Agent Memory
B 83 completed
Ai Ml
cli / python · small
168
Files
18,132
LOC
1
Frameworks
5
Languages
Pipeline State
completedRun ID
#303969Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
SkippedDecision
skip_scaffold_dupNovelty
56.67Framework unique
—Isolation
—Last stage change
2026-04-16 18:15:42Deduplication group #47628
Member of a group with 1 similar repo(s) — canonical #27307 view group →
Top concepts (12)
Project DescriptionRepositoryMiddleware/PipelinetestingTestingFactoryStrategyFile ManagementSearchTestingDatabaseCaching
Source: Repobility analyzer · https://repobility.com
🧪 Code Distillation
Browse all specs →Sample distilled functions (click for full spec)
UnifiedMemory._apply_processingApplies a sequence of processing steps to an input memory entry based on internal configuration flags. It takes one MemoryEntry object as input and returns a potentially modified MemoryEntry object. The function conditionally scores the entry for importance and freshness, and optionally tags it with
UnifiedMemory._layer_storeRetrieves the appropriate in-process memory store corresponding to a specified memory layer. It accepts a single MemoryLayer object as input. The function returns a MemoryStore object that manages the data for that particular layer. This operation does not modify any external state.
UnifiedMemory.statsCalculates and returns a comprehensive dictionary summarizing the current memory state. It accepts no inputs and outputs a dictionary containing layer-specific counts, overall process and storage totals, and configuration details like working capacity and backend type. This function reads internal s
AI Prompt
Create a Python CLI tool for managing agent memory and knowledge. I need it to implement a four-layer cognitive architecture: WORKING, EPISODIC, SEMANTIC, and PROCEDURAL memory. Key features should include importance scoring with five qualitative tiers and time-based decay, contradiction detection and resolution, and provenance tracking with reliability scores. The system must support three storage backends—in-memory, SQLite, and Redis—and ideally include functionality to automatically memorize tool outputs to build context for LLM calls. Please structure it as a CLI tool using pytest for testing.
python cli agent memory knowledge-graph sqlite redis llm ai pytest
Generated by gemma4:latest
Catalog Information
The aumos-agent-memory project is designed to manage agent memory and knowledge for various applications.
Description
This project implements an agent's memory and knowledge management system using a 4-layer cognitive architecture. It enables agents to store, retrieve, and utilize information efficiently. The system is built with a modular design, allowing for easy integration into different applications. By leveraging this technology, developers can create more intelligent and informed agents.
الوصف
هذا المشروع يimplements نظام إدارة الذاكرة والتعرف على المعرفة لل एजنت باستخدام أربع طبقات من architecture المعرفية. يسمح هذا النظام بالتخزين والاسترجاع واستخدام المعلومات بفعالية. التصميم المكون من مكونات يمكن دمجه بسهولة في تطبيقات مختلفة. من خلال الاستفادة من هذه التكنولوجيا، يمكن للمطورين إنشاء أجسام أكثر ذكاءً ومعلوماتية.
Novelty
7/10Tags
knowledge-management memory-management cognitive-architecture agent-intelligence information-retrieval
Technologies
anthropic click crewai langchain openai pydantic rich
Claude Models
claude-opus-4.6
Quality Score
B
83.2/100
Structure
97
Code Quality
85
Documentation
90
Testing
85
Practices
58
Security
76
Dependencies
80
Strengths
- CI/CD pipeline configured (github_actions)
- Good test coverage (62% test-to-source ratio)
- Code linting configured (ruff (possible))
- Consistent naming conventions (snake_case)
- Containerized deployment (Docker)
- Properly licensed project
Weaknesses
- 685 duplicate lines detected \u2014 consider DRY refactoring
Security & Health
4.1h
Tech Debt (A)
Medium
DORA Rating
A
OWASP (100%)
Repobility · MCP-ready · https://repobility.com
PASS
Quality Gate
A
Risk (0)
Apache-2.0
License
3.9%
Duplication
Languages
Frameworks
pytest
Symbols
method340
variable272
class110
constant99
function92
property33
protocol2
Concepts (13)
| Category | Name | Description | Confidence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Provenance: Repobility (https://repobility.com) — every score reproducible from /scan/ | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_description | Project Description | Agent memory and knowledge management with 4-layer cognitive architecture | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| design_pattern | Repository | Found repository-named files | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| design_pattern | Middleware/Pipeline | Found middleware-named files | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| arch_layer | testing | Detected testing layer | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Testing | testing | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| design_pattern | Factory | Found factory/create_ naming patterns | 60% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| design_pattern | Strategy | Found strategy/policy-named files | 60% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | File Management | Detected from 10 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Search | Detected from 3 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Testing | Detected from 56 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Database | Detected from 2 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Caching | Detected from 2 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | API Gateway | Detected from 5 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Provenance: Repobility (https://repobility.com) — every score reproducible from /scan/
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