Local-first TypeScript SDK
Shared semantic memory for AI agents
agentOrc gives multiple agents one persistent memory they can all write to and search — stored locally in SQLite, retrieved by meaning, with a tiny TypeScript API.
No Redis glue. No JSON scratch files. No custom locking. Just remember() and recall().
$ npm install agentorcPlanner writes · worker recalls · same shared memory
Why it exists
Agents forget. Shared state gets messy.
Most multi-agent setups store knowledge in process memory, JSON files, or a generic key-value store. That works until you need persistence, concurrency, and search by meaning — not just by key.
Without agentOrc
- Each agent keeps its own isolated context
- Developers invent shared state with globals or Redis
- JSON dumps become the source of truth
- No semantic search across what agents learned
- Concurrency and crash safety are DIY
With agentOrc
- One shared semantic memory for the whole system
- SQLite persistence with WAL and ACID writes
- Natural-language recall via embeddings
- A small, stable TypeScript API
- Runs locally with zero infrastructure
How it works
Write once. Recall by meaning.
The whole product fits in one loop: store text as vectors, keep the original, search later with natural language.
Step 1
Agent remembers
One agent calls remember() with a fact and optional metadata.
Step 2
Embedding generated
content.text is embedded through your OpenAI-compatible endpoint.
Step 3
Stored in SQLite + sqlite-vec
Original text, metadata, and vector land in a local ACID write.
Step 4
Another agent recalls
A different agent asks in natural language and gets ranked hits.
Core features
Built for real agent systems
Local-first
Everything lives in a SQLite file on disk. No hosted vector DB, no cloud dependency for the memory layer.
Semantic recall
Agents ask in natural language. agentOrc embeds the query and returns the closest memories by meaning.
Multi-agent by design
Many agents can write and read the same store safely. WAL mode and transactions handle concurrency.
SQLite + sqlite-vec
Durable storage with an in-process vector index. Back it up like any other database file.
Tiny API
remember, recall, compress, forget, history, stats. Easy to drop into an existing agent loop.
Zero infrastructure
No Redis cluster, no queue, no sidecar. Install the package and point it at a file path.
Install and go
One dependency. Point it at a SQLite path and an OpenAI-compatible embedding endpoint.
$ npm install agentorcRead the docs
API reference, configuration, concurrency notes, and guides for shared multi-agent memory.