Skip to main content

AI-IQ — Self-Hosted AI Agent Memory (Python)

Long-term memory for AI agents. One pip install. No servers, no paywall, no vendor lock-in. Graph memory, conflict detection, and semantic search — free, forever.

Python library for AI agent long-term memory. SQLite-based. Works with Claude, GPT-4, Gemini, or any LLM. Mem0 alternative. Zep alternative. No cloud required.

Part of the Claw Stack: AI-IQ is the memory + credential substrate of a larger pipeline — Memory → Credential → Commons → Runtime. Agents earn W3C Verifiable Credentials through proof-of-work, then present them to circus (agent commons where agents discover each other, join rooms, build trust) and run inside bot-circus (multi-bot Telegram orchestrator). Runs standalone or as part of the full stack.

Install the whole stack in one command:

/plugin marketplace add kobie3717/claw-stack

Or just this plugin:

/plugin marketplace add kobie3717/ai-iq

Install

pip install ai-iq

Quick Start

from ai_iq import Memory

memory = Memory()

# Add memories
memory.add("User prefers dark mode", tags=["preference", "ui"])
memory.add("Redis bug fixed with network_mode: host", category="learning")

# Search (hybrid keyword + semantic)
results = memory.search("redis networking")
for r in results:
    print(f"#{r['id']}: {r['content']}")

# Update and delete
memory.update(1, "User STRONGLY prefers dark mode")
memory.delete(1)

CLI

memory-tool add learning "Docker needs network_mode: host" --project MyApp
memory-tool search "docker networking"
memory-tool dream  # Consolidate duplicates, detect conflicts

Claude Code Plugin

Use AI-IQ directly in Claude Code with auto-capture:

/plugin marketplace add kobie3717/ai-iq
/plugin install ai-iq

See CLAUDE_CODE_PLUGIN.md for details.

Why AI-IQ?

  • Single SQLite file = your AI's brain — No servers, no vector DB, no setup
  • No cloud dependencies — Works offline, owns your data, zero API keys
  • Works with any Python agent — Not locked to Claude, OpenAI, or any vendor
  • Hybrid search — Keyword (FTS5) + semantic (vector) + graph traversal
  • Conflict detection — Catches contradictions automatically
  • Memories decay naturally — FSRS-6 algorithm like human memory

AI-IQ vs Mem0 vs Zep

Feature AI-IQ Mem0 Zep
Install pip install ai-iq pip + vector DB + LLM API Neo4j + FalkorDB + Graphiti
Graph memory ✅ Free ❌ $249/mo ❌ Paywalled
Conflict detection ✅ Built-in ❌ None ❌ None
Self-hostable ✅ Single SQLite file ⚠️ Complex setup ⚠️ 3 systems required
Fact recall Bayesian scoring ~17.5% (independent benchmark) ~58% (disputed)
Open source ✅ MIT ⚠️ Core only ❌ Community edition killed April 2025
Works offline ✅ Yes ❌ No ❌ No
Price Free $49-$249/mo for full features Paywalled

Advanced Features

See docs/REFERENCE.md for complete documentation:

  • Passport System — Complete identity card for any memory (graph connections, provenance chain, access patterns, confidence score)
  • Reflexion Self-Improvement — Learn from mistakes with structured reflections (20-40% task improvement)
  • Beliefs & Predictions — Confidence tracking with Bayesian updates
  • ReasoningBank Boost — Successful reasoning (confirmed predictions) ranks higher in retrieval (inspired by ruvnet/ruflo)
  • Knowledge Graph — Entities, relationships, spreading activation
  • Dream Mode — REM-like consolidation (dedup, conflict detection)
  • Identity Layer — Auto-discovers behavioral traits
  • Narrative Memory — Builds cause-effect stories from causal graph
  • Meta-Learning — Search improves from feedback loops

Passport System

Every memory has a "passport" — its complete identity card across all dimensions:

memory-tool passport 42

Shows:

  • Core identity: content, category, project, tags
  • Graph connections: linked entities with their relationships
  • Memory relationships: derived-from, related, supersedes chains
  • Provenance: citations, reasoning, source memories
  • Usage stats: access count, revisions, FSRS state
  • Passport score: composite 0-10 score from priority, access patterns, proof count, graph connections, and recency
  • Spreading activation: related entities discovered via graph traversal

Like a traveler's passport proves who you are and where you've been, a memory passport is its complete dossier.

Reflexion Self-Improvement

Learn from past mistakes with structured reflections (20-40% improvement on repeated tasks):

# Before starting a task
memory-tool reflect-load "nginx configuration"
# Shows: what failed before, what worked, what to do differently

# After completing a task
memory-tool reflect "Fixed nginx SSL config" \
  --outcome success \
  --worked "Tested syntax with nginx -t first" \
  --failed "None" \
  --next "Keep testing syntax before reload"

# Review patterns
memory-tool lessons
# Shows: task types with high failure rates needing attention

See docs/REFLEXION.md for complete guide.

Example

See examples/chatbot_with_memory.py

Documentation

Complete Reference • Examples • Architecture

Requirements

Python 3.8+ and SQLite 3.37+. Optional: pip install ai-iq[full] for semantic search.

License

MIT

Links

Release files for ai-iq 5.11.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for ai-iq 5.11.1
File Size Uploaded
ai_iq-5.11.1.tar.gz 224.0 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for ai-iq 5.11.1
File Interpreter ABI Platform
ai_iq-5.11.1-py3-none-any.whl Python 3 none any Details

Total release size: 394.2 kB

Release files / ai_iq-5.11.1.tar.gz

Download URL ai_iq-5.11.1.tar.gz
Size 224.0 kB
Tags Source
SHA-256 checksum
How to use checksums
41dc80214c3addf41e4346645333273ff17e7e275e2eba8b337c27e6c978ea5d
BLAKE2b-256 checksum
How to use checksums
963fe0b78760a3197e05dae0f8c3c5f7cb352afed82d0d35141df144761eca74
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.10.12

Release files / ai_iq-5.11.1-py3-none-any.whl

Download URL ai_iq-5.11.1-py3-none-any.whl
Size 170.2 kB
Tags Python 3
SHA-256 checksum
How to use checksums
284a7f2d7d743f153129e0c35c4da79a3bbf610727dc36110c631ac102c2ff47
BLAKE2b-256 checksum
How to use checksums
f84c6269df3a52feb164e42f5acad376beab6bba88cdad34d84077f3b4092efa
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.10.12

Release history Release notifications | RSS feed

This release

5.11.1 This release

2 release files

5.9.0

2 release files

5.8.0

2 release files

5.7.0

2 release files

5.6.0

2 release files

5.4.0

2 release files

5.3.0

2 release files

5.2.0

2 release files

5.1.0

2 release files

5.0.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page