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Hindsight gives an AI agent persistent memory through three operations: retain extracts memories from conversations or documents, recall retrieves relevant memories, and reflect searches and synthesizes an answer. To keep irrelevant details out, you can guide extraction with a retain mission and separately control which memories retrieval can see. The trade-off is important: if extraction filters too aggressively, useful information may never become searchable memory.
How Hindsight gives an AI agent memory
Hindsight is a memory system an application can use to turn source material into structured memories and retrieve them later. Its central operations have distinct roles:
- Retain: Processes conversations or documents into memory units. In LLM-based extraction mode, a retain mission tells the extraction process what to prioritize and what to ignore.
- Recall: Returns ranked memories so the calling agent or application can decide what to do with them.
- Reflect: Searches memories and synthesizes an answer. Hindsight identifies the memories used separately from the answer.
The distinction matters: retain shapes what gets written; recall and reflect shape how existing memories are used. A mission is guidance for extraction, not a replacement for the extraction process. Hindsight’s documentation says retain missions are ignored in chunks mode. See the Hindsight retain documentation.
Under the hood, Hindsight’s 2026 paper describes four logical memory networks: world, experience, observation, and opinion. Its retrieval pipeline combines vector search, keyword matching, graph traversal, and temporal filtering, with PostgreSQL and pgvector as the storage foundation. These design details explain why “memory” is not simply a transcript copied into a prompt: the system extracts, organizes, and later searches information.
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How to tell an agent what to ignore
Write the retain mission as a selection policy: state what should survive and what should not. For example, instruct it to preserve decisions, durable preferences, and recurring constraints while excluding greetings, routine scheduling logistics, and social exchanges. Hindsight’s retain documentation uses the example directive, “Ignore meeting logistics, greetings, and social exchanges.” That is an example instruction, not a promise that those items will always be excluded.
Specific instructions are more useful than broad requests such as “remember important things.” The Hindsight best-practices documentation warns that vague missions can produce low-quality memory and recommends naming both inclusions and exclusions. For third-party transcripts, identify who is speaking in the retain context; otherwise first-person statements can be attributed to the wrong person or entity.
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After changing a mission, inspect the resulting memories and test retrieval with questions that should succeed as well as questions that should not surface a memory. Treat this as a practical check, not proof of perfect selection: extraction is not fully deterministic, and a source may produce no memory units.
What happens when a mission filters out too much?
A document can remain stored while producing zero memory units. Recall and reflect search memories, not the original document as a substitute for those memories. If a document yields no memory units, those retrieval paths cannot find its contents. Hindsight’s retain documentation also warns that extraction can be nondeterministic, so a zero-fact result is a reason to review the mission or retry—not evidence that the source contains nothing worth keeping.
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For important material, check memory output after a mission change and ask a retrieval question whose answer depends on that material. If the expected information is absent, broaden the mission or choose a less selective extraction approach. Do not assume a tighter prompt is always safer: selectivity can reduce noise, but it can also make useful evidence disappear from the memory layer the agent searches.
Choose how much to extract
Hindsight offers several extraction approaches. The right choice depends on whether you value compact memories, richer detail, custom selection, exact source preservation, or avoiding LLM extraction.
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| Approach | What it is for | Trade-off |
|---|---|---|
| Concise default | A compact baseline for extracting useful memories. | Less verbose than richer extraction; inspect results to see whether the detail is sufficient. |
| Verbose extraction | Capturing more detail from source material. | Can create more material for retrieval to manage. |
| Custom rules or mission | Applying domain-specific include and exclude criteria. | Overly narrow rules can result in zero memory units. |
| Verbatim preservation | Keeping source text rather than relying only on extracted summaries. | Preserves more raw material, but does not by itself decide what should be retrieved for a later task. |
chunks mode |
Processing content without LLM-based extraction. | Retain missions do not apply in this mode. |
These options affect what is created during retain. They are separate from retrieval controls, which limit what can be returned from the memories that already exist.
Control what recall and reflect can use
Hindsight provides retrieval controls that operate independently of the retain mission:
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- Tags: Scope which memories are visible to a request. For user-specific information in a shared memory bank, use strict tag matching so a request cannot see memories that do not match the user’s tag. Hindsight’s best-practices documentation warns that retaining into a shared bank without a user tag can make that memory globally visible.
- Type filters: Limit retrieval to the memory types relevant to the task rather than exposing every kind of memory.
- Recall budget: Choose the breadth and depth of retrieval. The Hindsight recall documentation describes lower budgets for straightforward lookups, a middle setting for everyday balance, and higher budgets for indirect connections or broader coverage. More is not automatically better: broader retrieval can cost more and take longer.
prefer_observations: Prefer consolidated observations and suppress a raw fact when a returned observation already represents it. Observations can reduce duplication and capture durable patterns, while recent raw retains may be available before background consolidation catches up.
Use recall when your application should receive ranked evidence and control the next step. Use reflect when you want Hindsight to synthesize a response from retrieved memories; check the separately identified memories to understand what supports the answer. The best-practices documentation also cautions against retaining and recalling in the same turn with the expectation that the new item will be immediately searchable: retained memories are not yet indexed. A practical sequence is to retain at the end of one turn and recall on the next.
Keep personal memories isolated
If an agent handles multiple users, use separate memory banks to isolate their data. Tags and strict filtering can add another scope boundary within a shared bank. These are retrieval safeguards, not substitutes for correctly labeling information when it is retained. A missing user tag on a shared-bank retain can expose a memory beyond the intended user, according to Hindsight’s best-practices documentation.
What benchmark results do—and do not—show
The Association for Computational Linguistics paper record, dated July 2026, reports Hindsight accuracy of 83.6% on LongMemEval and 83.2% on LoCoMo with a 20B open-source model. It also reports 91.4% on LongMemEval with Gemini-3 Pro. These are results under the paper’s stated benchmark and model configurations; they do not predict how a particular agent, mission, or dataset will perform. The figures are reported in the ACL Anthology record.
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