What is Memary?
A memory layer for autonomous agents built on knowledge graphs. It tracks entities, relationships, and events with timestamps, supports multi-agent setups with isolated memory contexts, and integrates with LangChain and LlamaIndex to help agents evolve their knowledge through interactions.
Memary pricing
We don't have Memary's full plan breakdown yet (its pricing page resisted automated reading). Here's what we could confirm. Always check live pricing for exact numbers.
What Memary does
The capabilities that matter for agent memory, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- Architecture model
- Open source self hosted
- Graph based entity relationship extraction
- ✓
- Automatic context window rolling summarization
- -
- Semantic vs episodic event chronological memory
- ✓
- Cross session and multi thread user persistence
- ✓
- Memory conflict resolution and fact updating
- -
- Multi tenant user and agent isolation
- ✓
- Native langchain and llamaindex memory classes
- ✓
- Time decay and recency weighted retrieval
- -
- Zero data retention and pii redaction layer
- -
- SOC2 type ii
- -
- Mit or apache permissive oss license
- -
- Pricing model
- Free open source
Platform & deployment
Independently observed- CLI
- Web
- Self-hosted
Integrations (3)
Independently observed- FalkorDB
- LangChain
- LlamaIndex
Security & compliance
Known vulnerabilities: 0 (0 in the last 12 months) sourcea count reflects scale & disclosure, not quality
Memary alternatives
Other agent memory we track, ranked by the same independent score.
- CogneeOpen-source AI memory platform that builds knowledge graphs to help agents retrieve relevant context across sessionslow · 37%
- ZepTemporal knowledge graph system for AI agent memorymedium · 55%
- Supermemorylow · 42%
- Mastralow · 42%
- Mem0A persistent memory layer that enables AI agents to retain and learn from interactions across sessions and toolsmedium · 54%
- LangMemlow · 31%
Compare Memary
Side by side against other agent memory, attribute by attribute, with a source on every value.
The Vioscale score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for Memary, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Pricing transparency | 80 | 0.08 | 6.7 | ✓ |
| Capabilities | 75 | 0.08 | 6.3 | ✓ |
| Price level | 100 | 0.05 | 5.2 | ✓ |
| Release cadence | 94 | 0.05 | 4.9 | ✓ |
| Stars | 65 | 0.03 | 1.7 | ✓ |
| Integrations | 17 | 0.09 | 1.6 | ✓ |
| Reliability | 0 | 0.07 | 0.0 | - |
| Dependent projects | 0 | 0.06 | 0.0 | ✓ |
| Development activity | 0 | 0.09 | 0.0 | - |
| Security posture | 0 | 0.07 | 0.0 | - |
| Package downloads | 0 | 0.14 | 0.0 | - |
| Security score | 0 | 0.04 | 0.0 | - |
| Developer Q&A activity | 0 | 0.06 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Adoption
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | Pricing model: free_open_source · Architecture model: open_source_self_hosted · Multi tenant user and agent isolation: Yes · Graph based entity relationship extraction: Yes · Native langchain and llamaindex memory classes: Yes · Cross session and multi thread user persistence: Yes | mediumsource · 2026-08-21 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 3 | mediumsource · 2026-08-21 · 60% |
Language
| Attribute | Value | Evidence |
|---|---|---|
| Primary | Jupyter Notebook | highsource · 2026-08-26 · 90% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | MIT | highsource · 2026-08-26 · 95% |
Pricing
Release
Security
| Attribute | Value | Evidence |
|---|---|---|
| Vulnerabilities | Count: 0 · Source: https://advisories.ecosyste.ms/api/v1/advisories?ecosystem=pypi&package_name=memary&per_page=100 · Last 12m: 0 | highsource · 2026-08-26 · 90% |