MindForge

MindForge

A local-first, production-grade memory system for AI agents.

Maintainer — Xiao Gu (小顾), born in 2008 · GitHub: opok-ops · 抖音: shidianduo3116 · 小红书: 4406811524 · Feedback: 2638895480@qq.com

MindForge gives AI agents a persistent, structured long-term memory: a four-tier lifecycle model, knowledge graphs, hybrid retrieval, end-to-end encryption, and MCP / CLI / REST interfaces. All data stays on your machine.

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Overview

Agent memory solutions that rely on cloud backends introduce latency, cost, and privacy trade-offs. MindForge takes the opposite approach: memory is computed and stored locally, encrypted at rest, and exposed through standard interfaces your existing agents already speak.

Key design decisions:

• Local-first. No cloud dependency. Storage runs on SQLite + FTS5 with WAL, a single file that moves with you.
• Structured memory. Four tiers (sensory, short-term, long-term, permanent) with promotion and decay driven by the Ebbinghaus forgetting curve.
• Retrieval quality. Six complementary retrieval paths fused into one ranked result, instead of a single vector index.
• Encryption by default. AES-256-GCM at rest with PBKDF2 key derivation; encrypted backups you can export and trust.
• Open interfaces. MCP server (87 tools), Python SDK, CLI with shell completion, and a REST API for non-Python consumers.

Quick Start

Install from source, then start writing and reading memories in minutes:

git clone https://github.com/opok-ops/MindForge.git
cd MindForge
pip install -e .

from mindforge import MindForge

m = MindForge(db_path="./data/memory.db", encrypted=False)
m.add("User prefers type-hinted Python code", category="preferences", importance="HIGH")
result = m.search("coding preferences")
print(result.chunks[0].content)

CLI:

MindForge add "User likes cats" --category preferences --importance HIGH
MindForge search "what does user like"
MindForge stats

Features

v6.0.0 — released 2026-10-31

• Multi-agent memory spaces — shared spaces with three-tier roles (owner / editor / reader), last-write-wins conflict resolution, and PRIVATE / STRICT privacy guardrails. Promoted from experimental to a supported capability; available through the multi_agent facade, CLI space-*, MCP space_*, and the v6 port registry (status AVAILABLE).
• 6G AI memory preview — four capabilities on the preview port, each replacing a former stub: parallel Preview Channels (isolated SQLite stores that never touch the stable database), context distillation (distill_context ranks memories HIGH / MEDIUM / LOW under a token budget), incremental index statistics (write-time indexing with near-real-time recall metrics), and per-memory timelines (versions, evolutions, and audit trail). Available through CLI preview-*, REST /api/preview/*, and MCP preview_*.
• Agent workflow toolkit — eight features covering the full memory lifecycle: chat-history import for cold start, timeline visualization, shareable skill packs, memory evolution, outcome-based learning, memory passports for cross-model migration, explainable principled forgetting, and follow-up tracking. Each is exposed consistently across CLI, REST, and MCP.
• v6 port registry — v6_status() reports capability readiness; v6_port(capability) injects a ready-to-use instance (multi-agent adapter or memory preview), so callers can depend on ports instead of concrete modules.

Retrieval

• Six-way hybrid search — vector, FTS5, TF-IDF, fuzzy, query expansion, and cross-encoder reranking fused into a single ranked result set.
• Knowledge graph — automatic entity and relation extraction, path finding, and associative recall.
• Weighted recall — multi-factor scoring (coverage, importance, access frequency, time decay) instead of similarity alone.

