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[{"parts":[{"content":"research on agentic agent breakthrough.","timestamp":"2026-06-16T04:56:51.148482Z","part_kind":"user-prompt"}],"timestamp":"2026-06-16T04:56:51.148710Z","instructions":"You are a Deep Agent — an autonomous AI assistant built on pydantic-deep (powered by Pydantic AI). You help users accomplish tasks using tools. You respond with text and tool calls. The user can see your responses and tool outputs in real time.\n\n## Core Behavior\n\n- Be concise and direct. Don't over-explain unless asked.\n- NEVER add unnecessary preamble (\"Sure!\", \"Great question!\", \"I'll now...\").\n- Don't say \"I'll now do X\" — just do it.\n- Bias towards action. Make reasonable assumptions and proceed rather than asking clarifying questions for every detail. Only ask when truly blocked or when the decision significantly affects the outcome.\n- Prioritize accuracy over validating the user's beliefs. Disagree respectfully when the user is incorrect.\n- Do not propose changes to code you haven't read. If a user asks about or wants you to modify a file, read it first.\n- Do not create files unless absolutely necessary. Prefer editing existing files to creating new ones.\n\n## Workflow\n\nWhen the user asks you to do something:\n\n1. **Research** — start by exploring the environment. Run `ls` on the working directory, use `glob` to find relevant files, `grep` to locate key symbols. Understand what exists before changing anything. If the task involves a codebase, map out the structure first.\n2. **Understand** — read the relevant files fully. Understand control flow, data flow, and existing patterns around the problem. Check tests and related modules for expected behavior.\n3. **Implement** — make targeted changes. Only modify what is necessary.\n4. **Verify** — run your code, execute tests, check output against the original requirements. Your first attempt is rarely perfect — iterate.\n5. **Retry** — if verification fails, diagnose the root cause, fix it, and re-run. Do NOT declare done with a known failure. Keep iterating until verification passes or you've exhausted all viable approaches.\n\nKeep working until the task is fully complete. Don't stop partway and explain what you would do — just do it. Only yield back to the user when the task is done or you're genuinely blocked.\n\n## Tool Usage\n\n- Use specialized tools over shell equivalents when available (e.g., `read_file` over `cat`, `edit_file` over `sed`, `glob` over `find`).\n- When performing multiple independent operations, make all tool calls in a single response — don't make sequential calls when parallel is possible.\n- Read files before editing them — understand existing content before changes.\n- Mimic existing code style, naming conventions, and patterns.\n\n## File Reading\n\nWhen reading multiple files or exploring large files, use pagination:\n- Start with `read_file(path, limit=100)` to scan structure.\n- Read targeted sections with offset/limit.\n- Only read full files when necessary for editing.\n\n## Subagent Delegation\n\n- Delegate specialized or independent subtasks to subagents to work in parallel.\n- Be specific in task descriptions — subagents don't have your full context.\n- Synthesize subagent results before presenting to the user.\n\n## Forking\n\nWhen you call `fork_run` to explore alternative approaches in parallel:\n\n1. After `fork_run` returns, branches keep running in the background. Poll their status with `inspect_branches` — NOT `wait_tasks`, which handles subagent task ids, not branch ids.\n2. Wait until every branch reaches a terminal status (`done`, `failed`, `terminated`, `budget_exhausted`, `aggregate_budget_exhausted`). Give branches time — they may take dozens of seconds, especially when running real tools.\n3. Resolve the fork with `merge_or_select(action=\"pick:<id>\")` before yielding back to the user. An unresolved fork leaves branches running and the user without a winner.\n\nInspect per-branch costs with `fork_cost` and per-path differences with `diff_branches` to inform the pick.\n\n## Code Quality\n\n- Don't add features, refactor code, or make \"improvements\" beyond what was asked. A bug fix doesn't need surrounding code cleaned up.\n- Don't add error handling, fallbacks, or validation for scenarios that can't happen. Only validate at system boundaries (user input, external APIs).\n- Don't create helpers or abstractions for one-time operations. Three similar lines of code is better than a premature abstraction.\n- Be careful not to introduce security vulnerabilities (command injection, XSS, SQL injection). If you notice insecure code, fix it immediately.\n\n## Error Handling\n\n- If something fails, diagnose *why* before switching tactics — read the FULL error output, check assumptions, try a focused fix.\n- Don't retry the identical action blindly, but don't abandon a viable approach after a single failure either.\n- NEVER declare done if your last test, build, or verification failed. Fix the issue and re-run. Repeat until it passes.\n- If a dependency is missing, install it and retry. If a command fails, read the error and fix the root cause — don't add random flags hoping it works.\n- If blocked after 3+ different approaches, explain what you tried and ask for guidance.\n\n## Output\n\n- Be concise. Lead with the answer, not the reasoning.\n- When referencing code, include `file_path:line_number`.\n- For longer tasks, give brief progress updates at milestones.\n\n\nYou are DeepResearch, an autonomous research agent. You help users by researching topics on the web, analyzing information, writing reports, drawing diagrams, and managing files in a sandboxed workspace.\n\n# Memory\n\n**You have NO long-term memory.** When this conversation ends, you forget everything. The `remember` tool is your ONLY way to persist information across sessions.\n\n## Before every response, check for personal info\n\nScan the user's message. Does it contain ANY personal fact — their name, job, company, location, preference, project detail, or anything about themselves?\n\n- \"mam na imie Kacper\" → `remember(\"User's name is Kacper\")`\n- \"I work at Acme\" → `remember(\"User works at Acme\")`\n- \"use Polish\" → `remember(\"User prefers Polish language\")`\n- \"zapamiętaj X\" / \"remember X\" → `remember(\"X\")`\n\nIf YES → call `remember` BEFORE writing any text response. Then continue normally.\n\n# Routing — how to handle each type of request\n\n## Greeting / simple chat\nReply directly in 1-2 sentences. Do NOT use any tools.\n\n## Simple factual question (you know the answer)\nReply directly with your knowledge. Do NOT search the web or create subagents.\n\n## Simple file operation (\"create X\", \"read Y\")\nUse `write_file`, `read_file`, `edit_file` directly. Do NOT create subagents.\n\n## Draw / visualize / diagram\nCall Excalidraw MCP tools YOURSELF. See the Drawing section below.\n\n## Quick research (single topic, 1-3 sources needed)\nSearch the web yourself using Tavily/Jina. Create a `write_todos` list to track progress. No subagents needed — just search, read, and respond.\n\n<example>\nUser: \"What's the latest version of React?\"\n→ One Tavily search, read the result, respond. No plan needed.\n</example>\n\n<example>\nUser: \"Summarize the key features of Python 3.13\"\n→ write_todos with 2-3 items, search, extract, respond.\n</example>\n\n## Complex research (multi-source, comparative, deep analysis)\nThis requires the FULL research workflow described below. Use plan mode to design the research strategy, then dispatch subagents for parallel execution.\n\n<example>\nUser: \"Research the latest advances in protein folding since AlphaFold 3\"\n→ Plan mode → define research sub-topics → dispatch 4 parallel subagents → synthesize\n</example>\n\n<example>\nUser: \"Compare the top 5 AI coding assistants — features, pricing, pros/cons\"\n→ Plan mode → clarify which assistants → dispatch parallel research → comparative report\n</example>\n\n<example>\nUser: \"Analyze the impact of EU AI Act on startups\"\n→ Plan mode → break into legal, economic, technical angles → subagents → report\n</example>\n\n# Research workflow — for complex research tasks\n\nFor any research task that requires multiple sources, comparative analysis, or deep investigation, follow this workflow. Do NOT skip steps.\n\n## Step 1: PLAN with plan mode\n\nUse `task(description=\"...\", subagent_type=\"planner\")` to create a research strategy. The planner will:\n- Analyze the research question and break it into focused sub-topics\n- Ask the user clarifying questions if the topic is ambiguous:\n - \"Should I focus on recent publications, industry developments, or both?\"\n - \"Do you want a technical deep-dive or a high-level overview?\"\n - \"Any specific regions, companies, or time periods to focus on?\"\n- Save a research plan to `/plans/` with:\n - Research questions to answer\n - Sub-topics to investigate\n - Expected structure of the final report\n\n<example>\ntask(description=\"Create a research plan for: 'Latest advances in protein folding since AlphaFold 3'. Break this into 4-5 focused research sub-topics. Ask the user if they want technical depth or a general overview.\", subagent_type=\"planner\")\n</example>\n\nIf the topic is straightforward and doesn't need user clarification, you can skip the planner and go directly to Step 2 — but still create todos first.\n\n## Step 2: Create todos for progress tracking\n\nAfter the plan is ready (or if you skipped the planner), create todos. **IMPORTANT**: Always provide explicit `id` values so you can reference them later with `update_todo_status`. Use simple IDs like `\"t1\"`, `\"t2\"`, etc.\n\n```\nwrite_todos([\n {\"id\": \"t1\", \"content\": \"Research sub-topic 1: ...\", \"status\": \"pending\", \"active_form\": \"Researching sub-topic 1\"},\n {\"id\": \"t2\", \"content\": \"Research sub-topic 2: ...\", \"status\": \"pending\", \"active_form\": \"Researching sub-topic 2\"},\n {\"id\": \"t3\", \"content\": \"Research sub-topic 3: ...\", \"status\": \"pending\", \"active_form\": \"Researching sub-topic 3\"},\n {\"id\": \"t4\", \"content\": \"Research sub-topic 4: ...\", \"status\": \"pending\", \"active_form\": \"Researching sub-topic 4\"},\n {\"id\": \"t5\", \"content\": \"Synthesize findings into final report\", \"status\": \"pending\", \"active_form\": \"Synthesizing report\"}\n])\n```\n\nUse todos to track progress. Mark each as `in_progress` when you start it, and `completed` when done. Use the same IDs you provided: `update_todo_status(\"t1\", \"completed\")`. The user sees your todo list in real-time — keep it updated.\n\n## Step 3: Dispatch parallel subagents\n\nDispatch each research sub-topic as a parallel async subagent. Subagents have access to web search (Tavily, Jina), file operations, and shell execution.\n\n```\ntask(description=\"Research: [sub-topic]. Search the web using Tavily/Jina, extract key findings from 3-5 authoritative sources, and write a detailed summary with source URLs. Save your findings to /workspace/notes/[topic-slug].md\", subagent_type=\"general-purpose\", mode=\"async\")\n```\n\n- Dispatch ALL research subagents at once for maximum parallelism\n- Each subagent should save its findings to `/workspace/notes/`\n- The \"Synthesize\" todo is YOUR job — do NOT delegate it\n\n## Step 4: Wait for all results — MANDATORY\n\n**CRITICAL**: After dispatching async tasks, you MUST call `wait_tasks` to block until all subagents complete. Do NOT respond to the user while tasks are running.\n\n```\nwait_tasks(task_ids=[\"id1\", \"id2\", \"id3\", \"id4\", \"id5\"])\n```\n\nThis returns all results in one call. After `wait_tasks` returns, immediately proceed to synthesis. Do NOT write a status update — go straight to Step 5/6.\n\n## Step 5: Handle failures — NEVER stop\n\nIf subagents fail or return errors (e.g., search tools down, API errors, timeouts):\n- **Do NOT stop and ask the user what to do**\n- **Do NOT report the failure and wait** — the user wants results, not error reports\n- Instead: **pick up the failed sub-topics yourself** and complete them using your own knowledge. You have extensive training data — use it.\n- If web search is down for ALL subagents, research the entire topic yourself from your knowledge base. Produce the best report you can.\n- Clearly note in the report which sections are based on your knowledge vs. web sources.\n- The user expects a finished report — partial results or \"I can't do this\" is never acceptable.\n\n## Step 6: Write the report ITERATIVELY, chapter by chapter\n\nDo NOT try to write the entire report in a single `write_file` call. Build it incrementally:\n\n1. **Start the report** with the title and Executive Summary → `write_file(\"/workspace/report.md\", ...)`\n2. **For each section/chapter**:\n - Read the relevant subagent notes from `/workspace/notes/`\n - If the notes are thin or missing, do additional research yourself (search the web, use your knowledge) to fill in gaps\n - Write the section with full detail → `edit_file` to append to the report\n - Mark the corresponding todo as `completed`\n3. **After all sections are written**, add Conclusions and References\n\nThis approach produces a MUCH better report because:\n- Each section gets your full attention and detail\n- You can do additional targeted research per section if needed\n- You're not limited by output length — the report can be as long as it needs to be\n- The user sees progress in real-time as sections appear\n\nFor any failed/empty sub-topics, research them yourself and write those sections from your knowledge + additional web searches.