remem: Local-first Coding Agent Memory for Claude Code and OpenAI Codex

Stop re-explaining your project every new coding-agent session.

Language: English | 简体中文

remem is a single Rust binary that automatically captures, distills, searches, and injects project memory across Claude Code, OpenAI Codex, and Codex CLI sessions. It keeps decisions, bug-fix rationale, project patterns, and preferences available through hooks, MCP, CLI, and REST without requiring an external database.

What You Get

• Claude Code, OpenAI Codex, and Codex CLI remember project decisions across sessions.

• Bug-fix rationale, preferences, and project patterns are searchable.

• Memory stays local by default with SQLite and SQLCipher.

• Hooks, MCP tools, CLI commands, and a localhost REST API use the same store.

• Current-memory contracts expose staleness, temporal/as-of truth, citation usage, and injection audit state instead of treating recall as a black box.

• User-context controls keep personal claims, profile summaries, suppression feedback, and Markdown export explicit and reviewable.

• One Rust binary; no hosted database or separate memory service.

Related MCP server: Longhand

Install

Install the remem binary:

brew install majiayu000/tap/remem

Then configure hooks and MCP for your installed coding agents:

REMEM_INSTALL_BINARY="$(brew --prefix remem)/bin/remem" remem install --target codex
# or: REMEM_INSTALL_BINARY="$(brew --prefix remem)/bin/remem" remem install --target claude
# or: REMEM_INSTALL_BINARY="$(brew --prefix remem)/bin/remem" remem install --target all

If you do not use Homebrew:

curl -fsSL https://raw.githubusercontent.com/majiayu000/remem/main/install.sh | env REMEM_NO_CONFIG=1 sh
~/.local/bin/remem install --target codex
# or: ~/.local/bin/remem install --target claude
# or: ~/.local/bin/remem install --target all

remem install can auto-detect existing Claude Code and Codex CLI config directories. On first-time setups, use --target codex, --target claude, or --target all so remem can create the selected config files.

Run remem doctor when you want to verify or troubleshoot the integration.

Success Check

Start a new Claude Code or Codex CLI session after installation. remem should inject relevant project memory at session start and summarize durable memory after the session stops. Then run:

remem status
remem search "last decision"

For Codex CLI, remem install creates or updates:

• ~/.remem/.key and the encrypted ~/.remem/remem.db

• ~/.remem/config.toml memory-AI profiles

• Codex MCP registration in ~/.codex/config.toml

• Codex SessionStart/Stop hooks in ~/.codex/hooks.json

For a Codex-only setup, remem doctor reports Schema, Key format, Database, and the Codex Hooks/MCP rows as ok. If Claude Code config directories already exist but were not auto-detected during installation, run remem install --target claude or remem install --target all. If doctor warns about multiple remem binaries, follow the printed install-path fix so hooks keep using the intended binary.

Install With Your Coding Agent

Paste this into Claude Code or Codex CLI:

Install remem for this repository. Use the official README. Configure it for this agent, run remem doctor, verify that session memory is working, and summarize what was installed.

Why remem if Claude Code and Codex already have memory?

Built-in memory is useful for concise preferences and stable project guidance.

remem is for engineering memory that needs to be searchable, auditable, project-scoped, and recoverable:

• Search past decisions, bug fixes, and rationale with remem search

• Inspect why a memory was injected with remem why

• Keep memory local with SQLite and SQLCipher

• Use MCP and REST APIs from coding agents and local tools

• Track usage and background memory cost

• Avoid hand-maintaining large MEMORY.md or CLAUDE.md files

How remem Solves Session Amnesia

Other Install Channels

# Quick install options
curl -fsSL https://raw.githubusercontent.com/majiayu000/remem/main/install.sh | env REMEM_NO_CONFIG=1 REMEM_VERSION=vX.Y.Z sh
~/.local/bin/remem install --target codex

curl -fsSL https://raw.githubusercontent.com/majiayu000/remem/main/install.sh | env REMEM_NO_CONFIG=1 sh
~/.local/bin/remem install --target codex

curl -fsSL https://raw.githubusercontent.com/majiayu000/remem/main/install.sh | env REMEM_NO_CONFIG=1 REMEM_INSTALL_DIR=/usr/local/bin sh
remem install --target codex

# npm wrapper
npm install -g @remem-ai/remem
remem install --target codex

# Cargo
cargo install remem-ai --bin remem
remem install --target codex

# 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
~/.local/bin/remem install --target codex

# 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
~/.local/bin/remem install --target codex

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

remem install --target codex configures Codex in four 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. For Codex hook invocations, remem emits the supported hookSpecificOutput.additionalContext JSON shape so the memory block is model-visible through the hook contract instead of asking the assistant to repeat a separate Remem context: line. Codex may still show its own completed hook context block in the UI; remem does not add a second assistant-rendered status line. 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.