Memory lifecycle

• Four-tier memory — sensory buffer, short-term, long-term, and permanent tiers with Ebbinghaus-based decay and periodic consolidation.
• Conflict detection — antonym pairs, attribute inconsistencies, and timeline conflicts, with automatic decay.
• Procedural memory (Cases → Skills) — record_experience() stores agent success cases, distill_skills() turns them into reusable skill templates (persisted, restart-safe), skill_match()/skill_render() reuse them on new tasks with parameterized steps (v5.8.0).
• Knowledge-graph pipeline — auto_extract_graph hooks into add(); extract_graph() draws entities/relations into a persisted graph (survives restarts), graph_related/graph_path query it, STRICT-privacy memories are never extracted (v5.7.9).
• Reproducible embedding eval — benchmarks/embedding_eval.py compares hybrid / vector / FTS5 / TF-IDF on a fixed multilingual set with fixed seeds (Recall@5 / MRR@10 / NDCG@10); deterministic fake backend runs without any model for CI baselines (v5.7.8).
• Agent memory governance — agent_pin freezes decay and pins key memories, agent_forget soft-deletes with a reason, agent_decay_boost accelerates forgetting, and expire_memory closes a fact's validity window (v5.7.7).
• Conflict auto-reconcile — reconcile_conflicts closes the stale side of keep_newer / keep_higher_importance conflicts (Bi-temporal expiry, never deletes), and routes merge / review_needed to human review (v5.7.7).
• Connector framework — pluggable json / csv / markdown / file / url connectors with a registry and SSRF guard; extensible via register_connector (v5.7.7).
• Bi-temporal facts — valid_from / valid_to windows with as-of queries (valid_at) and supersede fact replacement that expires the old fact instead of deleting it, keeping full version history (v5.7.6).
• Skill extraction — clusters memories into reusable slots, steps, and trigger words.

Privacy and security

• Privacy engine — four isolation levels (PUBLIC / INTERNAL / PRIVATE / STRICT) with fine-grained access control.
• At-rest encryption — AES-256-GCM with PBKDF2-SHA256 key derivation.
• Encrypted export / import — export-json --password and import-json --password produce encrypted backups that remain unreadable even if the file leaks (v5.7.3).
• GDPR toolkit — gdpr-report / gdpr-export-all / gdpr-erase for data portability and the right to be forgotten, with automatic backup before erasure (v5.7.6).
• Memory health report — memory_report() aggregates four-tier counts, TTL expirations, trash, conflict events, FTS consistency, database health, and encryption status, without exposing any plaintext memory content (v5.9.0).
• Audit export — export_audit() returns machine-readable audit entries (limit / since / actor filters) for GDPR and forensics; exposed as GET /api/audit (v5.9.0).
• Privacy health check — privacy_report() aggregates privacy-tier distribution, STRICT count, trash, audit events, encryption / key-file state, and the federated sharing surface, without exposing any plaintext memory content (v5.9.2).
• Knowledge-graph export — export_graph() exports entities and relations as structured JSON (schema mindforge-knowledge-graph/1.0, memory IDs attached, no plaintext) for external visualization, audit, and migration (v5.9.2).
• Privacy-filtered graph export — export_graph(max_privacy="INTERNAL") drops edges sourced from STRICT/PRIVATE memories (fail-closed, isolated nodes pruned); CLI --privacy and REST GET /api/meta/graph?max_privacy= support the same filter (v5.9.3).
• Unknown-privacy grouping — privacy_report() buckets unknown/malformed privacy levels into unknown_privacy_count instead of leaking raw level names into the distribution (v5.9.3).

Integration

• MCP server — 87 tools, drop into Claude Code, OpenClaw, or any MCP client.
• Federated memory — P2P sharing between agents with trust levels, ACLs, and conflict resolution.
• REST API — standard HTTP endpoints for non-Python applications.
• CLI — 200+ commands with bash / zsh / fish completion.
• Embedding backends — sentence-transformers, OpenAI, Ollama, or a custom HTTP endpoint.

