Letta
Stateful agents with persistent, editable memory
You are leaving botconomy for the provider’s website. Link verified 9/21/2026.
What this product helps you do
Letta is designed around agents that keep state beyond a single context window. Its current agent harness uses a git-backed memory filesystem that agents can inspect and edit, supports tools and extensibility, and can run through desktop, CLI, web, local server, or cloud experiences. Developer plans also support API-driven workloads.
Problems it solves
- Agents lose useful context when a model context window is compacted
- Agent identity and learned preferences need to persist across conversations
- Developers need a consistent harness for tools, memory, and multi-step runs
- Users need local, application, and cloud paths for running the same agent concept
Letta builds stateful agents that retain memory, identity, tools, and conversation history across interactions. Its current MemFS system gives agents a git-backed memory filesystem they can inspect and edit, while initialization, explicit teaching, and background consolidation can update durable knowledge over time.
The current Letta experience spans desktop, CLI, web, local server, cloud, and developer interfaces. A free plan supports limited agents and bring-your-own model access, while personal, developer, team, and enterprise paths expand hosted usage, scale, and collaboration.
Information
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Letta builds stateful agents that retain memory, identity, tools, and conversation history across interactions. Its current MemFS system gives agents a git-backed memory filesystem they can inspect and edit, while initialization, explicit teaching, and background consolidation can update durable knowledge over time.
The current Letta experience spans desktop, CLI, web, local server, cloud, and developer interfaces. A free plan supports limited agents and bring-your-own model access, while personal, developer, team, and enterprise paths expand hosted usage, scale, and collaboration.
What this product helps you do
Letta is designed around agents that keep state beyond a single context window. Its current agent harness uses a git-backed memory filesystem that agents can inspect and edit, supports tools and extensibility, and can run through desktop, CLI, web, local server, or cloud experiences. Developer plans also support API-driven workloads.
Problems it solves
- Agents lose useful context when a model context window is compacted
- Agent identity and learned preferences need to persist across conversations
- Developers need a consistent harness for tools, memory, and multi-step runs
- Users need local, application, and cloud paths for running the same agent concept
Information
You are leaving botconomy for the provider’s website. Link verified 9/21/2026.
Related AI products
BabyAGI
BabyAGI is a free open-source experimental framework for registering, composing, executing, and monitoring functions, with dependency tracking, secrets, triggers, logs, a dashboard, and draft self-building agent examples.
Browser Use
Browser Use provides an open-source agent library plus hosted agents and cloud browsers for navigating sites, running browser tasks, reusing profiles, observing sessions, and integrating automation through APIs.
Haystack
Haystack is deepset's open-source Python framework for composing typed components into retrieval, generation, indexing, and agent pipelines that can use multiple model, database, and infrastructure providers.