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Show HN: Engrim – A universal, local-first SQLite memory engine for AI CLIs

Engrim, a universal local-first SQLite memory engine for AI CLIs, has been released on Hacker News, claiming to preserve project decisions and state across model switches with a 99%+ reduction in reloaded context cost. The tool, tested across 105 continuous sessions on a 50,000-line algorithmic trading system with zero regressions, integrates with Google Antigravity, Claude Code, Cursor MCP, and Windsurf, and is installable via pip.

read6 min views1 publishedSep 7, 2026
Show HN: Engrim – A universal, local-first SQLite memory engine for AI CLIs
Image: Michielbdejong (auto-discovered)

The Universal Cross-Model & Cross-Agent Episodic Memory Store.

A local-first, project-scoped SQLite memory engine that allows developers to freely switch between models and environments (Google Antigravity, Claude Code, Cursor MCP, Windsurf) on the SAME project without losing architectural decisions, user constraints, or project state.

"Why pay for 200,000 tokens of forgotten noise on every turn? The models are disposable utilities; your project's decisions are not."

As context windows scale to 1M+ tokens, developers face attention dilution: reasoning degrades, cost multiplies with every conversational turn, and clearing context causes total amnesia.

engrim replaces attention dilution with 4,000 characters of curated episodic working memory:

  • Switzerland of AI Memory : Decouples project intelligence from any single AI vendor or proprietary cloud silo. Switch from Gemini 3.8 in Antigravity to Claude 3.7 Sonnet in Claude Code to GPT-4o in Cursor mid-project — your agents pick up right where the others left off.
  • Save Button for Autonomous Coding : Externalize decisions, constraints, and state as you work. Clear your agent session freely (/clear ) and watch context reload intact.
  • **Smart, Hot Context ** : Combines SQLite FTS5 (bm25 keyword search) with static vector embeddings (model2vec ) in a zero-latency hybrid reciprocal-rank fusion engine.

Tested across 105 continuous sessions on a 50,000-line algorithmic trading system. Zero regressions across 186 unit tests, zero context amnesia across model switches.

In production testing on an active algorithmic trading codebase running real capital:

  • Over 153,000 tokens of work across days of architecture, parameter tuning, and debugging was consolidated into an active memory pack under1,000 tokens (<1% of the context window).
  • That is a 99%+ cut in reloaded context cost on every session restart.
  • Seamlessly switched between Google Antigravity CLI, Claude Code, and Cursor MCP on identical repos with zero model drift or architectural regression.
graph TD
    subgraph Agents ["Supported Agent Environments"]
        AGY["Google Antigravity<br/>(PreInvocation & Stop Hooks)"]
        CLAUDE["Claude Code<br/>(SessionStart & Stop Hooks)"]
        CURSOR["Cursor / Windsurf<br/>(Model Context Protocol stdio)"]
    end

    subgraph CoreEngine ["engrim Core Engine (v1.3.0)"]
        ADAPTERS["Adapters & Hooks<br/>(agy, claude, mcp)"]
        PROVENANCE["Agent Provenance Engine<br/>(origin_agent tracking)"]
        ROUTER["Hybrid Retrieval & Minder<br/>(bm25 lexical + vector cosine)"]
    end

    subgraph Storage ["Local-First SQLite Store (~/.engrim/memory.db)"]
        MEMORIES[("Curated Memories<br/>(decisions, facts, feedback)")]
        FTS5["FTS5 Full-Text Search<br/>(porter stemmer, triggers)"]
        VEC["Vector Embeddings<br/>(model2vec static embeddings)"]
        LOG["Flight Recorder Log<br/>(turns + action lines)"]
    end

    AGY <-->|"hook / CLI"| ADAPTERS
    CLAUDE <-->|"hook / CLI"| ADAPTERS
    CURSOR <-->|"JSON-RPC (stdio)"| ADAPTERS
    ADAPTERS --> PROVENANCE
    PROVENANCE --> ROUTER
    ROUTER --> MEMORIES
    MEMORIES --- FTS5
    MEMORIES --- VEC
    ADAPTERS --> LOG
pip install engrim

Run engrim setup without arguments. It automatically detects installed environments on your machine and configures them all:

engrim setup
  • If ~/.gemini exists$\rightarrow$ wires Antigravity lifecycle hooks, skill, and MCP server.
  • If ~/.claude exists$\rightarrow$ wires Claude Code SessionStart, Stop, status line, and CLAUDE.md.
  • If ~/.cursor exists$\rightarrow$ generates and merges Cursor MCP configuration.
engrim setup --agy
  • Configures ~/.gemini/config/hooks.json to executeengrim hook --agent agy --event boot onPreInvocation andengrim hook --agent agy --event stop onStop .
  • Deploys the canonical Antigravity skill to ~/.gemini/config/skills/engrim/SKILL.md .
  • Registers the MCP server in ~/.gemini/antigravity-cli/mcp_config.json and~/.gemini/config/mcp_config.json .
engrim setup --claude
  • Wires SessionStart ,SessionEnd ,Stop , andUserPromptSubmit hooks in~/.claude/settings.json .
  • Configures live ambient status line in Claude Code's status bar.
  • Appends memory usage notes to ~/.claude/CLAUDE.md .
engrim setup --cursor
  • Adds engrim to~/.cursor/mcp.json runningengrim serve --mcp .

