Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Local-first persistent agentic memory powered by Recursive Memory Harness (RMH). Open source must win.
| Date | Stars |
|---|---|
| 2026-07-24 | 314 |
| 2026-07-25 | 315 |
| 2026-07-28 | 315 |
| 2026-07-30 | 315 |
| 2026-07-31 | 316 |
| 2026-08-06 | 316 |
Today
— stars today
This week
+1 stars this week
This month
— stars this month
Momentum
6.0
growth rate 0.32%/day
# 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. **v0.6.0** · [npm](https://www.npmjs.com/package/ori-memory) · [Paper](https://orimnemos.com/rmh) · Apache-2.0 --- ## Benchmarks ### HotpotQA — Multi-Hop Retrieval Head-to-head against [Mem0](https://github.com/mem0ai/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](https://github.com/snap-research/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](https://arxiv.org/abs/2504.19413) (Table 1). Ori evaluated with GPT-4.1-mini for answer generation, BM25 + embedding + PageRank fusion for retrieval. More benchmarks coming — including [LoCoMo-Plus](https://github.com/snap-research/locomo) (Level-2 cognitive memory) and adversarial refusal evaluation. --- ## Quick Start ```bash npm install -g ori-memory ori init my-agent cd my-agent ``` Connect to your agent: ```bash # 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)
Excerpt of 24,588 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f9e5554834119ed2, topic:mcp, topic:model-context-protocol, topic:mcp-server
matched fp:f9e5554834119ed2, topic:vector-search, topic:semantic-search
matched fp:f9e5554834119ed2, topic:ai-agent, topic:ai-agents, readme:ai agents
matched fp:f9e5554834119ed2, topic:knowledge-graph, topic:memory, readme:knowledge graph