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  "overview": "From 2025-08-16 to 2025-08-17, elizaos/eliza had 0 new PRs (0 merged), 12 new issues, and 3 active contributors.",
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    {
      "id": "I_kwDOMT5cIs7GTMUc",
      "title": "Implement Dynamic Report Rendering",
      "author": "linear",
      "number": 5789,
      "repository": "elizaos/eliza",
      "body": "### **Ticket: Implement Dynamic Report Rendering**\n\n**ID:** `FEAT-133` (Example ID)\n\n**Epic:** `Performance Reporting Dashboard`\n\n**Tags:** `cli`, `reporting`, `feature`, `javascript`, `visualization`\n\n**Estimated Story Points:** `8`\n\n**Dependencies:** `FEAT-131` (Data Aggregation), `FEAT-132` (HTML Template)\n\n#### **1. Title**\n\n`feat(cli): Implement dynamic data injection and rendering for the HTML Performance Report`\n\n#### **2. Description**\n\nThis ticket brings the Performance Report to life. It involves creating the logic to take the aggregated `report.json` data and inject it into the static `report_template.html`, producing a final, fully-rendered, and interactive HTML file.\n\nThe work will primarily be in modifying the `elizaos report generate` command to read both the data and the template, inject the data, and write the final output. This includes writing the client-side JavaScript (embedded within the template) that will be responsible for all DOM manipulation, chart generation, and rendering of dynamic tables and lists.\n\n#### **3. Acceptance Criteria**\n\n1. **File I/O in** `generate` command:\n   * The `elizaos report generate` command is updated to:\n     * Read the aggregated `report.json` file into memory.\n     * Read the static `report_template.html` file into memory.\n     * Inject the entire JSON data object into the `<script id=\"report-data\">` tag within the HTML.\n     * Save the resulting string as the final report file (e.g., `performance_report.html`).\n2. **Client-Side Rendering Logic:**\n   * The JavaScript embedded in the `report_template.html` is implemented.\n   * On `DOMContentLoaded`, the script must:\n     * Parse the JSON from the `<script id=\"report-data\">` tag.\n     * Call a main `renderReport(data)` function with the parsed data.\n3. **DOM Population:**\n   * The `renderReport` function correctly populates all the simple data placeholders defined in the template (e.g., setting the `innerText` of `<span id=\"summary-total-runs\">`).\n4. **Chart Generation:**\n   * The script uses the embedded Chart.js library to render meaningful visualizations:\n     * A **Bar Chart** for \"Capability Success Rates,\" showing the success percentage for each capability.\n     * A **Grouped Bar Chart** for \"Results by Parameter,\" allowing for easy comparison of key metrics (like success rate and average execution time) across different parameter values (e.g., comparing `gpt-4` vs. `gpt-3.5`).\n   * Charts must be correctly labeled, include tooltips, and use colors that are consistent with the report's design.\n5. **Dynamic Table Rendering:**\n   * The \"Detailed Run Explorer\" table is dynamically populated by creating `<tr>` elements for each run in the `raw_results` array and appending them to the `<tbody id=\"detailed-runs-tbody\">`.\n   * The table should be searchable and sortable (using a lightweight, embedded library or custom JavaScript).\n6. **Trajectory Visualization:**\n   * The \"Common Action Trajectories\" section is populated.\n   * This includes logic to render the aggregated trajectory data, potentially as a simple ranked list or as a more advanced visual element like a Sankey diagram (using a library like D3 or Google Charts, if feasible to embed).\n\n#### **4. Technical Implementation Details**\n\n**Files to Modify:**\n\n* `packages/cli/src/commands/report/generate.ts`: Add the file I/O and data injection logic.\n* `packages/cli/src/commands/report/src/assets/report_template.html`: Implement the client-side JavaScript rendering logic within the `<script>` tags.