--- pretty_name: FreeInference Agentic Trace license: cc-by-4.0 tags: - agents - llm-inference - tool-use - prefix-caching - traces size_categories: - 1M/traces/.jsonl.zst ``` Each file is zstd-compressed JSONL with **one agent session per line**. A session contains its LLM requests in order. Each request records the model invocation and the tool calls produced by the response. If a tool call launches a subagent, the complete subagent session is nested under that call. A trace has the following structure: ```jsonc { "week": "2026-05-17_2026-05-23", "day": "2026-05-17", // Session field "session_id": "...", "user_id": "...", "harness": "claude-code", "start_ms": 1778977459236, "end_ms": 1778984067198, // LLM requests in chronological order. "requests": [ { // Position in the session and user turn. "step": 0, "turn_index": 0, // What triggered the request and how it connects // to the previous request. "step_trigger": "user", "link_reason": null, // Request timing at the gateway. "start_ms": 1778977459236, "end_ms": 1778977468828, "ttft_ms": 7889, // Requested model and anonymized upstream provider. "model": "glm-5.1", "provider": "B", "stream": true, // Token counts reported by the provider. "provider_tokens": { "prompt": 52731, "completion": 230, "cached": 1280 }, // Input tokens broken down by message role, tokenized with tiktoken. "role_tokens": { "system": 1790, "user": 1110, "assistant": 15916, "tool": 25725, "tool_definitions": 8638 }, // Shape of the input and tools exposed to the model. "n_messages": 179, "tool_definition_names": [ "read_file", "write_file", "terminal", "execute_code" ], "finish_reason": "tool_calls", // How the input differs from the previous request. // Null for the first request in a session. "mutation": null, // Input represented as chained 16-token blocks, // tokenized with tiktoken. "block_ids": [123, 456, 789, "..."], // Tool calls produced by the model response. "tool_calls": [ { "call_index": 0, "name": "terminal", // Normalized tool type. Shell calls additionally record // the programs found in the command. "category": "*Interpreter", "programs": ["python"], "parse_status": "success", // Arguments preserve their structure while sensitive // values are replaced with typed placeholders. "arguments": [ [ "command", "python --status" ], [ "timeout", "" ] ], // Tool outcome and the gap until the next LLM request. "outcome": "error", "result_tokens": 842, "latency_ms": 5272, // If this call starts another agent, its complete // session is nested here. "subagent": null } ] }, { "step": 1, "turn_index": 0, "step_trigger": "tool", "link_reason": "tool_call_id", // Later requests contain the same fields. // Here the previous input was extended without changing // any earlier content. "mutation": { "transition": "append", "cause": null }, "tool_calls": ["..."] } ] } ``` For convenience, `processed/` contains Parquet tables derived by parsing and flattening the traces into sessions, requests, and tool calls. ## Notes ## Notes - Request inputs are tokenized with `o200k_base`, not the provider's tokenizer and grouped into 16-token blocks. Inputs are rendered using the following order: **tool definitions → system prompt → messages**, Partial final blocks are dropped. - Block IDs are local to each week. When replaying multiple weeks, make them globally unique, e.g., by prefixing them with the week. - Sessions are reconstructed by linking related requests through repeated tool-call IDs and assistant messages. - Sessions do not cross week boundaries. A session that continues into the next week is released as a new session. - Rare tool names and commands used by fewer than 3 accounts are replaced with `other` or typed placeholders. - Each week's `manifest.json` reports sessions excluded from the release and their exclusion reasons. ## Usage ### DuckDB Query the derived Parquet tables directly from the Hugging Face Hub: ```sql SELECT sessions.harness, count(*) AS requests, approx_quantile(requests.ttft_ms, 0.5) AS p50_ttft_ms FROM 'hf://datasets/harvardMadsys/freeinference_agentic_trace/release/*/processed/requests.parquet' AS requests JOIN 'hf://datasets/harvardMadsys/freeinference_agentic_trace/release/*/processed/sessions.parquet' AS sessions USING (session_id) GROUP BY sessions.harness ORDER BY requests DESC; ``` ### Hugging Face Datasets ```python from datasets import load_dataset traces = load_dataset( "harvardMadsys/freeinference_agentic_trace", "traces", split="week_2026_08_30", ) ``` The derived request and tool-call tables are also available as separate configurations for convenience: ```python requests = load_dataset( "harvardMadsys/freeinference_agentic_trace", "requests", split="week_2026_08_30", streaming=True, ) tool_calls = load_dataset( "harvardMadsys/freeinference_agentic_trace", "tool_calls", split="week_2026_08_30", streaming=True, ) ``` ### Download Download the full dataset: ```bash hf download harvardMadsys/freeinference_agentic_trace \ --repo-type dataset \ --local-dir freeinference_trace ``` Or download one week: ```bash hf download harvardMadsys/freeinference_agentic_trace \ --repo-type dataset \ --local-dir freeinference_trace \ --include "release/2026-08-30_2026-09-05/*" ``` ## License This dataset is released under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). If you use this dataset, please cite the paper below. ## Citation If you use the dataset or artifact in your research, please cite our paper: ```bibtex @article{freeinference2026requests, title={From Requests to Sessions: A Large-Scale Characterization of Human-Driven Agentic Workloads}, author={Nixon, William and Tian, Muxin and Zheng, Yunjia and Gunawi, Haryadi S. and Yang, Juncheng}, year={2026} } ```