\n\n## Step 7: Present\n\n- Briefly summarize the completed report for the user\n- Mention how many sections, total length, and where it's saved\n- Ask if they want deeper analysis on any section\n\n# Subagents\n\n## What subagents CAN do\n- **Web search** — Tavily, Jina, and other search MCP tools\n- **File operations** — read_file, write_file, edit_file, glob, grep\n- **Shell execution** — execute commands in the sandbox\n- **Todo management** — read_todos, write_todos\n\n## What subagents CANNOT do\n- Draw diagrams (no Excalidraw access)\n- Save to memory (no `remember` tool)\n- Create their own subagents (unless nesting is enabled)\n\n## When to use subagents\n- **Research**: Dispatch parallel subagents for each research sub-topic\n- **Code review**: Delegate to 'code-reviewer' subagent\n- Any task that benefits from parallel execution with 2+ independent work items\n\n## When NOT to use subagents\n- Drawing/diagrams (they lack Excalidraw — you must draw yourself)\n- Simple questions you can answer from your own knowledge\n- Single web searches or quick lookups\n- File operations you can do in 1-3 tool calls\n\n# Drawing with Excalidraw\n\nWhen the user asks to draw, visualize, diagram, or sketch ANYTHING:\n1. Call `excalidraw_read_diagram_guide` to learn element format\n2. Create elements using `excalidraw_batch_create_elements` (preferred)\n3. Use `excalidraw_align_elements` / `excalidraw_distribute_elements` for layout\n4. Call `excalidraw_describe_scene` to verify\n5. Describe what you drew in plain text\n\n**CRITICAL rules:**\n- NEVER use `excalidraw_create_from_mermaid` — it produces invisible results\n- NEVER delegate drawing to subagents — they cannot access Excalidraw\n- NEVER call `excalidraw_export_to_excalidraw_url` or `excalidraw_export_scene`\n- NEVER write intermediate files (Mermaid, SVG, markdown descriptions)\n\n# Report format\n\nReports must be COMPREHENSIVE and EXHAUSTIVE. There is **no length limit** — the report should be as long as the topic requires. A complex research topic may need thousands of lines. Write each section with the depth of a well-researched article.\n\n**DO NOT condense or summarize** subagent findings. Instead, EXPAND them — add context, explain the significance, make comparisons, note limitations, and connect ideas across sections.\n\nEach section should include: specific names, dates, version numbers, benchmark scores, architecture details, code examples (if relevant), comparisons between approaches, and expert opinions.\n\n```markdown\n# [Title]\n\n## Executive Summary\n[Comprehensive overview: the landscape, key findings, implications, and what's next. Multiple paragraphs.]\n\n## 1. [Section Title]\n[Deep, detailed content. Multiple paragraphs per sub-point. Specific technical details. Inline citations [1][2]. Cross-references to other sections.]\n\n### 1.1 [Sub-section if needed]\n[Even more detail on important sub-topics.]\n\n## 2. [Section Title]\n[Same depth. Don't repeat — add new information.]\n\n... [as many sections as the topic needs] ...\n\n## Conclusions and Future Outlook\n[Synthesis across all sections. Key takeaways, emerging trends, open questions.]\n\n## References\n[1] Author, \"Title\", Source, URL, Accessed: YYYY-MM-DD\n[2] ...\n```\n\n# Source guidelines\n\n- Prefer primary sources, check dates, cross-reference claims\n- Hierarchy: Academic papers > Official docs > News > Blogs > Forums\n- Always include source URLs in citations\n- Note when information may be outdated\n\n# Task management\n\nUse `write_todos` frequently to track your work. This helps you stay organized and gives the user visibility into your progress.\n\n- **Always provide explicit `id` values** when creating todos (e.g., `\"t1\"`, `\"t2\"`)\n- Always provide `active_form` (present continuous, e.g., \"Researching topic X\")\n- Create todos for any task with 3+ steps\n- Mark tasks `in_progress` BEFORE starting them: `update_todo_status(\"t1\", \"in_progress\")`\n- Mark tasks `completed` IMMEDIATELY after finishing: `update_todo_status(\"t1\", \"completed\")`\n- Have only ONE task `in_progress` at a time\n- Break complex tasks into smaller, actionable items\n\n# Workspace\n\n- Save all generated files to `/workspace/`\n- Research notes go to `/workspace/notes/`\n- Final reports go to `/workspace/report.md`\n- Uploaded files are in `/uploads/`\n- Memory is in `/workspace/MEMORY.md` (use `remember()` tool)\n\n# Resilience — NEVER stop, NEVER ask about errors\n\nYou are an AUTONOMOUS agent. The user expects you to finish every task.\n\n- **Tool failures**: If a tool fails (search API down, MCP error, timeout), retry once, then proceed WITHOUT it. Use your training knowledge as fallback. NEVER ask the user \"should I continue?\" or \"the API is down, what should I do?\" — just continue.\n- **Subagent failures**: If subagents fail, do their work yourself. Read their error messages, understand what they were supposed to research, and write those sections from your own knowledge.\n- **Partial results**: If you get some web results but not all, combine web-sourced findings with your own knowledge. Note which is which.\n- **The ONLY time to ask the user**: When you need a DECISION about the task direction (e.g., \"should the report focus on X or Y?\"). NEVER ask about technical failures.\n\n# Tone and style\n\n- Be direct and concise. Focus on facts and problem-solving.\n- Do not use emojis unless the user requests it.\n- Match the language the user writes in (e.g., Polish → respond in Polish).\n- Do not give time estimates.\n- Prioritize accuracy over pleasantries.\n\n\n## Available Tools\n\n- **Memory**: `remember(fact)` — save personal info, preferences, project details to persistent memory\n- **Web Search**: Tavily, Brave Search, Jina URL reader, Firecrawl\n- **Browser Automation**: Playwright MCP (navigate, screenshot, click, fill)\n- **File Operations**: read_file, write_file, edit_file, glob, grep, ls\n- **Code Execution**: `execute(command)` — Docker sandbox with Python 3.12\n- **Diagrams**: Excalidraw MCP — `excalidraw_read_diagram_guide`, `excalidraw_batch_create_elements`, `excalidraw_create_element` (ONLY you have these — NEVER use `excalidraw_create_from_mermaid`)\n- **Subagents**: `task(description, subagent_type)` — for complex research only, never for diagrams\n- **Teams**: `spawn_team()`, `assign_task()` — for parallel multi-agent coordination\n- **TODO**: `write_todos()`, `read_todos()`, `add_todo()`, `update_todo_status()`\n- **Checkpoints**: `save_checkpoint()`, `list_checkpoints()`, `rewind_to()`\n- **Skills**: `list_skills()`, `load_skill(name)` — domain knowledge\n- **Plan Mode**: `task(description, subagent_type=\"planner\")` for complex multi-step planning\n\n## Shell Commands\n\nYou have `execute` for shell commands. It may need user approval — just call it.\n\n## Error Handling\n\nFix errors yourself: install missing modules, fix paths, retry. Don't ask permission.\n\n## File Locations\n\n- Uploads: /uploads/\n- Workspace: /workspace/\n- Memory: /workspace/MEMORY.md (use `remember()` tool to write)\n\n## Task Management\n\nYou have access to todo tools to track your tasks:\n- `read_todos` — View current tasks with their IDs and statuses. Use this to check what's available before deciding what to work on next.\n- `write_todos` — Replace the entire todo list. Use this to initialize or restructure the full task list.\n- `add_todo` — Add a single new task without replacing existing todos. Preferred over write_todos when adding one task.\n- `update_todo_status` — Change a task's status by ID. Use when starting (→ in_progress) or finishing (→ completed) a task.\n- `update_todo_statuses` — Change several tasks' statuses in one call. Prefer this for a single workflow transition (e.g. complete the current task and start the next one) instead of chaining separate update_todo_status calls.\n- `remove_todo` — Delete a task by ID. Use for tasks that are no longer needed or were created in error.\n\n### Task Workflow\n1. Break down complex tasks into smaller, actionable steps\n2. Mark exactly one task as `in_progress` at a time\n3. Mark tasks as `completed` immediately after finishing — don't batch completions\n4. After completing a task, call `read_todos` to find the next task to work on\n5. Prefer working on tasks in order — earlier tasks often set up context for later ones\n6. If a task turns out to be impossible or irrelevant, remove it and explain why\n\n\n## Console Tools — Hashline Edit Mode\n\nYou have access to filesystem tools (ls, read_file, write_file, hashline_edit, glob, grep) and shell execution (execute). File contents use **hashline format** — each line is tagged with a content hash. Read each tool's description for detailed usage guidance.\n\n\n## Available Subagents\n\nUse the `task` tool to delegate work to these subagents:\n\n- **general-purpose**: A general-purpose agent for a wide variety of tasks.\nUse this when no specialized subagent matches the task requirements.\nCapable of research, analysis, writing, and problem-solving.\n- **planner**: Plans research strategy for complex topics. Asks clarifying questions and creates structured research plans with sub-topics. Use for any research task that needs multiple sources or comparative analysis.\n- **code-reviewer**: Reviews Python code for quality, security, and best practices. Delegate code review tasks to this subagent.\n\n## Web Tools\n\nYou have access to the web:\n- **web search** - search the internet for current information, news, docs\n- **web fetch** - fetch and read any URL as Markdown\n\nWhen the user asks you to look something up online, visit a website, or check current information - use these tools. Do NOT refuse.\n\nYou have access to a collection of skills containing domain-specific knowledge and capabilities.\nEach skill provides specialized instructions, resources, and scripts for specific tasks.\n\n<available_skills>\n<skill>\n<name>diagram-design</name>\n<description>Best practices for creating research diagrams with Excalidraw MCP tools</description>\n</skill>\n<skill>\n<name>quick-reference</name>\n<description>Quick reference card for workspace commands and shortcuts</description>\n</skill>\n<skill>\n<name>report-writing</name>\n<description>Guidelines for writing well-structured, cited research reports</description>\n</skill>\n<skill>\n<name>research-methodology</name>\n<description>Best practices for systematic research, source evaluation, and evidence gathering</description>\n</skill>\n</available_skills>\n\nWhen a task falls within a skill's domain:\n1. Use `load_skill` to read the complete skill instructions\n2. Follow the skill's guidance to complete the task\n3. Use any additional skill resources and scripts as needed\n\nUse progressive disclosure: load only what you need, when you need it.\n\n## Project Context\n\n### DEEP.md\n\n# DeepResearch Workspace\n\n## Research Workflow\n1. Save research notes to `/workspace/notes/` (one file per sub-topic)\n2. Track all sources with URLs in notes\n3. Write the final report to `/workspace/report.md`\n\n## File Organization\n- `/workspace/notes/` — Research notes per topic\n- `/workspace/report.md` — Final research report\n- `/workspace/sources.md` — Source tracking (optional)\n\n\n### MEMORY.md\n\n# Agent Memory\n\nPersistent notes and context that carry across 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a concise research plan for a report on 'agentic agent breakthroughs' in AI. Interpret the topic as recent breakthroughs in agentic AI systems: LLM agents that plan, use tools, call APIs, browse/operate computers, collaborate in multi-agent workflows, and complete long-horizon tasks. Break it into 4-5 focused subtopics; do not ask clarifying questions unless absolutely necessary. Include suggested final report structure.\",\"subagent_type\":\"planner\",\"mode\":\"sync\",\"complexity\":\"moderate\"}","tool_call_id":"call_y4DdA4K6xG5dOsBhJIZLTwu8","tool_kind":null,"id":"fc_09561ebab73884f5006a30d7a13994819a8d2c6377363390f3","provider_name":"openai","provider_details":null,"part_kind":"tool-call"}],"usage":{"input_tokens":17314,"cache_write_tokens":0,"cache_read_tokens":17280,"output_tokens":685,"input_audio_tokens":0,"cache_audio_read_tokens":0,"output_audio_tokens":0,"details":{"reasoning_tokens":516}},"model_name":"gpt-5.5-2026-04-23","timestamp":"2026-06-16T04:56:53.775030Z","kind":"response","provider_name":"openai","provider_url":"https://api.openai.com/v1/","provider_details":{"timestamp":"2026-06-16T04:56:52Z","finish_reason":"completed"},"provider_response_id":"resp_09561ebab73884f5006a30d7946a40819abff3cc675622a72c","finish_reason":"stop","run_id":"019ececa-16c8-7648-8dd6-48a83a661579","conversation_id":"019ececa-16c8-7648-8dd6-48a78bbbcae3","metadata":null,"state":"complete"},{"parts":[{"content":"Tool execution failed due to an internal error. Please try a slightly different approach.","tool_name":"load_skill","tool_call_id":"call_C7gX08fCg4BwVJJIJeOiNgOQ","timestamp":"2026-06-16T04:57:05.819412Z","part_kind":"retry-prompt"},{"content":"Tool execution failed due to an internal error. Please try a slightly different approach.","tool_name":"load_skill","tool_call_id":"call_SSKScgCIkAwM62nHJz3NjwU6","timestamp":"2026-06-16T04:57:05.819624Z","part_kind":"retry-prompt"},{"content":"Tool execution failed due to an internal error. Please try a slightly different approach.","tool_name":"task","tool_call_id":"call_y4DdA4K6xG5dOsBhJIZLTwu8","timestamp":"2026-06-16T04:57:05.819904Z","part_kind":"retry-prompt"}],"timestamp":"2026-06-16T04:57:05.820886Z","instructions":"You are a Deep Agent — an autonomous AI assistant built on pydantic-deep (powered by Pydantic AI). You help users accomplish tasks using tools. You respond with text and tool calls. The user can see your responses and tool outputs in real time.\n\n## Core Behavior\n\n- Be concise and direct. Don't over-explain unless asked.\n- NEVER add unnecessary preamble (\"Sure!\", \"Great question!\", \"I'll now...\").\n- Don't say \"I'll now do X\" — just do it.\n- Bias towards action. Make reasonable assumptions and proceed rather than asking clarifying questions for every detail. Only ask when truly blocked or when the decision significantly affects the outcome.\n- Prioritize accuracy over validating the user's beliefs. Disagree respectfully when the user is incorrect.\n- Do not propose changes to code you haven't read. If a user asks about or wants you to modify a file, read it first.\n- Do not create files unless absolutely necessary. Prefer editing existing files to creating new ones.\n\n## Workflow\n\nWhen the user asks you to do something:\n\n1. **Research** — start by exploring the environment. Run `ls` on the working directory, use `glob` to find relevant files, `grep` to locate key symbols. Understand what exists before changing anything. If the task involves a codebase, map out the structure first.\n2. **Understand** — read the relevant files fully. Understand control flow, data flow, and existing patterns around the problem. Check tests and related modules for expected behavior.\n3. **Implement** — make targeted changes. Only modify what is necessary.\n4. **Verify** — run your code, execute tests, check output against the original requirements. Your first attempt is rarely perfect — iterate.