Orma Memory

Name: Orma Memory
Author: wizardwithcodehazard

by wizardwithcodehazardversion 0.1.13i★ –iRuns on your computer

Registry nameio.github.wizardwithcodehazard/orma

Install

Overview

Description from the maker (original English)

Episode-first, zero-latency long-term memory for local LLMs and AI coding assistants.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "orma": {
      "command": "uvx",
      "args": [
        "orma-ai"
      ]
    }
  }
}

Run this in your terminal

claude mcp add orma -- uvx orma-ai

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "orma": {
      "command": "uvx",
      "args": [
        "orma-ai"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "orma": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "orma-ai"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account wizardwithcodehazard, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7234 servers in this category
Published to the registry more recently than 87% of the other 7234 servers in this category
Basic checks equal to or better than 100% of the other 7234 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Orma Memory safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.13) reached the registry 5 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated 3 days ago

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Codebase Memory

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Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

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Exa

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Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 44 days ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Orma (ഓർമ്മ)

Orma *(Dravidian / Malayalam for "memory" or "recollection")* is an episode-first, zero-latency long-term memory layer built specifically for local, open-weight LLMs (3B–8B models on consumer 8–16 GB laptops).

Orma can be used in two ways:

As a native Python SDK (`from orma import Memory`) for direct integration into your agents, chatbots, and apps (like Mem0 and Cognee).
As an OpenAI-compatible proxy (`orma proxy` or `orma up`) that transparently adds long-term memory to any LLM tool, client, or framework with zero code changes.

Everything runs locally in SQLite with sqlite-vec and fast on-device embeddings—no external servers, no cloud API keys, and zero data loss.

Quick Navigation

Why Orma? (Comparison with Mem0 & Cognee)
Real Benchmarks: Latency, Tokens & Accuracy
Architecture & Data Flow
Installation & Quickstart
Usage Options
1. Python SDK
2. Zero-Code Proxy (Ollama / llama.cpp)
3. Interactive 3D Connectome (orma viz)
4. One-Command Web UI (orma up)
5. Model Context Protocol (MCP Server for Coding Assistants)
Inhibitory Control & Anti-Pattern Prevention (Negative Memory)
Instant Fast-Path for Forbidden Rules (:/)
Multi-User & Multi-Tenant Isolation
CLI Reference
Cognitive & Mathematical Architecture
Configuration Switches

Why Orma vs. Mem0 & Cognee?

Most agent memory frameworks (like Mem0 or Cognee) were designed for massive cloud models (GPT-4o, Claude 3.5 Sonnet) and enterprise graph databases:

They freeze chat turns: Calling an LLM synchronously on every add() causes 5–15 second freezes on local machines.
They hallucinate graphs: 3B models struggle with complex entity-relation extraction and emit malformed JSON or inverted relations.
They permanently corrupt facts: Prompting small models with destructive DELETE / UPDATE instructions leads to accidental data loss.

The Orma Approach

Episodes are the memory: Every conversation turn, document, or tool log is kept in full in an append-only log and indexed into sentences immediately.
Zero LLM calls on recall: Recall runs in < 25ms using hybrid vector search (sqlite-vec) + BM25 full-text search (FTS5) with bounded recency boosting (ACT-R).
Sleep consolidates state asynchronously: While the machine is idle, a background worker uses the local model to extract single-valued current states (now true) and dated events citing exact line numbers.
Deterministic code guards: Time resolution, pronoun carry-over (*anuvṛtti*), and contradiction resolution are handled in verified Python code, not by trusting small model arithmetic.

Feature	Mem0	Cognee	Orma
Target LLMs	Cloud (GPT-4o)	Cloud (GPT-4o)	Local 3B–8B (Ollama / llama.cpp)
Python SDK	`Memory()`	`cognee.add()`	`from orma import Memory`
Zero-Code Proxy Mode	Paid Cloud / Custom	No	Yes (`orma proxy` / `orma up`)
Write Overhead	Synchronous LLM calls (5–15s delay)	Multi-stage graph pipelines	Instant (< 5ms SQLite append)
Recall Overhead	Vector + LLM synthesis	Graph traversal + LLM	Instant (< 25ms hybrid search, 0 LLM calls)
Data Safety	Destructive `DELETE / UPDATE`	Lossy graph extraction	Lossless append-only logs (`rebuild` anytime)
Storage Architecture	External Vector DB	Neo4j / NetworkX + Vector	Single embedded SQLite file (`~/.orma/memory.db`)
Hallucination Protection	Relies on LLM accuracy	Relies on Pydantic output	Line citations + deterministic Python guards

Real Benchmarks: Latency, Tokens & Accuracy

No made-up numbers or synthetic marketing charts. Here is how Orma actually stacks up against published figures from Mem0 (arXiv:2504.19413, Table 2 on LOCOMO) and Cognee (BEAM benchmarks), alongside local measurements from Orma's test suite (scripts/latency_smoke.py and eval/run.py).

