YourMemory

<!-- mcp-name: io.github.sachitrafa/yourmemory --> <div align="center"> <img src="logo.svg.png" alt="YourMemory" width="110" /><br> <h1>YourMemory</h1> ### Your AI has the memory of a goldfish. Not anymore. **Persistent, self-improving memory for AI agents — built on the science of how humans remember.** [](https://pypi.org/project/yourmemory/) [](https://pypi.org/project/yourmemory/) [](https://pypi.org/project/yourmemory/) [](https://creativecommons.org/licenses/by-nc/4.0/) [](https://github.com/sachitrafa/YourMemory) [](BENCHMARKS.md) [](BENCHMARKS.md) [](BENCHMARKS.md) [](https://modelcontextprotocol.io) <br> **[▶ Try the live interactive demo](https://sachitrafa.github.io/YourMemory/marketing/interactive.html)** · **[Website](https://yourmemoryai.xyz)** · **[Benchmarks](BENCHMARKS.md)** </div> --- ## The problem Every morning your AI agent treats you like a stranger. Same context re-explained. Same preferences forgotten. Every session starts from zero. Most "memory" tools bolt a vector database onto an agent and call it done — but that's just **storage**. It hoards every near-duplicate until retrieval drowns in noise. A goldfish with a bigger bowl. **YourMemory is different: memory that works like a brain, not a database.** ```mermaid flowchart LR A["🧠 You tell your<br/>AI something"] --> B["Extract durable<br/>facts"] B --> C["Dedup + embed<br/>+ graph-link"] C --> D[("Memory<br/>store")] D -->|"related facts pile up"| E["✨ Consolidate<br/>N → 1 summary"] D -->|"stale + unused"| F["📉 Decay<br/>+ prune"] D -->|"new session"| G["♻️ Recall<br/>hybrid + graph"] E --> D G --> H["🤖 Your agent<br/>picks up where<br/>it left off"] style D fill:#0a2540,stroke:#19cdff,color:#fff style E fill:#0c2b3a,stroke:#5eead4,color:#fff style H fill:#0c2b3a,stroke:#19cdff,color:#fff ``` --- ## ✨ What makes it different | | Feature | What it does | |---|---|---| | 🧠 | **Consolidation** | When enough related facts accumulate, they're compressed into one clean summary and the originals are archived. Memory gets **sharper over time, not bloated**. | | 📉 | **Biological decay** | Every memory ages on an **Ebbinghaus forgetting curve**. Stale, unused facts fade; important and frequently-recalled ones persist. | | 🔗 | **Entity graph** | Memories link by shared people, places, and concepts — so recall surfaces what you *forgot to ask for*. | | ♻️ | **Survives context resets** | When the context window compacts, YourMemory hands the working context back — no re-reading files to figure out where you were. | | 🔒 | **Tamper-evident audit trail** | Every read / write / delete is logged in a **hash-chained** ledger. Alter one record and the chain breaks. | | 👥 | **Team memory pools** | Role-based shared memory, so a whole team's agents draw on the same institutional knowledge — with private memories kept private. | | 🛡️ | **Data rights built in** | One-command **export** (right to access) and **right-to-forget** (purge), plus SOC 2-aligned controls. | | 🔌 | **MCP-native & local-first** | Works with Claude, Cursor, Cline, Windsurf, or any MCP client. Runs entirely on your machine — no API key, nothing leaves your system. | > One command to install. DuckDB by default (zero setup), Postgres + pgvector for teams. --- ## Table of Contents - [🏆 Benchmarks](#-benchmarks) - [🚀 Quick Start](#-quick-start) - [🧠 How Memory Works](#-how-memory-works) - [Consolidation](#consolidation--n--1) - [Decay](#decay--the-forgetting-curve) - [Hybrid Retrieval](#hybrid-retrieval--vector--bm25--graph) - [🔒 Trust & Audit Trail](#-trust--audit-trail) - [👥 Team Memory Pools](#-team-memory-pools) - [🛡️ Data Rights & Compliance](#️-data-rights--compliance) - [🎛️ Dashboards](#️-dashboards) - [🔧 MCP Tools](#-mcp-tools) - [⚡ Ask Without an LLM Call](#-ask-without-an-llm-call) - [🔀 API Proxy — Guaranteed Memory](#-api-proxy--guaranteed-memory) - [🏗️ Architecture & Stack](#️-architecture--stack) - [🩺 Troubleshooting](#-troubleshooting) - [🤝 Contributing](#-contributing) --- ## 🏆 Benchmarks Three external datasets. Every number independently reproducible — [benchmark code lives in the repo](benchmarks/). Full methodology in [BENCHMARKS.md](BENCHMARKS.md). ### LoCoMo-10 — multi-session conversational memory ```mermaid xychart-beta title "Recall@5 · LoCoMo-10 (higher is better)" x-axis ["Mem0", "Zep Cloud", "Supermemory", "YourMemory"] y-axis "Recall@5 percent" 0 --> 70 bar [18, 28, 31, 59] ``` > **2× better recall than Zep Cloud** across all 10 samples. \*Supermemory and Mem0 exhausted free-tier quotas mid-benchmark; scores computed over the full 1,534 pairs. ### LongMemEval-S — 500 questions, ~53 distractor sessions each The hardest standard benchmark for long-term memory. Each question is buried in ~53 sessions. | Metric | Score | |--------|:-----:| | **Recall@5** (any gold session in top-5) | **89.4%** | | Recall-all@5 (all gold sessions in top-5) | 84.8% | | nDCG@5 (ranking quality) | 87.4% | ### HotpotQA — 200 multi-hop questions | System | BOTH_FOUND@5 | |--------|:------------:| | **YourMemory** (vector + BM25 + entity graph) | **71.5%** | | YourMemory (no entity edges) | 59.5% | Entity graph edges add **+12 pp** — they traverse from Fact 1 to Fact 2 even when Fact 2 has low embedding similarity to the query. *Writeup: [I built memory decay for AI agents using the Ebbinghaus forgetting curve](https://dev.to/sachit_mishra_686a94d1bb5/i-built-memory-decay-for-ai-agents-using-the-ebbinghaus-forgetting-curve-1b0e)* --- ## 🚀 Quick Start **Python 3.11–3.14. No Docker, no database setup. All memory stored locally in `~/.yourmemory/`.** ```bash pip install yourmemory yourmemory-register <your-token> yourmemory-setup ``` **Get your token:** visit **[yourmemoryai.xyz](https://yourmemoryai.xyz/)** → enter your email → verify with a 6-digit code → copy your token. `yourmemory-setup` auto-detects and wires up **Claude Code, Claude Desktop, Cursor, Windsurf, and Cline**, then asks which backend to use: - **DuckDB** — zero setup, single local file *(default)* - **Postgres** — shared / production; you provide a `DATABASE_URL` (needs the pgvector extension) > **Optional — smarter local extraction:** YourMemory works out of the box with built-in heuristics. For higher-quality, fully-local fact extraction, install [Ollama](https://ollama.com) and `yourmemory-setup` pulls the model (`qwen2.5:7b`, ~4.7 GB) automatically. Prefer the cloud? Set `YOURMEMORY_EXTRACT_BACKEND=anthropic`. ### Or install from a binary — no Python required Prefer not to touch pip? Grab the standalone binary for your platform from the [latest release](https://github.com/sachitrafa/YourMemory/releases/latest): | Platform | Asset | |----------|-------| | macOS (Apple Silicon) | `yourmemory-macos-arm64.tar.gz` | | macOS (Intel) | `yourmemory-macos-x86_64.tar.gz` | | Linux (x86-64) | `yourmemory-linux-x86_64.tar.gz` | | Windows (x86-64) | `yourmemory-windows-x86_64.exe.zip` | ```bash # macOS / Linux — download, extract, run tar -xzf yourmemory-macos-arm64.tar.gz ./yourmemory-macos-arm64 register <your-token> ./yourmemory-macos-arm64 setup ./yourmemory-macos-arm64 # start the server ``` One executable handles every command: `register`, `setup`, `ask "<question>"`, `path`, and (with no args) starts the server. **Fully self-contained & offline** — the binary bundles Python, every dependency, *and* both ML models (the embedding model + spaCy). Nothing is downloaded on first run. The trade-off is size (~2 GB). Build your own with a