Kian Jones 662ec082cf fix(core): handle MCP errors and API key whitespace (#9306)
* fix: strip whitespace from API keys in LLM client headers

Fixes httpx.LocalProtocolError when API keys contain leading/trailing whitespace.
Strips whitespace from API keys before using them in HTTP headers across:
- OpenAI client (openai.py)
- Mistral client (mistral.py)
- Anthropic client (anthropic_client.py)
- Anthropic schema provider (schemas/providers/anthropic.py)
- Google AI client (google_ai_client.py)
- Proxy helpers (proxy_helpers.py)

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

* fix: handle McpError gracefully in MCP client execute_tool

Return error as failed result instead of re-raising to avoid Datadog alerts for expected user-facing errors like missing tool arguments.

* fix: strip whitespace from API keys before passing to httpx client

Fixes httpx.LocalProtocolError by stripping leading/trailing whitespace
from API keys before passing them to OpenAI/AsyncOpenAI clients. The
OpenAI client library constructs Authorization headers internally, and
invalid header values (like keys with leading spaces) cause protocol
errors.

Applied fix to:
- azure_client.py (AzureOpenAI/AsyncAzureOpenAI)
- deepseek_client.py (OpenAI/AsyncOpenAI)
- openai_client.py (OpenAI/AsyncOpenAI via kwargs)
- xai_client.py (OpenAI/AsyncOpenAI)

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

* fix: handle JSONDecodeError in OpenAI client requests

Catches json.JSONDecodeError from OpenAI SDK when API returns invalid
JSON (typically HTML error pages from 500-series errors) and converts
to LLMServerError with helpful details.

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

* fix(core): strip API key whitespace at schema level on write/create

Add field_validator to ProviderCreate, ProviderUpdate, and ProviderCheck
schemas to strip whitespace from api_key and access_key fields before
persistence. This ensures keys are clean at the point of entry, preventing
whitespace from being encrypted and stored in the database.

Co-authored-by: Kian Jones <kianjones9@users.noreply.github.com>

* refactor: remove api_key.strip() calls across all LLM clients

Remove redundant .strip() calls on api_key parameters since pydantic models
now handle whitespace trimming at the validation layer. This centralizes
the validation logic and follows DRY principles.

- Updated 13 files across multiple LLM client implementations
- Removed 34 occurrences of api_key.strip()
- Includes: OpenAI, Anthropic, Azure, Google AI, Groq, XAI, DeepSeek, ZAI, Together, Mistral
- Also updated proxy helpers and provider schemas

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

* refactor: remove redundant ternary operators from api_key parameters

Remove `if api_key else None` ternaries since pydantic validation ensures
api_key is either a valid string or None. The ternary was defensive programming
that's now unnecessary with proper model-level validation.

- Simplified 23 occurrences across 7 files
- Cleaner, more concise client initialization code
- No behavioral change since pydantic already handles this

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

---------

Co-authored-by: Letta <noreply@letta.com>
Co-authored-by: letta-code <248085862+letta-code@users.noreply.github.com>
Co-authored-by: Kian Jones <kianjones9@users.noreply.github.com>
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

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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).

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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)

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