Recalld is a memory layer for AI agents and chats. It can ingest conversations, documents or code. The input is decomposed into facts, then saved into a vector database.
I built Recalld because I couldn't find a reliable memory layer that let my agents retrieve accurate data without noise.
The facts from each add operation are compared with existing facts, and Recalld then decides whether each one is an addition, an update or a replacement of existing facts.
Agents can retrieve context via two methods. The first is search, which is faster and cheaper and performs a vector search.
The second option is recall which uses an LLM to select relevant facts. In our published benchmark on LoCoMo benchmark run, it returned an average of about 243 context tokens per question. See the benchmarks for more details: https://github.com/bit-robotics/recalld-benchmarks
Recalld can be accessed via API or MCP. Benchmarks are public and can be rerun.
Recalld has a free tier plan with no card required. We offer two completely separate regions, EU and US, depending on where you want your data to be stored.
Recalld is hosted only and cannot be self-hosted yet
The facts from each add operation are compared with existing facts, and Recalld then decides whether each one is an addition, an update or a replacement of existing facts. Agents can retrieve context via two methods. The first is search, which is faster and cheaper and performs a vector search. The second option is recall which uses an LLM to select relevant facts. In our published benchmark on LoCoMo benchmark run, it returned an average of about 243 context tokens per question. See the benchmarks for more details: https://github.com/bit-robotics/recalld-benchmarks
Recalld can be accessed via API or MCP. Benchmarks are public and can be rerun.
Recalld has a free tier plan with no card required. We offer two completely separate regions, EU and US, depending on where you want your data to be stored.
Recalld is hosted only and cannot be self-hosted yet