remem-ai 0.5.11

Persistent memory for Claude Code and Codex — single binary, automatic context
Documentation

Remem Memory

Automatic memory for Claude Code and Codex.

Language: English | 简体中文

remem is a single Rust binary that automatically captures, distills, and injects project context across Claude Code and Codex sessions: decisions, patterns, preferences, and learnings. Stop re-explaining your project every new session.

CI License: MIT

Remem Memory terminal demo

The Problem

  • Session amnesia: every new Claude Code or Codex session starts from zero.
  • Lost context: bug-fix rationale and design decisions disappear after the session ends.
  • Preference fatigue: the same preferences must be repeated every session.
  • No continuity: long-running work is hard to resume with confidence.

How remem Solves This

Without remem With remem
"We use FTS5 trigram tokenizer..." (every session) Injected automatically from memory
"Do not use expect() in non-test code" (again) Preference surfaced before you ask
"Last session we decided to..." (reconstruct manually) Decision history with rationale
Bug context lost after session ends Root cause + fix preserved

Install

# Option 1: Homebrew
brew install majiayu000/tap/remem

# Option 2: Quick install (prebuilt GitHub Release binary, then auto-configures hooks)
curl -fsSL https://raw.githubusercontent.com/majiayu000/remem/main/install.sh | sh

# Pin a specific release or install into a custom bin directory
REMEM_VERSION=v0.4.5 curl -fsSL https://raw.githubusercontent.com/majiayu000/remem/main/install.sh | sh
REMEM_INSTALL_DIR=/usr/local/bin curl -fsSL https://raw.githubusercontent.com/majiayu000/remem/main/install.sh | sh
REMEM_NO_CONFIG=1 curl -fsSL https://raw.githubusercontent.com/majiayu000/remem/main/install.sh | sh

# Option 3: Manual GitHub Release download
curl -LO https://github.com/majiayu000/remem/releases/latest/download/remem-darwin-arm64.tar.gz
tar xzf remem-darwin-arm64.tar.gz
mv remem ~/.local/bin/
codesign -s - -f ~/.local/bin/remem  # required on macOS ARM

# Option 4: Cargo
cargo install remem-ai --bin remem

# Option 5: Build from source
git clone https://github.com/majiayu000/remem.git
cd remem
cargo build --release
cp target/release/remem ~/.local/bin/
codesign -s - -f ~/.local/bin/remem  # required on macOS ARM

# If you used Cargo or source install, configure detected Claude Code/Codex hooks + MCP
remem install

# Optional: target one host explicitly
remem install --target codex    # auto | claude | codex | all
remem install --dry-run         # preview config changes

Restart your AI coding tool after installation.

Use one canonical remem command on PATH. Standalone and source installs should normally live at ~/.local/bin/remem; Windows standalone installs should use %USERPROFILE%\.local\bin\remem.exe. If you install through a package manager such as Homebrew or Cargo, update through that same channel and avoid keeping a second manual copy earlier or later on PATH. remem doctor and remem install --dry-run warn when multiple remem executables are visible.

Updating an Existing Install

When you replace the binary manually, rerun remem install so existing Claude Code and Codex hook commands pick up the current host-aware settings:

cargo build --release
cp target/release/remem ~/.local/bin/
codesign -s - -f ~/.local/bin/remem  # required on macOS ARM
remem install --target all

Verify the installed hooks include host-specific context commands:

jq -r '.hooks.SessionStart[]?.hooks[]?.command' ~/.claude/settings.json
jq -r '.hooks.SessionStart[]?.hooks[]?.command' ~/.codex/hooks.json

Expected commands are host-only; model, executor, and context policy live in ~/.remem/config.toml:

/Users/you/.local/bin/remem context --host claude-code
/Users/you/.local/bin/remem context --host codex-cli

Use With Codex

For Codex-only setup:

remem install --target codex
remem doctor
remem status

remem install --target codex configures Codex in three ways:

  • Enables Codex hooks with [features].hooks = true in ~/.codex/config.toml
  • Registers remem as an MCP server in ~/.codex/config.toml
  • Writes Codex hook commands to ~/.codex/hooks.json
  • Creates or updates ~/.remem/config.toml memory-AI profiles

After restarting Codex, remem automatically injects relevant project memory at session start and summarizes the session at stop. Codex can also call the MCP tools exposed by remem mcp, including search, get_observations, save_memory, workstreams, and timeline.

