LLMSlim memory

LLMSlim

Long-term memory and context selection for AI agents and coding tools, with no LLM call.

LLMSlim stores what users and agents say word for word, then returns the parts that answer a question within a token budget you set. It runs as an MCP server, a Python library or a small HTTP server.

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A real Python session with llmslim 0.11.1 from PyPI, typed by a script and recorded with asciinema ( script , recording ).

Use it in 30 seconds

Each MCP setup below starts the same server, llmslim mcp, with uv (Python 3.10 or newer). Memory goes to a local SQLite file, ~/.llmslim/memory.db.

One click:

The first start downloads LLMSlim and its dependencies. If a client gives up waiting, run uvx --from "llmslim[mcp]" llmslim mcp --help once to fill uv's cache, then restart the client.

Python (pip install llmslim, Python 3.10 or newer):

from llmslim.memory import Memory

mem = Memory("memory.db") # a SQLite file; or "postgresql://..." (llmslim[postgres]), "mongodb://..." (llmslim[mongodb]) mem.add([{"role": "user", "content": "I'm allergic to peanuts."}], user_id="asha") context = mem.search("Am I allergic to anything?", user_id="asha", budget=1500) print(context.text) # put it in your prompt as data, not instructions

TypeScript (npm install llmslim, Node 18 or newer), against llmslim serve (pip install "llmslim[server]"):

import { LLMSlim } from "llmslim";

const slim = new LLMSlim(); // http://127.0.0.1:8080 unless LLMSLIM_BASE_URL is set await slim.memories.add({ userId: "asha", messages: "I'm allergic to peanuts." }); const context = await slim.memories.search({ userId: "asha", query: "Am I allergic to anything?", budget: 1500 });

Runnable examples, each with a test: Claude Code memory, LangChain agent, Pydantic AI agent, big logs and TypeScript.

If LLMSlim is useful, a star helps others find it. Feedback is welcome in issues.

What it does

• Five MCP tools: remember, recall, list_memories, forget and fit_context.
• Stores messages word for word in SQLite, Postgres or MongoDB. Nothing is summarized or extracted on the way in.
• Recalls within a token budget. recall returns the stored messages that answer a question, under their dates and whole where they fit, within the budget you set. fit_context shrinks a long log or document to a budget.
• Fixes the scope at server start. The user and agent ids are set when the server starts, not by the model, so a model steered by injected text can't read or delete another user's memory.
• Makes no LLM call. By default, storing and recalling are plain code: no model, no embedding API, no API key.
• MIT licensed. The library and the MCP server need Python 3.10 or newer.

Beyond memory, LLMSlim Core also compresses and plans prompts, chat history, retrieved documents and tool schemas into token budgets: see the sections below.

Why no LLM call

Storing is a database write and recall is deterministic ranking code. So what comes back is what was said, not a model's rewrite of it, and the same history and question always give the same context, which makes recall easy to test. The memory server and llmslim serve make no network calls of their own. What does reach the network: installing from PyPI (including uvx's first run of a version) and tiktoken downloading its tokenizer file once.

The trade-off: the default lexical mode matches words, not meanings, so a question phrased differently from the stored message can miss it. relevance="semantic" adds an optional cross-encoder for that (llmslim[semantic], which downloads the model the first time). See the limits.

Compress a prompt in 60 seconds

1. Install LLMSlim Core from PyPI (Python 3.10+). Running it needs no API key, model, or network call:

pip install llmslim

2. Run this snippet:

from llmslim import compress

ticket = """ Hi team, I hope you are all doing well and had a good weekend. I am writing about invoice INV-2291, which charged us $480 instead of $240. We upgraded to the Pro plan on 3 September, but the old Basic plan was billed as well. Our finance lead was asking about this during the weekly sync, which ran long as usual. Please refund the duplicate $240 charge to the card ending 4417. Thanks so much, and let me know if you need anything else from me. """

result = compress(ticket, target_ratio=0.6) # target: keep about 60% of the tokens print(result.compressed_text) print(f"{result.original_tokens} -> {result.compressed_tokens} tokens ({result.reduction_percent:.1f}% fewer)")

3. What you should see (llmslim 0.11.1, base install):

Hi team, I hope you are all doing well and had a good weekend. I am writing about invoice INV-2291, which charged us $480 instead of $240. We upgraded to the Pro plan on 3 September, but the old Basic plan was billed as well. Please refund the duplicate $240 charge to the card ending 4417.

112 -> 72 tokens (35.7% fewer)

Two of the six sentences are dropped; the invoice number, amounts, plan change, and refund request are kept. The default extractive mode only selects sentences from your input, so the same input always gives the same output. The base install estimates tokens at about 4 characters per token and prints a one-line warning saying so. With pip install "llmslim[fast-tokens]", tokens are counted with tiktoken instead: on this input the same four sentences are kept, and the count reads 107 -> 72 tokens (32.7% fewer).

Next: Docs • Pricing • Vault beta • Issues

LLMSlim Core, Orbit and Vault

The context layer for production AI agents. *This repository is LLMSlim Core (v0.12.0, MIT): local-first context planning and compression that fits prompts, conversation history, RAG, memory, and tool schemas into real token budgets while preserving instructions and execution boundaries.*

Core is and stays fully open source. Orbit and Vault are separate products that depend on Core; none of their code lives in this repo