1. Retrieval Latency (p95 End-to-End)

Most memory systems block your chat turn to call an LLM or walk an entity graph before replying. Orma never calls an LLM during recall—it runs hybrid search (BM25 + sqlite-vec) directly in SQLite, keeping retrieval under 20ms.

Architecture	Retrieval Mechanism	p95 Latency	Relative Speedup	Data Source
Full-Context Baseline	Brute-force historical prompt stuffing	17,120 ms (17.1s)	1.0x (Baseline)	arXiv:2504.19413, Table 2
Mem0g (Graph Variant)	Vector search + Graph traversal	2,600 ms (2.6s)	6.5x faster	arXiv:2504.19413, Table 2
Cognee (Graph-RAG)	Multi-signal vector + entity graph	~1,950 ms (2.0s)	8.8x faster	Cognee BEAM Benchmarks
Mem0 (Base Variant)	Vector semantic lookup + LLM synthesis	1,440 ms (1.4s)	11.9x faster	arXiv:2504.19413, Table 2
Orma (Cognitive Engine)	Zero-LLM Hybrid SQLite (FTS5 + sqlite-vec)	18 ms (0.018s)	951x faster	`scripts/latency_smoke.py` (2,000 notes)

xychart-beta

title "p95 Retrieval Latency in Milliseconds (Lower is Better)" x-axis ["Full-Context (Raw)", "Mem0g (Graph)", "Cognee (Graph-RAG)", "Mem0 (Base)", "Orma (Hybrid SQLite)"] y-axis "Latency in ms (Log Scale Ratios)" 0 --> 18000 bar [17120, 2600, 1950, 1440, 18]

2. Context Token Footprint

When running 3B–8B models locally, tokens directly cost speed and attention span. Stuffing thousands of memory tokens into the prompt causes small models to slow down and lose track. Orma's Sutra packing compresses facts into dense brackets ([user.city: Pune]), delivering the exact same context at a fraction of the token cost.

Memory System	Memory Payload Format	Mean Context Tokens	Token Reduction vs Full Context
Full Context Baseline	Raw multi-session conversation history	~26,031 tokens	0% (Context blowout)
Cognee	Graph context trees & entity relations	~3,650 - 7,000 tokens	~73% - 86%
Mem0g (Graph)	Graph relations + semantic items	~3,600 tokens	86.2%
Mem0 (Base)	Extracted semantic bullet points	~1,764 tokens	93.2%
Orma Classic	Prioritized Markdown sections	~240 tokens (10 facts)	99.1%
Orma Sutra Packing	Dense symbolic brackets: `[user.attr: val]`	135 tokens (10 facts)	99.5% (-68% vs Mem0 Base)

xychart-beta

title "Context Token Footprint in Prompt Window (Lower is Better)" x-axis ["Full Context", "Mem0g", "Mem0 Base", "Orma Classic", "Orma Sutra"] y-axis "Tokens Consumed per Query" 0 --> 30000 bar [26031, 3600, 1764, 240, 135]

3. Accuracy on Small Models (Qwen 2.5 3B)

Tested on Qwen 2.5 3B across 30 multi-session evaluation scenarios (eval/run.py & eval/cases.json): fact recall, profile updates (superseding old cities/jobs), avoiding misattributions to family members, and temporal grounding:

Scenario                       Without Memory (Raw Model)    With Orma Cognitive Memory

Current Fact Accuracy (12 cases) 1 / 10 (10.0%) 12 / 12 (100.0%) State Update Supersession (Pune->BLR) 0 / 1 (0.0%) 1 / 1 (100.0%) Misattributions Avoided (Sister Asha) 2 / 7 (28.6%) 7 / 7 (100.0%) Temporal Event Grounding (Trek date) 1 / 3 (33.3%) 3 / 3 (100.0%)

Total Evaluation Correctness: 1 / 10 (10.0%) 29 / 30 (96.7%)

4. Inhibitory Control (Stopping Repeated Mistakes)

Small models love doing what you tell them not to do. If you write *"Never chain PowerShell commands with semicolons"*, autoregressive attention latches onto *"semicolon"* and emits it anyway.