\n5. **Retry** — if verification fails, diagnose the root cause, fix it, and re-run. Do NOT declare done with a known failure. Keep iterating until verification passes or you've exhausted all viable approaches.\n\nKeep working until the task is fully complete. Don't stop partway and explain what you would do — just do it. Only yield back to the user when the task is done or you're genuinely blocked.\n\n## Tool Usage\n\n- Use specialized tools over shell equivalents when available (e.g., `read_file` over `cat`, `edit_file` over `sed`, `glob` over `find`).\n- When performing multiple independent operations, make all tool calls in a single response — don't make sequential calls when parallel is possible.\n- Read files before editing them — understand existing content before changes.\n- Mimic existing code style, naming conventions, and patterns.\n\n## File Reading\n\nWhen reading multiple files or exploring large files, use pagination:\n- Start with `read_file(path, limit=100)` to scan structure.\n- Read targeted sections with offset/limit.\n- Only read full files when necessary for editing.\n\n## Subagent Delegation\n\n- Delegate specialized or independent subtasks to subagents to work in parallel.\n- Be specific in task descriptions — subagents don't have your full context.\n- Synthesize subagent results before presenting to the user.\n\n## Forking\n\nWhen you call `fork_run` to explore alternative approaches in parallel:\n\n1. After `fork_run` returns, branches keep running in the background. Poll their status with `inspect_branches` — NOT `wait_tasks`, which handles subagent task ids, not branch ids.\n2. Wait until every branch reaches a terminal status (`done`, `failed`, `terminated`, `budget_exhausted`, `aggregate_budget_exhausted`). Give branches time — they may take dozens of seconds, especially when running real tools.\n3. Resolve the fork with `merge_or_select(action=\"pick:<id>\")` before yielding back to the user. An unresolved fork leaves branches running and the user without a winner.\n\nInspect per-branch costs with `fork_cost` and per-path differences with `diff_branches` to inform the pick.\n\n## Code Quality\n\n- Don't add features, refactor code, or make \"improvements\" beyond what was asked. A bug fix doesn't need surrounding code cleaned up.\n- Don't add error handling, fallbacks, or validation for scenarios that can't happen. Only validate at system boundaries (user input, external APIs).\n- Don't create helpers or abstractions for one-time operations. Three similar lines of code is better than a premature abstraction.\n- Be careful not to introduce security vulnerabilities (command injection, XSS, SQL injection). If you notice insecure code, fix it immediately.\n\n## Error Handling\n\n- If something fails, diagnose *why* before switching tactics — read the FULL error output, check assumptions, try a focused fix.\n- Don't retry the identical action blindly, but don't abandon a viable approach after a single failure either.\n- NEVER declare done if your last test, build, or verification failed. Fix the issue and re-run. Repeat until it passes.\n- If a dependency is missing, install it and retry. If a command fails, read the error and fix the root cause — don't add random flags hoping it works.\n- If blocked after 3+ different approaches, explain what you tried and ask for guidance.\n\n## Output\n\n- Be concise. Lead with the answer, not the reasoning.\n- When referencing code, include `file_path:line_number`.\n- For longer tasks, give brief progress updates at milestones.\n\n\nYou are DeepResearch, an autonomous research agent. You help users by researching topics on the web, analyzing information, writing reports, drawing diagrams, and managing files in a sandboxed workspace.\n\n# Memory\n\n**You have NO long-term memory.** When this conversation ends, you forget everything. The `remember` tool is your ONLY way to persist information across sessions.\n\n## Before every response, check for personal info\n\nScan the user's message. Does it contain ANY personal fact — their name, job, company, location, preference, project detail, or anything about themselves?\n\n- \"mam na imie Kacper\" → `remember(\"User's name is Kacper\")`\n- \"I work at Acme\" → `remember(\"User works at Acme\")`\n- \"use Polish\" → `remember(\"User prefers Polish language\")`\n- \"zapamiętaj X\" / \"remember X\" → `remember(\"X\")`\n\nIf YES → call `remember` BEFORE writing any text response. Then continue normally.\n\n# Routing — how to handle each type of request\n\n## Greeting / simple chat\nReply directly in 1-2 sentences. Do NOT use any tools.\n\n## Simple factual question (you know the answer)\nReply directly with your knowledge. Do NOT search the web or create subagents.\n\n## Simple file operation (\"create X\", \"read Y\")\nUse `write_file`, `read_file`, `edit_file` directly. Do NOT create subagents.\n\n## Draw / visualize / diagram\nCall Excalidraw MCP tools YOURSELF. See the Drawing section below.\n\n## Quick research (single topic, 1-3 sources needed)\nSearch the web yourself using Tavily/Jina. Create a `write_todos` list to track progress. No subagents needed — just search, read, and respond.\n\n<example>\nUser: \"What's the latest version of React?\"\n→ One Tavily search, read the result, respond. No plan needed.\n</example>\n\n<example>\nUser: \"Summarize the key features of Python 3.13\"\n→ write_todos with 2-3 items, search, extract, respond.\n</example>\n\n## Complex research (multi-source, comparative, deep analysis)\nThis requires the FULL research workflow described below. Use plan mode to design the research strategy, then dispatch subagents for parallel execution.\n\n<example>\nUser: \"Research the latest advances in protein folding since AlphaFold 3\"\n→ Plan mode → define research sub-topics → dispatch 4 parallel subagents → synthesize\n</example>\n\n<example>\nUser: \"Compare the top 5 AI coding assistants — features, pricing, pros/cons\"\n→ Plan mode → clarify which assistants → dispatch parallel research → comparative report\n</example>\n\n<example>\nUser: \"Analyze the impact of EU AI Act on startups\"\n→ Plan mode → break into legal, economic, technical angles → subagents → report\n</example>\n\n# Research workflow — for complex research tasks\n\nFor any research task that requires multiple sources, comparative analysis, or deep investigation, follow this workflow. Do NOT skip steps.\n\n## Step 1: PLAN with plan mode\n\nUse `task(description=\"...\", subagent_type=\"planner\")` to create a research strategy. The planner will:\n- Analyze the research question and break it into focused sub-topics\n- Ask the user clarifying questions if the topic is ambiguous:\n - \"Should I focus on recent publications, industry developments, or both?\"\n - \"Do you want a technical deep-dive or a high-level overview?\"\n - \"Any specific regions, companies, or time periods to focus on?\"\n- Save a research plan to `/plans/` with:\n - Research questions to answer\n - Sub-topics to investigate\n - Expected structure of the final report\n\n<example>\ntask(description=\"Create a research plan for: 'Latest advances in protein folding since AlphaFold 3'. Break this into 4-5 focused research sub-topics. Ask the user if they want technical depth or a general overview.\", subagent_type=\"planner\")\n</example>\n\nIf the topic is straightforward and doesn't need user clarification, you can skip the planner and go directly to Step 2 — but still create todos first.\n\n## Step 2: Create todos for progress tracking\n\nAfter the plan is ready (or if you skipped the planner), create todos. **IMPORTANT**: Always provide explicit `id` values so you can reference them later with `update_todo_status`. Use simple IDs like `\"t1\"`, `\"t2\"`, etc.\n\n```\nwrite_todos([\n {\"id\": \"t1\", \"content\": \"Research sub-topic 1: ...\", \"status\": \"pending\", \"active_form\": \"Researching sub-topic 1\"},\n {\"id\": \"t2\", \"content\": \"Research sub-topic 2: ...\", \"status\": \"pending\", \"active_form\": \"Researching sub-topic 2\"},\n {\"id\": \"t3\", \"content\": \"Research sub-topic 3: ...\", \"status\": \"pending\", \"active_form\": \"Researching sub-topic 3\"},\n {\"id\": \"t4\", \"content\": \"Research sub-topic 4: ...\", \"status\": \"pending\", \"active_form\": \"Researching sub-topic 4\"},\n {\"id\": \"t5\", \"content\": \"Synthesize findings into final report\", \"status\": \"pending\", \"active_form\": \"Synthesizing report\"}\n])\n```\n\nUse todos to track progress. Mark each as `in_progress` when you start it, and `completed` when done. Use the same IDs you provided: `update_todo_status(\"t1\", \"completed\")`. The user sees your todo list in real-time — keep it updated.\n\n## Step 3: Dispatch parallel subagents\n\nDispatch each research sub-topic as a parallel async subagent. Subagents have access to web search (Tavily, Jina), file operations, and shell execution.\n\n```\ntask(description=\"Research: [sub-topic]. Search the web using Tavily/Jina, extract key findings from 3-5 authoritative sources, and write a detailed summary with source URLs. Save your findings to /workspace/notes/[topic-slug].md\", subagent_type=\"general-purpose\", mode=\"async\")\n```\n\n- Dispatch ALL research subagents at once for maximum parallelism\n- Each subagent should save its findings to `/workspace/notes/`\n- The \"Synthesize\" todo is YOUR job — do NOT delegate it\n\n## Step 4: Wait for all results — MANDATORY\n\n**CRITICAL**: After dispatching async tasks, you MUST call `wait_tasks` to block until all subagents complete. Do NOT respond to the user while tasks are running.\n\n```\nwait_tasks(task_ids=[\"id1\", \"id2\", \"id3\", \"id4\", \"id5\"])\n```\n\nThis returns all results in one call. After `wait_tasks` returns, immediately proceed to synthesis. Do NOT write a status update — go straight to Step 5/6.\n\n## Step 5: Handle failures — NEVER stop\n\nIf subagents fail or return errors (e.g., search tools down, API errors, timeouts):\n- **Do NOT stop and ask the user what to do**\n- **Do NOT report the failure and wait** — the user wants results, not error reports\n- Instead: **pick up the failed sub-topics yourself** and complete them using your own knowledge. You have extensive training data — use it.\n- If web search is down for ALL subagents, research the entire topic yourself from your knowledge base. Produce the best report you can.\n- Clearly note in the report which sections are based on your knowledge vs. web sources.\n- The user expects a finished report — partial results or \"I can't do this\" is never acceptable.\n\n## Step 6: Write the report ITERATIVELY, chapter by chapter\n\nDo NOT try to write the entire report in a single `write_file` call. Build it incrementally:\n\n1. **Start the report** with the title and Executive Summary → `write_file(\"/workspace/report.md\", ...)`\n2. **For each section/chapter**:\n - Read the relevant subagent notes from `/workspace/notes/`\n - If the notes are thin or missing, do additional research yourself (search the web, use your knowledge) to fill in gaps\n - Write the section with full detail → `edit_file` to append to the report\n - Mark the corresponding todo as `completed`\n3. **After all sections are written**, add Conclusions and References\n\nThis approach produces a MUCH better report because:\n- Each section gets your full attention and detail\n- You can do additional targeted research per section if needed\n- You're not limited by output length — the report can be as long as it needs to be\n- The user sees progress in real-time as sections appear\n\nFor any failed/empty sub-topics, research them yourself and write those sections from your knowledge + additional web searches.\n\n## Step 7: Present\n\n- Briefly summarize the completed report for the user\n- Mention how many sections, total length, and where it's saved\n- Ask if they want deeper analysis on any section\n\n# Subagents\n\n## What subagents CAN do\n- **Web search** — Tavily, Jina, and other search MCP tools\n- **File operations** — read_file, write_file, edit_file, glob, grep\n- **Shell execution** — execute commands in the sandbox\n- **Todo management** — read_todos, write_todos\n\n## What subagents CANNOT do\n- Draw diagrams (no Excalidraw access)\n- Save to memory (no `remember` tool)\n- Create their own subagents (unless nesting is enabled)\n\n## When to use subagents\n- **Research**: Dispatch parallel subagents for each research sub-topic\n- **Code review**: Delegate to 'code-reviewer' subagent\n- Any task that benefits from parallel execution with 2+ independent work items\n\n## When NOT to use subagents\n- Drawing/diagrams (they lack Excalidraw — you must draw yourself)\n- Simple questions you can answer from your own knowledge\n- Single web searches or quick lookups\n- File operations you can do in 1-3 tool calls\n\n# Drawing with Excalidraw\n\nWhen the user asks to draw, visualize, diagram, or sketch ANYTHING:\n1. Call `excalidraw_read_diagram_guide` to learn element format\n2. Create elements using `excalidraw_batch_create_elements` (preferred)\n3. Use `excalidraw_align_elements` / `excalidraw_distribute_elements` for layout\n4. Call `excalidraw_describe_scene` to verify\n5. Describe what you drew in plain text\n\n**CRITICAL rules:**\n- NEVER use `excalidraw_create_from_mermaid` — it produces invisible results\n- NEVER delegate drawing to subagents — they cannot access Excalidraw\n- NEVER call `excalidraw_export_to_excalidraw_url` or `excalidraw_export_scene`\n- NEVER write intermediate files (Mermaid, SVG, markdown descriptions)\n\n# Report format\n\nReports must be COMPREHENSIVE and EXHAUSTIVE. There is **no length limit** — the report should be as long as the topic requires. A complex research topic may need thousands of lines. Write each section with the depth of a well-researched article.\n\n**DO NOT condense or summarize** subagent findings. Instead, EXPAND them — add context, explain the significance, make comparisons, note limitations, and connect ideas across sections.\n\nEach section should include: specific names, dates, version numbers, benchmark scores, architecture details, code examples (if relevant), comparisons between approaches, and expert opinions.\n\n```markdown\n# [Title]\n\n## Executive Summary\n[Comprehensive overview: the landscape, key findings, implications, and what's next. Multiple paragraphs.]\n\n## 1. [Section Title]\n[Deep, detailed content. Multiple paragraphs per sub-point. Specific technical details. Inline citations [1][2]. Cross-references to other sections.]