{"observation":{"id":"90d46d99-526b-4942-8825-01f7e7257c0f","tool":"cognee","tool_name":"Cognee","criterion":"memory-capture-quality","criterion_name":"Memory Capture Quality","criterion_definition":"Checks whether the tool stores useful durable context, not random conversation noise.","criterion_evidence_type":"transformation","criterion_rank_role":"decisive","criterion_rank_role_reason":"If the tool does not store useful durable context instead of noise, it is not doing the core memory job. (3 of 3 judges)","scenario":"personal-work-brain-memory","scenario_name":"Personal Work Brain Memory","group_tag":"memory-for-ai-agents","scenario_description":"A multi-session personal assistant memory test where the user first sets working-style preferences, then asks for an internal update, and finally requests a formal partner email to check whether the assistant applies memory selectively and appropriately across different writing tasks.","modality":"text","input_text":"Session 1:\nUse this under user_id: founder_001\n\nI run a small AI product/research team. When you help me, remember how I work:\n- Keep outputs short, direct, and copy-paste ready.\n- Do not make writing sound too polished or motivational.\n- Always mention what proof or artifact is needed before making a strong claim.\n- If a task is risky or unclear, tell me the safest next step instead of guessing.\n\nSession 2:\nUse this under user_id: founder_001\n\nToday I am testing tools for an AI memory use case. I want to show users that memory is not just \"remember my favorite color.\" It should help an assistant continue real work across days, remember my working style, and avoid repeating the same explanation again.\n\nCreate a short internal update for my team about what I worked on today and what we should test next.\n\nSession 3:\nUse this under user_id: founder_001\n\nNow write a formal email to a potential enterprise partner asking if they are open to a product demo next week. Keep it professional.","input_artifact_refs":[],"stresses":["work-style preference memory","cross-session retrieval","tone adaptation by task","proof-first behavior","avoiding overgeneralization of memory"],"verdict":"worked","score":null,"score_total":null,"note":"Stores a compact working-style profile as durable graph-backed memory: concise/direct output, no over-polished tone, proof-before-claims, and safest-next-step behavior for risky tasks.","evidence_state":"verified","source":null,"artifacts":[{"url":"https://d3epheqghktydj.cloudfront.net/memory-for-ai-agents-cognee-input1-session1-working-style-mem-7bfd6578718b.png","role":"output","alt":null}],"run_id":"6e31afbb-34d7-459a-b688-68ef76fc615a","study_title":"Memory for AI Agents","study_kind":"generation","research_task":"86ba16xrp","tested_at":null,"completeness":"input-and-output","input":{"state":"text","text":"Session 1:\nUse this under user_id: founder_001\n\nI run a small AI product/research team. When you help me, remember how I work:\n- Keep outputs short, direct, and copy-paste ready.\n- Do not make writing sound too polished or motivational.\n- Always mention what proof or artifact is needed before making a strong claim.\n- If a task is risky or unclear, tell me the safest next step instead of guessing.\n\nSession 2:\nUse this under user_id: founder_001\n\nToday I am testing tools for an AI memory use case. I want to show users that memory is not just \"remember my favorite color.\" It should help an assistant continue real work across days, remember my working style, and avoid repeating the same explanation again.\n\nCreate a short internal update for my team about what I worked on today and what we should test next.\n\nSession 3:\nUse this under user_id: founder_001\n\nNow write a formal email to a potential enterprise partner asking if they are open to a product demo next week. Keep it professional.","files":[],"modality":"text","stresses":["work-style preference memory","cross-session retrieval","tone adaptation by task","proof-first behavior","avoiding overgeneralization of memory"]},"tool_page_slug":"cognee","tool_url":"https://aidemos.com/tools/cognee","permalink":"https://aidemos.com/evidence/90d46d99-526b-4942-8825-01f7e7257c0f","api_url":"https://ai.aidemos.com/v1/observations/90d46d99-526b-4942-8825-01f7e7257c0f"},"peers":[{"id":"90662cab-d0f4-453a-8504-80a8f4ffed74","tool":"hindsight","tool_name":"Hindsight","verdict":"worked","score":null,"score_total":null,"note":"It durably stores work-style preferences such as short, direct output, proof-before-claim behavior, and safest-next-step handling, rather than treating them as throwaway chat noise.","artifact_count":1,"thumbnail":"https://d3epheqghktydj.cloudfront.net/memory-for-ai-agents-hindsight-input1-founder-work-preference-23e99e40f8f0.png","evidence_url":"https://aidemos.com/evidence/90662cab-d0f4-453a-8504-80a8f4ffed74"},{"id":"91b32059-a56e-4bad-b69f-9e48850f45da","tool":"mem0","tool_name":"Mem0","verdict":"worked","score":null,"score_total":null,"note":"Captures