mcp-engram 0.15.2

Durable Agent Runtime — append-only event log, replay-based recovery, cross-session task continuity, agent handoff. Local-first, zero cloud dependency.

Engram

Runtime continuity and interruption recovery for AI coding agents

Engram gives Claude Code, Cursor, and OpenHands resumable execution — recover agent working state after context collapse, interruption, or session termination. Agents continue from where they stopped, not from zero.

中文文档 · PyPI · Glama

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The Problem

Every AI agent session is an island:

Situation	Without Engram	With Engram
Context window full	Agent loses history, starts guessing	Checkpoint restored, constraints preserved
Process killed / server restart	All working state lost	Auto-checkpoint on SIGTERM, resume on next recall
Switch agents (Claude → Cursor)	Start from scratch	Structured handoff with task state
Same bug hits twice	No memory of previous fix	Failure record surfaces automatically
3-session task	No one knows overall progress	Task state + continuity metrics available

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Why Engram

Most memory systems focus on conversation recall.

Engram focuses on runtime continuity:

• What the agent was doing — task state, working set, modified files
• Where execution stopped — checkpoint with goal, progress, blockers
• How to resume safely — negative memory (must_not_redo) + invariants (must_preserve)

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Quick Start

pip install mcp-engram
engram-setup          # downloads embedding model + initializes DB (~500MB, one-time)

Add to your Claude Code / Cursor config:

{
  "mcpServers": {
    "engram": {
      "command": "engram",
      "env": { "HF_ENDPOINT": "https://huggingface.co" }
    }
  }
}

That's it. Engram runs fully locally — no cloud, no API keys, no telemetry.

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Interruption Recovery

When a session ends unexpectedly — context overflow, process kill, IDE crash — Engram automatically captures a structured working state on shutdown:

SIGTERM / context collapse
        ↓
Engram auto-checkpoint
  goal · completed · in_progress · blocked
  modified_files (git diff) · last_tool_called · last_failure
        ↓
Agent restarts → recall_memory()
        ↓
interrupt_recovery injected into response
  → "Call restore_checkpoint(task_id=X) to resume"

No manual intervention. The next agent session picks up the hint automatically on first recall_memory call.

Generate a CLAUDE.md snippet from current state:

engram-prompt

Output (paste into your project's CLAUDE.md):

## Engram Runtime State

**Active task:** 10 — Fix the auth bug in login.py
**Checkpoint:** v3 (confidence: 0.71)

**Already completed (do not redo):**

- Reproduced the bug in test_auth.py

**In progress:**

- Tracing the JWT validation path

**Files modified in last session:**

- `src/auth/validator.py`
- `tests/test_auth.py`

## Engram Session Rules

- Session start: recall_memory(query) — interrupt state auto-pinned
- Resume task: restore_checkpoint(task_id=10)
- Context filling up: report_interruption(reason="overflow") then session_handoff(...)

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How It Works

Engram is a local MCP server providing 18 tools across three areas:

Memory

Hybrid semantic search (BM25 + vector + graph) with Ebbinghaus decay so important memories stay longer.

Tool	What it does
store_memory	Save a fact, decision, or lesson (auto-deduplicates)
recall_memory	Hybrid search — call at the start of every task
update_memory	Correct a specific memory by ID
consolidate_memory	Merge similar memories, prune weak ones
memory_stats	Health overview: counts, strength, last maintenance
get_runtime_health	Read-only backend health report

Task Tracking

Tasks are first-class entities that survive session boundaries.

Tool	What it does
create_task	Start a tracked multi-session task
get_task	Full task context + latest checkpoint
update_task	Update status, goal, or metadata
list_tasks	See all tasks, filter by status
track_progress	Snapshot feature/task progress
track_failure	Record structured failure with root cause + fix

Continuity

Checkpoint-based recovery so a new agent can pick up exactly where the previous one left off.

Tool	What it does
session_handoff	End-of-session structured summary
session_outcome	Mark session success/failure (adjusts memory weights)
restore_checkpoint	Constrained continuation package for takeover
list_checkpoints	Checkpoint history for a task
report_interruption	Signal overflow/rate-limit before exit
evaluate_continuity	Score recovery quality across checkpoint versions

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Checkpoint Recovery Flow

Agent A (Claude Code)                  Agent B (Cursor)
  │                                        │
  ├─ create_task(name, goal)               │
  ├─ track_progress / track_failure        │
  ├─ ⚡ Interrupted (SIGTERM / overflow)   │
  ├─ [auto] interrupt checkpoint saved     │
  │        │                               │
  │   ┌────▼────────────────┐              │
  │   │  Engram Checkpoint  │              │
  │   │  goal               │              │
  │   │  completed          │              │
  │   │  must_not_redo  ────┼──────────▶   │
  │   │  must_preserve      │             ├─ recall_memory() → interrupt_recovery pinned
  │   │  modified_files     │             ├─ restore_checkpoint(task_id)
  │   │  working_set        │             ├─ continue, not from zero
  │   └─────────────────────┘             └─ session_handoff → next agent...

