jnjpng 39b25a0e3c fix: update ContextWindowCalculator to parse new system message sections (#9398)
* fix: update ContextWindowCalculator to parse new system message sections

The context window calculator was using outdated position-based parsing
that only handled 3 sections (base_instructions, memory_blocks, memory_metadata).
The actual system message now includes additional sections that were not
being tracked:

- <memory_filesystem> (git-enabled agents)
- <tool_usage_rules> (when tool rules configured)
- <directories> (when sources attached)

Changes:
- Add _extract_tag_content() helper for proper XML tag extraction
- Rewrite extract_system_components() to return a Dict with all 6 sections
- Update calculate_context_window() to count tokens for new sections
- Add new fields to ContextWindowOverview schema with backward-compatible defaults
- Add unit tests for the extraction logic

* update

* generate

* fix: check attached file in directories section instead of core_memory

Files are rendered inside <directories> tags, not <memory_blocks>.
Update validate_context_window_overview assertions accordingly.

* fix: address review feedback for context window parser

- Fix git-enabled agents regression: capture bare file blocks
  (e.g. <system/human.md>) rendered after </memory_filesystem> as
  core_memory via new _extract_git_core_memory() method
- Make _extract_top_level_tag robust: scan all occurrences to find
  tag outside container, handling nested-first + top-level-later case
- Document system_prompt tag inconsistency in docstring
- Add TODO to base_agent.py extract_dynamic_section linking to
  ContextWindowCalculator to flag parallel parser tech debt
- Add tests: git-enabled agent parsing, dual-occurrence tag
  extraction, pure text system prompt, git-enabled integration test
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
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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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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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