\n\n**Example Client-Side JavaScript Snippet:**\n\n```html\n<script>\n  document.addEventListener('DOMContentLoaded', () => {\n    const dataElement = document.getElementById('report-data');\n    if (dataElement) {\n      const reportData = JSON.parse(dataElement.textContent);\n      renderReport(reportData);\n    }\n  });\n  function renderReport(data) {\n    // 1. Populate simple values\n    document.getElementById('summary-total-runs').innerText = data.summary_stats.total_runs;\n    \n    // 2. Render Capability Chart\n    const ctx = document.getElementById('capability-chart').getContext('2d');\n    new Chart(ctx, {\n      type: 'bar',\n      data: {\n        labels: Object.keys(data.summary_stats.capability_success_rates),\n        datasets: [{\n          label: 'Success Rate',\n          data: Object.values(data.summary_stats.capability_success_rates).map(d => d * 100),\n          // ...chart options\n        }]\n      }\n    });\n    // 3. Render Detailed Runs Table\n    const tbody = document.getElementById('detailed-runs-tbody');\n    data.raw_results.forEach(run => {\n      const row = document.createElement('tr');\n      // ...create and append cells for the run data\n      tbody.appendChild(row);\n    });\n  }\n</script>\n```\n\n#### **5. Testing Requirements**\n\n* **Integration Tests:**\n  * Update the integration test for the `generate` command. After the command runs, the test should:\n    * Read the output `performance_report.html`.\n    * Use a tool like `jsdom` to parse the HTML and verify that the data has been correctly injected into the `<script id=\"report-data\">` tag.\n* **Manual End-to-End Testing:**\n  * Run the command on a sample `report.json` file.\n  * Open the resulting HTML file in a browser.\n  * Verify that all charts render correctly, all data points are populated, the table is functional, and there are no console errors.\n\n#### **6. Out of Scope**\n\n* The implementation of the optional PDF export feature. This ticket is only concerned with generating the final HTML file.\n* The data aggregation itself; this ticket assumes the `report.json` file is already correctly structured and complete.",
      "createdAt": "2025-08-16T05:52:36Z",
      "closedAt": "2025-08-18T05:15:37Z",
      "state": "CLOSED",
      "commentCount": 0
    },
    {
      "id": "I_kwDOMT5cIs7GTMCq",
      "title": "Design & Build HTML Report Template",
      "author": "linear",
      "number": 5788,
      "repository": "elizaos/eliza",
      "body": "### **Ticket: Design & Build HTML Report Template**\n\n**ID:** `FEAT-132` (Example ID)\n\n**Epic:** `Performance Reporting Dashboard`\n\n**Tags:** `ui`, `reporting`, `feature`, `html`, `css`\n\n**Estimated Story Points:** `5`\n\n**Dependencies:** `FEAT-131` (`report.json` data structure)\n\n#### **1. Title**\n\n`feat(cli): Design and build a static, self-contained HTML template for the Performance Report`\n\n#### **2. Description**\n\nThis ticket focuses on the user interface and design of the performance report. The goal is to create a professional, clean, and data-rich HTML file that will serve as the template for our reporting system. This template will be a static asset, later populated with dynamic data by the report generation logic.\n\nThe design should prioritize clarity and ease of navigation, allowing a developer to quickly understand the high-level results of a matrix run and then drill down into specific areas of interest. The final deliverable is a single, self-contained `.html` file with embedded CSS and JavaScript, ensuring the report is easily shareable and viewable in any modern web browser without requiring a web server.\n\n#### **3. Acceptance Criteria**\n\n1. **Report Structure and Layout:**\n   * A clear, hierarchical layout is established for the report. The design must include distinct sections for:\n     * **Report Header:** Title, date of generation, and a link to the matrix configuration used.\n     * **High-Level Summary:** Key metrics displayed prominently at the top (e.g., total runs, overall success rate, average execution time).