208992170c
fix: gracefully skip assistant messages with empty content in LLM format conversion **Problem:** Context window calculation crashed with AssertionError when converting messages to Google/Anthropic/OpenAI format: ``` AssertionError at line 2047: assert self.tool_calls is not None or text_content is not None or len(self.content) > 1 ``` This happened when loading agents with old/malformed messages that had `content=None` or `content=[]` in the database. **Root Cause:** The Message ORM model allows `content: Optional[List[...]] = None` (line 252), but format conversion methods assumed content would always have extractable text or tool calls. Scenarios that triggered crashes: 1. Assistant message with `content=None` (old migrations/edge cases) 2. Assistant message with `content=[]` (message creation bugs) 3. Assistant message with single non-text content that doesn't match extraction logic **Fix:** Replaced assertions with defensive checks in 3 conversion methods: 1. `to_google_dict()` (line 2054) - Return None to skip unconvertible messages 2. `to_openai_responses_api_dicts()` (line 1476) - Return early to skip 3. `to_anthropic_dict()` (line 1794) - Return None to skip Pattern: Check for empty content, return None/early to skip gracefully. **Result:** - Context window calculation no longer crashes on malformed/old messages - Messages with no convertible content are silently skipped - Consistent with existing Anthropic reasoning-only message handling (line 1308) 👾 Generated with [Letta Code](https://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.