chatmem

Local LLM chat history served over MCP. Embedded Postgres + pgvector, cross-tool memory, everything stays on your machine.

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chatmem

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Local LLM chat history, served over MCP.

chatmem is a single-binary utility that captures your LLM conversations via Model Context Protocol tools any client can call (Claude Code, Cursor, aider, custom SDKs), stores everything in an embedded Postgres 18 database with pgvector on your machine, and serves relevant past context back to any LLM on demand.

Only anonymous telemetry (install ID, aggregate counters, opt-in crash reports) leaves your machine — message content never does.

Table of contents

Status

Pre-alpha (v0.0.1-dev). Fully working platforms:

PlatformpgvectorDistributionVerified
darwin/arm640.8.5Homebrew tapLive use
linux/amd640.8.3RPM (zypper/dnf) + DEB + tarballDebian 12 container + openSUSE Leap 15.6 container
linux/arm640.8.3RPM (zypper/dnf) + DEB + tarballDebian 12 container + openSUSE Leap 15.6 container
darwin/amd64——Needs an Intel-Mac pgvector.dylib
windows/amd64——Needs a pgvector.dll + service story

The version skew between macOS and Linux is intentional (Homebrew ships 0.8.5, official Postgres apt ships 0.8.3 for PG18). Both are wire-compatible for our use — same operators, same HNSW support.

Working today:

CommandPurpose
chatmem initProvision the local database, apply schema, print MCP client config.
chatmem mcpSelf-contained stdio MCP server (starts + manages embedded Postgres).
chatmem daemonLong-lived Postgres process. Normally not invoked directly — chatmem install wraps this in a launchd/systemd service so it starts at login and stays warm.
chatmem install / uninstallInstall / remove the user-level background service (launchd on macOS, systemd --user on Linux) that runs chatmem daemon. Keeps embedded Postgres warm so MCP clients get instant startup instead of the 6-8s cold boot.
chatmem start / stop / restartStart / stop / restart the background service.
chatmem statusShow whether the service is installed + whether PG is listening.
chatmem doctorPrint a self-diagnostic report — HOME, EUID, data/cache paths, port availability, telemetry state, ingest reachability, Notion status. Run this first if anything's weird.
chatmem telemetry {enable,disable,status,dump}Manage anonymous telemetry; honors CHATMEM_TELEMETRY=0.
chatmem notion {connect,status,disconnect,list,resync,sample}Manage Notion integration for auto-synthesizing conversations into study/debug pages. See Notion synthesis below.
chatmem importBulk-load an existing chat transcript (JSONL or JSON array) into chatmem. Great for backfilling conversations that happened outside a chatmem-connected client. See Import an existing chat below.

Quickstart

# --- macOS or Linuxbrew (Homebrew tap — ships as a cask) ---
brew tap sid077/chatmem
brew install --cask chatmem

# --- openSUSE / SUSE (zypper self-hosted repo) ---
sudo zypper ar https://sid077.github.io/chatmem/chatmem.repo
sudo zypper --gpg-auto-import-keys refresh
sudo zypper in chatmem

# --- Fedora / RHEL (dnf, same repo) ---
sudo dnf config-manager --add-repo https://sid077.github.io/chatmem/chatmem.repo
sudo dnf install chatmem

# --- Debian / Ubuntu (direct .deb download; APT repo TBD) ---
# curl -sSLo /tmp/chatmem.deb https://github.com/sid077/chatmem/releases/latest/download/chatmem_<ver>_<arch>.deb
# sudo apt install /tmp/chatmem.deb

# --- direct download (any Linux) ---
# curl -sSL https://github.com/sid077/chatmem/releases/latest/download/chatmem_Linux_x86_64.tar.gz \
#     | tar -xz && sudo mv chatmem /usr/local/bin/

# 2) bootstrap the local database — prints the JSON snippet to paste into your MCP client
chatmem init

# 3) paste into ~/.claude/mcp.json (or ~/.cursor/mcp.json), restart the client

# 4) verify tools appear
#    In Claude Code, ask: "list your MCP tools" — you should see
#    record_message, search_history, get_conversation from the 'chatmem' server.

