letta-ai/letta
letta
Platform for stateful agents: AI with advanced memory that can learn and self-improve over time.
Usage guide
letta is an open-source project around ai-agents, llm, llm-agent with 23,560 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in Python, useful for judging integration effort in a similar stack.
- GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating letta for Python AI workflows.
- Comparing a GitHub project with 23,560 stars and current repository activity.
Pros
- letta has visible GitHub traction with 23,560 stars. Topics: ai, ai-agents, llm.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the Apache-2.0 terms fit your use case.
Production readiness
letta should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
Apache-2.0 is reported by GitHub; review the repository license before redistribution or commercial use.
letta architecture preview
letta's main path starts at the entry surface, runs through Coding agent runtime, combines Optional AI model, Runtime context, APIs / webhooks / Shell commands, and returns Assistant response / action result.
Entry
CLI / terminal entry
letta is primarily entered through a developer command or terminal workflow.
npm install @letta-ai/letta-client
Runtime
Coding agent runtime
The runtime reads developer intent, inspects repository context, plans edits, and returns code-oriented actions.
coding workflow
Model
Optional AI model
The project connects its core runtime to local models or hosted AI APIs when model inference is required.
model signal
Context
Runtime context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Tools
APIs / webhooks / Shell commands
Tool adapters let the runtime act outside the model through APIs / webhooks / Shell commands.
APIs / webhooks, Shell commands
Output
Assistant response / action result
The final result is a response, action, or task completion returned through the active channel.
assistant output
Featured video
Letta
Letta Code: A Memory-First Coding Agent (#1 OSS on Terminal-Bench)
15,099 views ยท 2025-12-16
Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- Node.js and the package manager used by the project
- A clean working directory for the first test run
Check the runtime environment
letta depends on a Python-style environment. Use venv, conda, or a container to keep dependencies isolated.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/letta-ai/letta.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ npm install @letta-ai/letta-clientAdoption guidance and sources
Practical use cases
Platform for stateful agents: AI with advanced memory that can learn a
This is one of the documented reasons to evaluate letta before choosing a stack.
Focus area: ai
This is one of the documented reasons to evaluate letta before choosing a stack.
All project comparison
Compare letta with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official letta setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented letta example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is letta?
letta is an open-source all project. Platform for stateful agents: AI with advanced memory that can learn and self-improve over time.
How do I install letta?
Start with the official README. The first detected setup step is: git clone https://github.com/letta-ai/letta.git.
Is letta beginner-friendly?
If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can letta be used commercially?
GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does letta need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt letta?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.