 100,000 vector memory entries, and paid tiers scale up from there as an agent's history grows.
Give your AI agents permanent memory today.
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