Letta
Open Source(Apache-2.0)
Memory-first agent
Open source
Best for developers
Self-improving AI
Model-agnostic
Build deeply personalized AI agents that remember, learn, and evolve.
Autonomous
Coding
Claude Sonnet 4.5
FREEMIUM
ADVANCED Complexity
HYBRID
Last updated: Apr 15, 2026
letta.com
Quick Answer
Letta Code is an open-source, memory-first AI agent platform that enables developers to build persistent, self-improving agents. It supports local execution, portable memory across various LLMs, and offers an API for application integration. Best for developers building production AI workflows and personalized agents. Available with a free plan and paid tiers.
Decision Intelligence
Best For
Building self-improving AI agents for personalized experiences
Developing memory-augmented coding agents for local tasks
Creating multi-agent systems with shared and archival memory
Integrating stateful AI agents into custom applications
Benchmarking and evaluating learning agents
Not Ideal For
Non-technical users seeking no-code agent solutions
Users who prefer fully managed, black-box AI services
Simple, stateless chatbot implementations
Users unwilling to manage their own LLM API keys
Teams without developer resources for integration and customization
Fit Score
Complexity
ADVANCED
Company Size
SOLO
SMALL
MEDIUM
ENTERPRISE
Team Size
INDIVIDUAL
SMALL_TEAM
LARGE_TEAM
Skill Level
EXPERT
About Letta
Letta Code is a memory-first AI agent platform for developers, enabling the creation of persistent, self-improving agents that learn over time. It offers local execution, portable memory across models, and an API for integrating agents into applications. Letta is open source and model-agnostic.
What Makes Letta Unique
Letta Code is designed as a memory-first agent, allowing for deeply personalized, persistent agents that learn and improve over time using background memory subagents. Its memory is transparent, modifiable, and portable across different LLM providers, a key differentiator from stateless LLM sessions.
Capabilities
Persistent memory management across sessions
Continuous learning and self-improvement
Memory portability between LLM models and providers
Local file system access and command execution
Programmatic tool calling and workflow generation
Multi-agent orchestration patterns
Context window management via memory blocks
Agent serialization using Agent File (.af)
Pros
- +Memory-first architecture enables truly persistent and learning agents
- +Model-agnostic, allowing users to connect their own LLM API keys
- +Open-source core project fosters transparency and community contributions
- +Portable agent memory and conversation histories across models and providers
- +Supports local execution via CLI and desktop app for privacy and control
- +Comprehensive SDKs for Python and TypeScript for easy integration
Limitations
- -Requires Node.js and npm for CLI installation, implying developer-centric usage
- -Desktop app currently only available for MacOS, limiting platform access
- -Server-side tool execution incurs credit costs, requiring budget management
- -Advanced features like programmatic tool calling and context engineering require technical expertise
- -No visual builder or low-code interface for non-developers
Frequently Asked Questions
Your Actions
Dimension Scores
Ease of Use4/10
Output Quality8/10
Value for Money7/10
Customization9/10
Support7/10
Integration8/10
Task Scores
Api Connectivity
9
Multi Step Chains
8
Autonomous Execution
8
Workflow Building
7
Action Library
7
Error Handling
6
Conditional Logic
6
Monitoring Logging
6
Trust Score
65/100
Fair
Based on 7 verified signals
Trust Signals
Verified CompanyLetta
Notable CustomersBilt Rewards, 11x, Hunt Club +1
Funding$10 million
Team Size1-10
Status PageView
GDPR CompliantYes
Data Residency
Switching & Migration
Migration Difficulty
Moderate Effort
Data portable as:
API
Email + Community Forum + Discord
Quick Stats
Agent TypeAutonomous
Memorypersistent
Planningadvanced
CategoryCoding
PlatformsWEB, DESKTOP, API
Views1
Reviews0
LaunchedSep 2024
Setup Time30 seconds
Status
active