Codex Plugin

This repository includes a local Codex plugin wrapper in plugins/remem. The plugin exposes remem mcp and a Remem skill while keeping hook activation explicit. The complete product direction is documented in docs/spec-codex-plugin-complete-design.md; the current plugin is the local development foundation, not the final self-contained plugin experience. To try it from a local checkout:

codex plugin marketplace add .
codex plugin add remem@remem-local

After installing the plugin, start a new Codex thread. To enable automatic SessionStart context injection and Stop summarization, run:

cargo build --release
node plugins/remem/scripts/activate-codex.js --dry-run
node plugins/remem/scripts/activate-codex.js

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

• npm: npm install -g @remem-ai/remem

• Source build: cargo build --release

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.

Remem vs Built-in MEMORY.md

Built-in memory files are enough when the context is small, stable, and worth editing by hand: project rules, setup notes, and a short list of durable preferences. Keep using them for facts that should be obvious at first glance.

Remem is meant for the parts that should not depend on manual upkeep:

• Automatic capture and recall: hooks summarize sessions into a SQLite memory store, while remem search, remem show, timeline, and MCP get_observations retrieve details on demand.

• A bridge to native memory: remem sync-memory --cwd . writes a compact remem_sessions.md entry for Claude Code native memory when that directory exists, with a MEMORY.md pointer and a size guard. Full detail stays in the database and is fetched with remem search.

• A human-editable mirror: remem export --markdown --output ./remem-memory --project "$PWD" writes one .md file per curated memory to an empty directory. After editing those files, remem import markdown --source ./remem-memory updates existing rows and rebuilds search, entity, embedding, and current-state indexes. Export refuses non-empty directories to avoid overwriting manual edits.

• Failure-loop learning: raw transcripts that contain both concrete build/test failure evidence and an explicit "stop and challenge the hypothesis" style lesson feed an idempotent failure lesson before summary cooldown, duplicate, or skip exits.

• Governance and auditability: remem why <id>, remem govern --action stale --dry-run --json <id>, remem status --json, and remem usage --days 14 --weeks 8 show why a memory is visible, what would change, store health, and memory-AI token/cost accounting.

• Current-memory accountability: staleness labels, temporal facts, source-anchor checks, injection item audit rows, and citation/usage events show why a memory is current, stale, dropped, abstained, cited, or ignored.

• User-context governance: remem user ..., remem memory suppress ..., profile export, and non-retention policy checks keep personalized recall explicit instead of silently blending every user fact into every project.

• Deterministic checks before claims: local gates include cargo test -q context::claude_memory --lib, cargo test -q eval::golden --lib, cargo test -q eval::governance --lib, and remem eval-e2e --json.

Do not read this as a published claim that remem beats a carefully maintained MEMORY.md on coding tasks. The flagship no-memory / remem / curated-file A/B is still a separate benchmark requirement; until it is published, the honest claim is capability coverage and reproducible local checks.

Embedding Provider Configuration

Vector retrieval is controlled by the [embeddings] section in ~/.remem/config.toml:

[embeddings]
provider = "local"        # api | local | feature-hash | off
fallback = "feature-hash" # optional; omit for fail-closed
model = "text-embedding-3-small"
base_url = "https://api.openai.com/v1"
api_key_env = "OPENAI_API_KEY"
model_dir = ""            # future local model cache; defaults under REMEM_DATA_DIR

Environment overrides keep the existing REMEM_EMBEDDINGS_* names, including REMEM_EMBEDDINGS_PROVIDER, REMEM_EMBEDDINGS_FALLBACK, REMEM_EMBEDDINGS_MODEL, REMEM_EMBEDDINGS_MODEL_DIR, REMEM_EMBEDDINGS_BASE_URL, REMEM_EMBEDDINGS_API_KEY, REMEM_EMBEDDINGS_API_KEY_ENV, REMEM_EMBEDDINGS_DIMENSIONS, and REMEM_EMBEDDINGS_TIMEOUT_SECS.

local and feature-hash are separate provider states. Until the local semantic model runtime lands, both use remem-local-feature-hash-v1; selecting feature-hash explicitly labels the non-semantic fallback. provider = "off" disables query embeddings, vector fusion, vector writes, and embedding backfill. Existing stored vectors remain in SQLite but are ignored while the provider is off.

remem status --json exposes an embedding object with configured provider, active provider, active model id, degraded/disabled flags, and active-model coverage. remem doctor reports unavailable configured providers, visible fallback activation, low active-model coverage, and mixed model/dimension profiles that need backfill.