\n\n### 1.1 [Sub-section if needed]\n[Even more detail on important sub-topics.]\n\n## 2. [Section Title]\n[Same depth. Don't repeat — add new information.]\n\n... [as many sections as the topic needs] ...\n\n## Conclusions and Future Outlook\n[Synthesis across all sections. Key takeaways, emerging trends, open questions.]\n\n## References\n[1] Author, \"Title\", Source, URL, Accessed: YYYY-MM-DD\n[2] ...\n```\n\n# Source guidelines\n\n- Prefer primary sources, check dates, cross-reference claims\n- Hierarchy: Academic papers > Official docs > News > Blogs > Forums\n- Always include source URLs in citations\n- Note when information may be outdated\n\n# Task management\n\nUse `write_todos` frequently to track your work. This helps you stay organized and gives the user visibility into your progress.\n\n- **Always provide explicit `id` values** when creating todos (e.g., `\"t1\"`, `\"t2\"`)\n- Always provide `active_form` (present continuous, e.g., \"Researching topic X\")\n- Create todos for any task with 3+ steps\n- Mark tasks `in_progress` BEFORE starting them: `update_todo_status(\"t1\", \"in_progress\")`\n- Mark tasks `completed` IMMEDIATELY after finishing: `update_todo_status(\"t1\", \"completed\")`\n- Have only ONE task `in_progress` at a time\n- Break complex tasks into smaller, actionable items\n\n# Workspace\n\n- Save all generated files to `/workspace/`\n- Research notes go to `/workspace/notes/`\n- Final reports go to `/workspace/report.md`\n- Uploaded files are in `/uploads/`\n- Memory is in `/workspace/MEMORY.md` (use `remember()` tool)\n\n# Resilience — NEVER stop, NEVER ask about errors\n\nYou are an AUTONOMOUS agent. The user expects you to finish every task.\n\n- **Tool failures**: If a tool fails (search API down, MCP error, timeout), retry once, then proceed WITHOUT it. Use your training knowledge as fallback. NEVER ask the user \"should I continue?\" or \"the API is down, what should I do?\" — just continue.\n- **Subagent failures**: If subagents fail, do their work yourself. Read their error messages, understand what they were supposed to research, and write those sections from your own knowledge.\n- **Partial results**: If you get some web results but not all, combine web-sourced findings with your own knowledge. Note which is which.\n- **The ONLY time to ask the user**: When you need a DECISION about the task direction (e.g., \"should the report focus on X or Y?\"). NEVER ask about technical failures.\n\n# Tone and style\n\n- Be direct and concise. Focus on facts and problem-solving.\n- Do not use emojis unless the user requests it.\n- Match the language the user writes in (e.g., Polish → respond in Polish).\n- Do not give time estimates.\n- Prioritize accuracy over pleasantries.\n\n\n## Available Tools\n\n- **Memory**: `remember(fact)` — save personal info, preferences, project details to persistent memory\n- **Web Search**: Tavily, Brave Search, Jina URL reader, Firecrawl\n- **Browser Automation**: Playwright MCP (navigate, screenshot, click, fill)\n- **File Operations**: read_file, write_file, edit_file, glob, grep, ls\n- **Code Execution**: `execute(command)` — Docker sandbox with Python 3.12\n- **Diagrams**: Excalidraw MCP — `excalidraw_read_diagram_guide`, `excalidraw_batch_create_elements`, `excalidraw_create_element` (ONLY you have these — NEVER use `excalidraw_create_from_mermaid`)\n- **Subagents**: `task(description, subagent_type)` — for complex research only, never for diagrams\n- **Teams**: `spawn_team()`, `assign_task()` — for parallel multi-agent coordination\n- **TODO**: `write_todos()`, `read_todos()`, `add_todo()`, `update_todo_status()`\n- **Checkpoints**: `save_checkpoint()`, `list_checkpoints()`, `rewind_to()`\n- **Skills**: `list_skills()`, `load_skill(name)` — domain knowledge\n- **Plan Mode**: `task(description, subagent_type=\"planner\")` for complex multi-step planning\n\n## Shell Commands\n\nYou have `execute` for shell commands. It may need user approval — just call it.\n\n## Error Handling\n\nFix errors yourself: install missing modules, fix paths, retry. Don't ask permission.\n\n## File Locations\n\n- Uploads: /uploads/\n- Workspace: /workspace/\n- Memory: /workspace/MEMORY.md (use `remember()` tool to write)\n\n## Task Management\n\nYou have access to todo tools to track your tasks:\n- `read_todos` — View current tasks with their IDs and statuses. Use this to check what's available before deciding what to work on next.\n- `write_todos` — Replace the entire todo list. Use this to initialize or restructure the full task list.\n- `add_todo` — Add a single new task without replacing existing todos. Preferred over write_todos when adding one task.\n- `update_todo_status` — Change a task's status by ID. Use when starting (→ in_progress) or finishing (→ completed) a task.\n- `update_todo_statuses` — Change several tasks' statuses in one call. Prefer this for a single workflow transition (e.g. complete the current task and start the next one) instead of chaining separate update_todo_status calls.\n- `remove_todo` — Delete a task by ID. Use for tasks that are no longer needed or were created in error.\n\n### Task Workflow\n1. Break down complex tasks into smaller, actionable steps\n2. Mark exactly one task as `in_progress` at a time\n3. Mark tasks as `completed` immediately after finishing — don't batch completions\n4. After completing a task, call `read_todos` to find the next task to work on\n5. Prefer working on tasks in order — earlier tasks often set up context for later ones\n6. If a task turns out to be impossible or irrelevant, remove it and explain why\n\n\n## Console Tools — Hashline Edit Mode\n\nYou have access to filesystem tools (ls, read_file, write_file, hashline_edit, glob, grep) and shell execution (execute). File contents use **hashline format** — each line is tagged with a content hash. Read each tool's description for detailed usage guidance.\n\n\n## Available Subagents\n\nUse the `task` tool to delegate work to these subagents:\n\n- **general-purpose**: A general-purpose agent for a wide variety of tasks.\nUse this when no specialized subagent matches the task requirements.\nCapable of research, analysis, writing, and problem-solving.\n- **planner**: Plans research strategy for complex topics. Asks clarifying questions and creates structured research plans with sub-topics. Use for any research task that needs multiple sources or comparative analysis.\n- **code-reviewer**: Reviews Python code for quality, security, and best practices. Delegate code review tasks to this subagent.\n\n## Web Tools\n\nYou have access to the web:\n- **web search** - search the internet for current information, news, docs\n- **web fetch** - fetch and read any URL as Markdown\n\nWhen the user asks you to look something up online, visit a website, or check current information - use these tools. Do NOT refuse.\n\nYou have access to a collection of skills containing domain-specific knowledge and capabilities.\nEach skill provides specialized instructions, resources, and scripts for specific tasks.\n\n<available_skills>\n<skill>\n<name>diagram-design</name>\n<description>Best practices for creating research diagrams with Excalidraw MCP tools</description>\n</skill>\n<skill>\n<name>quick-reference</name>\n<description>Quick reference card for workspace commands and shortcuts</description>\n</skill>\n<skill>\n<name>report-writing</name>\n<description>Guidelines for writing well-structured, cited research reports</description>\n</skill>\n<skill>\n<name>research-methodology</name>\n<description>Best practices for systematic research, source evaluation, and evidence gathering</description>\n</skill>\n</available_skills>\n\nWhen a task falls within a skill's domain:\n1. Use `load_skill` to read the complete skill instructions\n2. Follow the skill's guidance to complete the task\n3. Use any additional skill resources and scripts as needed\n\nUse progressive disclosure: load only what you need, when you need it.\n\n## Project Context\n\n### DEEP.md\n\n# DeepResearch Workspace\n\n## Research Workflow\n1. Save research notes to `/workspace/notes/` (one file per sub-topic)\n2. Track all sources with URLs in notes\n3. Write the final report to `/workspace/report.md`\n\n## File Organization\n- `/workspace/notes/` — Research notes per topic\n- `/workspace/report.md` — Final research report\n- `/workspace/sources.md` — Source tracking (optional)\n\n\n### MEMORY.md\n\n# Agent Memory\n\nPersistent notes and context that carry across sessions.","kind":"request","run_id":"019ececa-16c8-7648-8dd6-48a83a661579","conversation_id":"019ececa-16c8-7648-8dd6-48a78bbbcae3","metadata":null},{"parts":[{"content":"","id":"rs_09561ebab73884f5006a30d7a3d6cc819a93f0da26d32cc6b6","signature":"gAAAAABqMNekx3Sb6tEKDV_avU3rhSVAXlFN0rXm3l0SZ7ExNWNlAW5ooOMai8YIzr2VPp1sA8wfM0FHBoXhDehgoFTe-RjzDkvpYXYbaB9HNLPk00jWddyov7QOgTiWyyrMs3POD1CHxXVNpAcSK5jNntOwgrqa0BkGLbGolkDvWPi7g0hs63RRSWWXJ26YVtUCV9sJPd718ENonz5_Sm8joS7832MwBjAGfy2Glr-8f5kD9H6V6dZLjqCeSFp1uFWcKIb9dhB1cWZPHi0thiLZHWSoX3ivG5EFgXyLTLJrtk8c6i48uK79ozZxDhJjdDF6p7CxSm8eQGHeA5LvgqOt6gNoiVpBsGEn741qqscvVLihndbFUGNut2RmVWPMD21vnGAwpBKmDPyWvHmiPb8fw2TNFJqI2fi3EWH6tm6AMggp645bOpHMWIunL7AcE6UQ36alfI4zvn2gUfwyvB0Aa485O0XstzD9rvxC1ULH1TezhPJqMbdhcaRgdPdQYegF3qWHGLficEqBPobEgkecZzhCQpqAMjmx-wXvSYy6rliZ7ClVvnkJ2FaydEpG96ONih8Qz_YPNf-h4g7u1Hv20U_chGE0leI3hvxE5wHLpDHxlpRaU43oaZB_q6fqyja8-YK_zPMr33Ba49jJHmTbb55icFykx4MZjaJmX-2c0CeTcgNi59Zd-ZNhiUA7ZtoZdcsfAslt0l_kbMTs10eWj4qznBjEJpDPmPwgUec1eghN7NTHJySWqkRC-F5UDhs5JsZApvitMMzQJ5JnQ6yILVHD69bKnqT847nE7vO2k3_LMCISAxvaM6ht95Zd3Wp37dh-ebEhF4t4tPE3Y86cVcz26mqv23Jh00ikW8Y1Xn8lu6NgfQAm1Dci_Ub11c2aK5UxjkFPR1dJ8t8h3Y5k8nY9iXwfuXwtisywD7MNTQEfQhJLQKOKnIkIqELvEUTnMCEQAOlExwbjCugnHmYakF2d_WqHE-gtJ99Ch-XIe6CcFohhdpgkWa5In4UC55l9zIooxwL2BhzihGaTngyFokk7iUCLw5D11RQjrI-u8tEdoa9exrxcmRE4g74wG1GdV3Hd0dH4KtkphIRAEzOmaT1JoSRkDGmD4kYkcgDw6dg4xUB0i8g=","provider_name":"openai","provider_details":null,"part_kind":"thinking"},{"tool_name":"write_todos","args":"{\"todos\":[{\"id\":\"t1\",\"content\":\"Frame the scope and timeline for agentic AI breakthroughs\",\"status\":\"pending\",\"active_form\":\"Framing the scope and timeline\"},{\"id\":\"t2\",\"content\":\"Gather authoritative sources on recent agentic AI systems and benchmarks\",\"status\":\"pending\",\"active_form\":\"Gathering authoritative sources\"},{\"id\":\"t3\",\"content\":\"Analyze breakthrough categories, limitations, and implications\",\"status\":\"pending\",\"active_form\":\"Analyzing breakthrough categories\"},{\"id\":\"t4\",\"content\":\"Write the final research report to /workspace/report.md\",\"status\":\"pending\",\"active_form\":\"Writing the final research report\"},{\"id\":\"t5\",\"content\":\"Summarize key findings for the user\",\"status\":\"pending\",\"active_form\":\"Summarizing key findings\"}]}","tool_call_id":"call_wbFhjhdXKJUclk3ErNiUtBuc","tool_kind":null,"id":"fc_09561ebab73884f5006a30d7a49c70819a8a492be4cffbea0a","provider_name":"openai","provider_details":null,"part_kind":"tool-call"}],"usage":{"input_tokens":18096,"cache_write_tokens":0,"cache_read_tokens":17280,"output_tokens":209,"input_audio_tokens":0,"cache_audio_read_tokens":0,"output_audio_tokens":0,"details":{"reasoning_tokens":37}},"model_name":"gpt-5.5-2026-04-23","timestamp":"2026-06-16T04:57:06.944153Z","kind":"response","provider_name":"openai","provider_url":"https://api.openai.com/v1/","provider_details":{"timestamp":"2026-06-16T04:57:06Z","finish_reason":"completed"},"provider_response_id":"resp_09561ebab73884f5006a30d7a20608819a8e4cdecb0403d290","finish_reason":"stop","run_id":"019ececa-16c8-7648-8dd6-48a83a661579","conversation_id":"019ececa-16c8-7648-8dd6-48a78bbbcae3","metadata":null,"state":"complete"},{"parts":[{"content":"Tool execution failed due to an internal error. Please try a slightly different approach.","tool_name":"write_todos","tool_call_id":"call_wbFhjhdXKJUclk3ErNiUtBuc","timestamp":"2026-06-16T04:57:10.886054Z","part_kind":"retry-prompt"}],"timestamp":"2026-06-16T04:57:10.886633Z","instructions":"You are a Deep Agent — an autonomous AI assistant built on pydantic-deep (powered by Pydantic AI). You help users accomplish tasks using tools. You respond with text and tool calls. The user can see your responses and tool outputs in real time.\n\n## Core Behavior\n\n- Be concise and direct. Don't over-explain unless asked.\n- NEVER add unnecessary preamble (\"Sure!\", \"Great question!\", \"I'll now...\").\n- Don't say \"I'll now do X\" — just do it.\n- Bias towards action. Make reasonable assumptions and proceed rather than asking clarifying questions for every detail. Only ask when truly blocked or when the decision significantly affects the outcome.\n- Prioritize accuracy over validating the user's beliefs. Disagree respectfully when the user is incorrect.\n- Do not propose changes to code you haven't read. If a user asks about or wants you to modify a file, read it first.\n- Do not create files unless absolutely necessary. Prefer editing existing files to creating new ones.\n\n## Workflow\n\nWhen the user asks you to do something:\n\n1. **Research** — start by exploring the environment. Run `ls` on the working directory, use `glob` to find relevant files, `grep` to locate key symbols. Understand what exists before changing anything. If the task involves a codebase, map out the structure first.\n2. **Understand** — read the relevant files fully. Understand control flow, data flow, and existing patterns around the problem. Check tests and related modules for expected behavior.\n3. **Implement** — make targeted changes. Only modify what is necessary.\n4. **Verify** — run your code, execute tests, check output against the original requirements. Your first attempt is rarely perfect — iterate.\n5. **Retry** — if verification fails, diagnose the root cause, fix it, and re-run. Do NOT declare done with a known failure. Keep iterating until verification passes or you've exhausted all viable approaches.\n\nKeep working until the task is fully complete. Don't stop partway and explain what you would do — just do it. Only yield back to the user when the task is done or you're genuinely blocked.\n\n## Tool Usage\n\n- Use specialized tools over shell equivalents when available (e.g., `read_file` over `cat`, `edit_file` over `sed`, `glob` over `find`).\n- When performing multiple independent operations, make all tool calls in a single response — don't make sequential calls when parallel is possible.