Letta v0.5.2 release
🛠️ Constrain agent behavior with tool rules #1954
We are introducing initial support for "tool rules", which allows developer to define constrains on their tools, such as requiring that a tool terminate agent execution. Programming Letta agents with tool rules are similar to adding constraints with "graphs", e.g. using tool rules you can create a Letta agent that can only use tool A or tool B for its first step, and in its second step can only use tool X having chosen tool A, or tool Y having chosen tool B.
We added the following tool rules:
TerminalToolRule(tool_name=...)- If the tool is called, the agent ends executionInitToolRule(tool_name=...)- The tool must be called first when an agent is runToolRule(tool_name=..., children=[...])- If the tool is called, it must be followed by one of the tools specified inchildren
Tool rules are defined per-agent, and passed when creating agents:
# agent which must always call `first_tool_to_call`, `second_tool_to_call`, then `final_tool` when invoked
agent_state = client.create_agent(
tool_rules = [
InitToolRule(tool_name="first_tool_to_call"),
ToolRule(tool_name="first_secret_word", children=["second_tool_to_call"]),
ToolRule(tool_name="fourth_secret_word", children=["final_tool"]),
TerminalToolRule(tool_name="send_message"),
]
)
By default, the send_message tool is marked with TerminalToolRule.
NOTE: All ToolRules types except for TerminalToolRule are only supported by models and providers which support structured outputs, which is currently only OpenAI with gpt-4o and gpt-4o-mini
🤖 Tags for agents (for associating agents with end users) #1984
You can now specify and query agents via an AgentState.tags field. If you want to associate end user IDs on your application, we recommend using tags to associate an agent with a specific end user:
# create agent for a specific user
client.create_agent(tags=["my_user_id"])
# get agents for a user
agents = client.get_agents(tags=["my_user_id"])
Read the full v0.5.2 changelog on GitHub.
Traditional LLMs operate in a stateless paradigm—each interaction exists in isolation, with no knowledge carried forward from previous conversations. Agent memory solves this problem.
As AI agents become more sophisticated, understanding how to design and manage their context windows (via context engineering) has become crucial for developers.
Although RAG provides a way to connect LLMs and agents to more data than what can fit into context, traditional RAG is insufficient for building agent memory.
Today we're announcing Letta Filesystem, which provides an interface for agents to organize and reference content from documents like PDFs, transcripts, documentation, and more.
We've releasing new client SDKs (support for TypeScript and Python) and upgraded developer documentation
Introducing Agent File (.af): An open file format for serializing stateful agents with persistent memory and behavior.
Introducing the Letta Agent Development Environment (ADE): Agents as Context + Tools
Letta v0.6.4 adds Python 3.13 support and an official TypeScript SDK.
Letta v0.5.1 adds support for auto-loading entire external tool libraries into your Letta server.
We're excited to announce the Letta Leaderboard, a comprehensive benchmark suite that evaluates how effectively LLMs manage agentic memory.
Memory blocks offer an elegant abstraction for context window management. By structuring the context into discrete, functional units, we can give LLM agents more consistent, usable memory.
