memry 0.2.16

The open, self-hostable memory layer for AI agents. MCP-native, local-first, research-grade.

Memry

The open, self-hostable memory layer for AI agents - memry.tech

pip install memry
memry mcp        # your agent now has long-term memory

Memry gives any MCP-capable agent - Claude Code, Claude Desktop, Cursor, Windsurf, Codex - durable long-term memory. It distills conversations into discrete facts, reconciles each new fact against what it already knows, and serves the result back as token-budgeted context. All state is a single SQLite file on your machine: no vector database, no queue, no cloud account, and it works with zero API keys.

Why Memry

It runs anywhere, with nothing. The default install needs no services and no keys: storage is one SQLite file, retrieval falls back to FTS5 BM25 plus deterministic hash embeddings, and writes are stored verbatim. Set ANTHROPIC_API_KEY or OPENAI_API_KEY and the same pipeline upgrades itself to LLM extraction and real embeddings. Local-first is the default, not a demo mode.

It remembers the way you would want it to. New facts are reconciled against existing ones: duplicates are skipped, refinements update in place, and contradictions supersede the old memory instead of deleting it. Memories are bi-temporal (valid_from / invalid_at / superseded_by), so "moved to Amsterdam" does not erase "lived in Berlin" - it dates it. Importance decays with a half-life, and a sweep retires stale trivia, again without destroying anything.

Nothing is a black box. Raw episodes are stored immutably before anything is derived from them, every memory links back to its source episodes, every mutation is an inspectable event, and every search hit carries its score signals (BM25, vector, recency, importance). When a memory looks wrong, you can trace where it came from, or re-run a better extraction pipeline over the original episodes.

Entities are disambiguated, not name-matched. Mentions become first-class entities, and a shared name is never enough to merge two of them: unclear cases stay separate ("three Jonases") with a merge proposal you confirm or reject once the evidence is in.

Tags organize themselves. With an LLM, Memry periodically clusters your accumulated tags into a few higher-level ones (a synthetic health over running/diet/sleep) and applies them to matching memories, so a long tail of specific labels rolls up into navigable topics. Search and list by tag or by date window, not just by relevance.

It scales when needed and stays simple when not. Optional extras add a usearch HNSW index for larger stores (memry[ann]) and a PostgreSQL + pgvector backend for multi-writer deployments (memry[postgres]). Per-tenant API keys with strict isolation (MEMRY_TENANTS) let one server serve several teams. The default remains a zero-ops single file.

Multi-user, with real OAuth. Beyond static config tenants, one server can host runtime-managed accounts, each confined to its own namespace, created with memry account add. Set MEMRY_PUBLIC_URL and Memry becomes an OAuth 2.1 authorization server for those accounts (dynamic client registration, PKCE, refresh, revocation, discovery at the domain root), so any OAuth-capable MCP client - Claude, Cursor, VS Code - can sign in and get a token scoped to that account. No IdP required; Memry verifies against its own accounts. Off by default: the single-user path stays keyless. See docs/self-hosting.md.

You can measure it. A built-in eval harness scores retrieval (recall@k, MRR, latency percentiles) deterministically and offline, and the pluggable backend interface includes a Mem0 adapter so you can benchmark against it under identical conditions.

Quickstart

As an MCP server (any agent)

Memry speaks MCP two ways: stdio for agents on the same machine (zero config, no port, no auth) and streamable HTTP for a shared server that several agents and devices talk to.

Local, stdio - the fastest start:

# Claude Code
claude mcp add memry -- memry mcp

// Claude Desktop / Cursor / Windsurf config
{
  "mcpServers": {
    "memry": {
      "command": "memry",
      "args": ["mcp"],
      "env": { "ANTHROPIC_API_KEY": "sk-ant-..." }   // optional but recommended
    }
  }
}

Remote, streamable HTTP - point any MCP client at a self-hosted server (see below) and share one memory across every machine:

# Claude Code
claude mcp add --transport http memry https://memory.example.com/mcp \
  --header "Authorization: Bearer <MEMRY_API_KEY>"

// Cursor / Windsurf / anything that takes a config file
{
  "mcpServers": {
    "memry": {
      "type": "http",
      "url": "https://memory.example.com/mcp",
      "headers": { "Authorization": "Bearer <MEMRY_API_KEY>" }
    }
  }
}

claude.ai (web, desktop, mobile) - add Memry as a custom connector under Settings → Connectors → Add custom connector. The dialog has no header field, so embed the key in the URL instead:

https://memory.example.com/mcp/<MEMRY_API_KEY>

Full walkthrough with screenshots of the flow, security notes, and troubleshooting: docs/connect-claude-ai.md.

The server exposes save_memories, search_memories, get_memory_context, list_memories, list_categories, update_memory, delete_memory, memory_history, and memory_stats. Agents are instructed to recall context at the start of a task, save durable facts as they appear, and check the warnings field on saves (it reports anything distillation dropped).

As a Python library

from memry import MemoryStore

store = MemoryStore()

# write: extraction + reconciliation (or infer=False to store verbatim)
store.add(
    [{"role": "user", "content": "I'm Ada, a data engineer in Berlin. I prefer uv over pip."}],
    user_id="ada",
)

# read: hybrid search with explainable scores
for hit in store.search("what tooling does the user prefer?", user_id="ada"):
    print(hit.score, hit.memory.content, hit.signals)

# or a ready-to-inject, token-budgeted context block
ctx = store.reconstruct_context("help me set up a new project", user_id="ada", token_budget=1200)
print(ctx.text)

As a self-hosted server (REST + dashboard + MCP)

memry serve --host 0.0.0.0 --port 8787
# dashboard:  http://localhost:8787/
# REST API:   http://localhost:8787/api/v1/...
# MCP (HTTP): http://localhost:8787/mcp

The dashboard shows your memories with inline editing, search, JSONL export/import, and a galaxy map of your tags: heavily-used tags gravitate to the gold core, the working set orbits in the teal belt, and one-off tags drift at the violet rim. Links are co-occurrence; click a planet to filter.

