Engram

Persistent memory for AI coding agents One brain. Local or cloud. Agent-agnostic, single binary, zero dependencies.

Installation • Engram Cloud • Agent Setup • Codebase Guide • Architecture • Plugins • Team Usage • Contributing • Full Docs

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> engram /ˈen.ɡræm/ — neuroscience: the physical trace of a memory in the brain.

Your AI coding agent forgets everything when the session ends. Engram gives it a brain.

A Go binary with SQLite + FTS5 full-text search, exposed via CLI, HTTP API, MCP server, and an interactive TUI. Works with any agent that supports MCP — Claude Code, OpenCode, Gemini CLI, Codex, VS Code (Copilot), Antigravity, Cursor, Windsurf, or anything else.

Agent (Claude Code / OpenCode / Gemini CLI / Codex / VS Code / Antigravity / ...)
    ↓ MCP stdio
Engram (single Go binary)
    ↓
SQLite + FTS5 (~/.engram/engram.db)

Quick Start

Install

brew install gentleman-programming/tap/engram

Windows, Linux, and other install methods → [docs/INSTALLATION.md](docs/INSTALLATION.md)

Setup Your Agent

| Agent | One-liner | | --------------------------- | -------------------------------------------------------------------------------------------- | | Claude Code | claude plugin marketplace add Gentleman-Programming/engram && claude plugin install engram | | Pi | engram setup pi | | OpenCode | engram setup opencode | | Gemini CLI | engram setup gemini-cli | | Codex | engram setup codex | | Antigravity CLI | engram setup antigravity-cli | | Windsurf | engram setup windsurf | | Qwen Code | engram setup qwen | | Kiro | engram setup kiro | | Cursor | engram setup cursor | | VS Code (Copilot) | engram setup vscode-copilot | | Kilo Code | engram setup kilocode | | Any other MCP client | See [docs/AGENT-SETUP.md](docs/AGENT-SETUP.md) |

Full per-agent config, Memory Protocol, and compaction survival → [docs/AGENT-SETUP.md](docs/AGENT-SETUP.md)

What engram setup does — it writes MCP config and plugin files for the chosen agent. After setup, restart your agent and it is ready. No server to start manually.

> Do I need to run engram serve or engram mcp myself? > > For most agents (Claude Code, Gemini CLI, Codex, VS Code, Cursor, Windsurf) — no. Your agent launches engram mcp automatically as a short-lived stdio subprocess whenever it starts a session. You never run it manually. > > engram serve is only needed when a plugin uses the HTTP API for session tracking: the OpenCode plugin and the Pi extension both talk to engram serve in the background. engram setup opencode and engram setup pi note this; the plugins auto-start the server when possible. If your environment blocks background processes, start it manually in a separate terminal: > ``bash > engram serve # runs on port 7437 by default; keep it running > ` > You do not need engram serve` at all for stdio-only agents (Claude Code, Gemini CLI, Codex, VS Code, Cursor, Windsurf).

No Node.js, no Python, no Docker. One binary, one SQLite file.

Pi Package

Engram has a first-class Pi package: [gentle-engram](plugin/pi/README.md).

engram setup pi

It gives Pi persistent project memory, compaction recovery, and shared memory with other MCP agents through the same local-or-cloud Engram brain. The package is part of the Gentleman Programming agentic-coding ecosystem alongside Gentle-AI, SDD, skills, and Engram Cloud.

Setup FAQ

When do I need to manually add config to my agent's prompt or settings?

engram setup covers the MCP wiring automatically. Manual config — adding a Memory Protocol snippet to your CLAUDE.md, GEMINI.md, .cursorrules, etc. — is only needed if your agent keeps forgetting to use Engram after long sessions or context compaction. That manual step is called the "nuclear option" in the detailed docs because system prompts survive everything, including compaction. It is a reliability boost for heavy users, not a required first step. See [Agent Setup → Surviving Compaction](docs/AGENT-SETUP.md#surviving-compaction-recommended) for the snippets.

Can Docker agents (or remote agents) connect to Engram's MCP?

Engram's MCP transport is stdio only — there is no HTTP or network MCP endpoint. engram mcp speaks the MCP protocol over stdin/stdout; it cannot be reached over a TCP port.

If you have agents running in Docker that need to write to Engram on the host, the available paths are:

• HTTP REST API (engram serve): note that engram serve currently binds to 127.0.0.1 only, so it is not reachable from inside a container out of the box — a container cannot reach the host's loopback, and there is no bind-address flag yet. ENGRAM_URL lets the Pi plugin target an engram serve reachable on a routable host/port (e.g. ENGRAM_URL=http://host.docker.internal:7437 pi), but that only works once the server listens on a non-loopback interface, which is not supported today. The HTTP API is not the MCP protocol; Pi uses it for session capture and Pi-native mem_* tools. For Docker right now, prefer the stdio path below.
• Stdio MCP (mount the binary): the cleanest path for a Dockerized agent that needs MCP tools is to mount the engram binary into the container and let the agent launch engram mcp locally via stdio, pointing ENGRAM_DATA_DIR at a volume shared with the host.

