engrim

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.

1. The Core Value Proposition

"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 Codex CLI 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. The connected AI agents (Antigravity, Claude Code, Cursor, Codex, Codex CLI) can automatically write to memory via MCP tools when they make architectural decisions, or you can manually save them (engrim add). Clear your agent session freely (/clear) and watch context reload intact.
• Smart, Hot Context Loading: Combines SQLite FTS5 (bm25 keyword search) with static vector embeddings (model2vec) in a zero-latency hybrid reciprocal-rank fusion engine.

2. Empirical Proof (The 105-Session Case Study)

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 under 1,000 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.

3. Architecture

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)"]
        CODEX["Codex CLI<br/>(Hooks & MCP)"]
    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
    CODEX <-->|"hook / MCP"| ADAPTERS
    ADAPTERS --> PROVENANCE
    PROVENANCE --> ROUTER
    ROUTER --> MEMORIES
    MEMORIES --- FTS5
    MEMORIES --- VEC
    ADAPTERS --> LOG

4. Multi-Agent Quickstart

Installation

pip install engrim

Auto-Detection (Recommended)

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.
• If ~/.codex exists $\rightarrow$ wires Codex CLI hooks and MCP server.

Explicit Platform Setup

Google Antigravity

engrim setup --agy

• Configures ~/.gemini/config/hooks.json to execute engrim hook --agent agy --event boot on PreInvocation and engrim hook --agent agy --event stop on Stop.
• 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.

Claude Code

engrim setup --claude

• Wires SessionStart, SessionEnd, Stop, and UserPromptSubmit hooks in ~/.claude/settings.json.
• Configures live ambient status line in Claude Code's status bar.
• Appends memory usage notes to ~/.claude/CLAUDE.md.

Cursor

engrim setup --cursor

• Adds engrim to ~/.cursor/mcp.json running engrim serve --mcp.

Codex CLI

engrim setup --codex

• Wires SessionStart, SessionEnd, Stop, and UserPromptSubmit hooks in ~/.codex/hooks.json.
• Registers the MCP server in ~/.codex/config.toml.

Windsurf

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

{
  "mcpServers": {
    "engrim": {
      "command": "engrim",
      "args": ["serve", "--mcp"]
    }
  }
}

All Platforms

engrim setup --all

• Configures every supported environment in one command.

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

GitHub Actions (gh-aw)

See examples/gh-aw/ for engrim inside GitHub Agentic Workflows: memory across runs through artifacts and engrim merge, and a continue-as-clear restart instead of auto-compaction.

5. Agent Provenance Tracking

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, or user.
• Automatically populated based on the active hook, MCP client, or CLI session.
• Subtly surfaced in engrim context and engrim 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.

6. Hardened Model Context Protocol (MCP) Server

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

engrim serve --mcp
# or: engrim mcp

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

Core MCP Tools Exposed:

engrim_review returns safe_to_clear: null (unknown) when the project has no transcript log, even if it has saved memories. With logged turns, the field is a boolean heuristic verdict: false means possible uncaptured decisions were detected; true means none were detected in the reviewed log. It does not verify that logging captured the entire session.

7. CLI Reference

8. Continue-As-Clear Workflow

1. Capture as you work: Whenever a major decision or architectural rule is made, it needs to be saved to memory. The AI agent will often do this automatically via the engrim_add tool, but you can also manually intervene by running engrim add yourself.
2. Use resume-pointer: Before ending a session or clearing, add a record tagged resume-pointer describing the immediate next task. The newest pointer is pinned under [▶ RESUME HERE] at the top of the next session's boot pack. When that work is done, engrim retire marks the pointer(s) done so a finished task never leads a later 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.

9. How Does Engrim Compare?

There are several other memory solutions and coding assistants out there (such as gbrain, OpenCode, Codex, and Pi). Here is how engrim differs:

• vs gbrain: While gbrain is a great provider-agnostic memory tool, engrim sets itself apart by using a lightweight, local-first SQLite architecture. This keeps everything fast and offline without needing complex setup or cloud dependencies.
• vs OpenCode & Codex: While other solutions may have built-in SQLite or memory components, engrim is specifically designed as an episodic memory engine that tracks the provenance of decisions across multiple different agents (Antigravity, Claude Code, Cursor, Codex, Codex CLI). It operates as a unified backend that all your tools can share.
• vs Pi: Pi acts as a personal AI companion with a long-term memory. engrim is specifically tailored for coding projects and software architecture—capturing decisions, state, and constraints in a format that coding agents can efficiently query via hybrid search (FTS5 + vector).

10. Security & Privacy

• 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: Uses model2vec 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.

11. Author & Contact

Created by Tim Gordon (@timgordontg).

• LinkedIn: linkedin.com/in/timgordon1
• Email: timgordontg@gmail.com

Open to collaborations and Staff / Senior engineering opportunities in Agentic AI, Developer Tooling, and Systems Engineering.

12. License

MIT © 2026 Tim Gordon.