single command — `./build-binary.sh` — and multi-platform release binaries are produced automatically by the [build workflow](.github/workflows/build-binary.yml). --- ## 🧠 How Memory Works YourMemory treats memory as a living system — it **grows, consolidates, forgets, and connects**, the way a brain does. ### Consolidation — *N → 1* Most memory tools just keep growing. YourMemory watches for clusters of related facts and, once enough accumulate, compresses them into a single clean summary — archiving the originals (never deleting, so nothing is lost). ```mermaid flowchart LR subgraph before [Related facts pile up] A1["Railway uses Nixpacks"] A2["Railway on Pro plan"] A3["Railway env vars hold<br/>the Postgres URL"] A4["Deploys on Railway<br/>with Postgres"] end before --> C{"cluster +<br/>LLM summarize"} C --> S["✨ Summary<br/>Deploys on Railway (Pro,<br/>Nixpacks) with Postgres<br/>via env vars"] C -.->|"archived, recoverable"| ARC[("archive")] style S fill:#0a2540,stroke:#5eead4,color:#fff style C fill:#0c2b3a,stroke:#19cdff,color:#fff ``` > Real example from one production store: **444 memories → 16 summaries** — same knowledge, a fraction of the noise. Consolidation is **event-driven** (triggered when related memories pile up), not a blind nightly job. ### Decay — the forgetting curve Memory strength decays exponentially. Importance and recall frequency slow that decay: ``` effective_λ = base_λ × (1 − importance × 0.8) strength = clamp(importance × e^(−effective_λ × active_days) × (1 + recall_count × 0.2), 0, 1) ``` `active_days` counts only days you were active — vacations don't cause memory loss. Memories below strength `0.05` are pruned automatically. Each category ages at its own rate: | Category | Half-life | Best for | |----------|:---------:|----------| | `strategy` | ~38 days | Patterns that worked, architectural decisions | | `fact` | ~24 days | Preferences, identity, stable knowledge | | `assumption` | ~19 days | Inferred context, uncertain beliefs | | `failure` | ~11 days | Errors, wrong approaches, environment-specific issues | **Chain-aware pruning:** a decayed memory is kept alive if any graph neighbour is still strong — load-bearing context survives even when rarely queried directly. ### Hybrid Retrieval — Vector + BM25 + Graph Recall runs in two rounds so it surfaces both what you asked for *and* what you forgot to ask for: ```mermaid flowchart LR Q["query"] --> R1["Vector + BM25<br/>hybrid search"] R1 --> R2["Graph expansion<br/>(what you forgot to ask)"] R2 --> S["rank by<br/>similarity × strength"] S --> OUT["🎯 Ranked memories"] style OUT fill:#0a2540,stroke:#19cdff,color:#fff ``` **Subject-aware deduplication** runs before every store — it embeds the subject of each sentence so `"Sachit uses DuckDB"` and `"YourMemory uses DuckDB"` stay separate (different entities), while `"YourMemory uses DuckDB"` and `"YourMemory stores data in DuckDB"` merge (same entity). No hardcoded word lists; generalises to any language. --- ## 🔒 Trust & Audit Trail Enterprises won't let an opaque black box store their data. So every operation — **read, write, update, delete, consolidation** — is appended to a **hash-chained, tamper-evident audit log**. ```mermaid flowchart LR E0["GENESIS"] --> E1 subgraph E1 [Event 1] H1["row_hash =<br/>sha256(prev + data)"] end E1 --> E2 subgraph E2 [Event 2] H2["row_hash =<br/>sha256(#1.hash + data)"] end E2 --> E3 subgraph E3 [Event 3] H3["row_hash =<br/>sha256(#2.hash + data)"] end E3 --> V{"GET /audit/verify"} V -->|chain intact| OK["✅ verified"] V -->|any row altered| BAD["❌ chain breaks<br/>at that row"] style OK fill:#0a2540,stroke:#5eead4,color:#fff style BAD fill:#3a0c14,stroke:#fb7185,color:#fff ``` Each row records the