\n     * **Results by Parameter:** A section for each matrix parameter (e.g., \"Performance by LLM Model\"), with subsections for each value.\n     * **Capability Analysis:** A dedicated section showing the success rate for each defined agent capability.\n     * **Trajectory Analysis:** A section to visualize the most common action sequences.\n     * **Detailed Run Explorer:** A table or list view to browse the raw data for every individual run.\n2. **Visual Design and Styling:**\n   * The report uses a clean, modern aesthetic. A lightweight CSS framework like [Pico.css](https://picocss.com/) or a simple custom-written stylesheet should be used to maintain a small file size.\n   * Styling is embedded directly within the HTML file in a `<style>` tag to ensure portability.\n   * The design must be responsive and readable on both desktop and mobile screen sizes.\n   * Use of color should be intentional, highlighting key data points (e.g., green for success, red for failure) while remaining accessible.\n3. **Component Placeholders:**\n   * The HTML template must contain clearly identifiable placeholders for all dynamic data. This includes:\n     * `<span>` or `<div>` elements with specific `id` attributes for single data points (e.g., `<span id=\"total-runs\"></span>`).\n     * HTML `<canvas>` elements for charts, each with a unique `id`.\n     * Template blocks or empty table bodies (`<tbody>`) for data that will be rendered in loops (e.g., the detailed run list).\n4. **JavaScript Integration:**\n   * The chosen charting library ([Chart.js](https://www.chartjs.org/) is recommended) is embedded directly into the HTML file in a `<script>` tag.\n   * A second embedded `<script>` tag will contain the \"rendering\" logic. This script will:\n     * Define a function like `renderReport(data)`, where `data` is the `ReportData` JSON object.\n     * This function will contain the logic to find the placeholders in the DOM and populate them with the data.\n     * A placeholder for the data itself is included, e.g., `<script id=\"report-data\" type=\"application/json\">...</script>`.\n5. **Self-Contained Asset:**\n   * The final deliverable is a single `.html` file. All CSS and JavaScript must be embedded. No external network requests should be necessary to view the report's structure and styling (data will be injected later).\n\n#### **4. Technical Implementation Details**\n\n**File Structure:**\n\n```\npackages/cli/src/commands/report/src/\n└── assets/\n    └── report_template.html  # The new, self-contained HTML file\n```\n\n**Example HTML Placeholders:**\n\n```html\n<!-- For a single value -->\n<h2>Summary</h2>\n<p>Total Runs: <strong id=\"summary-total-runs\">[loading...]</strong></p>\n<!-- For a chart -->\n<h3>Capability Success Rates</h3>\n<canvas id=\"capability-chart\" width=\"400\" height=\"200\"></canvas>\n<!-- For a table to be populated by a loop -->\n<h3>Detailed Run Results</h3>\n<table>\n  <thead>\n    <tr>\n      <th>Run ID</th>\n      <th>Success</th>\n      <th>Model</th>\n      <th>Prompt</th>\n    </tr>\n  </thead>\n  <tbody id=\"detailed-runs-tbody\">\n    <!-- Rows will be injected here by JavaScript -->\n  </tbody>\n</table>\n<!-- For the data island -->\n<script id=\"report-data\" type=\"application/json\">\n  {}\n</script>\n```\n\n#### **5. Design Mockup / Wireframe**\n\n(A simple text-based wireframe should be included in the ticket to guide the developer)\n\n```\n+------------------------------------------------------+\n| Performance Report: GitHub Issue Analysis            |\n| Generated: 2023-10-27                                |\n+------------------------------------------------------+\n| SUMMARY                                              |\n| Total Runs: 18    Success Rate: 66%    Avg Time: 8.2s |\n+------------------------------------------------------+\n| CAPABILITY SUCCESS RATES                             |\n| [================\n| [===========     ] Formats Response (55%)             |\n+------------------------------------------------------+\n| PERFORMANCE BY LLM MODEL                             |\n| gpt-4-turbo: 9/9 (100%)    gpt-3.5-turbo: 