With Docker: docker compose up -d (see docker-compose.yml).

Or on a fresh Ubuntu/Debian VPS, one command installs Docker, Memry, and Caddy with automatic HTTPS (full guide):

curl -fsSL https://raw.githubusercontent.com/cosmin-novac/memry/main/deploy/install.sh \
  | MEMRY_DOMAIN=memory.example.com bash

Set MEMRY_API_KEY to require Authorization: Bearer <key> on the API. More in docs/self-hosting.md. To plug a hosted Memry into claude.ai as a custom connector, see docs/connect-claude-ai.md.

Multi-user accounts

One server can host isolated accounts, each confined to its own name::* namespace. Manage them with the CLI:

memry account add alice --password s3cret    # creates it, prints an API key (shown once)
memry account list
memry account issue-key alice --label laptop # another key for the same account
memry account disable alice                   # its keys + sessions stop working immediately

An account connects with its API key (Authorization: Bearer <key>, or https://<host>/mcp/<key>), or - once you set MEMRY_PUBLIC_URL=https://memory.example.com - by signing in through OAuth from any client that supports it (Claude, ChatGPT, Cursor, VS Code). On the dashboard, accounts sign in at /login with their name and password. Full walkthrough: docs/self-hosting.md.

From the CLI

memry add "I moved to Amsterdam and joined ASML" -u ada
memry search "where does ada work" -u ada
memry context "plan a commute" -u ada
memry history <memory_id>          # full audit trail
memry sweep                        # decay: soft-forget stale memories
memry eval --dataset evals/datasets/synthetic_v1.jsonl

How it works

flowchart LR
    A[conversation] --> E["episodes (immutable log)"]
    A --> X[extraction]
    X --> R{reconcile}
    R -->|new| ADD[add]
    R -->|refines| UPD[update in place]
    R -->|contradicts| SUP[supersede old]
    R -->|duplicate| SKIP[skip]
    ADD --> M[(memories)]
    UPD --> M
    SUP --> M
    Q[agent query] --> H[hybrid retrieval]
    M --> H
    H --> C[token-budgeted context]

1. Episodes first. Every message is stored verbatim before anything is derived from it. Memories are an index; episodes are the source of truth.
2. Extraction. An LLM distills discrete, self-contained facts with a type (semantic / episodic / procedural), importance, categories, and entities. Without an LLM key, messages are stored verbatim and everything still works.
3. Reconciliation. Each fact is compared to its most similar existing memories: duplicates are skipped, refinements rewrite in place, contradictions invalidate the old memory and link it to its successor.
4. Retrieval. BM25 and cosine similarity fused with reciprocal-rank fusion, then boosted by recency and importance. Keyword-only when no embedder is configured.
5. Forgetting. Effective importance decays over time; memry sweep invalidates memories that fall below threshold. Category filters (memry search -c diet) narrow any query.

Configuration

Everything works with defaults. Override via env vars, ~/.memry/config.json, or Config(...):

Env var	Default	Notes
MEMRY_DB_PATH	~/.memry/memry.db	single SQLite file
MEMRY_BACKEND	local	local | mem0 (needs memry[mem0])
MEMRY_DEFAULT_USER	default	user scope when the agent doesn't pass one
MEMRY_LLM_PROVIDER	auto	anthropic | openai | ollama | none - auto-detected from ANTHROPIC_API_KEY / OPENAI_API_KEY
MEMRY_LLM_MODEL	per provider	claude-opus-4-8 / gpt-5-mini / llama3.1 (use claude-haiku-4-5 for cheaper extraction)
MEMRY_EMBEDDING_PROVIDER	auto	openai | ollama | voyage | hash | none
MEMRY_API_KEY	-	bearer token for the REST/MCP HTTP server

Anthropic extraction requires the optional SDK: pip install "memry[anthropic]".

Evaluation

memry eval --dataset evals/datasets/synthetic_v1.jsonl -k 5

The harness ingests each case through the full write path, then scores retrieval (recall@k, MRR, latency p50/p95). It is deterministic and offline, so it runs in CI. LoCoMo and LongMemEval can be formatted into the same JSONL schema to compare providers, configs, and backends (including the Mem0 adapter) under identical conditions. The landscape survey behind the design is in docs/research/competitive-analysis.md.

Project layout

src/memry/
  models.py            # Episode / Memory / MemoryEvent (bi-temporal, provenance)
  config.py            # env + file config, provider auto-detection
  store.py             # MemoryStore - the public API
  retrieval.py         # hybrid search: RRF + recency + importance
  backends/            # storage interface, local SQLite engine, Mem0 adapter
  intelligence/        # extraction, reconciliation, decay, context building
  providers/           # LLMs (Anthropic/OpenAI/Ollama) & embeddings (+hash fallback)
  mcp_server.py        # MCP tools (stdio + streamable HTTP)
  rest.py              # REST API + dashboard + /mcp mount
  evals/               # retrieval eval harness

Development

pip install -e ".[dev]"
pytest

License

Apache-2.0