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MemGPT

Research

78·Strong

Overall Trust Score

Memory-enhanced LLM agent system enabling long-term context and conversation memory. Implements virtual context management inspired by operating systems to overcome LLM context window limitations for stateful agents.

memory
long-context
open-source
Version: 0.x
Last Evaluated: November 9, 2025
Official Website →

Trust Vector

Performance & Reliability

79
memory management
88
Methodology
Memory system testing
Evidence
MemGPT Documentation
Hierarchical memory system with virtual context management
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
long context handling
90
Methodology
Long context testing
Evidence
Research Paper
Enables unlimited context through memory pagination
Date: 2023-10-13
Confidence: highLast verified: 2025-11-09
conversation continuity
85
Methodology
Continuity testing
Evidence
Memory Features
Maintains conversation state across long interactions
Date: 2024-10-15
Confidence: highLast verified: 2025-11-09
llm integration
82
Methodology
LLM integration testing
Evidence
Model Support
Supports OpenAI, Anthropic, local models via backends
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
self editing memory
76
Methodology
Self-editing capability testing
Evidence
Memory Editing
Agent can edit its own memory structures
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
latency
Value: 1-5s (memory overhead)
Methodology
Performance benchmarking
Evidence
Performance
Memory management adds overhead to LLM calls
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Security

75
memory isolation
78
Methodology
Isolation testing
Evidence
Architecture
Per-agent memory isolation in storage backend
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
self hosting
87
Methodology
Deployment security assessment
Evidence
Deployment
Full self-hosting with pip install or Docker
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
data persistence
80
Methodology
Data security assessment
Evidence
Storage
Configurable storage backends (PostgreSQL, SQLite)
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
open source
90
Methodology
Open source assessment
Evidence
GitHub
Apache 2.0 license, 12k+ stars, research-backed
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
memory security
68
Methodology
Memory security assessment
Evidence
Security Features
Memory security requires careful configuration
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Privacy & Compliance

78
memory privacy
80
Methodology
Privacy architecture review
Evidence
Memory Storage
Memory stored in configured database backend
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
gdpr compliance
77
Methodology
Compliance capabilities assessment
Evidence
Self-Hosted
GDPR compliance possible with self-hosted deployment
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
local deployment
90
Methodology
Deployment options assessment
Evidence
Installation
Complete local deployment with local LLMs supported
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
llm data sharing
72
Methodology
Data flow analysis
Evidence
LLM Integration
Memory context sent to configured LLM provider
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
memory deletion
76
Methodology
Data deletion assessment
Evidence
Memory Management
Memory can be deleted, archival system available
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09

Trust & Transparency

82
documentation quality
85
Methodology
Documentation completeness review
Evidence
Documentation
Good documentation with research paper backing
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
research backed
92
Methodology
Research foundation assessment
Evidence
Research Paper
Published research paper explaining memory system
Date: 2023-10-13
Confidence: highLast verified: 2025-11-09
memory visibility
80
Methodology
Transparency assessment
Evidence
Memory Inspection
Agent memory can be inspected and debugged
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
open source
88
Methodology
Open source assessment
Evidence
GitHub
Apache 2.0, 12k+ stars, active community
Date: 2024-10-20
Confidence: highLast verified: 2025-11-09
community support
76
Methodology
Community engagement analysis
Evidence
Community
Active Discord community and GitHub discussions
Date: 2024-10-15
Confidence: mediumLast verified: 2025-11-09

Operational Excellence

76
ease of integration
80
Methodology
Integration complexity assessment
Evidence
Python Package
Pip install with CLI and Python API
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
scalability
74
Methodology
Scalability testing
Evidence
Architecture
Scalability depends on database backend and infrastructure
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
cost predictability
88
Methodology
Pricing model analysis
Evidence
Pricing
Free Apache 2.0, costs for LLM API and storage
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09
monitoring
70
Methodology
Monitoring features assessment
Evidence
Monitoring Features
Basic logging, requires external monitoring tools
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
production readiness
72
Methodology
Production readiness assessment
Evidence
Maturity
Active development, production use requires careful setup
Date: 2024-10-01
Confidence: mediumLast verified: 2025-11-09
cli interface
82
Methodology
CLI capabilities assessment
Evidence
CLI
Feature-rich CLI for agent management
Date: 2024-10-01
Confidence: highLast verified: 2025-11-09

✨ Strengths

  • •Breakthrough memory system enabling unlimited conversation context
  • •Research-backed approach (published paper) with novel virtual context
  • •Open source (Apache 2.0) with active community (12k+ stars)
  • •Self-hosting option for complete memory control
  • •Supports multiple LLM providers and local models
  • •Enables truly stateful, personalized agent interactions

⚠️ Limitations

  • •Memory management adds latency overhead to interactions
  • •Requires database backend setup for persistence
  • •Complexity in managing and debugging agent memory
  • •Active development, some features still experimental
  • •Limited production tooling and monitoring features
  • •Memory pagination can introduce unpredictability

📊 Metadata

license: Apache 2.0
supported models:
0: OpenAI
1: Anthropic
2: Azure OpenAI
3: Local LLMs via Ollama
programming languages:
0: Python
deployment type: Self-hosted (pip, Docker) or MemGPT Cloud
tool support:
0: Memory management
1: Tool use
2: Custom functions
pricing model: Free open source (MemGPT Cloud available)
github stars: 12000+
first release: 2023
research paper: https://arxiv.org/abs/2310.08560
storage backends: PostgreSQL, SQLite, Chroma
rebranding: Now called Letta (formerly MemGPT)
pricing: Free (open source)

Use Case Ratings

customer support

88

Excellent for long-term customer relationships with memory

code generation

82

Good for maintaining context across coding sessions

research assistant

90

Excellent for long-form research with extensive context

data analysis

85

Good for maintaining analysis context over time

content creation

84

Good for maintaining style and context in long projects

education

92

Excellent for personalized tutoring with student history

healthcare

86

Good for maintaining patient context and history

financial analysis

80

Useful for maintaining client context and history

legal compliance

87

Excellent for maintaining context across long documents

creative writing

85

Good for maintaining creative context across sessions