mirror of
https://github.com/agentscope-ai/ReMe.git
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d0c9d89092
* feat(memory): add ContextChecker component for context size management * refactor(memory): restructure file-based memory tools and update imports * docs(readme): update documentation with detailed architecture and components * docs(readme): update Chinese documentation with enhanced memory management diagrams * refactor(cookbook): move cookbook files to test directory and clean up docs * docs(readme): update link path for old version documentation * docs(readme): update documentation with improved architecture diagrams and component details * docs(readme): update documentation with improved clarity and structure * refactor(docs): update in-memory memory documentation * docs(readme): add experiment reproduction link to quickstart guide
87 lines
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6.9 KiB
JSON
87 lines
No EOL
6.9 KiB
JSON
[
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{
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"workspace_id": "test_workspace",
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"memory_id": "e1103be06ec24ffebf441257d385211b",
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"memory_type": "task",
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"when_to_use": "When analyzing a complex, multi-faceted company with historical, financial, operational, and competitive dimensions—especially in dynamic industries like tech or EVs.",
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"content": "The agent successfully decomposed the broad query 'Analyze the company Tesla' into four distinct subtasks: (1) historical context, (2) business model and revenue streams, (3) financial performance, and (4) innovation and market position. Each subtask was addressed via targeted web searches using specific, focused queries that extracted precise, high-value information. The use of multiple search iterations with varying angles (e.g., 'Tesla innovation technology advancements 2024' vs. 'market position competitors electric vehicles 2024') ensured comprehensive coverage across different domains. This structured, layered approach prevented information overload while ensuring depth in each critical area.",
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"score": 0.92,
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"time_created": "2025-09-07 15:57:06",
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"time_modified": "2025-09-07 15:57:06",
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"author": "qwen3-30b-a3b-instruct-2507",
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"metadata": {
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"when_to_use": "When analyzing a complex, multi-faceted company with historical, financial, operational, and competitive dimensions—especially in dynamic industries like tech or EVs.",
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"experience": "The agent successfully decomposed the broad query 'Analyze the company Tesla' into four distinct subtasks: (1) historical context, (2) business model and revenue streams, (3) financial performance, and (4) innovation and market position. Each subtask was addressed via targeted web searches using specific, focused queries that extracted precise, high-value information. The use of multiple search iterations with varying angles (e.g., 'Tesla innovation technology advancements 2024' vs. 'market position competitors electric vehicles 2024') ensured comprehensive coverage across different domains. This structured, layered approach prevented information overload while ensuring depth in each critical area.",
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"tags": [
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"decomposition",
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"multi-dimensional analysis",
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"targeted search",
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"information layering",
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"business model",
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"financials",
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"competitive landscape"
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],
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"confidence": 0.9,
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"step_type": "reasoning",
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"tools_used": [
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"web_search"
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]
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}
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},
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{
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"workspace_id": "test_workspace",
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"memory_id": "989fceaa659548d6b85740de02a3ea83",
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"memory_type": "task",
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"when_to_use": "When initial search results are insufficient or fragmented, especially for time-sensitive or evolving topics like AI advancements or quarterly financials.",
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"content": "After receiving partial results from the first three searches, the agent proactively initiated two additional web searches to fill critical knowledge gaps: one on recent technological innovations (2024) and another on current market competition. These follow-up queries were highly specific and timed to capture up-to-date developments (e.g., FSD V12.5, Optimus robot production plans). This iterative search strategy allowed the agent to identify real-time trends and emerging strategic moves, which were essential for a forward-looking analysis. The ability to dynamically adjust the research plan based on incomplete early data is a key indicator of adaptive intelligence.",
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"score": 0.85,
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"time_created": "2025-09-07 15:57:06",
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"time_modified": "2025-09-07 15:57:06",
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"author": "qwen3-30b-a3b-instruct-2507",
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"metadata": {
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"when_to_use": "When initial search results are insufficient or fragmented, especially for time-sensitive or evolving topics like AI advancements or quarterly financials.",
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"experience": "After receiving partial results from the first three searches, the agent proactively initiated two additional web searches to fill critical knowledge gaps: one on recent technological innovations (2024) and another on current market competition. These follow-up queries were highly specific and timed to capture up-to-date developments (e.g., FSD V12.5, Optimus robot production plans). This iterative search strategy allowed the agent to identify real-time trends and emerging strategic moves, which were essential for a forward-looking analysis. The ability to dynamically adjust the research plan based on incomplete early data is a key indicator of adaptive intelligence.",
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"tags": [
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"iterative research",
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"dynamic query refinement",
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"real-time updates",
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"gap detection",
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"adaptive planning",
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"AI innovation"
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],
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"confidence": 0.85,
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"step_type": "action",
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"tools_used": [
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"web_search"
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]
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}
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},
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{
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"workspace_id": "test_workspace",
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"memory_id": "1aa50187c2da41a483256a55aae8e260",
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"memory_type": "task",
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"when_to_use": "When synthesizing diverse data sources into a coherent, structured narrative for executive-level understanding.",
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"content": "The agent did not merely aggregate raw facts but synthesized findings into a well-organized, thematic report that connected history, business model, financials, innovation, and competition. It highlighted critical contradictions (e.g., declining profits despite strong Q4 growth) and strategic shifts (e.g., move toward software/services). By identifying key metrics (like carbon credit income and FSD safety record) as differentiators, it transformed data into insight. This demonstrates the importance of post-data synthesis reasoning—turning fragmented inputs into actionable, narrative-driven conclusions that reflect both factual accuracy and strategic interpretation.",
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"score": 0.85,
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"time_created": "2025-09-07 15:57:06",
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"time_modified": "2025-09-07 15:57:06",
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"author": "qwen3-30b-a3b-instruct-2507",
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"metadata": {
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"when_to_use": "When synthesizing diverse data sources into a coherent, structured narrative for executive-level understanding.",
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"experience": "The agent did not merely aggregate raw facts but synthesized findings into a well-organized, thematic report that connected history, business model, financials, innovation, and competition. It highlighted critical contradictions (e.g., declining profits despite strong Q4 growth) and strategic shifts (e.g., move toward software/services). By identifying key metrics (like carbon credit income and FSD safety record) as differentiators, it transformed data into insight. This demonstrates the importance of post-data synthesis reasoning—turning fragmented inputs into actionable, narrative-driven conclusions that reflect both factual accuracy and strategic interpretation.",
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"tags": [
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"synthesis",
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"narrative structuring",
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"insight generation",
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"strategic interpretation",
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"data integration",
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"executive summary"
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],
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"confidence": 0.9,
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"step_type": "reasoning",
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"tools_used": [
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"web_search"
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]
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}
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}
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] |