mirror of
https://github.com/furyhawk/deepresearch.git
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furyhawk
85c286b72f
- Created meta.json to track session details and research todos. - Added DEEP.md to outline the research workflow and file organization. - Introduced MEMORY.md for persistent notes and context management.
1819 lines
227 KiB
JSON
1819 lines
227 KiB
JSON
{"type": "session_created", "session_id": "763605a6-5162-4912-b66a-a4e32cb16cd2", "_ts": "2026-06-19T02:22:18.733939+00:00"}
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{"type": "canvas_ready", "session_id": "763605a6-5162-4912-b66a-a4e32cb16cd2", "_ts": "2026-06-19T02:22:18.746300+00:00"}
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{"type": "user_message", "content": "research on architect agentic ai solution.", "_ts": "2026-06-19T02:22:18.746402+00:00"}
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{"type": "start", "_ts": "2026-06-19T02:22:18.746677+00:00"}
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{"type": "status", "content": "Processing...", "_ts": "2026-06-19T02:22:18.750406+00:00"}
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{"type": "status", "content": "Researching...", "_ts": "2026-06-19T02:22:18.781374+00:00"}
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{"type": "tool_call_start", "tool_name": "task", "tool_call_id": "u0FiMlil43yUNHFBEU9dXiItJ9uwDZyh", "_ts": "2026-06-19T02:22:32.842939+00:00"}
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{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\"description", "_ts": "2026-06-19T02:22:32.905405+00:00"}
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{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\"", "_ts": "2026-06-19T02:22:38.012212+00:00"}
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{"type": "tool_args_delta", "tool_name": "task", "args_delta": "planner", "_ts": "2026-06-19T02:22:38.070193+00:00"}
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{"type": "tool_args_delta", "tool_name": "task", "args_delta": "\"", "_ts": "2026-06-19T02:22:38.128140+00:00"}
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{"type": "tool_args_delta", "tool_name": "task", "args_delta": "}", "_ts": "2026-06-19T02:22:38.185805+00:00"}
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{"type": "tool_start", "tool_name": "task", "args": "{\"description\":\"Create a research plan for \\\"architecting agentic AI solutions\\\". The plan should break this down into focused sub-topics (e.g., multi-agent orchestration patterns, memory architectures, tool use & planning, evaluation frameworks, and real-world deployment challenges). Ask the user if they want a high-level architectural overview or a deep technical dive into specific implementation details.\",\"subagent_type\":\"planner\"}", "_ts": "2026-06-19T02:22:38.311737+00:00"}
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{"type": "status", "content": "Running planner subagent...", "_ts": "2026-06-19T02:22:38.311865+00:00"}
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{"type": "tool_output", "tool_name": "task", "output": "Error executing task: AgentMemoryToolset 'deep-memory' defines a tool whose name conflicts with existing tool from AgentMemoryToolset 'deep-memory': 'read_memory'. Rename the tool or wrap the toolset in a `PrefixedToolset` to avoid name conflicts.", "_ts": "2026-06-19T02:22:38.315332+00:00"}
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{"type": "middleware_event", "event": "tool_audit", "tool_name": "task", "total_calls": 1, "tools_breakdown": {"task": 1}, "_ts": "2026-06-19T02:22:38.315505+00:00"}
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{"type": "status", "content": "Researching...", "_ts": "2026-06-19T02:22:38.315761+00:00"}
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{"type": "tool_call_start", "tool_name": "write_todos", "tool_call_id": "0RZ6hIA1zVLPQAYQaMSRKovpIEGuIUAq", "_ts": "2026-06-19T02:22:39.535894+00:00"}
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{"type": "tool_start", "tool_name": "write_todos", "args": "{\"todos\":[{\"active_form\":\"Planning research on architecting agentic AI solutions\",\"content\":\"Create a detailed research plan for architecting agentic AI solutions\",\"status\":\"pending\"},{\"active_form\":\"Researching multi-agent orchestration patterns\",\"content\":\"Research multi-agent orchestration patterns (e.g., Router, Sequential, Hierarchical, Joint-Collaboration)\",\"status\":\"pending\"},{\"active_form\":\"Researching memory architectures\",\"content\":\"Research memory architectures for agents (short-term vs long-term, RAG vs fine-tuning vs context window management)\",\"status\":\"pending\"},{\"active_form\":\"Researching tool use and planning strategies\",\"content\":\"Research advanced planning techniques (CoT, Tree of Thoughts, ReAct) and tool-calling mechanisms\",\"status\":\"pending\"},{\"active_form\":\"Researching evaluation frameworks\",\"content\":\"Research evaluation frameworks for agentic systems (benchmarks, human-in-the-loop, automated testing)\",\"status\":\"pending\"},{\"active_form\":\"Researching real-world deployment and production challenges\",\"content\":\"Research production challenges including latency, cost management, reliability, and security in agentic workflows\",\"status\":\"pending\"},{\"active_form\":\"Synthesizing findings into a final report\",\"content\":\"Synthesize all research notes into a comprehensive architectural guide in /workspace/report.md\",\"status\":\"pending\"}]}", "_ts": "2026-06-19T02:22:58.351422+00:00"}