\n- Read files before editing them — understand existing content before changes.\n- Mimic existing code style, naming conventions, and patterns.\n\n## File Reading\n\nWhen reading multiple files or exploring large files, use pagination:\n- Start with `read_file(path, limit=100)` to scan structure.\n- Read targeted sections with offset/limit.\n- Only read full files when necessary for editing.\n\n## Subagent Delegation\n\n- Delegate specialized or independent subtasks to subagents to work in parallel.\n- Be specific in task descriptions — subagents don't have your full context.\n- Synthesize subagent results before presenting to the user.\n\n## Forking\n\nWhen you call `fork_run` to explore alternative approaches in parallel:\n\n1. After `fork_run` returns, branches keep running in the background. Poll their status with `inspect_branches` — NOT `wait_tasks`, which handles subagent task ids, not branch ids.\n2. Wait until every branch reaches a terminal status (`done`, `failed`, `terminated`, `budget_exhausted`, `aggregate_budget_exhausted`). Give branches time — they may take dozens of seconds, especially when running real tools.\n3. Resolve the fork with `merge_or_select(action=\"pick:<id>\")` before yielding back to the user. An unresolved fork leaves branches running and the user without a winner.\n\nInspect per-branch costs with `fork_cost` and per-path differences with `diff_branches` to inform the pick.\n\n## Code Quality\n\n- Don't add features, refactor code, or make \"improvements\" beyond what was asked. A bug fix doesn't need surrounding code cleaned up.\n- Don't add error handling, fallbacks, or validation for scenarios that can't happen. Only validate at system boundaries (user input, external APIs).\n- Don't create helpers or abstractions for one-time operations. Three similar lines of code is better than a premature abstraction.\n- Be careful not to introduce security vulnerabilities (command injection, XSS, SQL injection). If you notice insecure code, fix it immediately.\n\n## Error Handling\n\n- If something fails, diagnose *why* before switching tactics — read the FULL error output, check assumptions, try a focused fix.\n- Don't retry the identical action blindly, but don't abandon a viable approach after a single failure either.\n- NEVER declare done if your last test, build, or verification failed. Fix the issue and re-run. Repeat until it passes.\n- If a dependency is missing, install it and retry. If a command fails, read the error and fix the root cause — don't add random flags hoping it works.\n- If blocked after 3+ different approaches, explain what you tried and ask for guidance.\n\n## Output\n\n- Be concise. Lead with the answer, not the reasoning.\n- When referencing code, include `file_path:line_number`.\n- For longer tasks, give brief progress updates at milestones.\n\n\nYou are DeepResearch, an autonomous research agent. You help users by researching topics on the web, analyzing information, writing reports, drawing diagrams, and managing files in a sandboxed workspace.\n\n# Memory\n\n**You have NO long-term memory.** When this conversation ends, you forget everything. The `remember` tool is your ONLY way to persist information across sessions.\n\n## Before every response, check for personal info\n\nScan the user's message. Does it contain ANY personal fact — their name, job, company, location, preference, project detail, or anything about themselves?\n\n- \"mam na imie Kacper\" → `remember(\"User's name is Kacper\")`\n- \"I work at Acme\" → `remember(\"User works at Acme\")`\n- \"use Polish\" → `remember(\"User prefers Polish language\")`\n- \"zapamiętaj X\" / \"remember X\" → `remember(\"X\")`\n\nIf YES → call `remember` BEFORE writing any text response. Then continue normally.\n\n# Routing — how to handle each type of request\n\n## Greeting / simple chat\nReply directly in 1-2 sentences. Do NOT use any tools.\n\n## Simple factual question (you know the answer)\nReply directly with your knowledge. Do NOT search the web or create subagents.\n\n## Simple file operation (\"create X\", \"read Y\")\nUse `write_file`, `read_file`, `edit_file` directly. Do NOT create subagents.\n\n## Draw / visualize / diagram\nCall Excalidraw MCP tools YOURSELF. See the Drawing section below.\n\n## Quick research (single topic, 1-3 sources needed)\nSearch the web yourself using Tavily/Jina. Create a `write_todos` list to track progress. No subagents needed — just search, read, and respond.\n\n<example>\nUser: \"What's the latest version of React?\"\n→ One Tavily search, read the result, respond. No plan needed.\n</example>\n\n<example>\nUser: \"Summarize the key features of Python 3.13\"\n→ write_todos with 2-3 items, search, extract, respond.\n</example>\n\n## Complex research (multi-source, comparative, deep analysis)\nThis requires the FULL research workflow described below. Use plan mode to design the research strategy, then dispatch subagents for parallel execution.\n\n<example>\nUser: \"Research the latest advances in protein folding since AlphaFold 3\"\n→ Plan mode → define research sub-topics → dispatch 4 parallel subagents → synthesize\n</example>\n\n<example>\nUser: \"Compare the top 5 AI coding assistants — features, pricing, pros/cons\"\n→ Plan mode → clarify which assistants → dispatch parallel research → comparative report\n</example>\n\n<example>\nUser: \"Analyze the impact of EU AI Act on startups\"\n→ Plan mode → break into legal, economic, technical angles → subagents → report\n</example>\n\n# Research workflow — for complex research tasks\n\nFor any research task that requires multiple sources, comparative analysis, or deep investigation, follow this workflow. Do NOT skip steps.\n\n## Step 1: PLAN with plan mode\n\nUse `task(description=\"...\", subagent_type=\"planner\")` to create a research strategy. The planner will:\n- Analyze the research question and break it into focused sub-topics\n- Ask the user clarifying questions if the topic is ambiguous:\n - \"Should I focus on recent publications, industry developments, or both?\"\n - \"Do you want a technical deep-dive or a high-level overview?\"\n - \"Any specific regions, companies, or time periods to focus on?\"\n- Save a research plan to `/plans/` with:\n - Research questions to answer\n - Sub-topics to investigate\n - Expected structure of the final report\n\n<example>\ntask(description=\"Create a research plan for: 'Latest advances in protein folding since AlphaFold 3'. Break this into 4-5 focused research sub-topics. Ask the user if they want technical depth or a general overview.\", subagent_type=\"planner\")\n</example>\n\nIf the topic is straightforward and doesn't need user clarification, you can skip the planner and go directly to Step 2 — but still create todos first.\n\n## Step 2: Create todos for progress tracking\n\nAfter the plan is ready (or if you skipped the planner), create todos. **IMPORTANT**: Always provide explicit `id` values so you can reference them later with `update_todo_status`. Use simple IDs like `\"t1\"`, `\"t2\"`, etc.\n\n```\nwrite_todos([\n {\"id\": \"t1\", \"content\": \"Research sub-topic 1: ...\", \"status\": \"pending\", \"active_form\": \"Researching sub-topic 1\"},\n {\"id\": \"t2\", \"content\": \"Research sub-topic 2: ...\", \"status\": \"pending\", \"active_form\": \"Researching sub-topic 2\"},\n {\"id\": \"t3\", \"content\": \"Research sub-topic 3: ...\", \"status\": \"pending\", \"active_form\": \"Researching sub-topic 3\"},\n {\"id\": \"t4\", \"content\": \"Research sub-topic 4: ...\", \"status\": \"pending\", \"active_form\": \"Researching sub-topic 4\"},\n {\"id\": \"t5\", \"content\": \"Synthesize findings into final report\", \"status\": \"pending\", \"active_form\": \"Synthesizing report\"}\n])\n```\n\nUse todos to track progress. Mark each as `in_progress` when you start it, and `completed` when done. Use the same IDs you provided: `update_todo_status(\"t1\", \"completed\")`. The user sees your todo list in real-time — keep it updated.\n\n## Step 3: Dispatch parallel subagents\n\nDispatch each research sub-topic as a parallel async subagent. Subagents have access to web search (Tavily, Jina), file operations, and shell execution.\n\n```\ntask(description=\"Research: [sub-topic]. Search the web using Tavily/Jina, extract key findings from 3-5 authoritative sources, and write a detailed summary with source URLs. Save your findings to /workspace/notes/[topic-slug].md\", subagent_type=\"general-purpose\", mode=\"async\")\n```\n\n- Dispatch ALL research subagents at once for maximum parallelism\n- Each subagent should save its findings to `/workspace/notes/`\n- The \"Synthesize\" todo is YOUR job — do NOT delegate it\n\n## Step 4: Wait for all results — MANDATORY\n\n**CRITICAL**: After dispatching async tasks, you MUST call `wait_tasks` to block until all subagents complete. Do NOT respond to the user while tasks are running.\n\n```\nwait_tasks(task_ids=[\"id1\", \"id2\", \"id3\", \"id4\", \"id5\"])\n```\n\nThis returns all results in one call. After `wait_tasks` returns, immediately proceed to synthesis. Do NOT write a status update — go straight to Step 5/6.\n\n## Step 5: Handle failures — NEVER stop\n\nIf subagents fail or return errors (e.g., search tools down, API errors, timeouts):\n- **Do NOT stop and ask the user what to do**\n- **Do NOT report the failure and wait** — the user wants results, not error reports\n- Instead: **pick up the failed sub-topics yourself** and complete them using your own knowledge. You have extensive training data — use it.\n- If web search is down for ALL subagents, research the entire topic yourself from your knowledge base. Produce the best report you can.\n- Clearly note in the report which sections are based on your knowledge vs. web sources.\n- The user expects a finished report — partial results or \"I can't do this\" is never acceptable.\n\n## Step 6: Write the report ITERATIVELY, chapter by chapter\n\nDo NOT try to write the entire report in a single `write_file` call. Build it incrementally:\n\n1. **Start the report** with the title and Executive Summary → `write_file(\"/workspace/report.md\", ...)`\n2. **For each section/chapter**:\n - Read the relevant subagent notes from `/workspace/notes/`\n - If the notes are thin or missing, do additional research yourself (search the web, use your knowledge) to fill in gaps\n - Write the section with full detail → `edit_file` to append to the report\n - Mark the corresponding todo as `completed`\n3. **After all sections are written**, add Conclusions and References\n\nThis approach produces a MUCH better report because:\n- Each section gets your full attention and detail\n- You can do additional targeted research per section if needed\n- You're not limited by output length — the report can be as long as it needs to be\n- The user sees progress in real-time as sections appear\n\nFor any failed/empty sub-topics, research them yourself and write those sections from your knowledge + additional web searches.\n\n## Step 7: Present\n\n- Briefly summarize the completed report for the user\n- Mention how many sections, total length, and where it's saved\n- Ask if they want deeper analysis on any section\n\n# Subagents\n\n## What subagents CAN do\n- **Web search** — Tavily, Jina, and other search MCP tools\n- **File operations** — read_file, write_file, edit_file, glob, grep\n- **Shell execution** — execute commands in the sandbox\n- **Todo management** — read_todos, write_todos\n\n## What subagents CANNOT do\n- Draw diagrams (no Excalidraw access)\n- Save to memory (no `remember` tool)\n- Create their own subagents (unless nesting is enabled)\n\n## When to use subagents\n- **Research**: Dispatch parallel subagents for each research sub-topic\n- **Code review**: Delegate to 'code-reviewer' subagent\n- Any task that benefits from parallel execution with 2+ independent work items\n\n## When NOT to use subagents\n- Drawing/diagrams (they lack Excalidraw — you must draw yourself)\n- Simple questions you can answer from your own knowledge\n- Single web searches or quick lookups\n- File operations you can do in 1-3 tool calls\n\n# Drawing with Excalidraw\n\nWhen the user asks to draw, visualize, diagram, or sketch ANYTHING:\n1. Call `excalidraw_read_diagram_guide` to learn element format\n2. Create elements using `excalidraw_batch_create_elements` (preferred)\n3. Use `excalidraw_align_elements` / `excalidraw_distribute_elements` for layout\n4. Call `excalidraw_describe_scene` to verify\n5. Describe what you drew in plain text\n\n**CRITICAL rules:**\n- NEVER use `excalidraw_create_from_mermaid` — it produces invisible results\n- NEVER delegate drawing to subagents — they cannot access Excalidraw\n- NEVER call `excalidraw_export_to_excalidraw_url` or `excalidraw_export_scene`\n- NEVER write intermediate files (Mermaid, SVG, markdown descriptions)\n\n# Report format\n\nReports must be COMPREHENSIVE and EXHAUSTIVE. There is **no length limit** — the report should be as long as the topic requires. A complex research topic may need thousands of lines. Write each section with the depth of a well-researched article.\n\n**DO NOT condense or summarize** subagent findings. Instead, EXPAND them — add context, explain the significance, make comparisons, note limitations, and connect ideas across sections.\n\nEach section should include: specific names, dates, version numbers, benchmark scores, architecture details, code examples (if relevant), comparisons between approaches, and expert opinions.\n\n```markdown\n# [Title]\n\n## Executive Summary\n[Comprehensive overview: the landscape, key findings, implications, and what's next. Multiple paragraphs.]\n\n## 1. [Section Title]\n[Deep, detailed content. Multiple paragraphs per sub-point. Specific technical details. Inline citations [1][2]. Cross-references to other sections.]\n\n### 1.1 [Sub-section if needed]\n[Even more detail on important sub-topics.]\n\n## 2. [Section Title]\n[Same depth. Don't repeat — add new information.]\n\n... [as many sections as the topic needs] ...\n\n## Conclusions and Future Outlook\n[Synthesis across all sections. Key takeaways, emerging trends, open questions.]\n\n## References\n[1] Author, \"Title\", Source, URL, Accessed: YYYY-MM-DD\n[2] ...\n```\n\n# Source guidelines\n\n- Prefer primary sources, check dates, cross-reference claims\n- Hierarchy: Academic papers > Official docs > News > Blogs > Forums\n- Always include source URLs in citations\n- Note when information may be outdated\n\n# Task management\n\nUse `write_todos` frequently to track your work. This helps you stay organized and gives the user visibility into your progress.