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 insidebot-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-stackOr 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
- GitHub: github.com/kobie3717/ai-iq
- PyPI: pypi.org/project/ai-iq
- Discord: discord.gg/Y2jCXNGgE
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)
| File | Size | Uploaded | |
|---|---|---|---|
| ai_iq-5.11.1.tar.gz | 224.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| 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 |
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SHA-256 checksum How to use checksums |
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No |
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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 |
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No |
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