Dev builds temporal memory layer for agents using SVO + dual pgvector
u/Difficult-Net-6067 built a temporal memory layer called Smriti for multi-step agent pipelines, using SVO extraction and dual pgvector stores to track event sequences and causal chains across sessions.
Score breakdown
Smriti addresses a gap in agent memory tooling where existing approaches — vector search, prompt stuffing, and metadata timestamps — all fail to reliably preserve the ordered, causal sequence of events that multi-step and multi-agent pipelines depend on.
- 01The core problem identified was temporal context loss across sessions, not hallucination or tool use failures.
- 02Vector search was found to handle 'what' but not 'when'; system prompt stuffing hit context limits; metadata timestamps broke on overlapping/conflicting events.
- 03The solution uses SVO (Subject-Verb-Object) extraction combined with dual pgvector stores — one for semantic similarity, one for temporal ordering.
u/Difficult-Net-6067 describes a recurring pain point in multi-step agent development: not hallucination or tool use failures, but the loss of temporal context across sessions. The agent struggled to track who triggered what, in what order, and what changed after which action — a problem that standard vector search couldn't solve because it handles *what* but not *when*.
Stuffing everything into the system prompt hit context limits quickly.
Several common workarounds were tried and found lacking. Stuffing everything into the system prompt hit context limits quickly. Metadata timestamps worked in simple cases but broke down around overlapping events and conflicting states. The resulting tool, Smriti, addresses this by combining SVO (Subject-Verb-Object) extraction with a dual pgvector architecture: one store handles semantic similarity lookups, while the other is optimized for temporal ordering. The interface is designed around three API calls — ingest an event, query by time range or entity, and reconstruct a causal chain — with a reported query time of around 80ms. A free tier requires no account to test.
The post also opens a discussion around multi-agent setups, where event attribution becomes especially complex, and invites others who have tackled agent memory sequencing problems to share their approaches.
Key facts
- 01The core problem identified was temporal context loss across sessions, not hallucination or tool use failures.
- 02Vector search was found to handle 'what' but not 'when'; system prompt stuffing hit context limits; metadata timestamps broke on overlapping/conflicting events.
- 03The solution uses SVO (Subject-Verb-Object) extraction combined with dual pgvector stores — one for semantic similarity, one for temporal ordering.
- 04Three API calls cover the full workflow: ingest an event, query by time range or entity, and reconstruct a causal chain.
- 05Current query time is around 80ms.
- 06The tool, called Smriti, offers a free tier with no account required.
- 07The author flags multi-agent event attribution as a particularly messy open problem.
Topics
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