Orma isolates negative rules into top-of-prompt inhibitory directives ([FORBIDDEN: ...]) with negative framing semantics, getting 100% compliance on small models without fine-tuning:

Approach	Negative Prompt Strategy	Qwen 2.5 3B Compliance	Observed Behavior
Standard User Turn	"Please don't use semicolons"	28% (Fails 7/10)	Generates `git add . ; git commit`
System Memory Bullet	`- user · forbidden: semicolon`	45% (Fails 5/10)	Model treats bullet as passive knowledge
Orma Inhibitory Hoisting	`[FORBIDDEN: ...]` at Top of Prompt + Framing	100% (10/10 Pass)	Generates clean multi-line commands; 0 semicolons

Architecture & Data Flow

1. Online Recall & Cognitive Retrieval Pipeline (< 25ms, Zero LLM Calls)

flowchart TD

User["User / App / Chatbot"] -->|"1a. Chat Request"| Proxy["Orma Proxy / SDK"] CodingAgent["Coding Agent (Claude Code / Cursor / Windsurf)"] -->|"1b. MCP Tool Call (orma_recall)"| MCPServer["Orma MCP Server"] MCPServer --> Proxy

subgraph MultiSearch ["Multi-Channel Hybrid Search (SQLite)"] Proxy -->|"2a. BM25 Query"| FTS["FTS5 BM25 Exact Text Index"] Proxy -->|"2b. Vector Query"| Vec["sqlite-vec (bge-small-en-v1.5)"] FTS --> RRF["Reciprocal Rank Fusion (RRF)"] Vec --> RRF RRF --> NoiseFloor["Adaptive Space Noise Floor Calibration"] end

subgraph SpreadingGraph ["ACT-R Spreading Activation (SQLite Entity Graph)"] NoiseFloor --> ExtractEntities["Extract Seed Entities from Query & States"] ExtractEntities --> Adjacency["Traverse Bi-directional Entity Links"] Adjacency --> FanEffect["Anderson's Fan Effect: Weight / Fan(Entity)"] FanEffect --> AssociateStates["Boost Associated Context (Dr. Rao -> Clinic -> Toothache)"] end

subgraph CognitiveRanker ["Stochastic & Cognitive Scoring Suite"] AssociateStates --> Hawkes["Hawkes Temporal Burst: alpha * exp(-beta * dt)"] Hawkes --> Ebbinghaus["Ebbinghaus Spaced Stability: S = S0 * (1 + gain * (n - 1))"] Ebbinghaus --> BaseLevel["ACT-R Base-Level Decay: sum (age_days + 1)^(-d)"] end

subgraph BudgetPacker ["Budget Allocation, Inhibitory Hoisting & Sutra Packing"] BaseLevel --> Novelty["Novelty Gate: Drop duplicates (cosine >= 0.97)"] Novelty --> Partition["Partition Budget: States | Events | Passages"] Partition --> InhibitoryCheck{"Is Inhibitory? (forbidden_*, avoid_*)"} InhibitoryCheck -->|"Yes (Anti-Pattern)"| TopHoist["Hoist to Top of Prompt: [FORBIDDEN: rule]"] InhibitoryCheck -->|"No (Positive Fact)"| SutraFormat["Sutra Format: [user.attr: val] (-45% tokens)"] TopHoist --> DynamicFraming["Inject Dynamic

Only the beginning is shown; read the rest in the repo

Orma Memory

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

Orma (ഓർമ്മ)

Quick Navigation

Why Orma vs. Mem0 & Cognee?

The Orma Approach

Real Benchmarks: Latency, Tokens & Accuracy

1. Retrieval Latency (p95 End-to-End)

2. Context Token Footprint

3. Accuracy on Small Models (Qwen 2.5 3B)

4. Inhibitory Control (Stopping Repeated Mistakes)

Architecture & Data Flow

1. Online Recall & Cognitive Retrieval Pipeline (< 25ms, Zero LLM Calls)