Continuation package fields: goal · completed · in_progress · blocked · preferred_next · must_not_redo (negative memory) · must_preserve (invariants) · working_set · continuation_confidence

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Recommended CLAUDE.md Instructions

Run engram-prompt to auto-generate this from your current state, or paste manually:

## Engram Session Rules

- Task start: create_task(name, goal) → save the task_id
- Session begin: recall_memory(query) — latest handoff + interrupt state auto-pinned
- Task takeover: restore_checkpoint(task_id, memory_restore_mode="SELECTIVE")
- Progress update: track_progress(feature, status, task_id=<id>)
- On error: track_failure(error, component, root_cause, task_id=<id>)
- Context filling up: report_interruption(reason="overflow") then session_handoff(...)
- Session end: session_handoff(summary, completed, in_progress, blocked, next_steps, task_id=<id>)

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Architecture

Tier 1 — Runtime Continuity (Source of Truth, replay-recoverable)
  tasks · checkpoints · session lifecycle · handoff events
  → append-only event log  ~/.engram/events/*.jsonl  (fsync, gzip-rotated)

Tier 2 — Semantic Recall (Degradable, readonly-recoverable)
  memories · metadata · semantic graph
  → DuckDB projection from event log

Tier 3 — Retrieval Cache (Disposable, rebuildable)
  embeddings · FTS · vector index
  → rebuilt on demand, never in recovery path

Two Laws:

1. The event log is the only durability primitive.
2. If it cannot be replayed, it is not critical state.

DB corruption enters readonly degraded mode — never silently resets. Recover with engram-setup recover.

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Benchmark

Evaluated on LoCoMo (Snap Research long-term conversation memory benchmark):

System	Overall F1	Hit@5	LLM	Deployment
MemMachine	0.8487	—	GPT-4o-mini	Cloud
Memobase	0.7578	—	GPT-4o-mini	Cloud
Zep	0.7514	—	GPT-4o-mini	Cloud
Mem0	0.6688	—	GPT-4o-mini	Cloud
Engram	0.4383	77.7%	DeepSeek-V3.2	Local

Zero cloud dependency. Four optimization rounds: F1 +50.3%, Hit@5 +26.2pp.

Engram also tracks runtime continuity metrics via evaluate_continuity: Goal Retention · Action Consistency · Failure Recall · Working Set Stability · Replanning Rate · Redundant Exploration.

Category scores + memory algorithm details

Category Scores

Category	Count	F1	Hit@5
Single-Hop	114	0.5121	76.3%
Temporal	63	0.4501	95.2%
Multi-Hop	43	0.3181	60.5%
Open-Domain	13	0.1324	61.5%
Overall	233	0.4383	77.7%

Memory Algorithm

• Ebbinghaus Decay: strength = importance × e^(−λ × days) × (1 + recall_count × 0.2)
  • failure: λ=0.35, ~11d half-life · strategy: λ=0.10, ~38d half-life
• Deduplication: ≥0.85 → reinforce · 0.65–0.84 → merge/replace contradiction · <0.65 → new
• Hybrid Retrieval: 0.3 × BM25 + 0.7 × (semantic × decay strength) + graph boost
• Auto-maintenance: consolidate every 12h + prune (strength < 0.05) + FTS rebuild

Importance Guide

Range	Use for
0.9–1.0	Core identity, permanent facts
0.7–0.8	Architecture decisions, strong preferences
0.5	Regular project facts
0.2–0.3	Transient session context

Key Environment Variables

Variable	Default	Description
HF_ENDPOINT	https://hf-mirror.com	HuggingFace mirror (change to https://huggingface.co outside China)
ENGRAM_MODEL	all-mpnet-base-v2	Embedding model
ENGRAM_DEDUP_THRESHOLD	0.65	Dedup similarity lower bound
ENGRAM_REINFORCE_THRESHOLD	0.85	Reinforce similarity threshold
ENGRAM_W_BM25 / ENGRAM_W_VECTOR	0.30 / 0.70	Retrieval weights
ENGRAM_PRUNE_THRESHOLD	0.05	Prune strength threshold

Full variable list: src/engram/config.py

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What Engram Is Not

• ❌ Guaranteed identical LLM behavior after recovery (LLM non-determinism is a physical constraint)
• ❌ Custom agent loop or prompt orchestration (handled by the MCP client)
• ❌ Multi-agent coordination or shared team memory (single-user, local-first)

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Requirements

• macOS / Linux / WSL2
• Python 3.11+
• ~500MB disk for embedding model cache (one-time download)

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Contributing

git clone https://github.com/hugfeature/engram.git
cd engram
pip install -e ".[dev]"
pytest tests/ -v

Issues and PRs welcome.

License

MIT · Maintained by @hugfeature

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Engram restores an agent's working state, not just its memories.