Full environment variable reference → [DOCS.md#environment-variables](DOCS.md#environment-variables)

How It Works

1. Agent completes significant work (bugfix, architecture decision, etc.)
2. Agent calls mem_save → title, type, What/Why/Where/Learned
3. Engram persists to SQLite with FTS5 indexing
4. Next session: agent searches memory, gets relevant context

Full details on session lifecycle, topic keys, and memory hygiene → [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md)

MCP Tools (20)

| Category | Tools | | ---------------------- | ---------------------------------------------------------------------------------------------------------------- | | Save & Update | mem_save, mem_update, mem_delete, mem_suggest_topic_key | | Search & Retrieve | mem_search, mem_context, mem_timeline, mem_get_observation | | Session Lifecycle | mem_session_start, mem_session_end, mem_session_summary | | Conflict Surfacing | mem_judge, mem_compare | | Lifecycle Review | mem_review | | Utilities | mem_save_prompt, mem_stats, mem_capture_passive, mem_merge_projects, mem_current_project, mem_doctor |

Full tool reference with parameters → [DOCS.md#mcp-tools-20-tools](DOCS.md#mcp-tools-20-tools)

Terminal UI

engram tui

Navigation: j/k vim keys, Enter to drill in, c to copy content to clipboard (OSC 52), / to search, Esc back. Catppuccin Mocha theme.

Git Sync

Share memories across machines. Uses compressed chunks — no merge conflicts, no huge files.

Local SQLite remains the source of truth. Cloud integration is opt-in replication.

engram sync                    # Export new memories as compressed chunk
git add .engram/ && git commit -m "sync engram memories"
engram sync --import           # On another machine: import new chunks
engram sync --status           # Check sync status

Full sync documentation → [DOCS.md](DOCS.md)

Cloud Integration (Opt-In Replication)

Cloud is optional. Local SQLite stays authoritative; cloud is replication/shared access only.

Recommended first path (local smoke):

docker compose -f docker-compose.cloud.yml up -d
engram cloud config --server http://127.0.0.1:18080
engram cloud enroll smoke-project
engram sync --cloud --project smoke-project

Cloud mode is always project-scoped (--project is required; engram sync --cloud --all is intentionally blocked). ENGRAM_CLOUD_ALLOWED_PROJECTS is required for engram cloud serve in both token-auth and insecure modes. Set it to * to allow all projects (useful for dev/internal deploys) — this bypasses per-project name enforcement while still requiring a non-empty project on each request. Known repairable cloud sync/upsert/canonicalization failures keep the original error visible and recommend the explicit doctor/repair flow below; Engram never auto-applies repair from sync or autosync. For blocked cloud sync, transport_failed, or legacy session directory repair, see [Engram Cloud Troubleshooting](docs/engram-cloud/troubleshooting.md). If cloud sync stays blocked after doctor/repair, download the rescue helper and run the recommended exported-row repair:

tools/repair-missing-session-directory.sh --apply --interactive --fix-exported 
engram sync --cloud --project 

--fix-exported repairs local exported sessions[].directory and observations[] required fields that can still break the final push after doctor reports ready. For sequential legacy sync_mutations blockers, use tools/repair-missing-session-directory.sh --apply --interactive --all .

After upgrading engram while an MCP client is already running:

engram setup claude-code

Then restart Claude Code so it reloads the Engram MCP subprocess and refreshed hook/config files. Updating the engram binary on disk does not replace an already-running stdio MCP process.

Upgrade flow for existing local databases (diagnose → repair → bootstrap → status):

engram cloud upgrade doctor --project smoke-project        # read-only readiness check
engram cloud upgrade repair --project smoke-project --dry-run
engram cloud upgrade repair --project smoke-project --apply
engram cloud upgrade bootstrap --project smoke-project     # resumable enroll + push + verify
engram cloud upgrade status --project smoke-project        # stage/class/reason

See [DOCS.md — Cloud upgrade flow](DOCS.md#cloud-upgrade-flow) for the full state machine.