Claude Code (terminal claude, 2.1.x+):

claude mcp add --scope user chatmem /opt/homebrew/bin/chatmem mcp
claude mcp list   # should show: chatmem ✓ Connected

Claude Desktop app — merge into ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "chatmem": {
      "command": "/opt/homebrew/bin/chatmem",
      "args": ["mcp"]
    }
  }
}

Fully quit + relaunch the app after editing.

Codex — merge into ~/.codex/config.toml:

[mcp_servers.chatmem]
command = "/opt/homebrew/bin/chatmem"
args = ["mcp"]
startup_timeout_sec = 60

Cursor / Windsurf / other MCP clients — same JSON block as Claude Desktop but in their MCP config file. Consult the client's docs for the path.

MCP tools reference

All three tools are registered by internal/mcp/server.go. Every write conveys model/provider/client_id explicitly — the daemon never infers them — so multiple LLM clients writing into the same database stay cleanly attributed.

record_message

Store a single chat message. Opens a new conversation when conversation_id is empty (then model/provider/client_id are required).

FieldTypeRequiredNotes
conversation_idUUID stringif continuing a conversationOmit to open a new one
roleuser | assistant | system | toolyes
contentstringyesMessage text
modelstringwhen opening a new conversatione.g. claude-opus-4-7
providerstringwhen opening a new conversatione.g. anthropic, openai
client_idstringwhen opening a new conversatione.g. claude-code, cursor, aider
token_countintnoOptional metadata

Returns:

{ "message_id": "<uuid>", "conversation_id": "<uuid>" }

search_history

Search stored chat history. MVP uses Postgres full-text ranking (to_tsvector + plainto_tsquery); semantic re-ranking via ONNX embeddings is the next milestone. Returns one hit per conversation (MMR-lite), packed to token_budget.

FieldTypeRequiredNotes
querystringyesFree-text query
top_kintnoDefault 10, max 100
token_budgetintnoTotal snippet tokens (default 4000)
modelstringnoFilter by model id
client_idstringnoFilter by client id
sinceRFC3339 stringnoLower bound on created_at
untilRFC3339 stringnoUpper bound on created_at
conversation_idsarray of UUIDsnoRestrict to these conversations

Returns both a rendered text block (visible to any MCP client) and a structured payload for programmatic use:

Rendered text (Content):

2 hit(s) for "kafka retention"

── hit 1 ──
role:         user
conversation: 8a2f…
message:      def6…
created:      2026-07-20T10:15:32Z
score:        0.2341
snippet:
kafka retention is set at 7 days for the ingest topic…

Structured (StructuredContent):

{
  "hits": [
    { "message_id": "<uuid>", "conversation_id": "<uuid>", "role": "user",
      "snippet": "...", "score": 0.147, "created_at": "2026-07-20T10:15:32Z" }
  ]
}

(Before v0.0.2 Content was a bare hit count — Windsurf/Cascade and any client that ignores StructuredContent would show no snippet text.)

get_conversation

Fetch a conversation and its messages, ordered by created_at ascending. Cursor-paginated via after.

FieldTypeRequiredNotes
conversation_idUUID stringyes
limitintnoDefault 100, max 500
afterRFC3339 stringnoReturn messages strictly after this timestamp

Returns both a rendered text block and a structured payload.