3/9 (33%)    |\n| [ Bar Chart comparing models on key metrics ]        |\n+------------------------------------------------------+\n| COMMON ACTION TRAJECTORIES                           |\n| 1. THINK -> LIST_ISSUES -> REPLY (12 runs)           |\n| 2. THINK -> SEARCH -> REPLY (6 runs)                 |\n+------------------------------------------------------+\n| DETAILED RUNS                                        |\n| [ A filterable, sortable table of all 18 runs ]      |\n+------------------------------------------------------+\n```\n\n#### **6. Testing Requirements**\n\n* **Manual Testing:** Open the final `report_template.html` file in multiple browsers (Chrome, Firefox, Safari) to ensure consistent rendering and responsiveness.\n* **Static Analysis:** Run an HTML validator and a linter on the file to ensure it is well-formed and follows best practices.\n\n#### **7. Out of Scope**\n\n* The logic for actually injecting the `ReportData` JSON into the template. This ticket is only about creating the static HTML shell.\n* The implementation of the `elizaos report generate` command itself.\n* The data aggregation logic.",
      "createdAt": "2025-08-16T05:50:44Z",
      "closedAt": "2025-08-18T05:15:20Z",
      "state": "CLOSED",
      "commentCount": 0
    },
    {
      "id": "I_kwDOMT5cIs7GTLk4",
      "title": "Implement `elizaos report generate` Command",
      "author": "linear",
      "number": 5787,
      "repository": "elizaos/eliza",
      "body": "### **Ticket: Implement** `elizaos report generate` Command\n\n**ID:** `FEAT-131` (Example ID)\n\n**Epic:** `Performance Reporting Dashboard`\n\n**Tags:** `cli`, `reporting`, `feature`, `data-analysis`\n\n**Estimated Story Points:** `8`\n\n**Dependencies:** `FEAT-130` (Centralize and Serialize Run Data)\n\n#### **1. Title**\n\n`feat(cli): Implement 'elizaos report generate' command for data aggregation and analysis`\n\n#### **2. Description**\n\nThis ticket introduces the user-facing entry point for the Performance Reporting Dashboard: the `elizaos report generate` command. The primary responsibility of this command is to ingest the raw JSON output from a Scenario Matrix run, process it, and perform the complex data aggregation required to generate an insightful report.\n\nThe command will read all individual `run-*.json` files, calculate high-level statistics, group results by the matrix parameters, and analyze agent trajectories. The final output of this command will be a single, structured `ReportData` object, which will serve as the complete data context for rendering the HTML report in a subsequent ticket.\n\nThis is a critical data processing step that transforms raw run data into meaningful, aggregated insights.\n\n#### **3. Acceptance Criteria**\n\n1. **CLI Command Registration:**\n   * A new top-level command `report` is created.\n   * Under `report`, a subcommand `generate` is registered.\n   * The command accepts one required argument, `<input_dir>`, which is the path to the output directory of a matrix run.\n   * It also accepts an optional `--output-path` flag to specify where to save the final `report.json` file. If not provided, it defaults to `<input_dir>/report.json`.\n2. **Data Ingestion and Validation:**\n   * The command recursively finds and reads all `run-*.json` files within the `<input_dir>`.\n   * It validates the structure of each JSON file against the `ScenarioRunResult` schema.\n   * Malformed or incomplete files are gracefully skipped, and a warning is logged.\n   * The command provides a clear error and exits if the input directory is not found or contains no valid run files.\n3. **Data Aggregation Logic:**\n   * The core of the command is an `AnalysisEngine` that processes the array of `ScenarioRunResult` objects.\n   * It must calculate **overall summary statistics**: total runs, average execution time, overall capability success rates, etc.\n   * It must calculate **grouped statistics**: The engine must group the results by each matrix parameter and value (e.g., group by `character.llm.model`). For each group, it calculates the same summary statistics, allowing for direct comparison (e.g., success rate of `gpt-4` vs. `gpt-3.5`).