a durable work-style preference as compact memory cards rather than raw chat history, including short, direct output expectations and proof-first guidance.","artifact_count":1,"thumbnail":"https://d3epheqghktydj.cloudfront.net/memory-for-ai-agents-mem0-input1-founder-work-preference-crea-35e7b82616e2.png","evidence_url":"https://aidemos.com/evidence/91b32059-a56e-4bad-b69f-9e48850f45da"},{"id":"0db4e3ed-3f70-4b20-8747-078fe9c03570","tool":"supermemory","tool_name":"Supermemory","verdict":"worked","score":null,"score_total":null,"note":"The tool captured a reusable working-style profile, not just a one-off fact: it stored the user's short, direct output preference, the anti-hype writing style, the requirement to mention proof or artifacts before strong claims, the safe-next-step rule for risky tasks, and the small AI product/research team context.","artifact_count":1,"thumbnail":"https://d3epheqghktydj.cloudfront.net/memory-for-ai-agents-supermemory-input1-session1-working-styl-0f1fbd209476.png","evidence_url":"https://aidemos.com/evidence/0db4e3ed-3f70-4b20-8747-078fe9c03570"},{"id":"9a9dd5b9-dd5d-49ba-9bce-af4ae801dcf2","tool":"zep","tool_name":"Zep","verdict":"worked","score":null,"score_total":null,"note":"Captured four durable work-style preferences for the user: keep outputs short and direct, make them copy-paste ready, avoid over-polished or motivational writing, require proof/artifacts for strong claims, and choose the safest next step when something is unclear.","artifact_count":1,"thumbnail":"https://d3epheqghktydj.cloudfront.net/zep-zep-input1-founder-work-preference-creat-83bebe96b8a3.png","evidence_url":"https://aidemos.com/evidence/9a9dd5b9-dd5d-49ba-9bce-af4ae801dcf2"}],"other_criteria":[{"id":"90dbd4a6-001a-4c33-a064-e8d46d67b736","criterion":"correct-application","criterion_name":"Correct Application","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"Applies the stored working style to a new internal update by keeping the reply short and proof-oriented while blending in the current memory-testing project context.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/90dbd4a6-001a-4c33-a064-e8d46d67b736"},{"id":"22461c92-39c7-4585-a0b6-7210ea110012","criterion":"observability-and-debugging","criterion_name":"Observability and Debugging","rank_role":"context","verdict":"worked","score":null,"score_total":null,"note":"Exposes recall provenance directly in the UI: the Last Recall Response panel shows the source graph completion, evidence chunks, dataset ID, and document/chunk IDs.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/22461c92-39c7-4585-a0b6-7210ea110012"},{"id":"c216c674-b0bf-4841-a588-fa1f4f037883","criterion":"relevant-retrieval","criterion_name":"Relevant Retrieval","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"Recalls prior working-style memory in later sessions through GRAPH_COMPLETION, exposing evidence chunks plus dataset and document/chunk IDs rather than a simple memory-hit flag.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/c216c674-b0bf-4841-a588-fa1f4f037883"}],"appears_in":[{"page_type":"ranking","slug":"ai-agent-memory-tools","title":"Best AI Tools for Memory for AI Agents","url":"https://aidemos.com/best/ai-agent-memory-tools","binding":"run"}],"same_scenario":[{"id":"90662cab-d0f4-453a-8504-80a8f4ffed74","tool":"hindsight","tool_name":"Hindsight","verdict":"worked","score":null,"score_total":null,"note":"It durably stores work-style preferences such as short, direct output, proof-before-claim behavior, and safest-next-step handling, rather than treating them as throwaway chat noise."},{"id":"91b32059-a56e-4bad-b69f-9e48850f45da","tool":"mem0","tool_name":"Mem0","verdict":"worked","score":null,"score_total":null,"note":"Captures a durable work-style preference as compact memory cards rather than raw chat history, including short, direct output expectations and proof-first guidance."},{"id":"0db4e3ed-3f70-4b20-8747-078fe9c03570","tool":"supermemory","tool_name":"Supermemory","verdict":"worked","score":null,"score_total":null,"note":"The tool captured a reusable working-style profile, not just a one-off fact: it stored the user's short, direct output preference, the anti-hype writing style, the requirement to mention proof or artifacts before strong claims, the safe-next-step rule for risky tasks, and the small AI product/research team context."},{"id":"9a9dd5b9-dd5d-49ba-9bce-af4ae801dcf2","tool":"zep","tool_name":"Zep","verdict":"worked","score":null,"score_total":null,"note":"Captured four durable work-style preferences for the user: keep outputs short and direct, make them copy-paste ready, avoid over-polished or motivational writing, require proof/artifacts for strong claims, and choose the safest next step when something is unclear."}]}