Comparison
automem vs deep-searcher
Verdict
Pick automem if autoMem leverages both graph and vector database technologies to provide AI assistants with durable relational memory; pick deep-searcher if deepSearcher is an open-source tool for reasoning and searching on private data, using vector databases and LLM integrations in Python under Apache-2.0 license.
Markdown twin · automem alternatives · deep-searcher alternatives
GraphCanon updated 3d
vs
Trust & integrity
| Signal | automem | deep-searcher |
|---|---|---|
| Maintenance | Active (7d since push) As of 3d · github_public_v1 | Slowing (272d since push) As of 6d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3d · github_public_v1 | Not a fork · Organization account As of 6d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- automem
- Graph-vector memory service for durable, relational AI assistant memory
- deep-searcher
- Open Source Deep Research Alternative to Reason and Search on Private Data.
Stars
- automem
- 802
- deep-searcher
- 8.1k
Forks
- automem
- 102
- deep-searcher
- 775
Open issues
- automem
- 15
- deep-searcher
- 53
Language
- automem
- Python
- deep-searcher
- Python
Adopt for
- automem
- AutoMem leverages both graph and vector database technologies to provide AI assistants with durable relational memory.
- deep-searcher
- DeepSearcher is an open-source tool for reasoning and searching on private data, using vector databases and LLM integrations in Python under Apache-2.0 license.
Persona
- automem
- -
- deep-searcher
- -
Runtime
- automem
- -
- deep-searcher
- -
License
- automem
- AutoMem is licensed under the MIT License, which means it is free to use, modify, and distribute as long as license terms are met.
- deep-searcher
- Apache-2.0
Last pushed
- automem
- Aug 14, 2026
- deep-searcher
- Nov 19, 2025
Categories
- automem
- AI Agents, Vector Databases
- deep-searcher
- AI Agents, LLM Frameworks, Vector Databases
Trust and health
Maintenance
- automem
- Active (82%)
- deep-searcher
- Slowing (36%)
Days since push
- automem
- 7d
- deep-searcher
- 272d
Open issues (now)
- automem
- 15
- deep-searcher
- 53
Stars delta
- automem
- +9 (30d)
- deep-searcher
- +59 (30d)
Open issues delta
- automem
- +4 (30d)
- deep-searcher
- 0 (30d)
Full report
- automem
- Trust report
- deep-searcher
- Trust report
Choose automem if…
- License: automem is MIT, deep-searcher is Apache-2.0.
- Pricing: Free for open-source use, with no explicit commercial licensing information provided..
- Tags unique to automem: ai-memory, anthropic, falkordb, graph-database.
- Use AutoMem when you need an AI assistant capable of maintaining rich, relational memories over time.
When NOT to use automem
- Avoid using AutoMem if your application does not benefit from persistent memory or relational context, as it might add unnecessary overhead.
- If you require a simpler key-value storage system for less complex or non-relational data, AutoMem's graph and vector capabilities may be overkill.
Choose deep-searcher if…
- License: deep-searcher is Apache-2.0, automem is MIT.
- Tags unique to deep-searcher: agent, agentic-rag, deep-research.
- Also covers LLM Frameworks.
- When you require custom search and reasoning capabilities on your private datasets with integration of multiple LLMs like Claude or Qwen3.
When NOT to use deep-searcher
- Avoid if your project demands proprietary solutions, as DeepSearcher is open-source and may not be suitable for closed systems.
- Not ideal when a single vector database suffices; DeepSearcher supports multiple databases which might be overkill and complicate setup unnecessarily.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (verygoodplugins/automem) · observed Aug 21, 2026
- GitHub forks (verygoodplugins/automem) · observed Aug 21, 2026
- Last push (verygoodplugins/automem) · observed Aug 14, 2026
- License file (MIT) · observed Aug 21, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (zilliztech/deep-searcher) · observed Aug 18, 2026
- GitHub forks (zilliztech/deep-searcher) · observed Aug 18, 2026
- Last push (zilliztech/deep-searcher) · observed Nov 19, 2025
- License file (Apache-2.0) · observed Aug 18, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: automem 802 · deep-searcher 8.1k (synced Aug 21, 2026).
Common questions
- What is the difference between automem and deep-searcher?
- automem: Graph-vector memory service for durable, relational AI assistant memory. deep-searcher: Open Source Deep Research Alternative to Reason and Search on Private Data.. See the comparison table for live GitHub stats and shared categories.
- When should I choose automem over deep-searcher?
- Choose automem over deep-searcher when License: automem is MIT, deep-searcher is Apache-2.0; Pricing: Free for open-source use, with no explicit commercial licensing information provided.; Tags unique to automem: ai-memory, anthropic, falkordb, graph-database; Use AutoMem when you need an AI assistant capable of maintaining rich, relational memories over time.
- When should I choose deep-searcher over automem?
- Choose deep-searcher over automem when License: deep-searcher is Apache-2.0, automem is MIT; Tags unique to deep-searcher: agent, agentic-rag, deep-research; Also covers LLM Frameworks; When you require custom search and reasoning capabilities on your private datasets with integration of multiple LLMs like Claude or Qwen3.
- When should I avoid automem?
- Avoid using AutoMem if your application does not benefit from persistent memory or relational context, as it might add unnecessary overhead. If you require a simpler key-value storage system for less complex or non-relational data, AutoMem's graph and vector capabilities may be overkill.
- When should I avoid deep-searcher?
- Avoid if your project demands proprietary solutions, as DeepSearcher is open-source and may not be suitable for closed systems. Not ideal when a single vector database suffices; DeepSearcher supports multiple databases which might be overkill and complicate setup unnecessarily.
- Is automem or deep-searcher more popular on GitHub?
- deep-searcher has more GitHub stars (8,060 vs 802). Stars measure visibility, not whether either tool fits your constraints.
- Are automem and deep-searcher open source?
- Yes - both are open-source projects on GitHub (automem: MIT, deep-searcher: Apache-2.0).
- Where can I find alternatives to automem or deep-searcher?
- GraphCanon lists graph-backed alternatives at automem alternatives and deep-searcher alternatives (automem markdown twin, deep-searcher 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, automem or deep-searcher?
- automem: Active. deep-searcher: Slowing. 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 automem and deep-searcher?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: automem trust report; deep-searcher trust report.