\n   * It must perform **trajectory analysis**: Aggregate all `trajectory` arrays to identify the most common action sequences and calculate the frequency of each path.\n4. **Structured** `ReportData` Output:\n   * The `AnalysisEngine` produces a single, large `ReportData` object.\n   * This object's schema is formally defined in TypeScript and contains all aggregated data in a clean, predictable structure, ready for a rendering engine.\n   * The command serializes this `ReportData` object to a pretty-printed JSON file at the specified output path.\n\n#### **4. Technical Implementation Details**\n\n**File Structure:**\n\n```\npackages/cli/src/commands/report/\n├── index.ts              # Registers the 'report' command\n├── generate.ts           # Implements the 'generate' subcommand\n└── src/\n    ├── analysis-engine.ts  # Core data aggregation logic\n    ├── report-schema.ts    # Defines the 'ReportData' interface\n    └── __tests__/\n        └── analysis-engine.test.ts\n```\n\n`ReportData` Interface (High-Level Example):\n\n```typescript\ninterface ReportData {\n  metadata: {\n    report_generated_at: string;\n    matrix_config: MatrixConfig; // From the original run\n  };\n  summary_stats: {\n    total_runs: number;\n    total_failed_runs: number;\n    average_execution_time: number;\n    capability_success_rates: Record<string, number>; // { \"Formats Response\": 0.75 }\n  };\n  results_by_parameter: {\n    [parameter_name: string]: { // e.g., \"character.llm.model\"\n      [parameter_value: string]: ReportSummaryStats; // e.g., \"gpt-4-turbo\" has its own summary_stats\n    };\n  };\n  common_trajectories: {\n    sequence: string[]; // e.g., [\"THINK\", \"LIST_ISSUES\", \"REPLY\"]\n    count: number;\n    average_duration: number;\n  }[];\n  raw_results: ScenarioRunResult[]; // Include all original data for detailed drill-downs\n}\n```\n\n**Example CLI Usage:**\n\n```bash\n# Generate a report from a previous matrix run\nelizaos report generate ./output/matrix-20231027-1000/\n# Specify a different output location for the aggregated data\nelizaos report generate ./output/matrix-20231027-1000/ --output-path ./reports/latest-report.json\n```\n\n#### **5. Testing Requirements**\n\n* **Unit Tests:**\n  * Extensive unit tests for the `AnalysisEngine` are critical. Provide it with a mock array of `ScenarioRunResult` objects and assert that the resulting `ReportData` object contains the correct aggregations, groupings, and statistical calculations.\n  * Test edge cases like empty input, single-run input, and runs with missing data points.\n* **Integration Tests:**\n  * Create a test that runs the `elizaos report generate` command on a pre-generated fixture directory containing a set of `run-*.json` files.\n  * The test should verify that the command exits successfully and that the output `report.json` file is created and contains valid, non-empty data.\n\n#### **6. Out of Scope**\n\n* The design or implementation of the final HTML template. This ticket's final artifact is the `report.json` file, not a user-facing document.\n* The logic for rendering charts or any other UI components (handled in Ticket 3.2 and 3.3).\n* Any modification to the scenario running or data collection process; this command is strictly read-only on the matrix run output.",
      "createdAt": "2025-08-16T05:47:08Z",
      "closedAt": "2025-08-18T05:15:07Z",
      "state": "CLOSED",
      "commentCount": 0
    },
    {
      "id": "I_kwDOMT5cIs7GTLOh",
      "title": "Centralize and Serialize Run Data",
      "author": "linear",
      "number": 5786,
      "repository": "elizaos/eliza",
