--- title: "TrueMemory vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/buildingjoshbetter-truememory-vs-sindresorhus-awesome" tools: ["buildingjoshbetter-truememory", "sindresorhus-awesome"] --- # TrueMemory vs awesome *GraphCanon updated Jul 12, 2026* ## 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. [TrueMemory](https://truememory.net) reports 365 GitHub stars, 47 forks, and 13 open issues, last pushed Jun 24, 2026. [awesome](https://github.com/sindresorhus/awesome) has 484k stars, 36k forks, and 92 open issues, last pushed Jun 30, 2026. Figures are from public GitHub metadata via [TrueMemory's repository](https://github.com/buildingjoshbetter/TrueMemory) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [TrueMemory](/tools/buildingjoshbetter-truememory.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | 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. | 😎 Curated list of awesome topics including hardware resources | | Stars | 365 | 484,026 | | Forks | 47 | 35,799 | | Open issues | 13 | 92 | | Language | Python | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | AGPL-3.0 | CC0-1.0 | | Categories | AI Agents, LLM Frameworks, Vector Databases | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [TrueMemory](/tools/buildingjoshbetter-truememory.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Days since push | 17d | 11d | | Open issues (now) | 13 | 92 | | Security scan | No MCP manifest | No lockfile | | Full report | [trust report](/tools/buildingjoshbetter-truememory/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### 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. ### 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 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 NOT to use awesome - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## 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](/tools/buildingjoshbetter-truememory/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([TrueMemory markdown twin](/tools/buildingjoshbetter-truememory/alternatives.md), [awesome markdown twin](/tools/sindresorhus-awesome/alternatives.md)), 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](/compare/buildingjoshbetter-truememory-vs-sindresorhus-awesome.md) 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](/tools/buildingjoshbetter-truememory/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=buildingjoshbetter-truememory`](/api/graphcanon/graph?tool=buildingjoshbetter-truememory) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_