--- title: "Agent_Memory_Techniques vs statewave" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nirdiamant-agent-memory-techniques-vs-smaramwbc-statewave" tools: ["nirdiamant-agent-memory-techniques", "smaramwbc-statewave"] --- # Agent_Memory_Techniques vs statewave *GraphCanon updated Aug 22, 2026* ## Verdict Pick Agent_Memory_Techniques if agent_Memory_Techniques provides thirty Jupyter Notebooks that detail advanced memory techniques for LLMs; pick statewave if statewave focuses on reproducibility and provenance in memory management for AI agents, using Postgres plus pgvector for self-hosted operations rather than relying on query-time retrieval. [Agent_Memory_Techniques](https://diamantai.substack.com/) reports 924 GitHub stars, 120 forks, and 0 open issues, last pushed Aug 15, 2026. [statewave](https://statewave.ai) has 289 stars, 22 forks, and 14 open issues, last pushed Jul 30, 2026. Figures are from public GitHub metadata via [Agent_Memory_Techniques's repository](https://github.com/NirDiamant/Agent_Memory_Techniques) and [statewave's repository](https://github.com/smaramwbc/statewave). | | [Agent_Memory_Techniques](/tools/nirdiamant-agent-memory-techniques.md) | [statewave](/tools/smaramwbc-statewave.md) | | --- | --- | --- | | Tagline | Agent memory for LLMs: runnable Jupyter notebooks on various memory and knowledge techniques. | Open-source memory runtime for AI agents | | Stars | 924 | 289 | | Forks | 120 | 22 | | Open issues | 0 | 14 | | Language | Jupyter Notebook | Python | | Adopt for | Agent_Memory_Techniques provides thirty Jupyter Notebooks that detail advanced memory techniques for LLMs. | Statewave focuses on reproducibility and provenance in memory management for AI agents, using Postgres plus pgvector for self-hosted operations rather than relying on query-time retrieval. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | AI Agents, Evaluation & Observability, Model Training, Vector Databases | AI Agents, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Agent_Memory_Techniques](/tools/nirdiamant-agent-memory-techniques.md) | [statewave](/tools/smaramwbc-statewave.md) | | --- | --- | --- | | Days since push | 6d | 1d | | Open issues (now) | 0 | 14 | | Stars delta | +119 (30d) | Unknown | | Open issues delta | -1 (30d) | Unknown | | Full report | [trust report](/tools/nirdiamant-agent-memory-techniques/trust.md) | [trust report](/tools/smaramwbc-statewave/trust.md) | ## Decision facts: Agent_Memory_Techniques - **Adopt for:** Agent_Memory_Techniques provides thirty Jupyter Notebooks that detail advanced memory techniques for LLMs. ## Decision facts: statewave - **Adopt for:** Statewave focuses on reproducibility and provenance in memory management for AI agents, using Postgres plus pgvector for self-hosted operations rather than relying on query-time retrieval. ## Choose when ### Choose Agent_Memory_Techniques if… - Agent_Memory_Techniques is primarily Jupyter Notebook; statewave is Python. - Tags unique to Agent_Memory_Techniques: anthropic, episodic-memory, generative-ai, graphiti. - Also covers Evaluation & Observability, Model Training. - Need to integrate multiple types of memory systems such as episodic, semantic, or vector stores ### Choose statewave if… - statewave is primarily Python; Agent_Memory_Techniques is Jupyter Notebook. - Tags unique to statewave: llm, memory, pgvector, postgres. - statewave ships Docker support for self-hosted deployment. - Use when needing reproducible and documented context bundles to ensure consistent agent performance over time, as Statewave's design specifically supports provenance tagging for each context bundle. ## When NOT to use Agent_Memory_Techniques - Looking for a lightweight solution with minimal setup; this has extensive notebooks and dependencies - Require real-time memory management without heavy computational overhead, as some techniques are more geared toward detailed offline analysis ## When NOT to use statewave - Avoid if your application cannot leverage or benefit from a self-hosted memory runtime setup specifically using Postgres and pgvector. - Not suitable for cases where query-time document retrieval is preferred over maintaining provenance-tagged, static context bundles. ## Common questions ### What is the difference between Agent_Memory_Techniques and statewave? Agent_Memory_Techniques: Agent memory for LLMs: runnable Jupyter notebooks on various memory and knowledge techniques.. statewave: Open-source memory runtime for AI agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose Agent_Memory_Techniques over statewave? Choose Agent_Memory_Techniques over statewave when Agent_Memory_Techniques is primarily Jupyter Notebook; statewave is Python; Tags unique to Agent_Memory_Techniques: anthropic, episodic-memory, generative-ai, graphiti; Also covers Evaluation & Observability, Model Training; Need to integrate multiple types of memory systems such as episodic, semantic, or vector stores. ### When should I choose statewave over Agent_Memory_Techniques? Choose statewave over Agent_Memory_Techniques when statewave is primarily Python; Agent_Memory_Techniques is Jupyter Notebook; Tags unique to statewave: llm, memory, pgvector, postgres; statewave ships Docker support for self-hosted deployment; Use when needing reproducible and documented context bundles to ensure consistent agent performance over time, as Statewave's design specifically supports provenance tagging for each context bundle. ### When should I avoid Agent_Memory_Techniques? Looking for a lightweight solution with minimal setup; this has extensive notebooks and dependencies Require real-time memory management without heavy computational overhead, as some techniques are more geared toward detailed offline analysis ### When should I avoid statewave? Avoid if your application cannot leverage or benefit from a self-hosted memory runtime setup specifically using Postgres and pgvector. Not suitable for cases where query-time document retrieval is preferred over maintaining provenance-tagged, static context bundles. ### Is Agent_Memory_Techniques or statewave more popular on GitHub? Agent_Memory_Techniques has more GitHub stars (924 vs 289). Stars measure visibility, not whether either tool fits your constraints. ### Are Agent_Memory_Techniques and statewave open source? Yes - both are open-source projects on GitHub (Agent_Memory_Techniques: Apache-2.0, statewave: Apache-2.0). ### Where can I find alternatives to Agent_Memory_Techniques or statewave? GraphCanon lists graph-backed alternatives at [Agent_Memory_Techniques alternatives](/tools/nirdiamant-agent-memory-techniques/alternatives) and [statewave alternatives](/tools/smaramwbc-statewave/alternatives) ([Agent_Memory_Techniques markdown twin](/tools/nirdiamant-agent-memory-techniques/alternatives.md), [statewave markdown twin](/tools/smaramwbc-statewave/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/nirdiamant-agent-memory-techniques-vs-smaramwbc-statewave.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Agent_Memory_Techniques or statewave? Agent_Memory_Techniques: Very active. statewave: 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 Agent_Memory_Techniques and statewave? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Agent_Memory_Techniques trust report](/tools/nirdiamant-agent-memory-techniques/trust); [statewave trust report](/tools/smaramwbc-statewave/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nirdiamant-agent-memory-techniques`](/api/graphcanon/graph?tool=nirdiamant-agent-memory-techniques) - 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/_