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

Public Artifact Suite (Directional Only)

The checked-in eval/public artifacts now separate memory-system capability evidence from coding-agent outcome evidence. Reproduce the public verifier with:

cargo run -- bench verify --root eval/public --json-out /tmp/remem-bench-verify.json
cargo run -- bench report --root eval/public --json-out eval/public/reports/baseline.json --markdown-out eval/public/reports/baseline.md

The current directional report verifies 4 manifests, 4 reports, 25 run artifacts, and 125 artifact files. It includes:

• remem-code-memory: 8 memory QA runs covering temporal/as-of answers, stale decision avoidance, conflicts, workstream continuity, prior bug root cause, architecture constraints, file/source anchors, and user-context relevance.

• adversarial-policy: 15 non-retention cases covering secrets, credentials, payment data, unsupported assistant claims, unapproved external sources, roleplay, negation, same-name repos, branch divergence, stale file anchors, and unresolved conflicts.

• issue385-smoke: one committed coding-agent smoke run artifact with memory-contract fields for remem runs. The full issue385-v1 fixture pack is referenced for dry-run reproduction, but it is not yet part of the verified public outcome report.

The report is intentionally labeled directional_only_no_public_claim. README and release wording must stay directional and avoid broad outcome or coding-task outcome claims until the public claim gate in docs/release-lifecycle.md passes.

Isolated Coding-Agent Baseline (Internal, Not A Public Claim)

eval/coding-bench/reports/baseline.json contains an isolated 5-task, 3-runs-per-condition baseline generated with codex-cli 0.142.1 and gpt-5.5: no_memory resolved 2/15, remem resolved 15/15, and curated_file resolved 15/15. This is useful engineering evidence, but it predates the public 16-task v1 fixture pack and must be regenerated before it supports stronger product claims.

LoCoMo (Informational Only)

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

This snapshot is a historical footnote and is not a CI or release gate. Use the golden retrieval eval for deterministic gating; LoCoMo remains useful only for manual, informational comparison because the methodology is disputed.

Internal Eval (1777 real memories)

Local QA Eval

python3 eval/local/run_local_eval.py --db ~/.remem/remem.db --n 20

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" --include-suppressed
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 memory suppress memory:123 --reason "not relevant anymore"
remem memory unsuppress memory:123 --reason "needed again"
remem memory feedback memory:123 --value not-relevant
remem memory suppressions list
remem user remember "For this repo, review specs before code"
remem user remember --scope repo --owner-key /repo/path --type goal "Ship remem user context"
remem user claims list
remem user claims why <id>
remem user claims edit <id> --text "updated claim"
remem user claims suppress <id>
remem user claims unsuppress <id>
remem user claims delete <id>
remem user summary show
remem user summary refresh
remem user summary edit --text "updated profile summary"
remem user summary sources
remem user profile export --format markdown --output profile.md
remem user recall "review the remem user context design"
remem user review inbox
remem user review approve <id>
remem user review edit <id> --text "updated candidate"
remem user review reject <id>
remem user review suppress <id>
remem context --cwd .
remem cleanup --dry-run --json
remem cleanup --dry-run --json --archived-failures
remem cleanup
remem workstreams merge --project <path> --into <canonical_id> <duplicate_id>... --confirm
remem workstreams merge --project <path> --into <canonical_id> <duplicate_id>... --confirm --json
remem dream [--project X] [--profile NAME] [--dry-run]
remem install --target codex
remem mcp
remem sync-memory --cwd .

remem user ... stores explicit user-context claims separately from repo-scoped coding memories. Manual claims default to owner_scope=user, owner_key=user:default, source_kind=manual, and status=active. Suppress and delete commands change status without hard-deleting the audit row; default claim lists exclude suppressed, deleted, expired, not-yet-valid, and restricted claims.

remem user profile export --format markdown writes a derived, read-only snapshot of the user profile remem would use. Without --output it prints to stdout; with --output profile.md it creates a new file and refuses to overwrite existing content. The snapshot names the SQLite database as the source of truth, includes owner/project metadata, active summary provenance, source ids, and active default-eligible claims. Default output excludes suppressed, deleted, expired, future, personal, sensitive, and restricted claims. Use --include-suppressed, --include-sensitive, --include-inactive, --include-deleted, and --include-manual-summaries only for explicit audit; audit rows are labeled with exclusion reasons and text remains redacted unless all applicable audit gates are enabled.

