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 from taking over, use a specific retain mission to guide what gets stored, then use retrieval controls such as tags, type filters, and budgets to limit what gets returned. The trade-off matters: an instruction that is too narrow can leave a document with no searchable memories at all.
How do you give an AI agent memory with Hindsight?
Hindsight separates memory into three jobs. retain processes conversations or documents into structured memories. recall returns ranked memories for an agent or application to use. reflect searches memory and synthesizes a response, identifying the memories it used.
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That separation helps clarify what “memory” means here: the agent does not simply keep a transcript in its prompt. Hindsight stores information in memory networks and provides operations for finding or reasoning over it later. The 2026 Association for Computational Linguistics paper describes four logical networks—world, experience, observation, and opinion—and a retrieval pipeline that combines vector search, keyword matching, graph traversal, and temporal filtering, backed by PostgreSQL with pgvector. Read the ACL Anthology record.
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How do you tell an agent what to ignore?
Write a retain mission that names both what to keep and what to leave out. For example, preserve durable preferences, decisions, and recurring constraints while excluding greetings, routine scheduling details, and one-off social exchanges. Hindsight’s retain documentation gives “Ignore meeting logistics, greetings, and social exchanges” as an example directive; it is an instruction example, not a guarantee of what extraction will produce.
The retain mission guides the extraction stage when Hindsight uses an LLM-based extraction mode. It does not replace the extraction logic, and the documentation says the mission is ignored in chunks mode. A vague directive such as “remember what matters” gives the system little basis for distinguishing a durable preference from a passing detail; specific include-and-exclude rules are more useful.
Choose the extraction behavior for the material
Hindsight documents several extraction approaches. A concise default can suit ordinary conversation; a richer or verbose approach can preserve more detail; custom rules let an application define its own selection criteria; verbatim preservation prioritizes the source wording; and chunks keeps material in chunks without LLM extraction. These are different trade-offs, not a ladder where the most selective setting is always best. In particular, a retain mission cannot steer extraction in chunks mode.
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For third-party transcripts, identify speakers in the retain context. Without that context, a first-person statement may be assigned to the wrong person or entity. This is especially important when a memory bank contains customer or team conversations.
What happens if memory instructions filter out too much?
A document can remain stored while producing zero memory units. Because recall and reflect search memories, not the unprocessed document itself, that source may then be unreachable through those paths. Tightening a mission can therefore improve relevance while making some source information disappear from the agent’s searchable memory layer.
Extraction is also not fully deterministic, according to Hindsight’s documentation. A zero-memory result does not prove that the source had nothing useful in it; it is a reason to inspect the output and reconsider the instruction or extraction mode.
Test the memory boundary, not just the prompt
- Set a specific mission. State the durable information to retain and the kinds of details to ignore.
- Inspect the result. Check which memories were created and whether important facts were omitted.
- Ask positive retrieval questions. Test questions that should be answerable from the retained material.
- Ask negative retrieval questions. Check that excluded details are not surfacing as if they were durable facts.
- Revise and retry when needed. If an important source yields no memories, loosen or clarify the mission and review the new extraction.
This process tests what the memory layer actually exposes; it does not guarantee perfect selection. The retain documentation warns about zero-fact outcomes and nondeterministic extraction.
How do retrieval controls differ from retain instructions?
A retain mission controls what memories are created. Retrieval controls determine which existing memories are visible to a particular request. Keeping those stages separate makes it easier to diagnose a missing answer: the fact may never have been extracted, or it may exist but be excluded by retrieval settings.
| Control | Stage | What it changes |
|---|---|---|
| Retain mission | Extraction | Guides what an LLM-based extraction mode attends to or ignores; it does not apply in chunks mode. |
| Tags and strict matching | Retrieval scope | Limits which tagged memories can be considered; strict matching can enforce user-specific scope. |
| Memory type filters | Retrieval scope | Restricts the kinds of memories returned for a request. |
| Recall budget | Retrieval effort | Trades retrieval breadth and depth against speed and cost. |
prefer_observations |
Result selection | Can suppress a raw fact when a returned observation already represents it. |
Hindsight’s best-practices documentation describes observations as evidence-grounded, consolidated knowledge that can be refined when new evidence supports, contradicts, or extends it. Observations can reduce duplication and capture durable patterns. However, recent raw retains may be available before background consolidation has caught up, so a search that prefers observations may not show the newest material in the same way as a raw-fact search.
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How should you choose recall or reflect settings?
Choose recall when the application should control the answer
Use recall when the agent needs ranked evidence and should decide how to combine it with the current conversation, tools, or other sources. A lower retrieval budget can suit a straightforward lookup; a middle setting offers an everyday balance; a higher budget can help when the answer depends on indirect connections or broader coverage. More retrieval is not automatically better: it can take more time and cost while adding material that is not useful to the question.
Choose reflect when Hindsight should synthesize
Use reflect when you want Hindsight to search and compose a response rather than return evidence alone. The response identifies the memories used, which helps an application inspect what informed the synthesis. Because reflection does more reasoning than retrieving ranked memories, it serves a different point in an agent’s workflow.
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Scope data deliberately
Memory banks provide data isolation, while tags can further scope retrieval. In a shared bank, Hindsight warns that retaining a user’s information without a user tag may make it globally visible. For user-scoped data, apply the appropriate tags consistently and use strict tag matching on retrieval rather than relying on a loose filter.
Also account for indexing timing: Hindsight’s best-practices documentation advises against retaining and recalling the new item in the same turn, because the retained memories are not yet indexed. A practical flow is to retain at the end of a turn and recall on the next turn.
What do Hindsight’s benchmark results establish?
The 2026 ACL paper reports 83.6% accuracy on LongMemEval and 83.2% on LoCoMo using a 20B open-source model. With Gemini-3 Pro, it reports 91.4% on LongMemEval. These are paper-reported results under the named benchmark and model conditions; they do not predict how a particular agent, retain mission, dataset, or retrieval configuration will perform.
The paper describes Hindsight as an MIT-licensed Python package and Docker image. Its benchmarks support the claim that the system was evaluated on long-term-memory tasks, not the stronger claim that any individual implementation will remember the right things or ignore the wrong ones without testing.
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