Rendered text (Content):

conversation 8a2f…
model:    anthropic / claude-opus-4-7
client:   claude-code
started:  2026-07-20T10:15:32Z
updated:  2026-07-20T10:20:15Z
messages: 3

── user @ 2026-07-20T10:15:32Z ──
hello from chatmem

── assistant @ 2026-07-20T10:15:35Z ──
hi back

Structured (StructuredContent):

{
  "conversation": {
    "id": "<uuid>", "client_id": "...", "model": "...", "provider": "...",
    "title": null, "started_at": "...", "updated_at": "..."
  },
  "messages": [
    { "id": "<uuid>", "role": "user", "content": "...", "token_count": 0, "created_at": "..." }
  ],
  "next_after": null
}

Notion synthesis (v0.3.0 — multi-pass with coverage guarantee)

After each conversation, chatmem publishes a structured, concept-organized Notion page — a study-mode page for learning conversations, or a debug-mode page for problem-solving sessions. The pages are optimized for revision: TL;DR at the top, Mermaid diagrams for anything with structure or flow, citations back to the original messages, full transcript collapsed at the bottom.

v0.3.0 quality guarantees: the LLM extracts atomic facts from every message first, then composes the Summary from that inventory. Chatmem refuses to write to Notion unless the Summary cites ≥ 95% of the messages that carry non-trivia facts. Result: no message silently dropped, even in a 4-hour, 200-message session.

suggest_synthesize=true triggers the client-side LLM to run:

┌─ Phase 1: extract facts ──────────────────────────┐
│  get_extraction_prompt → chunk of unextracted    │
│  record_facts          → store, return remaining │
│  loop until extraction_complete=true             │
└──────────────────────┬────────────────────────────┘
                       ▼
┌─ Phase 2: compose Summary ────────────────────────┐
│  get_synthesis_prompt → transcript + facts +     │
│                          schema + quality rules   │
└──────────────────────┬────────────────────────────┘
                       ▼
┌─ Phase 3: publish (coverage-gated) ───────────────┐
│  synthesize_to_notion → validates + writes to     │
│                          Notion, OR returns list  │
│                          of missed msg uuids for  │
│                          the LLM to fix and retry │
└───────────────────────────────────────────────────┘

Setup (once)

  1. Create an internal Notion integration at https://www.notion.so/my-integrations. Copy the secret token (starts with ntn_ or secret_).
  2. Create a Notion page that will hold your chatmem pages. Share it with the integration (⋯ → Add connections → your integration).
  3. On your machine: 
    chatmem notion connect ntn_XXXXX --parent <page-url-or-id>
chatmem notion status     # verify token + parent

Restart your MCP client (Windsurf/Cursor/Claude Code). Auto-synthesis fires when a conversation crosses 20 messages, or the client-side rule triggers on suggest_synthesize=true in record_message responses.

System-prompt rule for your MCP client

Paste this into your Windsurf / Cursor / Claude-Code rules so auto-fire lands:

Whenever a record_message tool response includes suggest_synthesize: true, immediately (a) call get_synthesis_prompt for the referenced conversation_id, (b) compose the Summary object per the schema in that prompt, (c) call synthesize_to_notion with {conversation_id, summary}. Do this once per conversation per trigger, before continuing the user's turn.

Page types

  • Study mode (concept-heavy conversations): TL;DR · Prerequisites · Core Concepts (definition callout + body + example + why-it-matters + citations) · Mermaid diagrams · Key Insights · Code · Further Study · References · Full Transcript.
  • Debug mode (fixing a broken thing): TL;DR · Status callout (resolved / partial / unresolved) · Timeline Mermaid diagram · What I Tried (attempts) · Root Cause · Resolution · Prevention · Full Transcript.

Session type is auto-classified per conversation. Diagrams are required (validation enforced) — Mermaid timeline for debug, appropriate diagram type for study when concepts warrant one.

Preview the templates without touching Notion

chatmem notion sample --type=study    # prints Summary JSON + rendered blocks JSON
chatmem notion sample --type=debug

Inspect coverage

chatmem notion coverage <conversation_id>
# → total messages, messages with facts, category breakdown,
#   msgs without any fact yet

Reliability

Failed Notion writes are persisted to ~/.local/share/chatmem/notion-pending/ and retried automatically on the next chatmem mcp start, or manually with chatmem notion resync.

