Home/Compare/TrueMemory vs awesome

Comparison

TrueMemory vs awesome

Verdict

Pick TrueMemory when license: TrueMemory is AGPL-3.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, TrueMemory is AGPL-3.0.

Markdown twin · TrueMemory alternatives · awesome alternatives

GraphCanon updated today

TrueMemory logo

TrueMemory

buildingjoshbetter/TrueMemory

365pushed Jun 24, 2026
vs
awesome logo

awesome

sindresorhus/awesome

484kpushed Jun 30, 2026

Trust & integrity

SignalTrueMemoryawesome
Maintenance
Active (17d since push)
As of today · github_public_v1
Active (11d since push)
As of today · github_public_v1
Provenance
Not a fork · Personal account
As of today · github_public_v1
Not a fork · Personal account
As of today · github_public_v1
Security (OSV)
No MCP manifest
As of today · mcp_manifest
No lockfile
As of today · none

Tagline

TrueMemory
The memory your AI should have had from the start. Automatic capture, automatic recall, 100% local. One SQLite file, zero cloud. Works with Claude Code, Claude CLI, Cursor, Codex CLI, Gemini CLI.
awesome
😎 Curated list of awesome topics including hardware resources

Stars

TrueMemory
365
awesome
484k

Forks

TrueMemory
47
awesome
36k

Open issues

TrueMemory
13
awesome
92

Language

TrueMemory
Python
awesome
-

Adopt for

TrueMemory
-
awesome
-

Persona

TrueMemory
-
awesome
-

Runtime

TrueMemory
-
awesome
-

License

TrueMemory
AGPL-3.0
awesome
CC0-1.0

Last pushed

TrueMemory
Jun 24, 2026
awesome
Jun 30, 2026

Categories

TrueMemory
AI Agents, LLM Frameworks, Vector Databases
awesome
LLM Frameworks

Trust and health

Days since push

TrueMemory
17d
awesome
11d

Open issues (now)

TrueMemory
13
awesome
92

Security scan

TrueMemory
No MCP manifest
awesome
No lockfile

Full report

TrueMemory
Trust report

Choose TrueMemory if…

  • License: TrueMemory is AGPL-3.0, awesome is CC0-1.0.
  • Tags unique to TrueMemory: agent-memory, ai, ai-agent, ai-agents.
  • Also covers AI Agents, Vector Databases.

When NOT to use TrueMemory

  • AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
  • Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

Choose awesome if…

  • License: awesome is CC0-1.0, TrueMemory is AGPL-3.0.
  • Tags unique to awesome: awesome-list, resources.
  • More GitHub stars (484k vs 365) - visibility, not fit.

When NOT to use awesome

  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: TrueMemory 365 · awesome 484k (synced Jul 11, 2026).

Common questions

What is the difference between TrueMemory and awesome?
TrueMemory: The memory your AI should have had from the start. Automatic capture, automatic recall, 100% local. One SQLite file, zero cloud. Works with Claude Code, Claude CLI, Cursor, Codex CLI, Gemini CLI.. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories.
When should I choose TrueMemory over awesome?
Choose TrueMemory over awesome when License: TrueMemory is AGPL-3.0, awesome is CC0-1.0; Tags unique to TrueMemory: agent-memory, ai, ai-agent, ai-agents; Also covers AI Agents, Vector Databases.
When should I choose awesome over TrueMemory?
Choose awesome over TrueMemory when License: awesome is CC0-1.0, TrueMemory is AGPL-3.0; Tags unique to awesome: awesome-list, resources; More GitHub stars (484k vs 365) - visibility, not fit.
When should I avoid TrueMemory?
AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
When should I avoid awesome?
LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Is TrueMemory or awesome more popular on GitHub?
awesome has more GitHub stars (484,026 vs 365). Stars measure visibility, not whether either tool fits your constraints.
Are TrueMemory and awesome open source?
Yes - both are open-source projects on GitHub (TrueMemory: AGPL-3.0, awesome: CC0-1.0).
Where can I find alternatives to TrueMemory or awesome?
GraphCanon lists graph-backed alternatives at TrueMemory alternatives and awesome alternatives (TrueMemory markdown twin, awesome 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, TrueMemory or awesome?
TrueMemory: Active. awesome: 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 TrueMemory and awesome?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: TrueMemory trust report; awesome trust report.