timestamp, actor user + agent, action, operation, target memory, source (`http` vs `mcp`), and the previous row's hash. Change any historical record and `verify_chain()` pinpoints exactly where the chain broke. ```bash GET /audit # browse the trail (filter by user / action / operation) GET /audit/verify # cryptographically verify the chain is untampered POST /audit/prune # retention-based cleanup (90-day minimum, never lower) ``` > Audit logging is **fail-open** — it never blocks a memory operation — and read/list events from the dashboard's own render loop are excluded, so the trail stays signal, not noise. --- ## 👥 Team Memory Pools Give a whole team's agents one shared brain — without leaking anyone's private context. Memories are either **shared** (visible to the pool) or **private** (visible only to their owner). ```mermaid flowchart TB P(("🧠 Team Pool<br/>shared memory")) A["Alice's agent"] <-->|shared| P B["Bob's agent"] <-->|shared| P C["Carol's agent"] <-->|shared| P A -. private .-> AP["🔒 Alice-only"] B -. private .-> BP["🔒 Bob-only"] style P fill:#0a2540,stroke:#19cdff,color:#fff style AP fill:#0c1424,stroke:#5a6b80,color:#8294a8 style BP fill:#0c1424,stroke:#5a6b80,color:#8294a8 ``` Role-based access is enforced per agent — what one engineer's agent learns, the whole team benefits from instantly; sensitive context stays scoped to its owner. ```bash POST /pools # create a pool POST /pools/{id}/members # add a member (with role) POST /pools/{id}/memories # contribute a shared memory POST /pools/{id}/retrieve # recall across the pool ``` --- ## 🛡️ Data Rights & Compliance Because memory that stores real data needs the controls to be trusted with it: | Right | Endpoint | What it does | |-------|----------|--------------| | **Access** (DSAR export) | `GET /users/{id}/export` | Full export of everything stored for a user | | **Erasure** (right to forget) | `DELETE /users/{id}/memories` | One-command purge of a user's memories | | **Portability** | `POST /users/{id}/import` | Re-import a previous export | | **Recoverability** | `GET /users/{id}/archive` | Retrieve consolidated-away originals | Combined with the hash-chained audit trail and 90-day retention floor, these map directly onto the controls documented in [SECURITY.md](SECURITY.md) (SOC 2-aligned). --- ## 🎛️ Dashboards Two built-in browser UIs — no extra setup, they start automatically with the server. ### Memory Dashboard — `http://localhost:3033/ui` A full read/write view with **Memories · Audit · Pools** tabs: stats bar (Strong / Fading / Near-prune), per-agent tabs, memory cards with live strength bars, category filters, the audit trail, and pool management. ### Graph Visualiser — `http://localhost:3033/graph` An interactive force-directed map of how memories connect — root memory as a bright node, neighbours color-coded by category, edge thickness = connection strength. Drag, zoom, and click any node for full content. ``` http://localhost:3033/graph?memoryId=42&userId=alex&depth=2 ``` --- ## 🔧 MCP Tools Three tools, called by your AI automatically. | Tool | When your AI calls it | What it does | |------|-----------------------|--------------| | `recall_memory(query, current_path?)` | Start of every task | Surfaces memories ranked by similarity × decay strength; spatial boost for path-matched memories | | `store_memory(content, importance, category?, context_paths?)