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{"type": "todos_update", "todos": [{"active_form": "Planning research on architecting agentic AI solutions", "content": "Create a detailed research plan for architecting agentic AI solutions", "status": "pending"}, {"active_form": "Researching multi-agent orchestration patterns", "content": "Research multi-agent orchestration patterns (e.g., Router, Sequential, Hierarchical, Joint-Collaboration)", "status": "pending"}, {"active_form": "Researching memory architectures", "content": "Research memory architectures for agents (short-term vs long-term, RAG vs fine-tuning vs context window management)", "status": "pending"}, {"active_form": "Researching tool use and planning strategies", "content": "Research advanced planning techniques (CoT, Tree of Thoughts, ReAct) and tool-calling mechanisms", "status": "pending"}, {"active_form": "Researching evaluation frameworks", "content": "Research evaluation frameworks for agentic systems (benchmarks, human-in-the-loop, automated testing)", "status": "pending"}, {"active_form": "Researching real-world deployment and production challenges", "content": "Research production challenges including latency, cost management, reliability, and security in agentic workflows", "status": "pending"}, {"active_form": "Synthesizing findings into a final report", "content": "Synthesize all research notes into a comprehensive architectural guide in /workspace/report.md", "status": "pending"}], "_ts": "2026-06-19T02:22:58.351733+00:00"}
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{"type": "tool_output", "tool_name": "write_todos", "output": "Updated 7 todos: 0 completed, 0 in progress, 7 pending", "_ts": "2026-06-19T02:22:58.353615+00:00"}
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{"type": "tool_start", "tool_name": "update_todo_status", "args": "{\"status\":\"in_progress\",\"todo_id\":\"[2278be22]\"}", "_ts": "2026-06-19T02:23:03.022929+00:00"}
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{"type": "tool_output", "tool_name": "update_todo_status", "output": "Todo with ID '[2278be22]' not found", "_ts": "2026-06-19T02:23:03.023533+00:00"}
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{"type": "status", "content": "Researching...", "_ts": "2026-06-19T02:23:03.023924+00:00"}
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{"type": "tool_output", "tool_name": "read_todos", "output": "Current todos:\n1. [ ] [2278be22] Create a detailed research plan for architecting agentic AI solutions\n2. [ ] [6bbced54] Research multi-agent orchestration patterns (e.g., Router, Sequential, Hierarchical, Joint-Collaboration)\n3. [ ] [47016bb3] Research memory architectures for agents (short-term vs long-term, RAG vs fine-tuning vs context window management)\n4. [ ] [33e682a9] Research advanced planning techniques (CoT, Tree of Thoughts, ReAct) and tool-calling mechanisms\n5. [ ] [18f0a837] Research evaluation frameworks for agentic systems (benchmarks, human-in-the-loop, automated testing)\n6. [ ] [e49943ef] Research production challenges including latency, cost management, reliability, and security in agentic workflows\n7. [ ] [bd6832e4] Synthesize all research notes into a comprehensive architectural guide in /workspace/report.md\n\nSummary: 0 completed, 0 in progress, 7 pending", "_ts": "2026-06-19T02:23:04.206799+00:00"}
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{"type": "tool_start", "tool_name": "task", "args": "{\"description\":\"Create a detailed research plan for \\\"architecting agentic AI solutions\\\". The plan should break this down into focused sub-topics (e.g., multi-agent orchestration patterns, memory architectures, tool use & planning, evaluation frameworks, and real-world deployment challenges). Ask the user if they want a high-level architectural overview or a deep technical dive into specific implementation details. Save the research plan to /plans/architecting_agentic_solutions.md.\",\"subagent_type\":\"planner\"}", "_ts": "2026-06-19T02:24:04.965956+00:00"}
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{"type": "status", "content": "Running planner subagent...", "_ts": "2026-06-19T02:24:04.966162+00:00"}
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{"type": "tool_output", "tool_name": "task", "output": "Error executing task: AgentMemoryToolset 'deep-memory' defines a tool whose name conflicts with existing tool from AgentMemoryToolset 'deep-memory': 'read_memory'. Rename the tool or wrap the toolset in a `PrefixedToolset` to avoid name conflicts.", "_ts": "2026-06-19T02:24:04.971318+00:00"}
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{"type": "middleware_event", "event": "tool_audit", "tool_name": "task", "total_calls": 9, "tools_breakdown": {"task": 2, "write_todos": 3, "update_todo_status": 3, "read_todos": 1}, "_ts": "2026-06-19T02:24:04.971432+00:00"}
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{"type": "status", "content": "Researching...", "_ts": "2026-06-19T02:24:04.971864+00:00"}
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{"type": "tool_call_start", "tool_name": "write_todos", "tool_call_id": "6raCUx1yXSDGmcsCyf34WLwFJwvzHqXV", "_ts": "2026-06-19T02:24:06.244514+00:00"}
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{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"todos", "_ts": "2026-06-19T02:24:06.304701+00:00"}
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{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\":[", "_ts": "2026-06-19T02:24:06.366452+00:00"}
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{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "{", "_ts": "2026-06-19T02:24:06.426478+00:00"}
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{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "\"active", "_ts": "2026-06-19T02:24:06.485516+00:00"}
