{"data":{"slug":"nirdiamant-agent-memory-techniques","name":"Agent_Memory_Techniques","tagline":"Agent memory for LLMs: runnable Jupyter notebooks on various memory and knowledge techniques.","github_url":"https://github.com/NirDiamant/Agent_Memory_Techniques","owner":"NirDiamant","repo":"Agent_Memory_Techniques","owner_avatar_url":"https://avatars.githubusercontent.com/u/28316913?v=4","primary_language":"Jupyter Notebook","stars":924,"forks":120,"topics":["agent-memory","ai-agents","anthropic","episodic-memory","generative-ai","graphiti","knowledge-graph","langchain","letta","llm","llm-agents","llm-memory","mem0","memgpt","openai","python","rag","semantic-memory","vector-database","zep"],"archived":false,"github_pushed_at":"2026-08-15T00:52:07+00:00","maintenance_label":"Very active","stars_delta_30d":119,"url":"https://www.graphcanon.com/tools/nirdiamant-agent-memory-techniques","markdown_url":"https://www.graphcanon.com/tools/nirdiamant-agent-memory-techniques.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/nirdiamant-agent-memory-techniques","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=nirdiamant-agent-memory-techniques","description":"Agent memory for LLMs: 30 runnable Jupyter notebooks covering conversation buffers, vector stores, knowledge graphs, episodic and semantic memory, MemGPT, Mem0, Letta, Zep, Graphiti, LoCoMo benchmarks, and production patterns.","homepage_url":"https://diamantai.substack.com/","license":"Apache-2.0","open_issues":0,"watchers":5,"ai_summary":"This repository offers thirty practical examples through Jupyter Notebooks focusing on the integration of advanced memory techniques with language models to store, retrieve, and use information effectively by AI agents. It explores conversation buffers, various vector stores, knowledge graph implementations, episodic and semantic memory designs, alongside benchmarking methods and production guidelines. Techniques like MemGPT, Mem0, Letta, Zep, Graphiti, LoCoMo benchmarks are covered.","readme_excerpt":"## 🚀 Quick Start\n\n> 💡  **Prefer not to install anything?** Every notebook renders on GitHub directly. Click a technique in the table above to read it in your browser. Or use the Colab badges to run it in the cloud.\n\n```bash\n\n---\n\n# Install dependencies\npip install -r requirements.txt\n\n---\n\n## 📄 License\n\nThis project is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for details.\n\n---","github_created_at":"2026-05-05T21:08:23+00:00","created_at":"2026-07-11T11:27:27.315724+00:00","updated_at":"2026-08-22T00:00:48.543927+00:00","categories":[{"slug":"ai-agents","name":"AI Agents","url":"https://www.graphcanon.com/categories/ai-agents","markdown_url":"https://www.graphcanon.com/categories/ai-agents.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/ai-agents"},{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"},{"slug":"vector-databases","name":"Vector Databases","url":"https://www.graphcanon.com/categories/vector-databases","markdown_url":"https://www.graphcanon.com/categories/vector-databases.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/vector-databases"}],"tags":[{"slug":"agent-memory","name":"agent-memory"},{"slug":"ai-agents","name":"ai-agents"},{"slug":"anthropic","name":"anthropic"},{"slug":"episodic-memory","name":"episodic-memory"},{"slug":"generative-ai","name":"generative-ai"},{"slug":"graphiti","name":"graphiti"},{"slug":"knowledge-graph","name":"knowledge-graph"},{"slug":"langchain","name":"langchain"}],"trust":{"provenance":{"is_fork":false,"github_id":1230280405,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-22T00:00:47.750Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":1,"days_since_push":6,"last_release_at":"2026-05-30T15:29:50Z","stars_delta_30d":119,"open_issues_delta_30d":-1},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:27:28.404Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-22T00:00:48.188Z"},"languages":{"value":["jupyter notebook"],"source":"github.language","observed_at":"2026-08-22T00:00:48.188Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-22T00:00:48.188Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["Need to integrate multiple types of memory systems such as episodic, semantic, or vector stores","Want hands-on examples on using MemGPT, Mem0, Letta, and other specialized technologies within the same package"],"when_not_to_use":["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"],"source":"enrich:decision_facts","observed_at":"2026-07-14T18:58:35.888Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Agent_Memory_Techniques provides thirty Jupyter Notebooks that detail advanced memory techniques for LLMs."}]}}