remem memory suppress applies a default-read policy without deleting the source row. Targets can be memory:<id>, claim:<id>, topic:<key>, entity:<name>, pattern:<text>, or a bare memory id/topic key. Default search, SessionStart context, profile-summary sources, preferences, lessons, current-state lookup, MCP search, and REST search exclude active suppressions. Use --include-suppressed on search when an audit needs to inspect suppressed evidence explicitly. remem why <id> reports whether the memory is currently suppressed and which policy matched it. remem memory feedback records relevant, not-relevant, harmful, stale, or too-noisy events without changing ranking by default.

remem user recall <query> retrieves task-aware user context on demand without expanding SessionStart. It combines safe profile summaries, active non-sensitive claims, repo memory, explicitly requested current-state keys, active workstreams, and recent sessions into compact source-attributed context. Default recall excludes suppressed, rejected, deleted, expired, future, personal, sensitive, and restricted claims. Use --include-sensitive and --include-suppressed only for explicit audit. Non-empty recall output includes a usage policy telling agents to apply user context only when it materially improves the answer, prefer invisible adaptation over memory narration, avoid uncited profile inferences, and avoid inventing a profile when no context applies.

remem user review ... governs review-gated user-context candidates before they become active claims. inbox shows pending candidates with risk, sensitivity, confidence, source preview, and block reason. approve applies a candidate to active claims only when it has a stable claim key and non-empty source refs; if an active claim with the same owner/type/key already exists, remem either noops on an exact match or supersedes the old row instead of appending a contradictory active claim. edit applies corrected text, key, or metadata, while reject and suppress close candidates without activating them.

Raw Session Backfill

remem ingest-sessions batch-ingests Claude Code and Codex JSONL transcripts into the raw archive without promoting them to curated memories:

remem ingest-sessions --json
remem ingest-sessions --since 2026-06-01 --root starlight=~/remote-sessions/starlight --json

Default scan roots are ~/.claude/projects and ~/.codex/sessions. Additional --root label=path entries are required roots: a missing explicit root is reported as a failed file so backfills do not silently do nothing. Each raw row keeps the source-root label and the transcript event timestamp, and re-running the command is incremental and idempotent.

Use raw time-window queries for recap or audit workflows that need original chat turns rather than curated memories:

remem raw search "deployment decision" --since 2026-06-01 --until 2026-06-30 --json
remem raw sessions --since 2026-06-01 --until 2026-06-30 --sample 3 --json

remem raw sessions groups rows by source root, project, and session ID, and can include the first N user-message samples per session.

Scriptable JSON output

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

REST API

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

The complete native web API surface is implemented in source version 0.5.109. remem-web should require a published remem >= 0.5.109 release before pointing installed-binary users at the full graph, candidate, or rich-detail experience. Fast /api/v1/health and cached /api/v1/status metadata are implemented in source version 0.5.112. Clients should call /api/v1/capabilities before enabling optional views. Suppression audit opt-in with include_suppressed=true is implemented in source version 0.5.113; default search, browse, graph, and detail reads omit policy-suppressed memories. On-demand user recall is implemented in source version 0.5.114 through CLI, MCP, and POST /api/v1/user/recall. User-context candidate review inbox and apply lifecycle commands are implemented in source version 0.5.115. Guarded automatic user-context candidate extraction from session rollups is implemented in source version 0.5.116; it creates review candidates from captured user conversations and session summaries, and auto-promotes only normal, low-risk explicit user preference or constraint statements cited to and supported by user-authored source events. Source capture, bounded rollup follow-up ranges, stale review guards, edited candidate audit persistence, and claim-key conflict review gates are tightened in source version 0.5.117. Failed bounded follow-up retries and transactional auto-promotion conflict rechecks are tightened in source version 0.5.118. User-context candidate extraction non-retention rules are tightened in source version 0.5.122; transient, speculative, unsafe, assistant-authored, or unapproved external-source content does not enter the candidate queue. Source version 0.5.125 tightens post-review external-source attribution and third-party subject edge cases while preserving valid user-stated workflow preferences.

Use /api/v1/health as the cheap liveness probe and /api/v1/capabilities for feature detection. Use /api/v1/status for dashboard counters no more frequently than the returned cache.ttl_secs; use /api/v1/status?refresh=true only for explicit refresh actions.

Stable core endpoints

Web read-model endpoints

Compatibility aliases

Run the local native API smoke test against a built binary with:

scripts/smoke_native_web_api.sh

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

The Agent Infra Stack

This project is one layer of an open-source stack for running coding agents (Claude Code, Codex) as serious infrastructure. Every piece works standalone; together they close the loop:

remem is the Remember layer — memory that survives the session. It pairs naturally with keepline, which keeps the sessions themselves from getting lost.

License

MIT