Disconnect

chatmem notion disconnect     # removes notion.json; published pages are untouched

Import an existing chat (v0.2.1)

Got a transcript from a chat that happened outside chatmem — ChatGPT web export, a Claude.ai conversation you saved, an aider log, etc.? Load it into chatmem with chatmem import. Then either the LLM auto-synthesizes it to Notion (if past the threshold) or you nudge it manually.

Input formats

Two formats accepted; auto-detected by the first non-whitespace character:

JSONL (one JSON object per line):

{"role":"user","content":"what is hnsw"}
{"role":"assistant","content":"a graph-based ANN index"}
{"role":"user","content":"pgvector defaults?"}

JSON array:

[
  {"role":"user","content":"what is hnsw"},
  {"role":"assistant","content":"a graph-based ANN index"}
]

Extra fields on each message (timestamps, source ids, tool_calls) are silently ignored — chatmem generates its own ids and timestamps.

Commands

# From a file, opening a new conversation:
chatmem import -f ./chatgpt-export.jsonl \
  --model gpt-5 --provider openai --client-id chatgpt-web

# Piping from stdin:
cat ./transcript.jsonl | chatmem import --stdin \
  --model claude-opus-4-7 --provider anthropic --client-id claude-web

# Appending to an existing chatmem conversation (e.g. resume a partial capture):
chatmem import -f ./followup.jsonl \
  --conversation-id 8a2f... \
  --model claude-opus-4-7 --provider anthropic --client-id claude-code

chatmem import attaches to a running chatmem instance's Postgres if one is up (Claude Code / Windsurf / etc. session live). Otherwise it starts its own embedded PG briefly. Either way it's safe to run.

On success it prints the new conversation UUID + a hint to synthesize it in your LLM client:

imported 47 messages into conversation 8a2f...

Next steps:
  chatmem notion status
  # From an LLM session with chatmem:
  #   "call get_synthesis_prompt for conversation 8a2f..., then synthesize_to_notion"

Converting from real chat exports

  • ChatGPT .zip export → JSONL: jq recipe (adjust for your export format): 
    jq -c '.[0].mapping | to_entries | map(select(.value.message != null)) |
       sort_by(.value.message.create_time) |
       .[] | {role: .value.message.author.role, content: (.value.message.content.parts | join("\n"))}' \
   < conversations.json > out.jsonl
  • Claude.ai export → JSONL: jq '.[] | {role: .sender, content: .text}' < conversation.json
  • Anything text-based: turn each turn into {"role":"...","content":"..."} and you're done.

Commands

Every subcommand has --help. Run chatmem with no arguments to see the top-level list.

chatmem init

Provisions the persistent data directory, extracts embedded Postgres, installs pgvector into the runtime, and applies the schema. On completion prints a ready-to-paste MCP JSON snippet with the actual binary path (os.Executable()).

Safe to re-run — all provisioning steps are idempotent.

chatmem mcp [--port <n>]

Runs a stdio MCP server for a single client. Starts embedded Postgres for the process lifetime and stops it cleanly on stdin close or SIGTERM.

Concurrent MCP clients cannot share one embedded Postgres in the MVP — each chatmem mcp process opens its own PG on the port supplied. See Roadmap for the daemon+shim architecture.

chatmem daemon [--port <n>]

Runs the long-lived Postgres foundation for the future daemon+shim architecture. Not required for MVP usage. Ends cleanly on SIGINT/SIGTERM.

chatmem telemetry {enable|disable|status}

Manage the anonymous telemetry setting. status prints the effective state, the precedence source (env | config | default), and the install id.