Add engrim to your ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "engrim": {
      "command": "engrim",
      "args": ["serve", "--mcp"]
    }
  }
}
engrim setup --all
  • Configures every supported environment in one command.

(Use --dry-run with any setup command to inspect changes without modifying disk).

When multiple agents collaborate on a single codebase, provenance matters. engrim records the origin of every memory entry with the origin_agent field:

  • Allowed values: antigravity ,claude-code ,cursor ,cli , oruser .
  • Automatically populated based on the active hook, MCP client, or CLI session.
  • Subtly surfaced in engrim context andengrim list :
🧠 engrim · memory restored for this project — you don't have to re-explain · /workspace
  18 of 54 curated records loaded (~3850 chars) · the rest one `recall` away

[DECISION]
- #961 [DECISION] (via Antigravity): Inverted stop loss matrix for high volatility  (risk, execution)
- #942 [DECISION] (via Claude Code): Switched primary database from MongoDB to PostgreSQL  (db, schema)
- #910 [DECISION] (via Cursor): Standardized on Pydantic v2 schemas across API boundaries  (api, types)

Existing databases are non-destructively migrated on first access via ALTER TABLE memories ADD COLUMN origin_agent TEXT.

Launch the zero-dependency, JSON-RPC 2.0 stdio MCP server:

engrim serve --mcp

stdout is strictly reserved for JSON-RPC messages, redirecting all diagnostic logs to stderr.

Tool Signature Purpose
engrim_recall (query: str, project: str = "auto", k: int = 5, type: str = None) Hybrid (keyword + semantic) search over project memory.
engrim_add (type: str, summary: str, detail: str = None, tags: list[str] = []) Write a durable memory record persisted across sessions.
engrim_context (project: str = "auto", budget: int = 4000) Retrieve the session-boot memory pack within a character budget.
engrim_review (project: str = "auto") Check uncaptured decisions from transcript logs before clearing.
Command Usage Description
engrim add engrim add -t decision -s "..." [--origin-agent agy] Insert memory record (types: decision ,fact ,feedback ,state ,user ,reference ).
engrim recall engrim recall -q "database" Ranked hybrid recall for the project ( --log searches raw turns).
engrim context engrim context [-b 4000] Priority-ordered, budget-capped session-boot pack.
engrim hook engrim hook --agent agy --event boot Agent lifecycle hook runner for Antigravity and Claude Code.
engrim setup engrim setup [--agy|--claude|--cursor|--all] Universal multi-agent environment configuration.
engrim serve engrim serve --mcp Start stdio MCP server for agent integrations.
engrim review engrim review "Safe to clear" coverage check: scans logs for uncurated decisions.
engrim list engrim list [-k 20] List recent memories for the current project.
engrim supersede engrim supersede --id 12 --status superseded Mark a record superseded without erasing history.
engrim sync engrim sync [DIR] Mirror markdown memories into the store (idempotent seed-once).
  1. Capture as you work : Whenever a major decision or architectural rule is made, runengrim add or invokeengrim_add via your agent.
  2. Use resume-pointer : Before ending a session or clearing, add a record taggedresume-pointer describing the immediate next task. The newest pointer is pinned under[▶ RESUME HERE] at the top of the next session's boot pack.
  3. Verify with engrim review : Check that all recent decisions are captured.
  4. Clear freely (/clear) : The session window is wiped clean; engrim automatically re-injects the active memory pack on the next prompt or invocation.
  • 100% Local & Offline : All memory records and logs reside in a local SQLite file (~/.engrim/memory.db ). No telemetry, no cloud sync, no tracking.
  • Model Storage : Usesmodel2vec for local static embeddings (~30ms load time, no GPU required, runs on CPU). Can run pure-lexical (ENGRIM_EMBED=off ) for zero extra dependencies.
  • POSIX File Permissions : Databases are created with restricted owner-only permissions (0600 ).
  • Git Protection :*.db is gitignored by default; your memories never accidentally commit to version control.

MIT © 2026 Tim Gordon.

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