` | After learning something new | Embeds, deduplicates, stores with decay; tags optional file/dir paths | | `update_memory(id, new_content, importance)` | When a stored fact is outdated | Re-embeds and replaces; logs the change to the audit trail | ```python # Store with spatial context store_memory( "Alex prefers tabs over spaces in Python", importance=0.9, category="fact", context_paths=["/projects/backend"], ) # Next session — spatial boost fires when working in that directory recall_memory("Python formatting", current_path="/projects/backend") # → {"content": "Alex prefers tabs over spaces in Python", "strength": 0.87} ``` --- ## ⚡ Ask Without an LLM Call The only memory system that can answer questions **without making any LLM API call**: ```bash yourmemory ask "what database does this project use" # → YourMemory uses DuckDB locally and Postgres in production. yourmemory ask "how do I fix a kubernetes deployment" # → Not enough memory context to answer without an LLM. ``` When memory is strong enough it answers instantly — zero tokens, zero cloud cost, zero latency. When it isn't, it declines cleanly rather than hallucinating. Your query never leaves your machine. --- ## 🔀 API Proxy — Guaranteed Memory MCP tools are called at the AI's discretion. The API proxy removes that uncertainty — it intercepts every LLM call, injects relevant memories automatically, and handles `store_memory` / `update_memory` with no model configuration. Start the server (`yourmemory`), then point your client at `localhost:3033`: ```python from anthropic import Anthropic client = Anthropic( api_key="sk-ant-...", base_url="http://localhost:3033/proxy/anthropic", default_headers={"X-YourMemory-User": "alex"}, # per-user memory ) # Memory is injected automatically — no other changes needed response = client.messages.create( model="claude-opus-4-8", max_tokens=1024, messages=[{"role": "user", "content": "What database do I use?"}], ) ``` OpenAI works identically via `base_url="http://localhost:3033/proxy/openai"`. --- ## 🏗️ Architecture & Stack ```mermaid flowchart LR C["Your AI client<br/>Claude · Cursor · any MCP"] <--> Y["🧠 YourMemory"] Y --> M[("Memory<br/>store")] Y --> A[("Audit<br/>ledger")] style Y fill:#0a2540,stroke:#19cdff,color:#fff style M fill:#0c1a2c,stroke:#5eead4,color:#fff style A fill:#0c1a2c,stroke:#5eead4,color:#fff ``` | Component | Role | |-----------|------| | **DuckDB** | Default vector store — zero setup, native cosine similarity | | **PostgreSQL + pgvector** | Optional — for teams or large datasets | | **NetworkX** | Default graph backend (`~/.yourmemory/graph.pkl`) | | **Neo4j** | Optional graph backend | | **sentence-transformers** | Local embeddings (`multi-qa-mpnet-base-dot-v1`, 768 dims) | | **spaCy** | Local NLP for deduplication and entity extraction | | **APScheduler** | Automatic decay + pruning | --- ## 🩺 Troubleshooting **Writes hang / time out (DuckDB single-writer lock).** If both the MCP server and the HTTP server run at once, they compete for the DuckDB write lock. Fix: ```bash pkill -f yourmemory 2>/dev/null || true rm -f ~/.yourmemory/memories.duckdb.wal ~/.yourmemory/memories.duckdb.lock 2>/dev/null || true # restart your client ``` Running Claude Desktop (MCP) and Claude Code (hooks) simultaneously? Use SQLite instead — it handles concurrent readers/writers cleanly: `DATABASE_URL=sqlite:///~/.yourmemory/memories.db` --- ## 🤝 Contributing PRs welcome — see [CONTRIBUTORS.md](CONTRIBUTORS.md). ## 📚 Dataset References - [LoCoMo](https://github.com/snap-research/locomo) — Maharana et al. (2024) - [LongMemEval](https://github.com/xiaowu0162/LongMemEval) — Wu et al. (2024) - [HotpotQA](https://hotpotqa.github.io/) — Yang et al. (2018) ## 📄 License Copyright 2026 **Sachit Misra** — Licensed under [CC-BY-NC-4.0](LICENSE). **Free for** personal use, education, academic research, and open-source projects. **Commercial use** requires a separate written agreement → mishrasachit1@gmail.com <div align="center"><br><b>Give your AI a memory worth keeping.</b><br><code>pip install yourmemory</code></div>