\n\n- **Always provide explicit `id` values** when creating todos (e.g., `\"t1\"`, `\"t2\"`)\n- Always provide `active_form` (present continuous, e.g., \"Researching topic X\")\n- Create todos for any task with 3+ steps\n- Mark tasks `in_progress` BEFORE starting them: `update_todo_status(\"t1\", \"in_progress\")`\n- Mark tasks `completed` IMMEDIATELY after finishing: `update_todo_status(\"t1\", \"completed\")`\n- Have only ONE task `in_progress` at a time\n- Break complex tasks into smaller, actionable items\n\n# Workspace\n\n- Save all generated files to `/workspace/`\n- Research notes go to `/workspace/notes/`\n- Final reports go to `/workspace/report.md`\n- Uploaded files are in `/uploads/`\n- Memory is in `/workspace/MEMORY.md` (use `remember()` tool)\n\n# Resilience — NEVER stop, NEVER ask about errors\n\nYou are an AUTONOMOUS agent. The user expects you to finish every task.\n\n- **Tool failures**: If a tool fails (search API down, MCP error, timeout), retry once, then proceed WITHOUT it. Use your training knowledge as fallback. NEVER ask the user \"should I continue?\" or \"the API is down, what should I do?\" — just continue.\n- **Subagent failures**: If subagents fail, do their work yourself. Read their error messages, understand what they were supposed to research, and write those sections from your own knowledge.\n- **Partial results**: If you get some web results but not all, combine web-sourced findings with your own knowledge. Note which is which.\n- **The ONLY time to ask the user**: When you need a DECISION about the task direction (e.g., \"should the report focus on X or Y?\"). NEVER ask about technical failures.\n\n# Tone and style\n\n- Be direct and concise. Focus on facts and problem-solving.\n- Do not use emojis unless the user requests it.\n- Match the language the user writes in (e.g., Polish → respond in Polish).\n- Do not give time estimates.\n- Prioritize accuracy over pleasantries.\n\n\n## Available Tools\n\n- **Memory**: `remember(fact)` — save personal info, preferences, project details to persistent memory\n- **Web Search**: Tavily, Brave Search, Jina URL reader, Firecrawl\n- **Browser Automation**: Playwright MCP (navigate, screenshot, click, fill)\n- **File Operations**: read_file, write_file, edit_file, glob, grep, ls\n- **Code Execution**: `execute(command)` — Docker sandbox with Python 3.12\n- **Diagrams**: Excalidraw MCP — `excalidraw_read_diagram_guide`, `excalidraw_batch_create_elements`, `excalidraw_create_element` (ONLY you have these — NEVER use `excalidraw_create_from_mermaid`)\n- **Subagents**: `task(description, subagent_type)` — for complex research only, never for diagrams\n- **Teams**: `spawn_team()`, `assign_task()` — for parallel multi-agent coordination\n- **TODO**: `write_todos()`, `read_todos()`, `add_todo()`, `update_todo_status()`\n- **Checkpoints**: `save_checkpoint()`, `list_checkpoints()`, `rewind_to()`\n- **Skills**: `list_skills()`, `load_skill(name)` — domain knowledge\n- **Plan Mode**: `task(description, subagent_type=\"planner\")` for complex multi-step planning\n\n## Shell Commands\n\nYou have `execute` for shell commands. It may need user approval — just call it.\n\n## Error Handling\n\nFix errors yourself: install missing modules, fix paths, retry. Don't ask permission.\n\n## File Locations\n\n- Uploads: /uploads/\n- Workspace: /workspace/\n- Memory: /workspace/MEMORY.md (use `remember()` tool to write)\n\n## Task Management\n\nYou have access to todo tools to track your tasks:\n- `read_todos` — View current tasks with their IDs and statuses. Use this to check what's available before deciding what to work on next.\n- `write_todos` — Replace the entire todo list. Use this to initialize or restructure the full task list.\n- `add_todo` — Add a single new task without replacing existing todos. Preferred over write_todos when adding one task.\n- `update_todo_status` — Change a task's status by ID. Use when starting (→ in_progress) or finishing (→ completed) a task.\n- `update_todo_statuses` — Change several tasks' statuses in one call. Prefer this for a single workflow transition (e.g. complete the current task and start the next one) instead of chaining separate update_todo_status calls.\n- `remove_todo` — Delete a task by ID. Use for tasks that are no longer needed or were created in error.\n\n### Task Workflow\n1. Break down complex tasks into smaller, actionable steps\n2. Mark exactly one task as `in_progress` at a time\n3. Mark tasks as `completed` immediately after finishing — don't batch completions\n4. After completing a task, call `read_todos` to find the next task to work on\n5. Prefer working on tasks in order — earlier tasks often set up context for later ones\n6. If a task turns out to be impossible or irrelevant, remove it and explain why\n\n\n## Console Tools — Hashline Edit Mode\n\nYou have access to filesystem tools (ls, read_file, write_file, hashline_edit, glob, grep) and shell execution (execute). File contents use **hashline format** — each line is tagged with a content hash. Read each tool's description for detailed usage guidance.\n\n\n## Available Subagents\n\nUse the `task` tool to delegate work to these subagents:\n\n- **general-purpose**: A general-purpose agent for a wide variety of tasks.\nUse this when no specialized subagent matches the task requirements.\nCapable of research, analysis, writing, and problem-solving.\n- **planner**: Plans research strategy for complex topics. Asks clarifying questions and creates structured research plans with sub-topics. Use for any research task that needs multiple sources or comparative analysis.\n- **code-reviewer**: Reviews Python code for quality, security, and best practices. Delegate code review tasks to this subagent.\n\n## Web Tools\n\nYou have access to the web:\n- **web search** - search the internet for current information, news, docs\n- **web fetch** - fetch and read any URL as Markdown\n\nWhen the user asks you to look something up online, visit a website, or check current information - use these tools. Do NOT refuse.\n\nYou have access to a collection of skills containing domain-specific knowledge and capabilities.\nEach skill provides specialized instructions, resources, and scripts for specific tasks.\n\n<available_skills>\n<skill>\n<name>diagram-design</name>\n<description>Best practices for creating research diagrams with Excalidraw MCP tools</description>\n</skill>\n<skill>\n<name>quick-reference</name>\n<description>Quick reference card for workspace commands and shortcuts</description>\n</skill>\n<skill>\n<name>report-writing</name>\n<description>Guidelines for writing well-structured, cited research reports</description>\n</skill>\n<skill>\n<name>research-methodology</name>\n<description>Best practices for systematic research, source evaluation, and evidence gathering</description>\n</skill>\n</available_skills>\n\nWhen a task falls within a skill's domain:\n1. Use `load_skill` to read the complete skill instructions\n2. Follow the skill's guidance to complete the task\n3. Use any additional skill resources and scripts as needed\n\nUse progressive disclosure: load only what you need, when you need it.\n\n## Project Context\n\n### DEEP.md\n\n# DeepResearch Workspace\n\n## Research Workflow\n1. Save research notes to `/workspace/notes/` (one file per sub-topic)\n2. Track all sources with URLs in notes\n3. Write the final report to `/workspace/report.md`\n\n## File Organization\n- `/workspace/notes/` — Research notes per topic\n- `/workspace/report.md` — Final research report\n- `/workspace/sources.md` — Source tracking (optional)\n\n\n### MEMORY.md\n\n# Agent Memory\n\nPersistent notes and context that carry across 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execution failed due to an internal error. Please try a slightly different approach.","tool_name":"ls","tool_call_id":"call_QjQBcFi8y57XHiyAJGmGuPfc","timestamp":"2026-06-16T04:57:42.779275Z","part_kind":"retry-prompt"}],"timestamp":"2026-06-16T04:57:42.780008Z","instructions":"You are a Deep Agent — an autonomous AI assistant built on pydantic-deep (powered by Pydantic AI). You help users accomplish tasks using tools. You respond with text and tool calls. The user can see your responses and tool outputs in real time.\n\n## Core Behavior\n\n- Be concise and direct. Don't over-explain unless asked.\n- NEVER add unnecessary preamble (\"Sure!\", \"Great question!\", \"I'll now...\").\n- Don't say \"I'll now do X\" — just do it.\n- Bias towards action. Make reasonable assumptions and proceed rather than asking clarifying questions for every detail. Only ask when truly blocked or when the decision significantly affects the outcome.\n- Prioritize accuracy over validating the user's beliefs. Disagree respectfully when the user is incorrect.\n- Do not propose changes to code you haven't read. If a user asks about or wants you to modify a file, read it first.\n- Do not create files unless absolutely necessary. Prefer editing existing files to creating new ones.\n\n## Workflow\n\nWhen the user asks you to do something:\n\n1. **Research** — start by exploring the environment. Run `ls` on the working directory, use `glob` to find relevant files, `grep` to locate key symbols. Understand what exists before changing anything. If the task involves a codebase, map out the structure first.\n2. **Understand** — read the relevant files fully. Understand control flow, data flow, and existing patterns around the problem. Check tests and related modules for expected behavior.\n3. **Implement** — make targeted changes. Only modify what is necessary.\n4. **Verify** — run your code, execute tests, check output against the original requirements. Your first attempt is rarely perfect — iterate.\n5. **Retry** — if verification fails, diagnose the root cause, fix it, and re-run. Do NOT declare done with a known failure. Keep iterating until verification passes or you've exhausted all viable approaches.\n\nKeep working until the task is fully complete. Don't stop partway and explain what you would do — just do it. Only yield back to the user when the task is done or you're genuinely blocked.\n\n## Tool Usage\n\n- Use specialized tools over shell equivalents when available (e.g., `read_file` over `cat`, `edit_file` over `sed`, `glob` over `find`).\n- When performing multiple independent operations, make all tool calls in a single response — don't make sequential calls when parallel is possible.\n- Read files before editing them — understand existing content before changes.\n- Mimic existing code style, naming conventions, and patterns.\n\n## File Reading\n\nWhen reading multiple files or exploring large files, use pagination:\n- Start with `read_file(path, limit=100)` to scan structure.\n- Read targeted sections with offset/limit.\n- Only read full files when necessary for editing.\n\n## Subagent Delegation\n\n- Delegate specialized or independent subtasks to subagents to work in parallel.\n- Be specific in task descriptions — subagents don't have your full context.\n- Synthesize subagent results before presenting to the user.\n\n## Forking\n\nWhen you call `fork_run` to explore alternative approaches in parallel:\n\n1. After `fork_run` returns, branches keep running in the background. Poll their status with `inspect_branches` — NOT `wait_tasks`, which handles subagent task ids, not branch ids.\n2. Wait until every branch reaches a terminal status (`done`, `failed`, `terminated`, `budget_exhausted`, `aggregate_budget_exhausted`). Give branches time — they may take dozens of seconds, especially when running real tools.\n3. Resolve the fork with `merge_or_select(action=\"pick:<id>\")` before yielding back to the user. An unresolved fork leaves branches running and the user without a winner.\n\nInspect per-branch costs with `fork_cost` and per-path differences with `diff_branches` to inform the pick.\n\n## Code Quality\n\n- Don't add features, refactor code, or make \"improvements\" beyond what was asked. A bug fix doesn't need surrounding code cleaned up.\n- Don't add error handling, fallbacks, or validation for scenarios that can't happen. Only validate at system boundaries (user input, external APIs).\n- Don't create helpers or abstractions for one-time operations. Three similar lines of code is better than a premature abstraction.\n- Be careful not to introduce security vulnerabilities (command injection, XSS, SQL injection). If you notice insecure code, fix it immediately.\n\n## Error Handling\n\n- If something fails, diagnose *why* before switching tactics — read the FULL error output, check assumptions, try a focused fix.\n- Don't retry the identical action blindly, but don't abandon a viable approach after a single failure either.\n- NEVER declare done if your last test, build, or verification failed. Fix the issue and re-run. Repeat until it passes.\n- If a dependency is missing, install it and retry. If a command fails, read the error and fix the root cause — don't add random flags hoping it works.\n- If blocked after 3+ different approaches, explain what you tried and ask for guidance.\n\n## Output\n\n- Be concise. Lead with the answer, not the reasoning.\n- When referencing code, include `file_path:line_number`.\n- For longer tasks, give brief progress updates at milestones.\n\n\nYou are DeepResearch, an autonomous research agent. You help users by researching topics on the web, analyzing information, writing reports, drawing diagrams, and managing files in a sandboxed workspace.\n\n# Memory\n\n**You have NO long-term memory.** When this conversation ends, you forget everything. The `remember` tool is your ONLY way to persist information across sessions.\n\n## Before every response, check for personal info\n\nScan the user's message. Does it contain ANY personal fact — their name, job, company, location, preference, project detail, or anything about themselves?\n\n- \"mam na imie Kacper\" → `remember(\"User's name is Kacper\")`\n- \"I work at Acme\" → `remember(\"User works at Acme\")`\n- \"use Polish\" → `remember(\"User prefers Polish language\")`\n- \"zapamiętaj X\" / \"remember X\" → `remember(\"X\")`\n\nIf YES → call `remember` BEFORE writing any text response. Then continue normally.\n\n# Routing — how to handle each type of request\n\n## Greeting / simple chat\nReply directly in 1-2 sentences. Do NOT use any tools.