For authenticated mode, upgrade flow, dashboard behavior, reason codes, and full runtime/env details:

• [Engram Cloud docs landing](docs/engram-cloud/README.md)
• [Engram Cloud quickstart](docs/engram-cloud/quickstart.md)
• [DOCS.md — Cloud CLI reference](DOCS.md#cloud-cli-opt-in)
• [DOCS.md — Cloud Autosync](DOCS.md#cloud-autosync)

Steps to Test (Beta — Phases 2+3+4)

Try the new memory-conflict-surfacing features in complete isolation from your existing engram setup. Docker uses non-default ports + a separate data dir + a beta-only token, so your prod cloud and ~/.engram/ are untouched. Cleanup is one command.

What's in the beta:

• 🔄 Cloud sync of conflict relations cross-machine
• 🔍 engram conflicts CLI + HTTP API for retroactive audit + scan
• 🧠 --semantic scan that uses your existing Claude Code or OpenCode CLI to judge FTS5 conflict candidates with LLM reasoning — $0 if you're on a Pro/Max/Plus subscription

Setup (4 commands)

git clone https://github.com/Gentleman-Programming/engram.git engram-beta-repo
cd engram-beta-repo && git checkout feat/memory-conflict-surfacing-cloud-sync
docker compose -f docker-compose.beta.yml up -d
go build -o ./engram-beta ./cmd/engram

# Isolated env (does NOT touch ~/.engram or your prod cloud)
export ENGRAM_DATA_DIR=/tmp/engram-beta-data
export ENGRAM_CLOUD_SERVER=http://127.0.0.1:28080
export ENGRAM_CLOUD_TOKEN=beta-token-CHANGE-ME-please-32chars
mkdir -p "$ENGRAM_DATA_DIR"

Use cases

1️⃣ Phase 1 — Conflict detection on save (sanity)

./engram-beta save \
  "Use Clean Architecture" \
  "Layers: entities, use cases, adapters." \
  --type architecture --project beta-test

./engram-beta save \
  "Use Hexagonal Architecture" \
  "Ports and adapters separate domain from infra." \
  --type architecture --project beta-test

✅ Second save returns candidates[] with the first memory's id.

2️⃣ Phase 2 — Cloud sync of relations cross-machine

./engram-beta cloud enroll beta-test
./engram-beta sync --cloud --project beta-test
./engram-beta cloud status

# Simulate a "second machine"
ENGRAM_DATA_DIR=/tmp/engram-beta-data-2 ./engram-beta cloud enroll beta-test
ENGRAM_DATA_DIR=/tmp/engram-beta-data-2 ./engram-beta sync --cloud --project beta-test
ENGRAM_DATA_DIR=/tmp/engram-beta-data-2 ./engram-beta search "Architecture"

✅ The "second machine" sees memories synced from the first.

3️⃣ Phase 3 — Admin CLI + HTTP API

./engram-beta conflicts list --project beta-test
./engram-beta conflicts stats --project beta-test
./engram-beta conflicts scan --project beta-test --dry-run
./engram-beta conflicts scan --project beta-test --apply --max-insert 10

# In another terminal: ./engram-beta serve
curl -s "http://127.0.0.1:7437/conflicts?project=beta-test" | jq

✅ List/scan/stats return sensible data.

4️⃣ Phase 4 — Semantic LLM-judge (the killer feature) 🎯

export ENGRAM_AGENT_CLI=claude   # or opencode

./engram-beta conflicts scan --project beta-test --semantic --apply \
  --max-semantic 5 --concurrency 3 --yes

✅ Your agent's LLM judges semantic similarity. $0 if on a subscription.

5️⃣ The case where FTS5 finds a candidate, then the LLM judges meaning

Lexically related candidate titles with a semantic conflict:

./engram-beta save \
  "Use Postgres for the user database" \
  "Postgres 15 is our SQL store for users." \
  --type architecture --project beta-test

./engram-beta save \
  "Replace the user database with MongoDB" \
  "Document store now backs the user collection. SQL is gone." \
  --type decision --project beta-test

./engram-beta conflicts scan --project beta-test --semantic --apply \
  --max-semantic 5 --yes

./engram-beta conflicts list --project beta-test --status judged

✅ FTS5 supplies the candidate pair through shared title terms like user / database; the LLM then judges whether it is supersedes / conflicts_with. --semantic does not discover totally lexically unrelated pairs on its own.

Cleanup (zero residue)

docker compose -f docker-compose.beta.yml down -v
rm -rf /tmp/engram-beta-data /tmp/engram-beta-data-2 ./engram-beta

Your production engram is fully untouched throughout.

Full guide + troubleshooting

→ [docs/BETATESTING.md](docs/BETATESTING.md)

→ Report feedback: [issues with beta-phase-2-3-4 label](

…

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Gentleman-Programming
• Source: Gentleman-Programming/engram
• License: MIT

Install and usage instructions live in the source repository linked above.