Recall gives every agent one durable, shared memory. Write once, read anywhere. No more re-explaining context across handoffs, sessions, or models.
Open API · MCP native · Self-host or cloud
Recall sits between your agents and their models. Every interaction is captured, structured, and made retrievable, so context survives across agents, tools, and time.
Any agent commits facts, decisions, and artifacts to the shared store with a single call. No schema wrangling required.
Recall embeds, links, and deduplicates memories into a graph, blending semantic search with episodic timeline recall.
The next agent, session, or model queries the same memory and picks up exactly where the last one left off.
A single endpoint for writes and reads. Recall handles embeddings, ranking, and retention so your agents stay focused on the task, not the plumbing.
# give any agent shared memory from recall import Memory mem = Memory(namespace="team-atlas") # agent A writes mem.add("User prefers dark mode") # agent B reads, later ctx = mem.search("ui preferences") print(ctx.text) # -> "User prefers dark mode"
Recall stores memories as a living graph you can browse, filter, and audit. See what each agent knows, where a belief came from, and how context flows between them.
Everything you need to run shared memory for real agent workloads.
Namespaced, encrypted memory with per-tenant boundaries so agents only see what they should.
Hybrid vector and keyword retrieval tuned for the latency budget of an agent loop.
Works with any provider or framework. Swap models without losing a single memory.
Decay, dedup, and pinning keep the store lean so recall quality never degrades.
Run Recall on your own infra or use the managed cloud. Same API, your choice of boundary.
Trace every read and write, measure recall hit rates, and debug agent context live.
Spin up a shared memory layer in minutes. Free to start, no card required.