Letta
An open-source platform for building stateful agents with persistent, editable, and external memory.
By Letta · reviewed 2026-08-15
What Letta does
Letta focuses on agents that maintain structured memory across long-running interactions. It provides local tools, an API, model-provider integrations, and development interfaces for inspecting and controlling agent state.
Pricing model
Free and open source; managed services available
Position in the stack
Where Letta fits
Letta belongs primarily in the agents automation layer. Letta focuses on agents that maintain structured memory across long-running interactions. It provides local tools, an API, model-provider integrations, and development interfaces for inspecting and controlling agent state. It should be evaluated as one part of a complete stack, because model files, inference providers, storage, identity, and external integrations remain separate operational choices.
Typical deployment flow
A practical sequence for evaluating Letta before making it part of a permanent stack.
- STEP 1
Confirm the deployment boundary
Choose among desktop-local, self-hosted, managed-cloud based on users, data sensitivity, network access, and who will operate updates.
- STEP 2
Validate the complete stack
Check Command line, Python package, Docker, Managed cloud, connected providers, supported formats, and exact hardware or accelerator compatibility before rollout.
- STEP 3
Run a representative workflow
Test stateful agents with inspectable memory, record versions and settings, then review security, backups, observability, and failure recovery.
Best for
- Stateful agents with inspectable memory
- Long-running personalized assistants
- Agent applications that need explicit memory control
Not the right layer for
- A simple one-off chat interface
- Serving foundation models without a provider
Capabilities
Capabilities refer to the tool's application layer. Hardware and model support can still depend on a connected inference engine.
Deployment and compatibility
- Deployment
- Desktop / localSelf-hostedManaged cloud
- Platforms
- macOS · Windows · Linux · Docker
- Hardware backends
- Uses connected model runtime
- Install methods
- Command line · Python package · Docker · Managed cloud
Models and integrations
- Model formats
- Uses connected model runtime
- Common integrations
- OpenAI-compatible APIsOllamaModel providersAgent tools
Strengths
- Memory-first agent architecture
- Inspectable agent state
- Model-provider flexibility
Limitations
- Agent quality depends on model and memory design
- Persistent agents introduce data governance requirements
Planning checklist
Before you choose Letta
Answer these questions with the exact models, hardware, users, and data you expect to operate.
- Does Letta support the exact model, provider, data source, and operating system required by the workflow?
- Can the available hardware and memory handle the selected models, context, concurrency, and runtime overhead?
- Do the Apache-2.0 terms fit internal use, modification, redistribution, and any commercial service being planned?
- Who will own upgrades, credentials, backups, monitoring, and recovery when this tool becomes part of a real workflow?
Letta FAQ
What layer does Letta replace?
Letta primarily covers agents automation. It does not automatically replace every model runtime, application, storage service, or infrastructure dependency connected to that layer.
Can Letta run entirely locally?
Yes, a local or self-hosted path is available. Privacy still depends on the model providers, connectors, telemetry, and external tools that you enable.
What should be tested before adopting Letta?
Use the exact models, documents, integrations, hardware, concurrency, and security boundary expected in production. Feature lists and public benchmarks cannot validate that complete combination.
Official sources
Use these links to confirm current compatibility and installation requirements.
Related tools
Execution evidence
Known working recipes using Letta
Recipes connect hardware, a model artifact, tools, settings, verification, and a reportable result.
No verified recipe is linked to this record yet.
Compatibility estimates remain available in the planner. A recipe appears here only after its exact stack and verification protocol are documented.