      "body": "### **Ticket: Centralize and Serialize Run Data**\n\n**ID:** `FEAT-130` (Example ID)\n\n**Epic:** `Advanced Evaluation & Data Collection`\n\n**Tags:** `cli`, `scenario-testing`, `feature`, `data-pipeline`\n\n**Estimated Story Points:** `5`\n\n**Dependencies:** `FEAT-126` (Run Orchestration), `FEAT-127` (Structured JSON Output), `FEAT-129` (Agent Trajectory Logging)\n\n#### **1. Title**\n\n`feat(cli): Centralize and serialize all scenario run data into a structured JSON output file`\n\n#### **2. Description**\n\nThis ticket focuses on the final step of the data collection phase: bringing together all the rich data gathered from a single scenario run and saving it to a well-defined, structured JSON file. This file will be the canonical source of truth for a single execution and will serve as the input for the Performance Reporting Dashboard (Epic 3).\n\nThe implementation will create a new service or utility responsible for aggregating data from various sources—the matrix configuration, the evaluation engine, the agent runtime's trajectory log, and performance timers—and ensuring it conforms to our master `ScenarioRunResult` schema before being written to disk.\n\n#### **3. Acceptance Criteria**\n\n1. `ScenarioRunResult` Schema:\n   * A comprehensive `ScenarioRunResult` interface is finalized in `packages/cli/src/commands/scenario/src/schema.ts`.\n   * This master interface must consolidate all data points from the previous tickets and include:\n     * `run_id`: A unique identifier for the run.\n     * `matrix_combination_id`: An identifier linking it to a specific set of parameters.\n     * `parameters`: An object detailing the specific matrix configuration for this run (e.g., `{ \"character.llm.model\": \"gpt-4-turbo\" }`).\n     * `metrics`: An object containing performance data (`execution_time_seconds`, `llm_calls`, `total_tokens`, etc.).\n     * `evaluations`: The array of `EvaluationResult` objects from the `EvaluationEngine` (Ticket 2.1).\n     * `trajectory`: The array of `TrajectoryStep` objects from the `AgentRuntime` (Ticket 2.3).\n     * `final_agent_response`: The final text/object response from the agent to the user.\n     * `error`: A field to store any fatal error message if the run failed unexpectedly.\n2. **Data Aggregation Utility:**\n   * A new utility or class, e.g., `RunDataAggregator`, is created.\n   * It will have methods to collect data from different parts of the system throughout a run's lifecycle.\n   * It will have a final `buildResult()` method that assembles all the collected pieces into a single, validated `ScenarioRunResult` object.\n3. **File Serialization Logic:**\n   * The `MatrixOrchestrator` (from Ticket 1.4) will use this new utility to generate the result for each run.\n   * After a run completes (or fails), the orchestrator will call a function to serialize the `ScenarioRunResult` object to a JSON file.\n   * The output file will be named according to a consistent pattern (e.g., `run-<run_id>.json`) and saved within the unique output directory created for the matrix execution.\n   * The JSON output must be cleanly formatted and human-readable (pretty-printed with an indentation of 2 spaces).\n4. **Integration with Matrix Orchestrator:**\n   * The main execution loop in the `MatrixOrchestrator` is updated to wrap each scenario run in a `try...catch` block.\n   * In both success and failure cases, the orchestrator ensures that the data aggregator is called and a result file is written, capturing the error details if the run failed.\n\n#### **4. Technical Implementation Details**\n\n**Files to Modify / Create:**\n\n* `packages/cli/src/commands/scenario/src/schema.ts`: Finalize the `ScenarioRunResult` master interface.\n* `packages/cli/src/commands/scenario/src/data-aggregator.ts`: New file for the `RunDataAggregator` class.\n* `packages/cli/src/commands/scenario/src/matrix-orchestrator.ts`: Integrate the aggregator and serialization logic into the main execution loop.\n\n**Final** `ScenarioRunResult` JSON Structure (Example):\n\n```json\n{\n  \"run_id\": \"run-20231027-015\",\n  \"matrix_combination_id\": \"combo-003\",\n  \"parameters\": {\n    \"character.llm.model\": \"gpt-4-turbo\",\n    \"run[0].input\": \"Show me what's open in the elizaOS/eliza GitHub.\"\n  },\n  \"metrics\": {\n    \"execution_time_seconds\": 14.7,\n    \"llm_calls\": 2,\n    \"total_tokens\": 2100\n  },\n  \"final_agent_response\": \"Here are the open issues for elizaOS/eliza: #123 Fix the login button, #124 Improve documentation...