atomicmemory-python

Python client SDK for AtomicMemory memory and artifact storage.

Docs: docs.atomicstrata.ai

AtomicMemory Core currently reaches cost-Pareto SOTA on BEAM-100K, BEAM-1M, and LoCoMo10, with BEAM-10M parity against the strongest published Mem0-new result. This package brings that memory layer to Python services, agents, notebooks, and evaluation workflows.

A backend-agnostic memory and storage client: ingest conversations and documents, search them semantically, package retrieval-ready context, register or upload raw artifacts, and access AtomicMemory-specific features (lifecycle, audit, lessons, agents/trust, runtime config) through typed namespace handles.

This is a Python port of the TypeScript atomicmemory-sdk. It mirrors the public surface 1:1 while staying idiomatic to Python (Pydantic models, httpx sync + async clients, match statements, snake_case).

Status

Stable releases are available on PyPI. This source tree prepares version 1.1.3; consult PyPI for publication status.

Installation

pip install atomicmemory                    # core + local search + SQLite store
pip install 'atomicmemory[embeddings]'      # + sentence-transformers for local embeddings

Quick start

Prerequisite: start atomicmemory-core first. Follow the Core Quickstart if you do not already have a backend at http://localhost:17350.

from atomicmemory import AtomicMemoryClient

with AtomicMemoryClient({
    "apiUrl": "http://localhost:17350",
    "apiKey": "server-api-key",
    "userId": "demo",
}) as client:
    client.memory.initialize()

    client.memory.ingest({
        "mode": "messages",
        "messages": [
            {"role": "user", "content": "I prefer aisle seats on flights."},
        ],
        "scope": {"user": "demo"},
    })

    page = client.memory.search({"query": "seat preference", "scope": {"user": "demo"}})
    for hit in page.results:
        print(hit.memory.content, hit.score)

    artifact = client.storage.put({
        "mode": "pointer",
        "uri": "https://example.com/manual.pdf",
        "contentType": "application/pdf",
    })
    print(artifact.artifact_id)

Async usage

import asyncio
from atomicmemory import AsyncAtomicMemoryClient

async def main() -> None:
    async with AsyncAtomicMemoryClient({
        "apiUrl": "http://localhost:17350",
        "apiKey": "server-api-key",
        "userId": "demo",
    }) as client:
        await client.memory.initialize()
        results = await client.memory.search({"query": "seat preference", "scope": {"user": "demo"}})
        for hit in results.results:
            print(hit.memory.content)

asyncio.run(main())

AtomicMemory-specific features

When configured with the atomicmemory provider, the client exposes a typed handle for backend-specific routes:

trail = client.memory.atomicmemory.audit.trail(memory_id="mem-123", user_id="demo")
health = client.memory.atomicmemory.config.health()

Categories: lifecycle, audit, lessons, config, agents.

Memory providers

The memory namespace supports the same provider family as the TypeScript SDK:

• atomicmemory — AtomicMemory core backend.
• mem0 — Mem0 OSS or hosted backend.
• hindsight — Hindsight Cloud or self-hosted backend.

from atomicmemory import MemoryClient

with MemoryClient(
    providers={
        "hindsight": {
            "apiUrl": "http://localhost:8888",
            "apiVersion": "v1",
            "projectId": "default",
        }
    }
) as memory:
    memory.initialize()
    page = memory.search({"query": "seat preference", "scope": {"user": "demo"}})

Artifact storage

The client.storage namespace mirrors the TypeScript SDK's direct storage API:

• capabilities() reports active backend support.
• put({"mode": "pointer", ...}) registers a pointer to caller-owned bytes.
• put({"mode": "managed", "body": b"...", ...}) uploads known-length bytes to the configured raw content store.
• get, get_content, head, delete, and verify address artifacts by artifact_id.
• stream_content streams large artifact bodies without buffering the entire response in memory.

Every storage request sends Authorization: Bearer <apiKey> and X-AtomicMemory-User-Id. The SDK never sends the legacy ?user_id= URL parameter.

