Agent memory and knowledge management with 4-layer cognitive architecture
Project description
agent-memory
Agent memory and knowledge management with 4-layer cognitive architecture
Part of the AumOS open-source agent infrastructure portfolio.
Features
- Four-layer cognitive memory model —
WORKING(active context),EPISODIC(events),SEMANTIC(facts and concepts), andPROCEDURAL(how-to knowledge) — each with its own storage, retrieval, and eviction semantics - Importance scoring with five qualitative tiers (
CRITICALthroughTRIVIAL) and a numeric[0, 1]score, plus time-based decay that reduces importance automatically as memories age - Contradiction detection and resolution:
ContradictionDetectoridentifies conflictingMemoryEntrypairs andContradictionResolverapplies configurable resolution strategies - Provenance tracking records the source and trust level of every memory item, with
reliabilityscores that factor into retrieval ranking - Three storage backends — in-memory, SQLite, and Redis — all behind the
MemoryStoreABC;UnifiedMemoryprovides a single facade across all four layers AutoMemorizeMiddlewareandContextBuilderwork together to automatically memorize tool outputs and construct retrieval-augmented context windows for LLM calls- Freshness validation and forced refresh policies prevent stale semantic memories from being surfaced in time-sensitive retrieval queries
Quick Start
Install from PyPI:
pip install agent-memory
Verify the installation:
agent-memory version
Basic usage:
import agent_memory
# See examples/01_quickstart.py for a working example
Documentation
Enterprise Upgrade
For production deployments requiring SLA-backed support and advanced integrations, contact the maintainers or see the commercial extensions documentation.
Contributing
Contributions are welcome. Please read CONTRIBUTING.md before opening a pull request.
License
Apache 2.0 — see LICENSE for full terms.
Part of AumOS — open-source agent infrastructure.
Project details
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