\",\n  \"evaluations\": [\n    {\n      \"evaluator_type\": \"llm_judge\",\n      \"success\": false,\n      \"summary\": \"...\",\n      \"details\": {\n        \"qualitative_summary\": \"...\",\n        \"capability_checklist\": [\n          { \"capability\": \"Formats Final Response\", \"achieved\": false, \"reasoning\": \"...\" }\n        ]\n      }\n    }\n  ],\n  \"trajectory\": [\n    { \"type\": \"thought\", \"content\": \"...\" },\n    { \"type\": \"action\", \"content\": { \"name\": \"LIST_GITHUB_ISSUES\", \"parameters\": { \"owner\": \"elizaOS\", \"repo\": \"eliza\" } } },\n    { \"type\": \"observation\", \"content\": \"[...]\" },\n    { \"type\": \"thought\", \"content\": \"...\" }\n  ],\n  \"error\": null\n}\n```\n\n#### **5. Testing Requirements**\n\n* **Unit Tests:**\n  * Test the `RunDataAggregator` to ensure it correctly assembles the `ScenarioRunResult` object from mock inputs.\n  * Test the file serialization logic, including pretty-printing and correct file pathing.\n  * Test the error handling path, ensuring that a valid result file (with the `error` field populated) is created even when a run fails catastrophically.\n* **Integration Tests:**\n  * Run a full matrix test with a small number of combinations.\n  * After the test completes, manually inspect the generated JSON files in the output directory to verify their structure and content are correct and complete.\n\n#### **6. Out of Scope**\n\n* The creation of the final `summary.json` file for the entire matrix run. This ticket is only concerned with the individual `run-*.json` files.\n* Any form of report generation or data visualization (this is handled in Epic 3).\n* The implementation of any of the data sources themselves (e.g., trajectory logging); this ticket only consumes and aggregates the data.",
      "createdAt": "2025-08-16T05:44:32Z",
      "closedAt": "2025-08-18T05:14:54Z",
      "state": "CLOSED",
      "commentCount": 0
    },
    {
      "id": "I_kwDOMT5cIs7GTK4e",
      "title": "Implement Agent Trajectory Logging",
      "author": "linear",
      "number": 5785,
      "repository": "elizaos/eliza",
      "body": "### **Ticket: Implement Agent Trajectory Logging**\n\n**ID:** `FEAT-129` (Example ID)\n\n**Epic:** `Advanced Evaluation & Data Collection`\n\n**Tags:** `core`, `agent-runtime`, `scenario-testing`, `feature`, `instrumentation`\n\n**Estimated Story Points:** `5`\n\n**Dependencies:** `FEAT-127` (Structured JSON Output)\n\n#### **1. Title**\n\n`feat(core): Instrument AgentRuntime to capture and expose agent's cognitive trajectory`\n\n#### **2. Description**\n\nTo effectively analyze and debug an agent's behavior—especially for complex tasks involving action chaining—we need to record its step-by-step cognitive process. This \"trajectory\" consists of the agent's reasoning (thoughts), the actions it takes, and the results of those actions (observations).\n\nThis ticket covers the work to instrument the core `AgentRuntime` to capture this sequence of events during the processing of a single user message. The captured trajectory will then be exposed to the Scenario Runner, making it a critical data source for the `LLMJudge` and the final performance reports.\n\n#### **3. Acceptance Criteria**\n\n1. `TrajectoryStep` Interface Definition:\n   * A new set of interfaces is defined in `packages/core/src/types.ts` to represent the trajectory.\n   * `TrajectoryStep`: A union type representing a single step, containing `type`, `timestamp`, and `content`.\n   * `ThoughtStep`: `type: 'thought'`, `content: string` (The LLM's reasoning before taking an action).\n   * `ActionStep`: `type: 'action'`, `content: { name: string, parameters: object }` (The action and its arguments).\n   * `ObservationStep`: `type: 'observation'`, `content: any` (The result returned from the action).\n2. `AgentRuntime` Instrumentation:\n   * The `AgentRuntime` is modified to maintain an internal, ordered log of `TrajectoryStep` objects for the duration of a single `handleMessage` or equivalent processing cycle.