Entities

The client.entities namespace (on AtomicMemoryClient and AsyncAtomicMemoryClient) provides typed access to the /v1/entities API — profiles, attributes, memory history, settings, and entity merge.

from atomicmemory import AtomicMemoryClient

with AtomicMemoryClient({
    "apiUrl": "http://localhost:17350",
    "apiKey": "server-api-key",
    "userId": "demo",
}) as client:
    # fetch the synthesized profile for a user
    profile = client.entities.profile("alice")
    print(profile.entity_id, profile.summary)

    # list all entities (paginated)
    result = client.entities.list(page=1, page_size=20)
    for entity in result.entities:
        print(entity.entity_id, entity.memory_count)

The async surface is identical — call await client.entities.profile("alice") on AsyncAtomicMemoryClient.

Memory pipelines

MemoryProcessingPipeline (and its async twin AsyncMemoryProcessingPipeline) let you attach optional pre- and post-processing hooks to any registered provider. All hook fields are None by default, so a pipeline with only one hook populated is valid.

from atomicmemory import AtomicMemoryClient
from atomicmemory.memory.pipeline import MemoryProcessingPipeline
from atomicmemory.memory.registry import ProviderRegistration, default_registry

def split_long_content(input):
    # return a list of IngestInput items; here we pass through unchanged
    return [input]

def log_ingest_result(result, original_input):
    print(f"ingested: {len(result.created)} created, {len(result.updated)} updated")

pipeline = MemoryProcessingPipeline(
    preprocess_ingest=split_long_content,   # optional — splits one input into many
    postprocess_ingest=log_ingest_result,   # optional — runs after each per-item ingest
)

# Register the pipeline alongside a provider factory
def my_provider_factory(config):
    from atomicmemory.memory.provider import BaseMemoryProvider
    # ... build and return your provider ...
    provider = ...
    return ProviderRegistration(provider=provider, pipeline=pipeline)

default_registry.register("my_provider", my_provider_factory)

If preprocess_ingest splits one input into N items and a per-item ingest raises mid-loop, earlier items are already persisted and no merged result is returned — keep splitting pipelines idempotent.

v1 wire contract

atomicmemory.contract.v1 is the wire codec for the v1 provider-contract encoding. The wire form is deliberately mixed-case — Memory.createdAt/updatedAt and SearchResult.rankingScore are camelCase; version_id, observed_at, and retrieval-receipt fields are snake_case — as pinned by the vendored contract/CONTRACT.md. This module is the only place that mapping lives; in-process models and provider mappers are unchanged.

from atomicmemory.contract import v1

# decode a wire search response (e.g. from a cross-SDK provider call)
wire_page = {
    "results": [
        {
            "memory": {
                "id": "mem_1",
                "content": "I prefer aisle seats on flights.",
                "scope": {"user": "demo"},
                "kind": "fact",
                "createdAt": "2026-05-30T12:00:00.000Z",
            },
            "score": 0.91,
            "rankingScore": 0.87,
        }
    ],
    "retrieval": {
        "embedding_model": "text-embedding-x",
        "embedding_model_version": "1",
        "embedding_dimensions": 1536,
        "query_text": "deploy gate",
        "candidate_ids": ["mem_1"],
        "trace_id": "trace-1",
    },
}

page = v1.decode_search_result_page(wire_page)
for hit in page.results:
    print(hit.memory.content, hit.score)  # snake_case in-process models

# re-encode to the exact v1 wire form (millisecond-precision UTC datetimes)
wire_out = v1.encode_search_result_page(page)

Two behaviors to know: naive datetimes passed to encode functions are assumed UTC (bare astimezone() would shift by the host's UTC offset); encode_ingest_input rejects models carrying content_class with a clear error because the v1 schemas have additionalProperties: false and no such field — this is a Python-ahead field pending TS contract alignment.

This is NOT the AtomicMemory core HTTP API. That boundary stays in the provider mappers. The import path is atomicmemory.contract — deliberately not re-exported from the package root to keep the root namespace focused on the core provider API.

Development

uv sync --extra dev --extra embeddings
uv run pytest
uv run ruff check .
uv run ruff format --check .
uv run mypy atomicmemory --strict
uv run vulture atomicmemory tests .vulture_whitelist.py --min-confidence 90

Live provider smoke tests

Live provider tests are opt-in and are not required for normal development. They assume the backend is already running and configured with its own model.

ATOMICMEMORY_HINDSIGHT_INTEGRATION=1 \
HINDSIGHT_API_URL=http://localhost:8890 \
HINDSIGHT_TIMEOUT_SECONDS=120 \
uv run pytest tests/providers/hindsight/test_integration.py -m integration -ra

License

Apache-2.0