Data & config paths

KindmacOSLinuxEnv override
Data~/.local/share/chatmem/~/.local/share/chatmem/CHATMEM_HOME
Cache~/Library/Caches/chatmem/~/.cache/chatmem/CHATMEM_CACHE
Install id<data>/install_id<data>/install_id—
Telemetry cfg<data>/telemetry.json<data>/telemetry.json—
Postgres data<data>/pgdata/<data>/pgdata/—
Postgres runtime<cache>/pg-runtime/<cache>/pg-runtime/—

Telemetry

chatmem can send anonymous usage pings to help track adoption. Message content, query strings, and filenames are never sent, ever. What is sent when enabled:

  • install UUID (generated locally on first run, in <data>/install_id)
  • chatmem version
  • counters for the flush window (default 5 minutes): captures, searches, gets, errors
  • model + client distributions — e.g. {"claude-opus-4-7": 12, "gpt-5": 4}, {"windsurf": 8, "cursor": 8}
  • latency percentiles per operation — p50/p95/p99 in ms

Modes

The client operates in three modes depending on config:

ModeBehavior
Disabled (env or config)Nothing accumulated. Aggregator.Record* still runs but Flush is a no-op.
Enabled, no ingest URLAccumulate + periodic flush → slog.Info("telemetry flush (local-only, no ingest URL set)", ...). Local observability only.
Enabled, ingest URL setAccumulate + flush → POST to <URL>/v1/ping with 3-attempt exponential backoff. Failed sends persist to <data>/pending/*.json and get drained on the next successful flush (24h TTL). Release binaries have this URL baked in (points at the maintainer's Worker); CHATMEM_TELEMETRY_URL overrides it, and chatmem telemetry status prints the effective value.

Precedence (highest wins)

  1. CHATMEM_TELEMETRY=0 (also false, off) — hard off
  2. <data>/telemetry.json ({"enabled": true|false}) — persistent choice
  3. chatmem telemetry {enable|disable} — writes the above
  4. Default: enabled

chatmem init prompts on first run (interactive TTY only) and writes the config. Non-TTY inits print a notice and leave the default; opt out non-interactively with chatmem telemetry disable or the env var.

Commands

chatmem telemetry status     # current state + source + ingest URL
chatmem telemetry enable     # persist enabled = true
chatmem telemetry disable    # persist enabled = false
chatmem telemetry dump       # list <data>/pending/*.json (unshipped pings)

Standing up your own ingest

The client posts to any endpoint that speaks POST /v1/ping with the payload documented in internal/telemetry/client.go:Payload. A ready-to-deploy Cloudflare Worker + D1 lives in server/telemetry-worker/ — one wrangler deploy and you have an endpoint. See that README for the 5-command setup.

Architecture

LLM client (Claude Code, Cursor, aider, custom)
        │
        ▼  MCP over stdio
   chatmem mcp
        │
        ▼
  embedded Postgres 18 + pgvector 0.8.5
        │
        ▼
  chunks (with vector(384) column, HNSW cosine index)
  messages (btree on conv_id + created_at)
  conversations (append-only event log ready for future sync)
  • Language: Go 1.26, single static binary (CGO off).
  • Storage: fergusstrange/embedded-postgres v1.34 driving Postgres 18.3 in a per-user data dir. Cold start ~600 ms on M-series (~7 s on the very first run because of initdb).
  • Vector column: vector(384) on the chunks table with an HNSW cosine index (m=16, ef_construction=64), primed for semantic search. Values are zero-vectors until the ONNX embedder lands.
  • Search (MVP): Postgres full-text — to_tsvector('english', content) + plainto_tsquery, GIN index (chunks_tsv_idx). Ranked with ts_rank_cd.
  • MCP: official modelcontextprotocol/go-sdk v1.6.1. Stdio transport.
  • CLI: spf13/cobra v1.10.

The pgvector .dylib for darwin_arm64 is committed under internal/pg/assets/darwin_arm64/ and shipped inside the Go binary via //go:embed. On first Start(), internal/pg copies the dylib into <runtimeDir>/lib/postgresql/ and the control/SQL files into <runtimeDir>/share/postgresql/extension/ — then CREATE EXTENSION vector works out of the box.