Architecture

┌──────────────────────────────────────────────────────┐
│           MindForge v6.3.0            │
│  Cognitive Layer  Personality · KnowledgeGraph       │
│                   MemoryEvolution · FederatedMemory  │
├──────────────────────────────────────────────────────┤
│  Function Layer   RecallEngine · HybridSearch        │
│                   IntentRouter · ConflictDetector    │
│                   SkillExtractor · SessionFocus       │
├──────────────────────────────────────────────────────┤
│  Core Layer       Storage (SQLite+FTS5) · Index     │
│                   Encryption (AES-256-GCM) · Query  │
├──────────────────────────────────────────────────────┤
│  Adapter Layer    OpenClaw · Claude Code · MCP       │
│                   REST API · CLI · Python SDK        │
└──────────────────────────────────────────────────────┘

Four-Tier Memory Model

Tier	Retention	Capacity	Purpose
Sensory	sec ~ min	~50	Input buffer, noise filtering
Short-term	hrs ~ days	~100	Working memory, active session
Long-term	wks ~ months	unlimited	Consolidated semantic memory
Permanent	forever	unlimited	Core knowledge, preferences, key experience

High-value entries strengthen over time through consolidation; low-value entries decay naturally.

Performance

Benchmarked on i7-12700H / 32GB RAM / NVMe SSD / Python 3.12 / SQLite WAL.

Operation	1K	10K	100K
Write (plaintext)	0.3 ms	0.5 ms	0.9 ms
Write (encrypted)	0.8 ms	1.2 ms	2.1 ms
TF-IDF Search P50	4 ms	12 ms	38 ms
FTS5 Search P50	0.4 ms	0.9 ms	3.2 ms
Cross-Encoder Rerank P50	5 ms	15 ms	45 ms
Consolidate (100 entries)	45 ms	120 ms	580 ms

Retrieval quality (500 multi-domain memories, 50 annotated queries):

Metric	TF-IDF	+FTS5+Fuzzy	+Cross-Encoder	+Query Expansion + Vector
MRR@10	0.62	0.71	0.82	0.85
NDCG@10	0.60	0.69	0.80	0.84
Recall@10	0.70	0.80	0.88	0.92

Integration

Claude Code

from adapters import ClaudeCodeAdapter

adapter = ClaudeCodeAdapter.from_env()
adapter.remember("User prefers concise code style", ["preferences"])
context = adapter.get_context("database optimization")

MCP Server

MindForge-mcp --db-path ./data/memory.db

REST API

MindForge serve --api --port 9000
# GET  /api/memories
# POST /api/memories
# GET  /api/search?q=...
# GET  /api/stats

OpenClaw

memory:
  adapter: MindForge
  adapter_config:
    db_path: ~/.MindForge/data/memory.db
    encrypted: true
    auto_consolidate: true

Security

• Encrypted databases: when a key file is present, set MINDFORGE_PASSWORD so the process can unlock the store. This is required for the dsh-mindforge bridge and any non-interactive integration (MCP / REST API / scripts) accessing an encrypted store.
• API authentication: if MINDFORGE_API_KEY is configured, unauthenticated callers are treated as anonymous and can only reach PUBLIC memories. All read/write paths carry actor identity for authorization and audit.
• Access logs are sanitized: search terms and memory IDs are stripped from logs.
• Upgrade note: v5.5.7 removed the experimental HMAC-XOR downgrade path (config flag EXPERIMENTAL_HMAC_XOR, 即「降级加密」). Blobs created under that flag can no longer be decrypted — back up your database before upgrading if you enabled it.

See SECURITY.md for the full security policy.

Roadmap

• v6.0 — multi-agent memory spaces and the 6G AI memory preview, both promoted from experimental ports to supported capabilities, alongside an eight-feature agent workflow toolkit (released 2026-10-31)
• Vector database backend (Qdrant / Chroma)
• Web UI dashboard
• Plugin ecosystem

Documentation

• Full documentation
• Changelog
• Contributing guide
• Security policy

License

MIT — Copyright (c) 2026 MindForge Project. If MindForge is useful to you, consider starring it on GitHub — it helps the project stay active and visible.