Ori Mnemos

Ori Mnemos

Open-source persistent memory infrastructure for AI agents.

Ori implements human cognition as mathematical models on a knowledge graph. Activation decay from ACT-R. Spreading activation along wiki-link edges. Hebbian co-occurrence from retrieval patterns. Reinforcement learning on retrieval itself. Recursive graph traversal with sub-question decomposition. The system learns what matters, forgets what doesn't, and optimizes its own retrieval pipeline.

Persistent memory across sessions, clients, and machines. Zero-infrastructure retrieval that [matches and in several cases strongly outperforms incumbents on benchmarks](#benchmarks) — and you own every byte of your data. Markdown on disk. Wiki-links as graph edges. Git as version control. No database lock-in, no cloud dependency, no vendor capture.

v0.5.0 · npm · Paper · Apache-2.0

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Benchmarks

HotpotQA — Multi-Hop Retrieval

Head-to-head against Mem0, the most widely adopted agent memory system. HotpotQA tests multi-hop reasoning — questions that require connecting information across multiple documents to answer.

| Metric | Ori Mnemos | Mem0 | Δ | |--------|:----------:|:----:|:-:| | Recall@5 | 90% | 29% | 3.1× | | F1 Score | 0.68 | 0.33 | 2.1× | | Latency (avg) | 120ms | 1,140ms | 9.5× faster | | Infrastructure | Markdown + SQLite | Redis + Qdrant + cloud | — |

Ori retrieves the right information 3× more often, scores 2× higher on answer quality, and does it 9.5× faster — on markdown files with a SQLite index. No cloud services. No API keys. Full evaluation code in [bench/](./bench/).

LoCoMo — Long-Term Conversational Memory

Evaluated on LoCoMo (Maharana et al., 2024) — the standard benchmark for long-term conversational memory. 10 conversations, 695 questions across single-hop, multi-hop, and temporal reasoning.

| System | Single-hop | Multi-hop | Infrastructure | |--------|:----------:|:---------:|----------------| | MemoryBank | 5.00 | — | Custom server | | ReadAgent | 9.15 | — | LLM-based | | A-Mem | 20.76 | — | Cloud APIs | | MemGPT / Letta | 26.65 | — | PostgreSQL + cloud | | LangMem | 35.51 | 26.04 | Cloud APIs | | OpenAI Memory | 34.30 | — | OpenAI proprietary | | Zep | 35.74 | 19.37 | PostgreSQL + cloud | | Mem0 | 38.72 | 28.64 | Redis + Qdrant + cloud | | Ori Mnemos | 37.69 | 29.31 | Markdown on disk |

Baseline numbers from Mem0 paper (Table 1). Ori evaluated with GPT-4.1-mini for answer generation, BM25 + embedding + PageRank fusion for retrieval.

More benchmarks coming — including LoCoMo-Plus (Level-2 cognitive memory) and adversarial refusal evaluation.

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Quick Start

npm install -g ori-memory
ori init my-agent
cd my-agent

Connect to your agent:

# Full adapters — auto-orient at session start, capture at session end
ori bridge claude-code --vault ~/brain                 # hooks + MCP + CLAUDE.md
ori bridge hermes --vault ~/brain                      # native plugin + MCP + HERMES.md
ori bridge opencode --vault ~/brain                    # plugin + MCP + AGENTS.md

# MCP-only adapters — tools available, no lifecycle automation
ori bridge cursor --vault ~/brain                      # .cursor/mcp.json
ori bridge codex --vault ~/brain                       # ~/.codex/config.toml

# Any MCP client
ori bridge generic --vault ~/brain                     # prints config for manual setup

Claude Code, Hermes Agent, and OpenCode get full lifecycle integration — the agent orients at session start, captures insights at session end, and validates notes on write. Cursor, Codex, and other MCP clients get access to all 16 tools but manage their own session lifecycle.

Manual MCP config (works with any client that speaks MCP):

{
  "mcpServers": {
    "ori": {
      "command": "ori",
      "args": ["serve", "--mcp", "--vault", "/path/to/brain"],
      "env": { "ORI_VAULT": "/path/to/brain" }
    }
  }
}

Start a session. The agent receives its identity automatically and begins onboarding on first run.

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Recursive Memory Harness

Ori is the first implementation of the Recursive Memory Harness (RMH) framework — a set of constraints on how persistent memory should behave for AI agents.

The core insight comes from Recursive Language Models (Zhang, Krassa & Khattab, 2026). RLM treats context not as input to be stuffed into a window, but as an environment to be navigated. The model doesn't get a bigger desk — it gets legs and walks into the library. RMH applies the same principle to persistent memory.

Three constraints define the framework:

1. Retrieval must follow the graph. Memory is not a flat vector store. Notes are nodes, wiki-links are edges. Retrieval walks the structure — Personalized PageRank at α=0.45, spreading activation along edges, community-aware traversal. The topology of the graph shapes what gets found.