\n   * Instrumentation points are added to capture:\n     * The `thought` generated by the LLM.\n     * The `action` call, including its name and parameters, immediately before execution.\n     * The `observation` (the return value or error) immediately after the action completes.\n   * The trajectory log must be cleared at the beginning of each new message processing cycle to ensure logs don't mix.\n3. **Trajectory Data Exposure:**\n   * The `AgentRuntime` must provide a mechanism to retrieve the trajectory for the most recently processed message. This could be a new method like `getLatestTrajectory(): TrajectoryStep[]`.\n   * The Scenario Runner is updated to call this method after the agent generates its response.\n4. **Integration with Data Collection:**\n   * The collected `TrajectoryStep[]` array is added to the final structured JSON output for each scenario run.\n   * A new top-level key, `trajectory`, will be added to the run result schema defined in `packages/cli/src/commands/scenario/src/schema.ts`.\n\n#### **4. Technical Implementation Details**\n\n**Files to Modify:**\n\n* `packages/core/src/runtime.ts`: The main location for instrumentation. The `private trajectory: TrajectoryStep[]` property will be added, and hooks will be placed within the core action/decision loop.\n* `packages/core/src/types.ts`: Add the new `TrajectoryStep` related interfaces.\n* `packages/cli/src/commands/scenario/src/EvaluationEngine.ts` (or the main runner logic): Update to fetch the trajectory from the runtime after a run.\n* `packages/cli/src/commands/scenario/src/schema.ts`: Add the `trajectory` field to the main run result interface.\n\n**Example** `trajectory` array in the final JSON output:\n\n```json\n\"trajectory\": [\n  {\n    \"type\": \"thought\",\n    \"timestamp\": \"2023-10-27T10:00:01Z\",\n    \"content\": \"The user wants to know the open issues for a GitHub repository. I need to use the GitHub plugin to get this information. The repository is 'elizaOS/eliza'.\"\n  },\n  {\n    \"type\": \"action\",\n    \"timestamp\": \"2023-10-27T10:00:02Z\",\n    \"content\": {\n      \"name\": \"LIST_GITHUB_ISSUES\",\n      \"parameters\": {\n        \"owner\": \"elizaOS\",\n        \"repo\": \"eliza\"\n      }\n    }\n  },\n  {\n    \"type\": \"observation\",\n    \"timestamp\": \"2023-10-27T10:00:04Z\",\n    \"content\": [\n      { \"id\": 123, \"title\": \"Fix the login button\", \"state\": \"open\" },\n      { \"id\": 124, \"title\": \"Improve documentation for scenarios\", \"state\": \"open\" }\n    ]\n  },\n  {\n    \"type\": \"thought\",\n    \"timestamp\": \"2023-10-27T10:00:05Z\",\n    \"content\": \"I have the list of issues. Now I need to format this information into a clear, readable list and reply to the user.\"\n  }\n]\n```\n\n#### **5. Testing Requirements**\n\n* **Unit Tests:**\n  * Add tests to `AgentRuntime` to verify that the trajectory is correctly captured for a single action.\n  * Test that the trajectory is correctly captured for a sequence of multiple actions (if the agent supports multi-step execution).\n  * Test that the trajectory log is cleared properly between separate message handling calls.\n  * Test the case where an action throws an error, ensuring the `observation` step correctly records the error object.\n* **Integration Tests:**\n  * Run an existing scenario (like `test-github-issues.scenario.yaml`).\n  * After the run completes, parse the output JSON file and assert that the `trajectory` field exists and contains a logically correct sequence of steps.\n\n#### **6. Out of Scope**\n\n* The use of the trajectory data by any evaluator, including the `LLMJudge`. This ticket's responsibility ends once the data is successfully captured and saved in the run result file.\n* The visualization or rendering of the trajectory in the final HTML report. This is part of Epic 3.",
      "createdAt": "2025-08-16T05:42:18Z",
      "closedAt": "2025-08-18T05:14:42Z",
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