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{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "form", "_ts": "2026-06-19T02:24:06.607477+00:00"}
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{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": "Planning", "_ts": "2026-06-19T02:24:06.790378+00:00"}
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{"type": "tool_args_delta", "tool_name": "write_todos", "args_delta": " research", "_ts": "2026-06-19T02:24:06.854539+00:00"}
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{"type": "tool_start", "tool_name": "task", "args": "{\"description\":\"Create a detailed research plan for \\\"architecting agentic AI solutions\\\". The plan should break this down into focused sub-topics (e.g., multi-agent orchestration patterns, memory architectures, tool use & planning, evaluation frameworks, and real-world deployment challenges). Ask the user if they want a high-level architectural overview or a deep technical dive into specific implementation details. Save the research plan to /plans/architecting_agentic_solutions.md.\",\"subagent_type\":\"planner\"}", "_ts": "2026-06-19T02:24:37.068997+00:00"}
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{"type": "tool_output", "tool_name": "task", "output": "Error executing task: AgentMemoryToolset 'deep-memory' defines a tool whose name conflicts with existing tool from AgentMemoryToolset 'deep-memory': 'read_memory'. Rename the tool or wrap the toolset in a `PrefixedToolset` to avoid name conflicts.", "_ts": "2026-06-19T02:24:37.072371+00:00"}
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{"type": "text_delta", "content": "level architectural", "_ts": "2026-06-19T02:24:49.502746+00:00"}
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{"type": "text_delta", "content": " overview**", "_ts": "2026-06-19T02:24:49.624677+00:00"}
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{"type": "text_delta", "content": " (suitable", "_ts": "2026-06-19T02:24:49.743893+00:00"}
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{"type": "text_delta", "content": " for strategy", "_ts": "2026-06-19T02:24:49.863172+00:00"}
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{"type": "text_delta", "content": " and system", "_ts": "2026-06-19T02:24:49.984612+00:00"}
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{"type": "text_delta", "content": " design)?", "_ts": "2026-06-19T02:24:50.105457+00:00"}
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{"type": "text_delta", "content": "\n-", "_ts": "2026-06-19T02:24:50.225764+00:00"}
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{"type": "text_delta", "content": " A **", "_ts": "2026-06-19T02:24:50.345454+00:00"}
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{"type": "text_delta", "content": "deep technical", "_ts": "2026-06-19T02:24:50.464231+00:00"}
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{"type": "text_delta", "content": " dive**", "_ts": "2026-06-19T02:24:50.585674+00:00"}
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{"type": "text_delta", "content": " (focused", "_ts": "2026-06-19T02:24:50.706211+00:00"}
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{"type": "text_delta", "content": " on specific", "_ts": "2026-06-19T02:24:50.826422+00:00"}
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{"type": "text_delta", "content": " implementation details", "_ts": "2026-06-19T02:24:50.948075+00:00"}
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{"type": "text_delta", "content": ", code", "_ts": "2026-06-19T02:24:51.067844+00:00"}
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{"type": "text_delta", "content": " patterns,", "_ts": "2026-06-19T02:24:51.190831+00:00"}
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{"type": "text_delta", "content": " and infrastructure", "_ts": "2026-06-19T02:24:51.312635+00:00"}
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{"type": "text_delta", "content": ")?", "_ts": "2026-06-19T02:24:51.393652+00:00"}
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{"type": "status", "content": "Completed!", "_ts": "2026-06-19T02:24:51.395642+00:00"}
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{"type": "checkpoint_saved", "checkpoint_id": "7f58e04f-801c-45a4-8d44-ac9d14d6b10f", "label": "turn-12", "turn": 12, "message_count": 23, "metadata": {}, "_ts": "2026-06-19T02:24:51.395942+00:00"}
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{"type": "response", "content": "I am encountering a technical error when trying to use the specialized planner agent due to a tool naming conflict in the system configuration. \n\nTo ensure we don't lose momentum, I will proceed with the research manually. I have already initialized a task list covering the key pillars of architecting agentic AI solutions:\n1. **Multi-agent orchestration patterns** (Router, Sequential, Hierarchical)\n2. **Memory architectures** (Short/Long-term, RAG vs Fine-tuning)\n3. **Advanced planning & Tool-calling** (CoT, ReAct, Tree of Thoughts)\n4. **Evaluation frameworks** (Benchmarks, Human-in-the-loop)\n5. **Production challenges** (Latency, Cost, Reliability, Security)\n\nBefore I begin the deep dive into these topics, could you please clarify if you would like me to focus on:\n- A **high-level architectural overview** (suitable for strategy and system design)?\n- A **deep technical dive** (focused on specific implementation details, code patterns, and infrastructure)?", "_ts": "2026-06-19T02:24:51.419838+00:00"}
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{"type": "done", "_ts": "2026-06-19T02:24:51.419920+00:00"}
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