1. Unresolved queries must recurse. When a single retrieval pass is insufficient, the system decomposes the question into sub-questions, retrieves against each, and synthesizes. Convergence detection stops recursion when new passes stop surfacing new information. This is what ori explore does.

1. Every retrieval must reshape the graph. Retrieval is not read-only. Co-occurrence edges grow between notes retrieved together (Hebbian learning). Q-values update based on whether retrieved notes were actually useful. The graph learns from how it is used — every query makes the next query better.

Most memory systems treat retrieval as search. RMH treats retrieval as navigation, recursion, and learning — on a graph that evolves with every session.

Read the full paper: Introducing Recursive Memory Harness

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What It Does

• Persistent identity. Agent state — name, personality, goals, methodology — is stored in plain markdown and auto-injected at session start via MCP instructions. Identity survives client switches, machine migrations, and model changes without reconfiguration.

• Knowledge graph. Every [[wiki-link]] is a directed edge. PageRank authority, Louvain community detection, betweenness centrality, bridge detection, orphan and dangling link analysis. Structure is queryable through MCP tools and CLI.

• Three memory spaces. Identity (self/) decays at 0.1x — barely fades. Knowledge (notes/) decays at 1.0x — lives and dies by relevance. Operations (ops/) decays at 3.0x — burns hot and clears itself. The separation is architectural, not cosmetic.

• Cognitive forgetting. Notes decay using ACT-R base-level learning equations, not arbitrary TTLs. Used notes stay alive. Their neighbors stay warm through spreading activation along wiki-link edges. Structurally critical nodes are protected by Tarjan's algorithm. ori prune analyzes the full activation topology before archiving anything.

• Four-signal fusion. Semantic embeddings, BM25 keyword matching, personalized PageRank, and associative warmth fused through score-weighted Reciprocal Rank Fusion. Intent classification (episodic, procedural, semantic, decision) shifts signal weights automatically.

• Dampening pipeline. Three post-fusion stages validated by ablation testing: gravity dampening halves cosine-similarity ghosts with zero query-term overlap, hub dampening applies a P90 degree penalty to prevent map notes from dominating results, and resolution boost surfaces actionable knowledge (decisions, learnings) over passive observation.

• Learning retrieval (v0.4.0). Three intelligence layers improve retrieval quality from session to session, synthesized from 63 research sources. See [Retrieval Intelligence](#retrieval-intelligence-v040) below.

• Capture-promote pipeline. ori add captures to inbox. ori promote classifies (idea, decision, learning, insight, blocker, opportunity), detects links, suggests areas. 50+ heuristic patterns. Optional LLM enhancement.

• Zero cloud dependencies. Local embeddings via all-MiniLM-L6-v2 running in-process. SQLite for vectors and intelligence state. Everything on your filesystem. Zero API keys required for core functionality.

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Retrieval Intelligence (v0.4.0)

Three learning layers that improve retrieval quality over time without manual tuning. Synthesized from 63 research sources across reinforcement learning, information retrieval, cognitive science, and bandit theory.

Layer 1 — Q-Value Reranking

Notes earn Q-values from session outcomes via exponential moving average updates. Over time, genuinely useful notes rise and noise sinks.

| Signal | Reward | What triggers it | |--------|--------|-----------------| | Forward citation | +1.0 | You [[link]] a retrieved note in new content | | Update after retrieval | +0.5 | You edit a note you just retrieved | | Downstream creation | +0.6 | You create a new note after retrieving | | Within-session re-recall | +0.4 | Same note surfaces across different queries | | Dead end (top-3, no follow-up) | −0.15 | Retrieved in top 3 but nothing follows |

After RRF fusion, Phase B reranks the candidate set with a lambda blend of similarity score and learned Q-value, plus a UCB-Tuned exploration bonus that ensures under-retrieved notes still get discovered. Exposure-aware correction prevents the same notes from dominating every session. A cumulative bias cap (MAX=3.0, compression=0.3) prevents runaway score inflation.

Layer 2 — Co-Occurrence Edges

Notes that are retrieved together grow edges between them — Hebbian learning on the knowledge graph. Edge weights are computed using NPMI normalization (genuine association beyond base rate), GloVe power-law frequency scaling, and Ebbinghaus decay with strength accumulation (frequently co-retrieved pairs decay slower).

Per-node Turrigiano homeostasis prevents hub notes from absorbing all edge weight. Bibliographic coupling bootstraps day-0 edges from existing wiki-link structure before any queries have been run.

The combined wiki-link + co-occurrence graph feeds a Personalized PageRank walk (HippoRAG, α=0.5) that surfaces notes semantic search alone would never find.

Layer 3 — Stage Meta-Learning

Each pipeline stage (BM25, PageRank, warmth, hub dampening, Q-reranking, co-occurrence PPR) is wrapped in a LinUCB contextual bandit with an 8-dimensional query feature vector. The system learns which stages help for which query types and auto-skips stages that consistently hurt.