Repository layout

chatmem/
├── cmd/chatmem/                # cobra CLI entrypoint + subcommands
│   ├── main.go
│   ├── init.go                 # chatmem init
│   ├── daemon.go               # chatmem daemon (+ dataHome / cacheHome helpers)
│   ├── mcp.go                  # chatmem mcp (stdio MCP server)
│   ├── telemetry.go            # chatmem telemetry {enable,disable,status}
│   └── mcp_e2e_test.go         # spawns built binary, drives stdio MCP as a real client
├── internal/
│   ├── pg/                     # embedded-postgres wrapper + pgvector install
│   │   ├── embedded.go
│   │   └── assets/
│   │       ├── darwin_arm64/vector.dylib + extension/{vector.control,vector--0.8.5.sql}
│   │       ├── linux_amd64/vector.so     + extension/{vector.control,vector--0.8.3.sql}
│   │       └── linux_arm64/vector.so     + extension/{vector.control,vector--0.8.3.sql}
│   ├── telemetry/
│   │   ├── telemetry.go        # State/Config, install_id, opt-out precedence
│   │   ├── aggregator.go       # Thread-safe counters + latency reservoir + percentiles
│   │   └── client.go           # Flush loop, HTTP POST with retry, local pending dir
│   ├── store/                  # schema + pgx-backed data access
│   │   ├── schema.sql
│   │   ├── store.go            # EnsureSchema, RecordMessage, SearchHistory, GetConversation
│   │   └── store_test.go
│   ├── mcp/                    # MCP tool registration
│   │   ├── server.go           # NewServer, register{RecordMessage,SearchHistory,GetConversation}
│   │   └── server_test.go      # in-process MCP round-trip
│   └── telemetry/              # install_id + opt-out gate
│       └── telemetry.go
├── server/telemetry-worker/    # Cloudflare Worker + D1 for the telemetry ingest
├── docs/
│   └── marketplace-submissions.md  # Playbook: awesome-mcp / Smithery / PulseMCP / Glama
├── smithery.yaml               # Smithery registry config (stdio start command)
├── .goreleaser.yaml            # cross-platform build + Homebrew tap + rpm/deb via nfpm
├── .github/workflows/release.yml  # tag push → goreleaser + gh-pages RPM repo publish
├── scripts/build-rpm-repo.sh   # assemble zypper/dnf repo tree locally (uses createrepo_c via docker)
├── README.md
├── CLAUDE.md                   # in-repo dev docs, auto-loaded by Claude Code
└── LICENSE                     # Apache-2.0

Building from source

Requires Go 1.26+ and, for now, Homebrew's pgvector 0.8.5 (only if you want to refresh the vendored dylib — the committed copy is enough to build).

git clone https://github.com/sid077/chatmem
cd chatmem
go build ./...

To refresh the vendored pgvector artifacts:

# darwin (from Homebrew) — refreshes internal/pg/assets/darwin_arm64/
brew install pgvector
cp /opt/homebrew/Cellar/pgvector/0.8.5/lib/postgresql@18/vector.dylib \
   internal/pg/assets/darwin_arm64/vector.dylib
cp /opt/homebrew/Cellar/pgvector/0.8.5/share/postgresql@18/extension/{vector.control,vector--0.8.5.sql} \
   internal/pg/assets/darwin_arm64/extension/

# linux amd64/arm64 (from official PostgreSQL apt, pgdg11+1 for glibc 2.31 baseline)
for arch in amd64 arm64; do
  curl -sSLo /tmp/pgv-$arch.deb \
    "https://apt.postgresql.org/pub/repos/apt/pool/main/p/pgvector/postgresql-18-pgvector_0.8.3-1.pgdg11+1_${arch}.deb"
  tmp=$(mktemp -d); cd "$tmp"; ar x /tmp/pgv-$arch.deb; tar -xf data.tar.xz
  cp "$tmp/usr/lib/postgresql/18/lib/vector.so" internal/pg/assets/linux_${arch}/vector.so
  cp "$tmp/usr/share/postgresql/18/extension/"{vector.control,vector--0.8.3.sql} \
     internal/pg/assets/linux_${arch}/extension/
done

The Linux .so is built against Debian 11's glibc 2.31 for maximum runtime compatibility — anything with glibc ≥ 2.31 works (Debian 11+, Ubuntu 22.04+, RHEL 9+, Alpine with libc6-compat, etc.).

