Stores project continuity context as memory, including the use case goal, proof-first rules, and the direction change toward real workflows.
What was measured
Memory Capture Quality
Checks whether the tool stores useful durable context, not random conversation noise.
decisive for this rankingtransformation
If the tool does not store useful durable context instead of noise, it is not doing the core memory job. (3 of 3 judges)
What was given, what came back
Test input: Team Handoff / Project Continuity Memory · text · group: memory-for-ai-agents
Input — what we sent
The exact prompt
Session 1: Use this under project_id: ai_demos_memory_use_case We are working on an AI Demos use case called Memory for AI Agents. The goal is to help users understand which memory tools are actually useful for real agent workflows. We are not promoting any tool. We are testing whether memory can help with real continuity: personal work brain, client relationship memory, and team handoff. Session 2: Use this under project_id: ai_demos_memory_use_case Important project rules: - No observation without proof. - Screenshots and artifacts are primary evidence. - Inputs must help rank tools, not just prove that tools can store one fact. - Memory should be checked for retrieval, update handling, scope control, deletion or retirement, and observability. - The page should stay practical and user-facing, not only technical. Session 3: Use this under project_id: ai_demos_memory_use_case Project direction changed slightly. The old input set was too QA-style and not relatable enough. The new direction is to use real workflows: personal work brain memory, client relationship memory, and team handoff/project continuity memory. Session 4: Use this under project_id: ai_demos_memory_use_case I am unavailable tomorrow. Create a handoff note for an intern who needs to continue this use case. The note should explain: 1. What this use case is about. 2. What the current testing direction is. 3. What rules they must follow before writing observations. 4. What artifacts they need to capture while testing. Session 5: Use this under project_id: unrelated_sales_agent_project We are building a sales email agent for a different project. Create a short kickoff note for the team.
A project continuity and handoff test where the assistant must remember project goals, project rules, and a changed testing direction, then produce a useful handoff note for an unavailable team member without leaking context into an unrelated project.
Why this input is hard
- · project-level memory
- · decision and rule retention
- · changed direction handling
- · handoff continuity
- · scope separation across projects
Output — unretouched
Also checked on this input — same tool, 3 other criteria
Relevant Retrieval✓ WorkedRetrieves the project rules needed for the handoff task, showing that the assistant can pull the right project context for a continuation note.Scope Control✓ WorkedKeeps an unrelated project isolated, so the sales-agent kickoff note does not inherit the AI Demos memory rules.Update and Correction Handling✓ WorkedApplies the updated project direction in the handoff output, so the note reflects the newer real-workflow framing rather than the older QA-style framing.
Provenance
- Observation
- 93919730-5b52-4713-966a-cafd867a5e3c
- Evidence run
- 6e31afbb-34d7-459a-b688-68ef76fc615a
- Study
- Memory for AI Agents
- Research task
- 86ba16xrp
- Tested at
- not recorded
- Source
- first-party
- Evidence state
- verified
- Proof shown
- input + output shown
- Cost / latency
- not captured
- Repeat run
- not captured
- Tester
- not captured
The last three rows are honest blanks, not placeholders — our capture has no field for them yet.
Query this
get_evidence({
tool: "mem0",
scenario: "memory-for-ai-agents"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 4 other tools
measured on Memory Capture Quality
Cognee◐ MixedStores project context, rules, and direction changes under the project container, but the visible replies were only generic acknowledgments rather than a visible restatement of the stored content.Hindsight✓ WorkedIt stores the project goal, proof-first rules, evaluation dimensions, and the shift toward real workflows instead of QA-style inputs.Supermemory◐ MixedThe tool stored the raw project-session content, but the report flags a granularity problem: it often kept full conversational turns rather than only distilled reusable project facts, which increases retrieval noise as the memory set grows.Zep✓ WorkedCaptured project continuity context including the Memory for AI Agents goal, the three real-work scenarios, and proof-first evaluation rules covering screenshots, ranking value, retrieval, update handling, scope control, deletion or retirement, and observability.
This evidence is published in
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com