Skip to main content

Memory for your AI. It forgets the junk, keeps what matters.

Project description

forget

Memory for your AI. It forgets the junk, keeps what matters.

PyPI npm LongMemEval License

forget — tell Claude Code once, Codex remembers. Local-first memory for AI agents, 78.4% on LongMemEval fully local.

On LongMemEval, the standard long-term-memory benchmark (full 500-question set, GPT-4o reader and judge):

configuration accuracy
GPT-4o full-context, no memory system (paper baseline) 60.6%
Forget, fully local memory pipeline (Qwen 14B observer) 76.2%
Forget, best configuration (GPT-4o observer) 81.8%
GPT-4o oracle — evidence sessions handed to the reader (paper ceiling) 87.0%

Knowledge-update questions, where memory products usually fail: 92.3% (best config). Our weakest category, so you don't have to dig for it: single-session-preference, 43.3%. Per-question outputs and run configs are in research/longmemeval/runs/.

Every LLM session starts from zero. Forget gives your AI a long-term memory that it actually maintains: an observation gate decides what is worth keeping at all, stale facts get retired non-destructively when new ones supersede them, and a consolidation loop keeps the store honest while you sleep.

The secret is in the name. Good memory is not storing everything — it is forgetting well.

  • Forget re-explaining. Your AI remembers your decisions, preferences, and context across sessions and across tools.
  • Forget asking. Memory arrives on its own: a context capsule opens each session, relevant memories are pushed mid-conversation, and a conflict alert fires if you're about to act on a fact that was later corrected.
  • Forget context limits. Durable facts live outside the window and come back only when relevant.
  • Forget trusting us. Everything runs on your machine, in one SQLite file you own. End-to-end encrypted sync is next — built so that we cannot read what we carry. (Why this matters →)
  • Forget nothing — that matters.

How it works

conversation ──▶ observation gate ──▶ durable facts (SQLite)
                     │                     │
                     ▼                     ▼
               "junk, skip it"      search ◀── temporal rerank
                                           │
              consolidation loop ◀── supersede (non-destructive)
  • Observation gate — extraction keeps only durable, useful facts. Questions, chit-chat, and assistant filler never become "memories".
  • Supersede / confirm — when a fact changes, the old memory is demoted and linked to its replacement, not deleted; when an unverified claim gets its receipt, confirm_memory promotes it. History stays auditable.
  • Trust labels — every memory carries provenance (who vouches for it) and recall returns a permission: green (user-stated or tool-observed) = safe to act on, yellow (agent-inferred) = verify first, red (superseded) = reference only.
  • Temporal rerank — recent facts outrank stale ones at recall time.
  • Consolidation — a background pass merges, dedupes, and retires.
  • Single file — everything lives in one SQLite database. No vector DB, no external services. Dependencies: FastAPI and httpx. That's it.

Quickstart

pip install 'forget-ai[server]'
forget-server install-service   # login service (launchd/systemd) — survives reboots
# or, to try it in the foreground first:
forget-server run

forget-server status tells you what's true; forget-server uninstall-service removes it. The server binds to localhost only. Data lives at ~/.forget/mem1.sqlite3, created 0600 in a private directory — set MEM1_DB_PATH to put it elsewhere.

Store and recall:

curl -X POST localhost:8000/v1/memories/ \
  -H 'Content-Type: application/json' \
  -d '{"text": "We settled on Paddle for payments.", "user_id": "me"}'

curl -X POST localhost:8000/v1/memories/search/ \
  -H 'Content-Type: application/json' \
  -d '{"query": "what did we pick for payments?", "user_id": "me"}'

Connect your AI (MCP)

Forget speaks MCP over streamable HTTP at /mcp — 42 tools including search_memories, add_memory, supersede_memory, confirm_memory, and prepare_context_autopilot.

Connect the local server started above without hand-editing config files:

npx forget-connect

The CLI preserves other MCP servers, backs up existing files once, installs the marked instruction layer (Claude Code / Codex / Claude Desktop), and — for Claude Code — installs the hooks layer: a session-start context capsule, per-turn push recall with conflict-zone alerts, and session capture feeding a usage-outcome flywheel. Hooks are fail-open (a stopped server never blocks a session), preserve any foreign hooks byte-for-byte, and are skippable with --no-hooks. npx forget-connect doctor diagnoses config, rules, hooks, and the MCP connection; disconnect reverses everything.