The default Codex integration is intentionally low-noise: it uses SessionStart for context injection and Stop for background summarization. Codex uses strict duplicate-injection gating via [memory_ai.hosts."codex-cli"].context_gate = "strict", so a mid-chat SessionStart repeat stays silent after the first injection for the same session. It does not install high-frequency Bash observation by default.

Distribution Channels

Currently published:

  • Homebrew: brew install majiayu000/tap/remem
  • GitHub Releases: prebuilt binaries for macOS and Linux on x64/arm64
  • crates.io: cargo install remem-ai --bin remem
  • Source build: cargo build --release

Prepared but not published:

  • npm wrapper package in npm/remem; publish requires npm auth or an NPM_TOKEN secret

Good next channels:

  • apt/yum packages: useful later, after the binary install path and service story are stable across Linux distributions

How It Works

remem uses host-specific hook strategies:

Claude Code workflow
        |
        |- SessionStart      -> Inject memories + preferences
        |- UserPromptSubmit  -> Register session, flush stale queues
        |- PostToolUse       -> Capture tool operations (queued, <1ms)
        '- Stop              -> Summarize in background (~6ms return)

Codex workflow
        |
        |- SessionStart      -> Inject memories + preferences
        '- Stop              -> Summarize in background with Codex CLI

Codex does not install a high-frequency PostToolUse(Bash) observe hook by default. Shell-heavy sessions must use the coalesced capture pipeline before per-command capture is enabled again; otherwise Bash output can create an unbounded backlog. Existing legacy hooks are also ignored unless REMEM_ENABLE_CODEX_BASH_OBSERVE=1 is set explicitly.

The capture pipeline starts with an append-only ledger: captured_events stores raw hook/session evidence, event_blobs keeps large payloads out of prompt-sized rows, and extraction_tasks coalesces work by host/project/session instead of creating one LLM job per tool call. Curated memory remains the promoted output of this pipeline, not the raw event itself.

Search Architecture

remem uses 4-channel Reciprocal Rank Fusion (RRF) inspired by Hindsight:

Query: "database encryption"
        |
   +----+------------------------------------+
   |          4 parallel channels            |
   +-----------------------------------------+
   | 1. FTS5 (BM25)   trigram + OR           |
   | 2. Entity Index  1600+ entities         |
   | 3. Temporal      "yesterday"/"last week" |
   | 4. LIKE fallback short tokens           |
   +-------------+---------------------------+
                 |
        RRF score = sum(1 / (60 + rank_i))
                 |
             Top-K merged results

Enhancements:

  • Entity graph expansion (2-hop multi-hop retrieval)
  • Project-scoped entity search (no cross-project leakage)
  • CJK segmentation support
  • Chinese-English synonym expansion
  • Title-weighted BM25 (bm25(fts, 10.0, 1.0))
  • Content-hash deduplication via topic_key
  • Multi-step retrieval guidance in MCP tool descriptions

Benchmark Snapshot

LoCoMo

Full LoCoMo benchmark (10 conversations, 1540 QA pairs after adversarial skip):

Config Overall Single-hop Multi-hop Temporal Open-domain Ingest Model
v1 (fair) 56.8% 67.1% 39.0% 53.9% 28.1% per-turn gpt-5.4
v2 (optimized) 62.7% 72.3% 61.3% 40.5% 56.2% session_summary gpt-5.4

Internal Eval (1777 real memories)

Metric Value
MRR 0.858
Hit Rate@5 1.000
Dedup rate 1.0%
Project leak 0%
Self-retrieval 100%

Local QA Eval

python3 eval/local/run_local_eval.py --db ~/.remem/remem.db --n 20
Metric Score
Overall 85.0%
Decision 77.8%
Discovery 87.5%
Preference 100%
Source in top-20 90.0%

Requires explicit --db plus .env with OPENAI_API_KEY (optional OPENAI_BASE_URL, OPENAI_MODEL).

Sandboxed E2E Eval

remem eval-e2e
remem eval-e2e --json

Runs a deterministic coding-agent memory corpus through the real local REST API boundary (POST /api/v1/memories, then GET /api/v1/search) with a temporary REMEM_DATA_DIR. The default run removes the sandbox directory afterward, so it does not touch ~/.remem or other real memory data. Use --keep-data-dir when you need to inspect the generated database.