Testing

Every test that touches storage spins up a real embedded Postgres — expect ~7–10 s per test package on cold cache.

# unit + integration
go test ./... -count=1 -timeout=240s

# just the storage layer round-trip
go test ./internal/store -count=1 -v

# just the in-process MCP round-trip
go test ./internal/mcp -count=1 -v

# end-to-end: spawn the built binary as a subprocess, drive stdio MCP
go test ./cmd/chatmem -run TestBinaryStdioMCP -count=1 -v

Tests use distinct hard-coded ports (54334, 54335, 54336) — run one test package at a time if you have a real chatmem daemon running on 54329.

Development workflow

  1. Edit — code lives under cmd/chatmem and internal/.
  2. Test — go test ./... after every change; add a test alongside any new store/MCP behavior.
  3. Update the docs on every functional change:
    • README.md for user-facing changes (new tool, new command, changed defaults).
    • CLAUDE.md for developer-facing changes (new package, new invariant, new gotcha).
    • ~/.claude/skills/chatmem/SKILL.md for cross-session context (kept in sync automatically).
  4. Commit — one focused change per commit, imperative subject line.
  5. go mod tidy if dependencies changed.

Troubleshooting

On macOS: "Apple could not verify … may be malware" dialog — the release binary isn't Apple-Developer-ID signed or notarized yet. Two-step workaround for now:

# 1. Strip the "downloaded from internet" flag (needed once per install):
xattr -d com.apple.quarantine "$(brew --prefix)/bin/chatmem"

# 2. Ad-hoc-sign the binary so launchd doesn't re-prompt on every daemon start:
codesign --force --sign - "$(brew --prefix)/bin/chatmem"

chatmem install runs step 2 automatically as of v0.3.2. Real fix (Developer ID + notarization) is wired into the release workflow; it activates for future releases once the MACOS_CERT_P12 + APP_STORE_CONNECT_KEY secrets are added. See docs/release-signing-setup.md.

On Linux: "Package chatmem is not signed! Continue anyway?" warning from zypper/dnf — the release RPMs aren't GPG-signed yet. Same story as macOS — plumbing lands in v0.3.2, activates when the GPG_PRIVATE_KEY secret is added. Until then it's safe to accept the warning (--allow-unsigned-rpm).

Anything weird? Run chatmem doctor first — it prints HOME, EUID, effective data + cache paths, port availability, telemetry state, and ingest reachability, with a green/red check for each. Most install-time issues surface here in one screen.

$HOME (…) is owned by uid X but you are uid Y — looks like sudo -E preserved a different HOME — you ran sudo -E chatmem …, which kept HOME=/root but dropped to a non-root uid. Do one of:

sudo -H -u <user> chatmem init         # -H rewrites HOME
su - <user> -c "chatmem init"          # login shell resets HOME
chatmem init                           # or just don't sudo — chatmem must run as your normal user

$HOME is not set — you're in a stripped environment (systemd unit without Environment=HOME=…, env -i, etc.). Set HOME explicitly to your login user's home directory.

chatmem cannot run as root — on Linux, Postgres refuses to run under uid 0 and chatmem now refuses too, up-front. Re-run as an unprivileged user: su - <username> -c 'chatmem init' (or sudo -u <username> chatmem init).

chatmem mcp client sees "invalid character 'T' looking for beginning of value" — the MCP protocol runs over stdout; something is writing non-JSON there. Most likely embedded-postgres was configured with logger: os.Stdout instead of os.Stderr (default here is os.Stderr; check internal/pg/embedded.go if you customized).

no embedded pgvector assets for <goos>/<goarch> — you are running on an unsupported platform. Copy a matching prebuilt pgvector into internal/pg/assets/<goos>_<goarch>/ (see Building from source for the file layout) and rebuild.

