p-memory

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An embedded memory library: durable memories, a knowledge graph, notes, and retrieval in one local directory, usable from Rust and Python.

p-memory embeds into your application; there is no separate service to run. Open a data directory from Rust or Python and it gives you the whole path — write, index, filter, recall — with no external database and nothing leaving your machine.

Quick start

use p_memory::{KnowledgeBase, MemoryInput, SearchRequest};

fn main() -> p_memory::Result<()> {
    let kb = KnowledgeBase::open("./data/app")?;
    kb.memories().upsert(MemoryInput::new("User prefers unsweetened tea"))?;
    let hits = kb.search(&SearchRequest { query: "unsweetened tea".into(), ..Default::default() })?;
    println!("{:?}", hits);
    kb.close()
}

from p_memory import KnowledgeBase

with KnowledgeBase("./data/app") as kb:
    kb.memories.upsert_by_judgment(judgment="User prefers unsweetened tea")
    print(kb.search("unsweetened tea")["hits"])

Features

• Changing the embedding model doesn't break the database. Register a new embedding space for the new model; the old data stays where it is, with no full rebuild.
• As small as possible. Text lives in normalized tables — one shared string dictionary, tag links as plain integer ids, nothing stored twice. For personal scale, keeping the store minimal is a deliberate goal.
• Memory, graph, and notes in a single search. They share one index, so one query returns all three, fused with RRF.
• Fast. For LLM memory, responsiveness comes first: local, single-file, no network round trips.
• Vectorization and reranking live inside. The host registers an embedder (one model = one space) and a reranker, then hands over content and search terms; the library batches, truncates, retries, and degrades on its own.

Try the demo

One small set of memories and notes, an offline embedder and reranker, and one search — no keys, no network, reproducible. Vectors and reranking both happen inside the library:

cargo run --example minimal_demo
python -m p_memory.demo

Both print the hit count, which paths ran, and whether reranking truncated. The same fixture is the end-to-end acceptance sample for this interface.

Memory, the graph, and notes

Memory — holds both loose fragments and structured user profiles, and the two coexist. It also scales to a chat room of a thousand people: each person's memory stays its own, with no bleed between them.

The graph — entities, relations, and events. Find an entity by name, look at its neighborhood, or trace the shortest way two entities are connected.

Notes — documents chunked by paragraph; each chunk keeps only a line and character range into the note, not its own copy of the text. The searchable text lives in the full-text index.

Isolation — memories, the graph, and notes all support separation by domain and by agent. One store can serve several domains and several agents at once, each seeing only its own.

How they combine — the three are wired together: tag a memory and that tag can be an entity in the graph, so the memory and the entity point at the same person or thing and can be walked between without building a link by hand. Retrieval is undivided too — one query returns memories, the graph, and notes together.

Every host application uses its own data directory. p-memory ships the library and the import tooling; wiring it into P-ai, astrbot_plugin_angel_memory, or another host happens in that host's repository.

Rust

[dependencies]
p-memory = "0.2"

Graph search is built in and needs no feature flags:

use p_memory::{KnowledgeBase, ReadFilter};

let kb = KnowledgeBase::open("./data/rust-app")?;
let filter = ReadFilter { namespace: "default".into(), scopes: vec!["public".into()], tags: vec![] };

let alice = kb.graph().resolve("Alice", &filter, 1)?[0].header.id;
let bob   = kb.graph().resolve("Bob",   &filter, 1)?[0].header.id;

let ring  = kb.graph().ego(alice, 2, &filter, 50)?;   // entities within two hops
let chain = kb.graph().path(alice, bob, &filter)?;    // shortest bridge between two entities

The Python binding exposes the same capabilities: ego / path / strongly_connected / component_count.

Python

CPython 3.10+. Prebuilt wheels for Windows, Linux and macOS need no Rust; building from source needs Rust 1.88+ and a C/C++ toolchain.

python -m pip install p-memory

from p_memory import KnowledgeBase

with KnowledgeBase("./data/python-app") as kb:
    receipt = kb.memories.upsert_by_judgment(
        judgment="User prefers unsweetened tea", tags=["preference"]
    )
    print(receipt["value"]["id"])
    print(kb.search("unsweetened tea")["hits"])

Async hosts use async with AsyncKnowledgeBase(...) as kb, then await kb.memories.upsert(...) / await kb.search(...). Operations run on a worker thread and release the GIL; cancelling a wait cannot roll back a write that has already started.

Simple agent

# Model name and endpoint come from your provider. The API key is read from the environment only.
$env:OPENAI_BASE_URL = "http://localhost:8000/v1"
$env:P_MEMORY_MODEL = "your-tool-calling-model"
# Set OPENAI_API_KEY if the endpoint requires authentication.

p-memory chat --data ./data/agent --prompt "Remember: I like unsweetened tea" --trace
p-memory chat --data ./data/agent --prompt "What do I like to drink?" --trace
p-memory chat --data ./data/agent

Uses Chat Completions tools / tool_calls, with custom endpoint, model, timeout, round cap, read-only tool mode, and multi-tool replies. BM25 is the default; --embedding-model and --embedding-dimension enable semantic retrieval. Implementation: agent.py.

Importing existing data

p-memory import --source p_ai --source-id my-host `
  --database ./backup/memory_store.db --destination ./data/imported
# Review the report, then add --apply. The default is a dry run that creates no destination.

Supports importing snapshots from other memory systems; run p-memory import --help for the available sources. Original fields, lifecycle, scopes, and stable ID mappings are preserved; the source database is read-only. Graph lookup and note roots can be set explicitly. Old vector / FAISS / Tantivy caches are rebuilt; register an embedder afterward and run embeddings.sync to fill vectors — the import itself stays offline.

Documentation and testing

• Overview · Usage
• Architecture · Data model
• Rust API · Python API
• Search · Lifecycle
• Graph generation · Graph search
• Performance
• Examples

cargo test
python -m pytest

Apache-2.0. See LICENSE.