* feat: add tags support to blocks * fix: add timestamps and org scoping to blocks_tags Addresses PR feedback: 1. Migration: Added timestamps (created_at, updated_at), soft delete (is_deleted), audit fields (_created_by_id, _last_updated_by_id), and organization_id to blocks_tags table for filtering support. Follows SQLite baseline pattern (composite PK of block_id+tag, no separate id column) to avoid insert failures. 2. ORM: Relationship already correct with lazy="raise" to prevent implicit joins and passive_deletes=True for efficient CASCADE deletes. 3. Schema: Changed normalize_tags() from Any to dict for type safety. 4. SQLite: Added blocks_tags to SQLite baseline schema to prevent table-not-found errors. 5. Code: Updated all tag row inserts to include organization_id. 🐾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * fix: add ORM columns and update SQLite baseline for blocks_tags Fixes test failures (CompileError: Unconsumed column names: organization_id): 1. ORM: Added organization_id, timestamps, audit fields to BlocksTags ORM model to match database schema from migrations. 2. SQLite baseline: Added full column set to blocks_tags (organization_id, timestamps, audit fields) to match PostgreSQL schema. 3. Test: Added 'tags' to expected Block schema fields. This ensures SQLite and PostgreSQL have matching schemas and the ORM can consume all columns that the code inserts. 🐾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * revert change to existing alembic migration * fix: remove passive_deletes and SQLite support for blocks_tags 1. Removed passive_deletes=True from Block.tags relationship to match AgentsTags pattern (neither have ondelete CASCADE in DB schema). 2. Removed SQLite branch from _replace_block_pivot_rows_async since blocks_tags table is PostgreSQL-only (migration skips SQLite). 🐾 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * api sync --------- 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.