Token Usage And Cost Reporting

remem records an AI usage ledger for its own background extraction, summary, compression, and promotion calls. The CLI can report daily and weekly token usage and estimated cost:

remem usage --days 14 --weeks 8
remem usage --project /path/to/project --days 30 --weeks 12

The report includes calls, input tokens, cache tokens, output tokens, reasoning tokens, total tokens, estimated USD cost, and a precision note. Usage rows are tagged by source:

  • anthropic_usage: provider-reported usage from the Anthropic Messages API
  • codex_log: exact token counts parsed from the current codex exec --json turn.completed.usage event
  • text_estimate: fallback estimate from prompt/response text length

Cost is an estimate, not an invoice. Historical rows may be text estimates or may have been repriced from older rows that did not store the exact model.

Memory AI Configuration

Memory AI execution is configured in ~/.remem/config.toml (override path with REMEM_CONFIG). Hooks pass only --host; the config maps each host to one profile used by summarize, flush/extract, compress, and dream.

remem config path
remem config show
remem config set memory_ai.profiles.codex.model gpt-5.2

For normal model switching, prefer the higher-level remem model commands:

remem model current
remem model list
remem model use cheap
remem model use balanced --dry-run
remem model use gpt-5.2 --reasoning medium
remem model use haiku --host claude-code
remem model test
remem model test --live
remem model rollback

remem model test only validates the selected config unless --live is set. remem model use saves a rollback backup before writing the config. Built-in presets are Codex-focused; use explicit model names for Claude Code profiles.

Default Codex profile:

[memory_ai.hosts."codex-cli"]
memory_profile = "codex"
context_gate = "strict"
context_color = true
capture_adapter = "codex-cli"

[memory_ai.profiles.codex]
executor = "codex-cli"
model = "gpt-5.2"
path = "codex"

Commands

remem install
remem uninstall
remem doctor
remem search "query"
remem search "query" --branch main --type decision --multi-hop --offset 10
remem search "query" --json
remem show <id>
remem show <id> --json
remem eval
remem eval-e2e --json
remem eval-local
remem backfill-entities
remem encrypt
remem api --port 5567
remem status
remem status --json
remem config show
remem config set memory_ai.profiles.codex.model gpt-5.2
remem model current
remem model list
remem model use balanced --dry-run
remem model use gpt-5.2 --reasoning medium
remem model use haiku --host claude-code
remem model test [--live]
remem model rollback
remem usage --days 14 --weeks 8
remem pending list-failed
remem pending list-failed --json
remem pending retry-failed --dry-run
remem pending purge-failed --dry-run --older-than-days 7
remem govern --action stale --dry-run --json <id>
remem review list
remem review approve <id>
remem review discard <id>
remem review edit <id> --text "updated memory"
remem preferences list
remem preferences add "text"
remem preferences remove 42
remem context --cwd .
remem cleanup --dry-run --json
remem cleanup
remem dream [--project X] [--profile NAME] [--dry-run]
remem install --target codex
remem mcp
remem sync-memory --cwd .

Scriptable JSON output

These commands emit one JSON object and no human text on stdout when --json is set:

Command Stable top-level fields
remem status --json version, database, totals, capture_pipeline, pending_observations, jobs, worker_daemon, today, top_projects
remem cleanup --dry-run --json dry_run, retention_days, plan, applied
remem search ... --json query, project, memory_type, limit, offset, branch, include_stale, multi_hop_requested, explain_requested, count, has_more, next_offset, results, raw_hits, multi_hop, explain_details
remem show <id> --json found, id, memory
remem pending list-failed --json project, limit, count, failed
remem govern ... --json dry_run, action, reason, affected

REST API

remem api --port 5567
TOKEN=$(cat ~/.remem/.api-token)
curl -H "Authorization: Bearer $TOKEN" http://127.0.0.1:5567/api/v1/status

Library users who build the router directly should call remem::api::ensure_api_token() before remem::api::build_router(...).

Endpoint Method Description
/api/v1/search?query=&project=&type=&limit=&offset=&branch=&multi_hop= GET Search memories
/api/v1/memory?id= GET Get one memory
/api/v1/memories POST Save memory
/api/v1/status GET System status

Security

  • SQLCipher encryption at rest (remem encrypt)
  • Data directory permissions (0700)
  • Key file permissions (0600)
  • REST API binds localhost only (127.0.0.1) and requires Authorization: Bearer $(cat ~/.remem/.api-token)
  • API token file permissions (0600)

Architecture Docs

See docs/ARCHITECTURE.md for full internals and data flow.

Uninstall

remem uninstall
rm -rf ~/.remem

License

MIT