Ari Webb 21765d16c9 fix(core): add OpenAI 24h prompt cache retention for supported models (#9509)
* fix(core): add OpenAI prompt cache key and model-gated 24h retention (#9492)

* fix(core): apply OpenAI prompt cache settings to request payloads

Set prompt_cache_key using agent and conversation context on both Responses and Chat Completions request builders, and enable 24h retention only for supported OpenAI models while excluding OpenRouter paths.

👾 Generated with [Letta Code](https://letta.com)

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

* fix(core): prefix prompt cache key with letta tag

Add a `letta:` prefix to generated OpenAI prompt_cache_key values so cache-related entries are easier to identify in provider-side logs and diagnostics.

👾 Generated with [Letta Code](https://letta.com)

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

* add integration test

* skip test

---------

Co-authored-by: Letta <noreply@letta.com>
Co-authored-by: Ari Webb <ari@letta.com>

* fix(core): only set prompt_cache_retention, drop prompt_cache_key

Two issues with the original prompt_cache_key approach:
1. Key exceeded 64-char max (agent-<uuid>:conv-<uuid> = 90 chars)
2. Setting an explicit key disrupted OpenAI's default prefix-hash
   routing, dropping cache hit rates from 40-45% to 10-13%

OpenAI's default routing (hash of first ~256 tokens) already provides
good cache affinity since each agent has a unique system prompt.
We only need prompt_cache_retention="24h" for extended retention.

Also fixes:
- Operator precedence bug in _supports_extended_prompt_cache_retention
- Removes incorrect gpt-5.2-codex exclusion (it IS supported per docs)

🐾 Generated with [Letta Code](https://letta.com)

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

---------

Co-authored-by: Charles Packer <packercharles@gmail.com>
Co-authored-by: Letta <noreply@letta.com>
2026-02-24 10:55:11 -08:00
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2026-02-24 10:55:11 -08:00
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2024-12-27 11:28:00 +04:00
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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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