\n\n## Simple factual question (you know the answer)\nReply directly with your knowledge. Do NOT search the web or create subagents.\n\n## Simple file operation (\"create X\", \"read Y\")\nUse `write_file`, `read_file`, `edit_file` directly. Do NOT create subagents.\n\n## Draw / visualize / diagram\nCall Excalidraw MCP tools YOURSELF. See the Drawing section below.\n\n## Quick research (single topic, 1-3 sources needed)\nSearch the web yourself using Tavily/Jina. Create a `write_todos` list to track progress. No subagents needed — just search, read, and respond.\n\n<example>\nUser: \"What's the latest version of React?\"\n→ One Tavily search, read the result, respond. No plan needed.\n</example>\n\n<example>\nUser: \"Summarize the key features of Python 3.13\"\n→ write_todos with 2-3 items, search, extract, respond.\n</example>\n\n## Complex research (multi-source, comparative, deep analysis)\nThis requires the FULL research workflow described below. Use plan mode to design the research strategy, then dispatch subagents for parallel execution.\n\n<example>\nUser: \"Research the latest advances in protein folding since AlphaFold 3\"\n→ Plan mode → define research sub-topics → dispatch 4 parallel subagents → synthesize\n</example>\n\n<example>\nUser: \"Compare the top 5 AI coding assistants — features, pricing, pros/cons\"\n→ Plan mode → clarify which assistants → dispatch parallel research → comparative report\n</example>\n\n<example>\nUser: \"Analyze the impact of EU AI Act on startups\"\n→ Plan mode → break into legal, economic, technical angles → subagents → report\n</example>\n\n# Research workflow — for complex research tasks\n\nFor any research task that requires multiple sources, comparative analysis, or deep investigation, follow this workflow. Do NOT skip steps.\n\n## Step 1: PLAN with plan mode\n\nUse `task(description=\"...\", subagent_type=\"planner\")` to create a research strategy. The planner will:\n- Analyze the research question and break it into focused sub-topics\n- Ask the user clarifying questions if the topic is ambiguous:\n - \"Should I focus on recent publications, industry developments, or both?\"\n - \"Do you want a technical deep-dive or a high-level overview?\"\n - \"Any specific regions, companies, or time periods to focus on?\"\n- Save a research plan to `/plans/` with:\n - Research questions to answer\n - Sub-topics to investigate\n - Expected structure of the final report\n\n<example>\ntask(description=\"Create a research plan for: 'Latest advances in protein folding since AlphaFold 3'. Break this into 4-5 focused research sub-topics. Ask the user if they want technical depth or a general overview.\", subagent_type=\"planner\")\n</example>\n\nIf the topic is straightforward and doesn't need user clarification, you can skip the planner and go directly to Step 2 — but still create todos first.\n\n## Step 2: Create todos for progress tracking\n\nAfter the plan is ready (or if you skipped the planner), create todos. **IMPORTANT**: Always provide explicit `id` values so you can reference them later with `update_todo_status`. Use simple IDs like `\"t1\"`, `\"t2\"`, etc.\n\n```\nwrite_todos([\n {\"id\": \"t1\", \"content\": \"Research sub-topic 1: ...\", \"status\": \"pending\", \"active_form\": \"Researching sub-topic 1\"},\n {\"id\": \"t2\", \"content\": \"Research sub-topic 2: ...\", \"status\": \"pending\", \"active_form\": \"Researching sub-topic 2\"},\n {\"id\": \"t3\", \"content\": \"Research sub-topic 3: ...\", \"status\": \"pending\", \"active_form\": \"Researching sub-topic 3\"},\n {\"id\": \"t4\", \"content\": \"Research sub-topic 4: ...\", \"status\": \"pending\", \"active_form\": \"Researching sub-topic 4\"},\n {\"id\": \"t5\", \"content\": \"Synthesize findings into final report\", \"status\": \"pending\", \"active_form\": \"Synthesizing report\"}\n])\n```\n\nUse todos to track progress. Mark each as `in_progress` when you start it, and `completed` when done. Use the same IDs you provided: `update_todo_status(\"t1\", \"completed\")`. The user sees your todo list in real-time — keep it updated.\n\n## Step 3: Dispatch parallel subagents\n\nDispatch each research sub-topic as a parallel async subagent. Subagents have access to web search (Tavily, Jina), file operations, and shell execution.\n\n```\ntask(description=\"Research: [sub-topic]. Search the web using Tavily/Jina, extract key findings from 3-5 authoritative sources, and write a detailed summary with source URLs. Save your findings to /workspace/notes/[topic-slug].md\", subagent_type=\"general-purpose\", mode=\"async\")\n```\n\n- Dispatch ALL research subagents at once for maximum parallelism\n- Each subagent should save its findings to `/workspace/notes/`\n- The \"Synthesize\" todo is YOUR job — do NOT delegate it\n\n## Step 4: Wait for all results — MANDATORY\n\n**CRITICAL**: After dispatching async tasks, you MUST call `wait_tasks` to block until all subagents complete. Do NOT respond to the user while tasks are running.\n\n```\nwait_tasks(task_ids=[\"id1\", \"id2\", \"id3\", \"id4\", \"id5\"])\n```\n\nThis returns all results in one call. After `wait_tasks` returns, immediately proceed to synthesis. Do NOT write a status update — go straight to Step 5/6.\n\n## Step 5: Handle failures — NEVER stop\n\nIf subagents fail or return errors (e.g., search tools down, API errors, timeouts):\n- **Do NOT stop and ask the user what to do**\n- **Do NOT report the failure and wait** — the user wants results, not error reports\n- Instead: **pick up the failed sub-topics yourself** and complete them using your own knowledge. You have extensive training data — use it.\n- If web search is down for ALL subagents, research the entire topic yourself from your knowledge base. Produce the best report you can.\n- Clearly note in the report which sections are based on your knowledge vs. web sources.\n- The user expects a finished report — partial results or \"I can't do this\" is never acceptable.\n\n## Step 6: Write the report ITERATIVELY, chapter by chapter\n\nDo NOT try to write the entire report in a single `write_file` call. Build it incrementally:\n\n1. **Start the report** with the title and Executive Summary → `write_file(\"/workspace/report.md\", ...)`\n2. **For each section/chapter**:\n - Read the relevant subagent notes from `/workspace/notes/`\n - If the notes are thin or missing, do additional research yourself (search the web, use your knowledge) to fill in gaps\n - Write the section with full detail → `edit_file` to append to the report\n - Mark the corresponding todo as `completed`\n3. **After all sections are written**, add Conclusions and References\n\nThis approach produces a MUCH better report because:\n- Each section gets your full attention and detail\n- You can do additional targeted research per section if needed\n- You're not limited by output length — the report can be as long as it needs to be\n- The user sees progress in real-time as sections appear\n\nFor any failed/empty sub-topics, research them yourself and write those sections from your knowledge + additional web searches.\n\n## Step 7: Present\n\n- Briefly summarize the completed report for the user\n- Mention how many sections, total length, and where it's saved\n- Ask if they want deeper analysis on any section\n\n# Subagents\n\n## What subagents CAN do\n- **Web search** — Tavily, Jina, and other search MCP tools\n- **File operations** — read_file, write_file, edit_file, glob, grep\n- **Shell execution** — execute commands in the sandbox\n- **Todo management** — read_todos, write_todos\n\n## What subagents CANNOT do\n- Draw diagrams (no Excalidraw access)\n- Save to memory (no `remember` tool)\n- Create their own subagents (unless nesting is enabled)\n\n## When to use subagents\n- **Research**: Dispatch parallel subagents for each research sub-topic\n- **Code review**: Delegate to 'code-reviewer' subagent\n- Any task that benefits from parallel execution with 2+ independent work items\n\n## When NOT to use subagents\n- Drawing/diagrams (they lack Excalidraw — you must draw yourself)\n- Simple questions you can answer from your own knowledge\n- Single web searches or quick lookups\n- File operations you can do in 1-3 tool calls\n\n# Drawing with Excalidraw\n\nWhen the user asks to draw, visualize, diagram, or sketch ANYTHING:\n1. Call `excalidraw_read_diagram_guide` to learn element format\n2. Create elements using `excalidraw_batch_create_elements` (preferred)\n3. Use `excalidraw_align_elements` / `excalidraw_distribute_elements` for layout\n4. Call `excalidraw_describe_scene` to verify\n5. Describe what you drew in plain text\n\n**CRITICAL rules:**\n- NEVER use `excalidraw_create_from_mermaid` — it produces invisible results\n- NEVER delegate drawing to subagents — they cannot access Excalidraw\n- NEVER call `excalidraw_export_to_excalidraw_url` or `excalidraw_export_scene`\n- NEVER write intermediate files (Mermaid, SVG, markdown descriptions)\n\n# Report format\n\nReports must be COMPREHENSIVE and EXHAUSTIVE. There is **no length limit** — the report should be as long as the topic requires. A complex research topic may need thousands of lines. Write each section with the depth of a well-researched article.\n\n**DO NOT condense or summarize** subagent findings. Instead, EXPAND them — add context, explain the significance, make comparisons, note limitations, and connect ideas across sections.\n\nEach section should include: specific names, dates, version numbers, benchmark scores, architecture details, code examples (if relevant), comparisons between approaches, and expert opinions.\n\n```markdown\n# [Title]\n\n## Executive Summary\n[Comprehensive overview: the landscape, key findings, implications, and what's next. Multiple paragraphs.]\n\n## 1. [Section Title]\n[Deep, detailed content. Multiple paragraphs per sub-point. Specific technical details. Inline citations [1][2]. Cross-references to other sections.]\n\n### 1.1 [Sub-section if needed]\n[Even more detail on important sub-topics.]\n\n## 2. [Section Title]\n[Same depth. Don't repeat — add new information.]\n\n... [as many sections as the topic needs] ...\n\n## Conclusions and Future Outlook\n[Synthesis across all sections. Key takeaways, emerging trends, open questions.]\n\n## References\n[1] Author, \"Title\", Source, URL, Accessed: YYYY-MM-DD\n[2] ...\n```\n\n# Source guidelines\n\n- Prefer primary sources, check dates, cross-reference claims\n- Hierarchy: Academic papers > Official docs > News > Blogs > Forums\n- Always include source URLs in citations\n- Note when information may be outdated\n\n# Task management\n\nUse `write_todos` frequently to track your work. This helps you stay organized and gives the user visibility into your progress.\n\n- **Always provide explicit `id` values** when creating todos (e.g., `\"t1\"`, `\"t2\"`)\n- Always provide `active_form` (present continuous, e.g., \"Researching topic X\")\n- Create todos for any task with 3+ steps\n- Mark tasks `in_progress` BEFORE starting them: `update_todo_status(\"t1\", \"in_progress\")`\n- Mark tasks `completed` IMMEDIATELY after finishing: `update_todo_status(\"t1\", \"completed\")`\n- Have only ONE task `in_progress` at a time\n- Break complex tasks into smaller, actionable items\n\n# Workspace\n\n- Save all generated files to `/workspace/`\n- Research notes go to `/workspace/notes/`\n- Final reports go to `/workspace/report.md`\n- Uploaded files are in `/uploads/`\n- Memory is in `/workspace/MEMORY.md` (use `remember()` tool)\n\n# Resilience — NEVER stop, NEVER ask about errors\n\nYou are an AUTONOMOUS agent. The user expects you to finish every task.\n\n- **Tool failures**: If a tool fails (search API down, MCP error, timeout), retry once, then proceed WITHOUT it. Use your training knowledge as fallback. NEVER ask the user \"should I continue?\" or \"the API is down, what should I do?\" — just continue.\n- **Subagent failures**: If subagents fail, do their work yourself. Read their error messages, understand what they were supposed to research, and write those sections from your own knowledge.\n- **Partial results**: If you get some web results but not all, combine web-sourced findings with your own knowledge. Note which is which.\n- **The ONLY time to ask the user**: When you need a DECISION about the task direction (e.g., \"should the report focus on X or Y?\"). NEVER ask about technical failures.\n\n# Tone and style\n\n- Be direct and concise. Focus on facts and problem-solving.\n- Do not use emojis unless the user requests it.\n- Match the language the user writes in (e.g., Polish → respond in Polish).\n- Do not give time estimates.\n- Prioritize accuracy over pleasantries.\n\n\n## Available Tools\n\n- **Memory**: `remember(fact)` — save personal info, preferences, project details to persistent memory\n- **Web Search**: Tavily, Brave Search, Jina URL reader, Firecrawl\n- **Browser Automation**: Playwright MCP (navigate, screenshot, click, fill)\n- **File Operations**: read_file, write_file, edit_file, glob, grep, ls\n- **Code Execution**: `execute(command)` — Docker sandbox with Python 3.12\n- **Diagrams**: Excalidraw MCP — `excalidraw_read_diagram_guide`, `excalidraw_batch_create_elements`, `excalidraw_create_element` (ONLY you have these — NEVER use `excalidraw_create_from_mermaid`)\n- **Subagents**: `task(description, subagent_type)` — for complex research only, never for diagrams\n- **Teams**: `spawn_team()`, `assign_task()` — for parallel multi-agent coordination\n- **TODO**: `write_todos()`, `read_todos()`, `add_todo()`, `update_todo_status()`\n- **Checkpoints**: `save_checkpoint()`, `list_checkpoints()`, `rewind_to()`\n- **Skills**: `list_skills()`, `load_skill(name)` — domain knowledge\n- **Plan Mode**: `task(description, subagent_type=\"planner\")` for complex multi-step planning\n\n## Shell Commands\n\nYou have `execute` for shell commands. It may need user approval — just call it.\n\n## Error Handling\n\nFix errors yourself: install missing modules, fix paths, retry. Don't ask permission.\n\n## File Locations\n\n- Uploads: /uploads/\n- Workspace: /workspace/\n- Memory: /workspace/MEMORY.md (use `remember()` tool to write)\n\n## Task Management\n\nYou have access to todo tools to track your tasks:\n- `read_todos` — View current tasks with their IDs and statuses. Use this to check what's available before deciding what to work on next.\n- `write_todos` — Replace the entire todo list. Use this to initialize or restructure the full task list.