* catch contextwindowexceeded error * fix(core): detect Google token limit errors as ContextWindowExceededError Google's error message says "input token count exceeds the maximum number of tokens allowed" which doesn't contain the word "context", so it was falling through to generic LLMBadRequestError instead of ContextWindowExceededError. This means compaction won't auto-trigger. Expands the detection to also match "token count" and "tokens allowed" in addition to the existing "context" keyword. 🐾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix(core): add missing message arg to LLMBadRequestError in OpenAI client The generic 400 path in handle_llm_error was constructing LLMBadRequestError without the required message positional arg, causing TypeError in prod during summarization. 🐾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * ci: add adapters/ test suite to core unit test matrix 🐾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix(tests): update adapter error handling test expectations to match actual behavior The streaming adapter's error handling double-wraps errors: the AnthropicStreamingInterface calls handle_llm_error first, then the adapter catches the result and calls handle_llm_error again, which falls through to the base class LLMError. Updated test expectations to match this behavior. 🐾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix(core): prevent double-wrapping of LLMError in stream adapter The AnthropicStreamingInterface.process() already transforms raw provider errors into LLMError subtypes via handle_llm_error. The adapter was catching the result and calling handle_llm_error again, which didn't recognize the already-transformed LLMError and wrapped it in a generic LLMError("Unhandled LLM error"). This downgraded specific error types (LLMConnectionError, LLMServerError, etc.) and broke retry logic that matches on specific subtypes. Now the adapter checks if the error is already an LLMError and re-raises it as-is. Tests restored to original correct expectations. 🐾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> --------- Co-authored-by: Letta <noreply@letta.com>
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.
