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Deep Research Agent That Remembers
The work deep research agents throw away goes well beyond the final answer.
It often includes the source text, the leads they rejected, the claims that contradicted each other, the papers worth keeping, and the trail that led from one document to the next.
So what would a research agent look like if it kept that evidence and treated every run as an addition to a growing knowledge base?
If we want to run a research harness inside Claude Code or OpenAI Codex, we have to ideally combine a tier-adaptive research pipeline, specialized subagents, a Markdown and SQLite vault, scholarly search integrations, and verification checks that run before a report ships.
Let’s have a look at several patterns for any long-running agentic application.
The Memory Problem in Agentic Research
Your context window can hold the immediate working set of a research session but if your product investigates hundreds of sources across many runs, you need a system of record that lives outside the model.
Hyperresearch writes every fetched source into research/notes/ as Markdown with YAML frontmatter, and it maintains a SQLite index for searching those notes.
If you have a new run, it searches the existing vault before it collects more material, and the source trail survives across model sessions, and anyone can inspect it without an agent in the loop.
