Comparison
mengram vs memsearch
Verdict
Pick mengram if mengram provides human-like memory capabilities for AI agents, including semantic, episodic, and procedural memory, enabling learning from experiences and failures. It offers free API access, Python and JS SDKs, and is a; pick memsearch if memsearch is a hybrid memory management solution for AI agents with Markdown and Milvus backing, ideal for rich semantic search and long-term.
Markdown twin · mengram alternatives · memsearch alternatives
GraphCanon updated Sep 20, 2026
5views this month
vs
Trust & integrity
| Signal | mengram | memsearch |
|---|---|---|
| Maintenance | Very active (1d since push) As of Sep 18, 2026 · github_public_v1 | Very active (0d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- mengram
- Human-like memory for AI agents
- memsearch
- A persistent, unified memory layer for all your AI agents backed by Markdown and Milvus.
Stars
- mengram
- 196
- memsearch
- 2.6k
Forks
- mengram
- 34
- memsearch
- 253
Open issues
- mengram
- 25
- memsearch
- 254
Language
- mengram
- Python
- memsearch
- Python
Adopt for
- mengram
- mengram provides human-like memory capabilities for AI agents, including semantic, episodic, and procedural memory, enabling learning from experiences and failures. It offers free API access, Python and JS SDKs, and is a
- memsearch
- memsearch is a hybrid memory management solution for AI agents with Markdown and Milvus backing, ideal for rich semantic search and long-term data storage.
Persona
- mengram
- -
- memsearch
- -
Runtime
- mengram
- -
- memsearch
- -
License
- mengram
- Apache-2.0
- memsearch
- MIT
Last pushed
- mengram
- Sep 16, 2026
- memsearch
- Sep 20, 2026
Categories
- mengram
- AI Agents, Data & Retrieval
- memsearch
- AI Agents, Data & Retrieval, Vector Databases
Trust and health
Days since push
- mengram
- 1d
- memsearch
- 0d
Open issues (now)
- mengram
- 25
- memsearch
- 254
Stars delta
- mengram
- +12 (30d)
- memsearch
- +291 (30d)
Open issues delta
- mengram
- -2 (30d)
- memsearch
- +23 (30d)
Owner type
- mengram
- User
- memsearch
- Organization
OSV dependency advisories
- mengram
- Published findings
- memsearch
- No lockfile (source not queried)
Full report
- mengram
- Trust report
- memsearch
- Trust report
Choose mengram if…
- License: mengram is Apache-2.0, memsearch is MIT.
- Tags unique to mengram: ai-agents, ai-memory, claude-desktop, cognitive-architecture.
- mengram ships Docker support for self-hosted deployment.
- When you need an AI agent that can learn from its experiences and failures, leveraging semantic, episodic, and procedural memory capabilities.
When NOT to use mengram
- If your project does not require the specific memory capabilities of semantic, episodic, and procedural memory, mengram might offer more features than necessary.
- If you are looking for a tool that does not require the setup of an MCP server, mengram might not be the best fit as it involves configuring this server.
- For projects that do not need integration with LangChain, CrewAI, or OpenClaw, the specific integrations provided by mengram may not be beneficial.
- If your AI agent development does not benefit from the use of pgvector, mengram's recent debugging efforts in this area may not be relevant to your needs.
Choose memsearch if…
- License: memsearch is MIT, mengram is Apache-2.0.
- Tags unique to memsearch: long-term-memory, milvus, semantic-search.
- Also covers Vector Databases.
- When you need robust integration with AI agents like Claude Code or Codex
When NOT to use memsearch
- If your application doesn't require integration with specific AI agents like Claude Code
- In cases where only simple text data storage without semantic search is needed
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (alibaizhanov/mengram) · observed Sep 20, 2026
- GitHub forks (alibaizhanov/mengram) · observed Sep 20, 2026
- Last push (alibaizhanov/mengram) · observed Sep 16, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (zilliztech/memsearch) · observed Sep 20, 2026
- GitHub forks (zilliztech/memsearch) · observed Sep 20, 2026
- Last push (zilliztech/memsearch) · observed Sep 20, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: mengram 196 · memsearch 2.6k (synced Sep 20, 2026).
Common questions
- What is the difference between mengram and memsearch?
- mengram: Human-like memory for AI agents. memsearch: A persistent, unified memory layer for all your AI agents backed by Markdown and Milvus.. See the comparison table for live GitHub stats and shared categories.
- When should I choose mengram over memsearch?
- Choose mengram over memsearch when License: mengram is Apache-2.0, memsearch is MIT; Tags unique to mengram: ai-agents, ai-memory, claude-desktop, cognitive-architecture; mengram ships Docker support for self-hosted deployment; When you need an AI agent that can learn from its experiences and failures, leveraging semantic, episodic, and procedural memory capabilities.
- When should I choose memsearch over mengram?
- Choose memsearch over mengram when License: memsearch is MIT, mengram is Apache-2.0; Tags unique to memsearch: long-term-memory, milvus, semantic-search; Also covers Vector Databases; When you need robust integration with AI agents like Claude Code or Codex.
- When should I avoid mengram?
- If your project does not require the specific memory capabilities of semantic, episodic, and procedural memory, mengram might offer more features than necessary. If you are looking for a tool that does not require the setup of an MCP server, mengram might not be the best fit as it involves configuring this server. For projects that do not need integration with LangChain, CrewAI, or OpenClaw, the specific integrations provided by mengram may not be beneficial. If your AI agent development does not benefit from the use of pgvector, mengram's recent debugging efforts in this area may not be relevant to your needs.
- When should I avoid memsearch?
- If your application doesn't require integration with specific AI agents like Claude Code In cases where only simple text data storage without semantic search is needed
- Is mengram or memsearch more popular on GitHub?
- memsearch has more GitHub stars (2,627 vs 196). Stars measure visibility, not whether either tool fits your constraints.
- Are mengram and memsearch open source?
- Yes - both are open-source projects on GitHub (mengram: Apache-2.0, memsearch: MIT).
- Where can I find alternatives to mengram or memsearch?
- GraphCanon lists graph-backed alternatives at mengram alternatives and memsearch alternatives (mengram markdown twin, memsearch markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, mengram or memsearch?
- mengram: Very active. memsearch: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for mengram and memsearch?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: mengram trust report; memsearch trust report.