First chatmem init takes ~15–20 s — that is initdb for a brand-new data directory plus Postgres binary extraction. Subsequent runs (warm caches) are ~1 s.

Port 54329 already in use — either another chatmem daemon/mcp process is running (kill it) or something else grabbed the port. Use --port to override.

CREATE EXTENSION vector fails with could not access file "$libdir/vector" — the pgvector .dylib didn't get copied into <runtimeDir>/lib/postgresql/. Check internal/pg/embedded.go's installPgvector output; usually a wiped-out runtime dir mid-run.

Uninstall

brew uninstall chatmem                # or delete the binary you built
rm -rf ~/.local/share/chatmem         # data (delete only if you're sure)
rm -rf ~/Library/Caches/chatmem       # cache (macOS) — safe to delete anytime
rm -rf ~/.cache/chatmem               # cache (linux) — safe to delete anytime
rm -f  ~/.claude/mcp.json             # or hand-remove the chatmem entry

Roadmap

Next up (still MVP-scope):

  • ONNX MiniLM int8 embedder → upgrade search_history from keyword to semantic.
  • Remaining platforms: darwin/amd64 (Intel Mac) and windows/amd64.
  • Cloudflare Worker ingest endpoint for real telemetry pings.
  • chatmem daemon HTTP MCP + chatmem mcp stdio-to-HTTP shim so multiple MCP clients can share one Postgres.

Post-MVP (v1.0):

  • Optional E2E-encrypted sync (hosted, open-core).
  • macOS notarization + signed .pkg.
  • Windows Service, launchd/systemd autostart.
  • Python + TypeScript SDK wrappers around the MCP tools.
  • Distribution to apt, dnf/COPR, zypper/OBS, AUR, winget, scoop.
  • Docs site.

Publishing a release

Tag-triggered — the release GitHub Actions workflow does everything.

git tag -a v0.0.1 -m "v0.0.1"
git push origin v0.0.1

On tag push the workflow:

  1. Runs goreleaser release --clean — cross-compiles darwin/arm64 + linux/amd64 + linux/arm64, packages .tar.gz + .rpm + .deb, uploads the archives to the GitHub Release, and pushes the updated Homebrew formula into sid077/homebrew-chatmem.
  2. Runs createrepo_c on the .rpms to build a zypper/dnf-compatible repo tree.
  3. Pushes the repo tree to the gh-pages branch of this repository.

Users then get updates via zypper refresh / brew upgrade / dnf update without any further action from you.

To dry-run locally before tagging:

goreleaser release --snapshot --clean --skip=publish   # produces dist/*
scripts/build-rpm-repo.sh                              # produces dist/rpm-repo/ (needs Docker for createrepo_c)

Verify with an openSUSE container:

cd dist/rpm-repo && python3 -m http.server 8765 &
docker run --rm --platform linux/arm64 --add-host=host.docker.internal:host-gateway \
  opensuse/leap:15.6 bash -c '
    echo -e "[chatmem]\nbaseurl=http://host.docker.internal:8765/\$basearch/\nenabled=1\ngpgcheck=0" > /etc/zypp/repos.d/chatmem.repo
    zypper --non-interactive refresh chatmem
    zypper --non-interactive install chatmem
    chatmem --version'

GPG signing (recommended for production)

MVP ships unsigned (gpgcheck=0 in the .repo file). To sign:

  1. Generate a GPG key: gpg --gen-key.
  2. Export the public key: gpg --armor --export you@example.com > chatmem.gpg.
  3. In .goreleaser.yaml, add signs: for the rpms with your key id.
  4. Copy chatmem.gpg alongside the .repo file in dist/rpm-repo/ and change gpgcheck=1 + gpgkey=<URL>/chatmem.gpg.

License

Apache-2.0. See LICENSE.