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Manage AI agent memory cheaply with budgeted recall and savings monitoring.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "thrift-memory" yet — see the docs or source repo.
Use thrift-memory to design a memory strategy for my multi-agent customer support system: archive long-term conversations to low-cost storage, set a token budget for each recall, and output traceable savings receipts.
A memory tiering and recall plan with token budget settings, storage strategy, and example savings receipts.
Connect to the thrift-memory local dashboard and summarize the past week's memory storage costs, recall counts, and token savings for each agent, then identify the best optimization opportunities.
A per-agent cost and savings analysis with actionable optimization recommendations.
Use thrift-memory to configure budgeted recall rules for engineering agents: prioritize recent task context, then historical decision summaries; keep each recall within a fixed token budget and explain the cost savings.
An implementable recall configuration with budget control logic and an explanation of expected savings.
Store agent memories cheaply and recall relevant context within strict token budgets.
Give AI agents persistent memory, retrieval, and context management across conversations.
Give AI agents persistent memory, recall, and context management across sessions
Give AI agents privacy-first memory storage with fine-grained access control.
Give AI agents persistent memory with semantic search and automatic linking.
Store and query namespaced key-value memory for persistent agent context.