Kian Jones b9c4ed3b15 fix: catch contextwindowexceeded error on gemini (#9450)
* 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.

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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.

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Co-Authored-By: Letta <noreply@letta.com>

* ci: add adapters/ test suite to core unit test matrix

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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.

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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.

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Co-Authored-By: Letta <noreply@letta.com>

---------

Co-authored-by: Letta <noreply@letta.com>
2026-02-24 10:52:07 -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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