--- title: "TrueMemory vs Prompt-Engineering-Guide" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/buildingjoshbetter-truememory-vs-dair-ai-prompt-engineering-guide" tools: ["buildingjoshbetter-truememory", "dair-ai-prompt-engineering-guide"] --- # TrueMemory vs Prompt-Engineering-Guide *GraphCanon updated Jul 11, 2026* ## Verdict Pick TrueMemory when trueMemory is primarily Python; Prompt-Engineering-Guide is MDX; pick Prompt-Engineering-Guide when prompt-Engineering-Guide is primarily MDX; TrueMemory is Python. [TrueMemory](https://truememory.net) reports 365 GitHub stars, 47 forks, and 13 open issues, last pushed Jun 24, 2026. [Prompt-Engineering-Guide](https://www.promptingguide.ai/) has 76k stars, 8.4k forks, and 274 open issues, last pushed Mar 11, 2026. Figures are from public GitHub metadata via [TrueMemory's repository](https://github.com/buildingjoshbetter/TrueMemory) and [Prompt-Engineering-Guide's repository](https://github.com/dair-ai/Prompt-Engineering-Guide). | | [TrueMemory](/tools/buildingjoshbetter-truememory.md) | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.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. | Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents | | Stars | 365 | 76,349 | | Forks | 47 | 8,361 | | Open issues | 13 | 274 | | Language | Python | MDX | | Adopt for | - | Decision-critical facts for Prompt-Engineering-Guide | | Persona | - | - | | Runtime | - | - | | License | AGPL-3.0 | MIT | | Categories | AI Agents, LLM Frameworks, Vector Databases | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [TrueMemory](/tools/buildingjoshbetter-truememory.md) | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 17d | 121d | | Open issues (now) | 13 | 274 | | Owner type | User | Organization | | Security scan | No MCP manifest | No criticals | | Full report | [trust report](/tools/buildingjoshbetter-truememory/trust.md) | [trust report](/tools/dair-ai-prompt-engineering-guide/trust.md) | ## Decision facts: Prompt-Engineering-Guide - **Adopt for:** Decision-critical facts for Prompt-Engineering-Guide ## Choose when ### Choose TrueMemory if… - TrueMemory is primarily Python; Prompt-Engineering-Guide is MDX. - License: TrueMemory is AGPL-3.0, Prompt-Engineering-Guide is MIT. - Tags unique to TrueMemory: agent-memory, ai, ai-agent, ai-memory. - Also covers Vector Databases. ### Choose Prompt-Engineering-Guide if… - Prompt-Engineering-Guide is primarily MDX; TrueMemory is Python. - License: Prompt-Engineering-Guide is MIT, TrueMemory is AGPL-3.0. - Tags unique to Prompt-Engineering-Guide: agent, agents, chatgpt, deep-learning. - When you seek comprehensive documentation and educational materials specifically focused on the nuance of prompt engineering techniques. ## 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 Prompt-Engineering-Guide - Avoid using if your focus is entirely on deep-learning frameworks without a need for detailed instructions or examples related to prompt crafting. - Not suitable when you require tools that go beyond guiding materials, such as custom prompts or direct software plugins provided by competitors focused more on practical implementation over learning. ## Common questions ### What is the difference between TrueMemory and Prompt-Engineering-Guide? 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.. Prompt-Engineering-Guide: Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose TrueMemory over Prompt-Engineering-Guide? Choose TrueMemory over Prompt-Engineering-Guide when TrueMemory is primarily Python; Prompt-Engineering-Guide is MDX; License: TrueMemory is AGPL-3.0, Prompt-Engineering-Guide is MIT; Tags unique to TrueMemory: agent-memory, ai, ai-agent, ai-memory; Also covers Vector Databases. ### When should I choose Prompt-Engineering-Guide over TrueMemory? Choose Prompt-Engineering-Guide over TrueMemory when Prompt-Engineering-Guide is primarily MDX; TrueMemory is Python; License: Prompt-Engineering-Guide is MIT, TrueMemory is AGPL-3.0; Tags unique to Prompt-Engineering-Guide: agent, agents, chatgpt, deep-learning; When you seek comprehensive documentation and educational materials specifically focused on the nuance of prompt engineering techniques. ### 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 Prompt-Engineering-Guide? Avoid using if your focus is entirely on deep-learning frameworks without a need for detailed instructions or examples related to prompt crafting. Not suitable when you require tools that go beyond guiding materials, such as custom prompts or direct software plugins provided by competitors focused more on practical implementation over learning. ### Is TrueMemory or Prompt-Engineering-Guide more popular on GitHub? Prompt-Engineering-Guide has more GitHub stars (76,349 vs 365). Stars measure visibility, not whether either tool fits your constraints. ### Are TrueMemory and Prompt-Engineering-Guide open source? Yes - both are open-source projects on GitHub (TrueMemory: AGPL-3.0, Prompt-Engineering-Guide: MIT). ### Where can I find alternatives to TrueMemory or Prompt-Engineering-Guide? GraphCanon lists graph-backed alternatives at [TrueMemory alternatives](/tools/buildingjoshbetter-truememory/alternatives) and [Prompt-Engineering-Guide alternatives](/tools/dair-ai-prompt-engineering-guide/alternatives) ([TrueMemory markdown twin](/tools/buildingjoshbetter-truememory/alternatives.md), [Prompt-Engineering-Guide markdown twin](/tools/dair-ai-prompt-engineering-guide/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-dair-ai-prompt-engineering-guide.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, TrueMemory or Prompt-Engineering-Guide? TrueMemory: Active. Prompt-Engineering-Guide: 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 TrueMemory and Prompt-Engineering-Guide? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [TrueMemory trust report](/tools/buildingjoshbetter-truememory/trust); [Prompt-Engineering-Guide trust report](/tools/dair-ai-prompt-engineering-guide/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/_