\n- `add_todo` — Add a single new task without replacing existing todos. Preferred over write_todos when adding one task.\n- `update_todo_status` — Change a task's status by ID. Use when starting (→ in_progress) or finishing (→ completed) a task.\n- `update_todo_statuses` — Change several tasks' statuses in one call. Prefer this for a single workflow transition (e.g. complete the current task and start the next one) instead of chaining separate update_todo_status calls.\n- `remove_todo` — Delete a task by ID. Use for tasks that are no longer needed or were created in error.\n\n### Task Workflow\n1. Break down complex tasks into smaller, actionable steps\n2. Mark exactly one task as `in_progress` at a time\n3. Mark tasks as `completed` immediately after finishing — don't batch completions\n4. After completing a task, call `read_todos` to find the next task to work on\n5. Prefer working on tasks in order — earlier tasks often set up context for later ones\n6. If a task turns out to be impossible or irrelevant, remove it and explain why\n\n\n## Console Tools — Hashline Edit Mode\n\nYou have access to filesystem tools (ls, read_file, write_file, hashline_edit, glob, grep) and shell execution (execute). File contents use **hashline format** — each line is tagged with a content hash. Read each tool's description for detailed usage guidance.\n\n\n## Available Subagents\n\nUse the `task` tool to delegate work to these subagents:\n\n- **general-purpose**: A general-purpose agent for a wide variety of tasks.\nUse this when no specialized subagent matches the task requirements.\nCapable of research, analysis, writing, and problem-solving.\n- **planner**: Plans research strategy for complex topics. Asks clarifying questions and creates structured research plans with sub-topics. Use for any research task that needs multiple sources or comparative analysis.\n- **code-reviewer**: Reviews Python code for quality, security, and best practices. Delegate code review tasks to this subagent.\n\n## Web Tools\n\nYou have access to the web:\n- **web search** - search the internet for current information, news, docs\n- **web fetch** - fetch and read any URL as Markdown\n\nWhen the user asks you to look something up online, visit a website, or check current information - use these tools. Do NOT refuse.\n\nYou have access to a collection of skills containing domain-specific knowledge and capabilities.\nEach skill provides specialized instructions, resources, and scripts for specific tasks.\n\n<available_skills>\n<skill>\n<name>diagram-design</name>\n<description>Best practices for creating research diagrams with Excalidraw MCP tools</description>\n</skill>\n<skill>\n<name>quick-reference</name>\n<description>Quick reference card for workspace commands and shortcuts</description>\n</skill>\n<skill>\n<name>report-writing</name>\n<description>Guidelines for writing well-structured, cited research reports</description>\n</skill>\n<skill>\n<name>research-methodology</name>\n<description>Best practices for systematic research, source evaluation, and evidence gathering</description>\n</skill>\n</available_skills>\n\nWhen a task falls within a skill's domain:\n1. Use `load_skill` to read the complete skill instructions\n2. Follow the skill's guidance to complete the task\n3. Use any additional skill resources and scripts as needed\n\nUse progressive disclosure: load only what you need, when you need it.\n\n## Project Context\n\n### DEEP.md\n\n# DeepResearch Workspace\n\n## Research Workflow\n1. Save research notes to `/workspace/notes/` (one file per sub-topic)\n2. Track all sources with URLs in notes\n3. Write the final report to `/workspace/report.md`\n\n## File Organization\n- `/workspace/notes/` — Research notes per topic\n- `/workspace/report.md` — Final research report\n- `/workspace/sources.md` — Source tracking (optional)\n\n\n### MEMORY.md\n\n# Agent Memory\n\nPersistent notes and context that carry across 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Research brief: agentic AI “breakthroughs” as of June 16, 2026\n\n**Short answer:** the breakthrough is not one single agent. It is the convergence of **reasoning models + tool use + computer/browser control + long-context memory + multi-agent orchestration + better evaluation**. Agents are moving from “chatbots that answer” toward systems that can **plan, use tools, browse, write code, operate GUIs, check their own work, and complete multi-step workflows**.\n\n## 1. What changed: from LLMs to agents\n\nEarly LLMs generated text. Agentic systems add a loop:\n\n1. understand goal \n2. plan next step \n3. call a tool / browse / run code / edit files / click UI \n4. observe result \n5. revise plan \n6. repeat until task completion\n\nTwo foundational research steps were **ReAct** and **Toolformer**. ReAct showed that interleaving reasoning traces with actions helps models update plans, use external information, and handle exceptions; Toolformer showed that models can learn when and how to call external APIs such as search, calculators, QA systems, translation, and calendars. ([arxiv.org](https://arxiv.org/abs/2210.03629))\n\n## 2. Key breakthrough categories\n\n### A. Tool-using reasoning agents\n\nThe first major agentic breakthrough was the realization that an LLM becomes much more useful when it can **act through tools** instead of only predicting text. This includes search, calculators, code execution, file search, databases, CRMs, browsers, calendars, and business APIs. OpenAI’s March 2025 Responses API and Agents SDK formalized this into a developer platform with built-in web search, file search, computer use, single/multi-agent orchestration, handoffs, guardrails, and tracing. ([openai.com](https://openai.com/index/new-tools-for-building-agents/))\n\n**Why it matters:** tool use lets agents ground answers in live data, perform actions, verify outputs, and chain operations across systems.\n\n### B. Computer-use / GUI agents\n\nA major practical leap was letting agents use software the way humans do: screenshots, clicks, typing, scrolling, and form interaction. Anthropic’s October 2024 Claude computer-use beta let Claude perceive and interact with computer interfaces; Anthropic reported Claude 3.5 Sonnet scoring 14.9% on OSWorld screenshot-only and 22.0% with more steps, while warning that the capability was still imperfect and should begin with low-risk tasks. ([anthropic.com](https://www.anthropic.com/news/3-5-models-and-computer-use))\n\nOpenAI followed with **Operator** in January 2025, powered by a Computer-Using Agent model that combines GPT-4o vision with reasoning through reinforcement learning; Operator could use a browser by clicking, typing, scrolling, self-correcting, and handing control back to the user when needed. ([openai.com](https://openai.com/index/introducing-operator/)) Google’s Gemini 2.0 push similarly framed Gemini as built for the “agentic era,” with Project Mariner using browser pixels and web elements to complete tasks, achieving 83.5% on WebVoyager in a single-agent setup while Google noted it was still early and sometimes slow or inaccurate. ([blog.google](https://blog.google/innovation-and-ai/models-and-research/google-deepmind/google-gemini-ai-update-december-2024/))\n\n**Why it matters:** GUI agents can automate software that has no API, including legacy enterprise systems.\n\n### C. Research agents\n\nOpenAI’s **deep research** launched in February 2025 as an agentic capability for multi-step web research. It searches, reads, analyzes, and synthesizes many sources into cited reports, using reasoning plus browsing/data-analysis tools. OpenAI described it as doing in tens of minutes work that might take humans many hours. ([openai.com](https://openai.com/index/introducing-deep-research/))\n\n**Why it matters:** this is one of the clearest production examples of an agent doing a complete knowledge-work workflow: scoping, searching, reading, filtering, synthesizing, citing, and reporting.\n\n### D. Unified “think + act” agents\n\nThe next step was combining research, browser action, code execution, files, and connectors into one system. OpenAI’s July 2025 **ChatGPT agent** unified Operator-style website interaction, deep research-style synthesis, and ChatGPT conversational intelligence. It can use a virtual computer, switch among visual browser, text browser, terminal, and APIs, and complete workflows like competitor analysis, calendar briefings, spreadsheet updates, and slide generation. ([openai.com](https://openai.com/index/introducing-chatgpt-agent/))\n\n**Why it matters:** this is the shift from specialized agents to **general workflow agents**.\n\n### E. Coding agents\n\nSoftware engineering became the clearest benchmark domain for agents because coding tasks have explicit tools: repositories, tests, terminals, diffs, and issue descriptions. SWE-agent, published at NeurIPS 2024, showed that agent-computer interface design significantly improves an agent’s ability to edit files, navigate repositories, run tests, and solve software tasks; it achieved 12.5% pass@1 on SWE-bench and 87.7% on HumanEvalFix at the time. ([papers.nips.cc](https://papers.nips.cc/paper_files/paper/2024/hash/5a7c947568c1b1328ccc5230172e1e7c-Abstract-Conference.html))\n\n**Why it matters:** coding agents are not just “code generators”; they are iterative repair systems that inspect projects, run tests, patch code, and retry.\n\n### F. Lifelong-learning / memory agents\n\nVoyager, a 2023 Minecraft agent, demonstrated another important pattern: agents can build a reusable skill library. It used an automatic curriculum, executable-code skill memory, and iterative prompting with environment feedback and self-verification. Voyager achieved 3.3× more unique items, 2.3× longer travel distance, and up to 15.3× faster tech-tree milestones than prior state of the art in that setting. ([arxiv.org](https://arxiv.org/abs/2305.16291))\n\n**Why it matters:** persistent skill memory is a path toward agents that improve over time instead of starting from scratch every task.\n\n## 3. The real breakthrough: agent scaffolding, not just bigger models\n\nThe main insight is that agent performance depends on the whole system:\n\n- model reasoning quality \n- tool-call reliability \n- context window and memory \n- environment access \n- planning loop \n- self-checking / verification \n- rollback and retry \n- UI/API permissions \n- observability and tracing \n- safety guardrails \n- benchmark design \n\nThis is why the same model can perform very differently depending on its agent scaffold. OpenAI’s Agents SDK reflects this shift by making handoffs, guardrails, tracing, and multi-agent orchestration first-class parts of agent development. ([openai.com](https://openai.com/index/new-tools-for-building-agents/))\n\n## 4. Benchmarks show progress, but also measurement problems\n\nMETR proposed measuring agent capability by the length of tasks agents can complete autonomously. Its March 2025 work estimated that the task length frontier for generalist agents had been doubling roughly every seven months over the prior six years, while also emphasizing that current agents still could not reliably substitute for humans on many substantive projects. ([metr.org](https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/))\n\nSWE-bench and SWE-bench Verified became popular coding-agent benchmarks, but by 2026 OpenAI argued SWE-bench Verified no longer measured frontier coding capability well because public benchmark exposure can cause contamination and because automated tests can be flawed or overly narrow. OpenAI recommended using SWE-bench Pro and more privately authored, expert-graded benchmarks instead. ([openai.com](https://openai.com/index/why-we-no-longer-evaluate-swe-bench-verified/))\n\n**Takeaway:** agent benchmarks are improving, but headline scores can overstate real-world reliability.\n\n## 5. Current limitations\n\nAgentic AI is impressive but still brittle. The main blockers are:\n\n- **Long-horizon reliability:** agents often fail after many steps due to compounding errors.\n- **Prompt injection:** web pages, emails, documents, or UI text can try to manipulate the agent.\n- **Weak judgment around irreversible actions:** purchases, emails, financial moves, and account changes need human confirmation.\n- **Evaluation contamination:** public benchmarks may leak into training data.\n- **Tool fragility:** websites change, APIs fail, UI elements move, and agents can misclick.\n- **Cost and latency:** multi-step agent runs can be slow and expensive.\n- **Verification gaps:** agents may appear successful without truly satisfying the task.\n\nGoogle, Anthropic, and OpenAI all explicitly emphasize human supervision, safety mitigations, and staged rollout for computer-use and browser-use agents. ([anthropic.com](https://www.anthropic.com/news/3-5-models-and-computer-use))\n\n## 6. Bottom line\n\nThe “agentic agent breakthrough” is best understood as a **systems breakthrough**: LLMs are becoming controllers for tools, browsers, code environments, files, and workflows. The strongest evidence is in:\n\n1. **research agents** that browse and synthesize sources, \n2. **computer-use agents** that operate GUIs, \n3. **coding agents** that edit repositories and run tests, \n4. **unified agents** that combine browsing, code, files, APIs, and user collaboration, \n5. **new evaluation methods** focused on long-horizon autonomous task completion.\n\nThe field is moving fast, but the frontier is still **supervised autonomy**, not fully reliable independence.","id":"msg_09561ebab73884f5006a30d7f17878819a9bdb003afabce755","provider_name":"openai","provider_details":{"phase":"final_answer"},"part_kind":"text"}],"usage":{"input_tokens":104489,"cache_write_tokens":0,"cache_read_tokens":17280,"output_tokens":3742,"input_audio_tokens":0,"cache_audio_read_tokens":0,"output_audio_tokens":0,"details":{"reasoning_tokens":1746}},"model_name":"gpt-5.5-2026-04-23","timestamp":"2026-06-16T04:57:44.801034Z","kind":"response","provider_name":"openai","provider_url":"https://api.openai.com/v1/","provider_details":{"timestamp":"2026-06-16T04:57:43Z","finish_reason":"completed"},"provider_response_id":"resp_09561ebab73884f5006a30d7c71650819aab2bc629b0016a65","finish_reason":"stop","run_id":"019ececa-16c8-7648-8dd6-48a83a661579","conversation_id":"019ececa-16c8-7648-8dd6-48a78bbbcae3","metadata":null,"state":"complete"}] |