Three-way decisions per stage: run / skip / abstain (stop the pipeline early). Cost-sensitive thresholds ensure expensive stages face a higher bar. Essential stages (semantic search, RRF fusion) never skip. An ACQO two-phase curriculum runs all stages during exploration (first 50 samples), then optimizes.

Session Learning Loop

Query → Retrieve → Use (cite, update, create) → Reward signals
  ↓                                                    ↓
  Co-occurrence edges grow                Q-values update (session-end batch)
  ↓                                                    ↓
  Stage meta-learner updates              Better retrieval next session

All updates happen in a single SQLite transaction at session end, in order: co-occurrence → Q-values → stage learning.

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The Stack

Layer 6: MCP Server                    16 tools, 5 resources — any agent talks to this
Layer 5: Recursive Exploration         PPR graph traversal, sub-question decomposition, convergence detection
Layer 4: Retrieval Intelligence        Q-value reranking, co-occurrence learning, stage meta-optimization
Layer 3: Dampening Pipeline            gravity, hub, resolution — ablation-validated
Layer 2: Four-Signal Fusion            semantic + BM25 + PageRank + warmth → score-weighted RRF
Layer 1: Knowledge Graph + Vitality    wiki-links, ACT-R decay, spreading activation, zone classification
Layer 0: Markdown files on disk        git-friendly, human-readable, portable

16 MCP tools · 5 resources · 17 CLI commands · 579 tests

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Token Economics

Without retrieval, every question requires dumping the entire vault into context. With Ori, the cost stays flat.

| Vault Size | Without Ori | With Ori | Savings | |:----------:|:-----------:|:--------:|:-------:| | 50 notes | 10,100 tokens | 850 tokens | 91% | | 200 notes | 40,400 tokens | 850 tokens | 98% | | 1,000 notes | 202,000 tokens | 850 tokens | 99.6% | | 5,000 notes | 1,010,000 tokens | 850 tokens | 99.9% |

A typical session costs ~$0.10 with Ori. Without it: ~$6.00+.

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Architecture

                          Any MCP Client
                    (Claude, Cursor, Windsurf,
                     Cline, Hermes, custom agents, VPS)
                              │
                        MCP Protocol
                        (stdio / JSON-RPC)
                              │
                    ┌───────────────────┐
                    │    Ori MCP Server  │
                    │                   │
                    │  instructions     │   identity auto-injected
                    │  resources  (5)   │   ori:// endpoints
                    │  tools    (16)    │   full memory operations
                    └─────────┬─────────┘
                              │
            ┌─────────────────┼─────────────────┐
            │                 │                 │
      ┌───────────┐    ┌───────────┐    ┌───────────┐
      │ Knowledge │    │ Identity  │    │Operations │
      │   Graph   │    │  Layer    │    │  Layer    │
      │           │    │           │    │           │
      │  notes/   │    │  self/    │    │  ops/     │
      │  inbox/   │    │  identity │    │  daily    │
      │  templates│    │  goals    │    │  reminders│
      └─────┬─────┘    │  method.  │    │  sessions │
            │          └───────────┘    └───────────┘
      ┌─────┴──────┐
      │            │
   Wiki-link   Embedding        ┌──────────────────────┐
    Graph       Index            │ Retrieval Intelligence│
   (in-mem)    (SQLite)          │                      │
      │            │             │  Q-values  (note_q)  │
   PageRank    Semantic          │  Co-occur  (edges)   │
   Spreading   BM25              │  Stage Q   (LinUCB)  │
   Activation  4-Signal          │  Dampening (3 stages)│
   Communities Fusion            │  Explore   (PPR+RMH) │
                                 └──────────────────────┘

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MCP Tools

| Tool | What it does | |------|-------------| | ori_orient | Session briefing: daily status, goals, reminders, vault health, index freshness | | ori_update | Write to identity, goals, methodology, daily, or reminders | | ori_status | Vault overview | | ori_health | Full diagnostics | | ori_add | Capture to inbox | | ori_promote | Promote with classification, linking, and area assignment | | ori_validate | Schema validation | | ori_query | Graph queries: orphans, dangling, backlinks, cross-project | | ori_query_ranked | Full retrieval with Q-value reranking, co-occurrence PPR, and stage meta-learning | | ori_warmth | Inspect the associative warmth field | | ori_query_similar | Semantic search (vector only, faster) | | ori_query_important | PageRank authority ranking | | ori_query_fading | Vitality-based decay detection | | ori_explore | Recursive graph traversal — PPR, sub-question decomposition, convergence detection | | ori_prune | Activation topology analysis and archive candidates | | ori_index_build | Build/update embedding index and bootstrap co-occurrence edges |

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CLI

# Vault management
ori init [dir]                    # Scaffold a new vault
ori status                        # Vault overview
ori health

…

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [aayoawoyemi](https://github.com/aayoawoyemi)
- **Source:** [aayoawoyemi/Ori-Mnemos](https://github.com/aayoawoyemi/Ori-Mnemos)
- **License:** Apache-2.0
- **Homepage:** https://orimnemos.com.

Install and usage instructions live in the source repository linked above.