ddfa922cde
* fix(core): prevent event loop saturation from ClickHouse and socket trace writes Two issues were causing the event loop watchdog to fire and liveness probes to fail under load: 1. LLMTraceWriter held an asyncio.Lock across each ClickHouse write, and wait_for_async_insert=1 meant each write held that lock for ~1s. Under high request volume, N background tasks all queued for the lock simultaneously, saturating the event loop with task management overhead. Fix: switch to wait_for_async_insert=0 (ClickHouse async_insert handles server-side batching — no acknowledgment wait needed) and remove the lock (clickhouse_connect uses a thread-safe connection pool). The sync insert still runs in asyncio.to_thread so it never blocks the event loop. No traces are dropped. 2. SocketProviderTraceBackend spawned one OS thread per trace with a 60s socket timeout. During crouton restarts, threads accumulated blocking on sock.sendall for up to 3 minutes each (3 retries x 60s). Fix: reduce socket timeout from 60s to 5s — the socket is local (Unix socket), so 5s is already generous, and fast failure lets retries resolve before threads pile up. Root cause analysis: event_loop_watchdog.py was detecting saturation (lag >2s) every ~60s on gke-letta-default-pool-c6915745-fmq6 via thread dumps. The saturated event loop caused k8s liveness probes to time out, triggering restarts. * chore(core): sync socket backend with main and document ClickHouse thread safety
Letta (formerly MemGPT)
Letta is the platform for building stateful agents: AI with advanced memory that can learn and self-improve over time.
- Letta Code: run agents locally in your terminal
- Letta API: build agents into your applications
Get started in the CLI
Requires Node.js 18+
- Install the Letta Code CLI tool:
npm install -g @letta-ai/letta-code - Run
lettain your terminal to launch an agent with memory running on your local computer
When running the CLI tool, your agent help you code and do any task you can do on your computer.
Letta Code supports skills and subagents, and bundles pre-built skills/subagents for advanced memory and continual learning. Letta is fully model-agnostic, though we recommend Opus 4.5 and GPT-5.2 for best performance (see our model leaderboard for our rankings).
Get started with the Letta API
Use the Letta API to integrate stateful agents into your own applications. Letta has a full-featured agents API, and a Python and Typescript SDK (view our API reference).
Installation
TypeScript / Node.js:
npm install @letta-ai/letta-client
Python:
pip install letta-client
Hello World example
Below is a quick example of creating a stateful agent and sending it a message (requires a Letta API key). See the full quickstart guide for complete documentation.
TypeScript:
import Letta from "@letta-ai/letta-client";
const client = new Letta({ apiKey: process.env.LETTA_API_KEY });
// Create your agent
const agentState = await client.agents.create({
model: "openai/gpt-5.2",
memory_blocks: [
{
label: "human",
value:
"Name: Timber. Status: dog. Occupation: building Letta, infrastructure to democratize self-improving superintelligence",
},
{
label: "persona",
value:
"I am a self-improving superintelligence. Timber is my best friend and collaborator.",
},
],
tools: ["web_search", "fetch_webpage"],
});
console.log("Agent created with ID:", agentState.id);
// Send your agent a message
const response = await client.agents.messages.create(agentState.id, {
input: "What do you know about me?",
});
for (const message of response.messages) {
console.log(message);
}
Python:
from letta_client import Letta
import os
client = Letta(api_key=os.getenv("LETTA_API_KEY"))
# Create your agent
agent_state = client.agents.create(
model="openai/gpt-5.2",
memory_blocks=[
{
"label": "human",
"value": "Name: Timber. Status: dog. Occupation: building Letta, infrastructure to democratize self-improving superintelligence"
},
{
"label": "persona",
"value": "I am a self-improving superintelligence. Timber is my best friend and collaborator."
}
],
tools=["web_search", "fetch_webpage"]
)
print(f"Agent created with ID: {agent_state.id}")
# Send your agent a message
response = client.agents.messages.create(
agent_id=agent_state.id,
input="What do you know about me?"
)
for message in response.messages:
print(message)
Contributing
Letta is an open source project built by over a hundred contributors from around the world. There are many ways to get involved in the Letta OSS project!
- Join the Discord: Chat with the Letta devs and other AI developers.
- Chat on our forum: If you're not into Discord, check out our developer forum.
- Follow our socials: Twitter/X, LinkedIn, YouTube
Legal notices: By using Letta and related Letta services (such as the Letta endpoint or hosted service), you are agreeing to our privacy policy and terms of service.