Kian Jones 6f746c5225 fix(core): handle Anthropic overloaded errors and Unicode encoding issues (#9305)
* fix: handle Anthropic overloaded_error in streaming interfaces

* fix: handle Unicode surrogates in OpenAI requests

Sanitize Unicode surrogate pairs before sending requests to OpenAI API.
Surrogate pairs (U+D800-U+DFFF) are UTF-16 encoding artifacts that cause
UnicodeEncodeError when encoding to UTF-8.

Fixes Datadog error: 'utf-8' codec can't encode character '\ud83c' in
position 326605: surrogates not allowed

* fix: handle UnicodeEncodeError from lone Unicode surrogates in OpenAI requests

Improved sanitize_unicode_surrogates() to explicitly filter out lone
surrogate characters (U+D800 to U+DFFF) which are invalid in UTF-8.

Previous implementation used errors='ignore' which could still fail in
edge cases. New approach directly checks Unicode code points and removes
any surrogates before data reaches httpx encoding.

Also added sanitization to stream_async_responses() method which was
missing it.

Fixes: 'utf-8' codec can't encode character '\ud83c' in position X:
surrogates not allowed
2026-02-24 10:52:06 -08:00
2024-12-10 19:20:27 -08:00
2025-04-21 08:43:29 -07:00
2024-12-27 11:28:00 +04:00
2024-07-04 14:45:35 -07:00
2024-10-11 15:51:14 -07:00
2024-11-06 23:00:17 -08:00
2025-05-13 15:32:09 -07:00
2026-01-18 13:50:17 -08:00

Letta logo

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+

  1. Install the Letta Code CLI tool: npm install -g @letta-ai/letta-code
  2. Run letta in 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!


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.

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