The legacy hosted service remains reachable with npx forget-connect --hosted --user-id <memory-user> --app-id <project> while it is phased out in favor of local-first.

Manual configuration — prefer the scoped endpoint /mcp/<app>/http/<user> so each user × client pair keeps its own memory pool (the connect CLI configures this for you; plain /mcp falls back to a server-side default scope of your OS username and says so in each write):

Claude Code

{ "mcpServers": { "forget": { "type": "http", "url": "http://localhost:8000/mcp/claude-code/http/<your-username>" } } }

Codex (~/.codex/config.toml)

[mcp_servers.forget]
url = "http://localhost:8000/mcp/codex/http/<your-username>"

Claude Desktop (bridge via mcp-remote, claude_desktop_config.json)

{ "mcpServers": { "forget": { "command": "npx",
  "args": ["-y", "mcp-remote@latest", "http://localhost:8000/mcp/claude-desktop/http/<your-username>"] } } }

Tip — make agents actually use it. npx forget-connect handles this: it installs both the instruction rules and the hooks that push memory into sessions unasked. If you configure manually instead, at minimum add to your global instruction file (~/.claude/CLAUDE.md, ~/.codex/AGENTS.md): "ALWAYS call search_memories on forget FIRST — before any shell command — whenever the user refers to their own past decisions."

Security

Forget is local-first: with no configuration it binds to localhost and accepts unauthenticated requests. Before exposing it to a network, set:

FORGET_REQUIRE_AUTH=true
FORGET_API_KEY=<your key>        # sent as "Authorization: Bearer <key>"

Compatibility

The REST surface and MCP tool names are API-compatible with mem0 and OpenMemory clients — point an existing client at Forget and it works.

Sync — end-to-end encrypted (in design)

The engine is local today. What's next is multi-device sync that cannot betray you: memories — and their embeddings — are encrypted on your device before they touch a server. The server stores ciphertext and nothing else. Not us, not an acquirer, not a subpoena.

The reasoning is in MANIFESTO.md; the key hierarchy, record format, and device-auth design are in docs/vault-design.md.

Building AI for therapy, law, or health? Your users' memories are your liability. We're taking design partners — founder@multi-turn.ai.

License

Apache-2.0.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

forget_ai-0.3.5.tar.gz (2.0 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

forget_ai-0.3.5-py3-none-any.whl (265.9 kB view details)

Uploaded Python 3

File details

Details for the file forget_ai-0.3.5.tar.gz.

File metadata

  • Download URL: forget_ai-0.3.5.tar.gz
  • Upload date:
  • Size: 2.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for forget_ai-0.3.5.tar.gz
Algorithm Hash digest
SHA256 3a5a27ef4789630b328bbab8ed669943a2d5d9154aa7517c6ffdaef40843f556
MD5 d25977896b7f1c4dc879c0157c46d126
BLAKE2b-256 ad353fe773fee2f8c7d78121d1bcfacd93bb5af6acbfe503821ea30e70431941

See more details on using hashes here.

Provenance

The following attestation bundles were made for forget_ai-0.3.5.tar.gz:

Publisher: publish.yml on multi-turn-inc/forget

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file forget_ai-0.3.5-py3-none-any.whl.

File metadata

  • Download URL: forget_ai-0.3.5-py3-none-any.whl
  • Upload date:
  • Size: 265.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for forget_ai-0.3.5-py3-none-any.whl
Algorithm Hash digest
SHA256 a308cc7ed50b0f460de24c538fbf95cfc04433bf6f86ea53017e7c73f03ece1b
MD5 e511c207e32938077e456a278da7cf1b
BLAKE2b-256 8e5fbeb5109d68afdbf7d2e147d20a8d4bc5b95d92a02a62c4126a2bc15d09a8

See more details on using hashes here.

Provenance

The following attestation bundles were made for forget_ai-0.3.5-py3-none-any.whl:

Publisher: publish.yml on multi-turn-inc/forget

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page