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Tencent Open-Sources Long-Term Memory for AI Agents Under MIT

July 7, 2026 at 11:45 EDT

AI Agents
Open Source
Software Dev & Coding

Tencent Cloud's TencentDB team has open-sourced TencentDB Agent Memory, a long-term memory system for AI agents, under the MIT license. It runs on local SQLite with no cloud dependency, and is pitched as cutting token usage while improving recall. The code is published on GitHub, and MarkTechPost reported details on May 23.

May 2026 · TencentDB Agent Memory

A Local-First Memory Engine That Cuts Agent Tokens and Sharpens Recall

Open-sourced under MIT and built on SQLite, it runs with zero external-API dependency — replacing opaque flat vector storage with a 4-tier, human-readable memory pyramid. Over 5,000 stars within weeks.

−61%

Peak token reduction (WideSearch)

48→76%

Recall accuracy (PersonaMem)

Semantic memory tiers (L0→L3)

Token usage before vs. after

Measured over 50 consecutive long-horizon tasks. Columns scaled per benchmark (baseline = full height).

221M

86M

WideSearch
−61%

3474M

2375M

SWE-bench
−33%

112M

77M

AA-LCR
−31%

Baseline With Agent Memory

The 4-tier semantic pyramid — progressive disclosure

L3 · Persona
User profile & decisions

L2 · Scenario
Grouped scenes & summaries

L1 · Atom
Atomic facts

L0 · Conversation
Raw logs — consulted only when evidence is needed

Upper layers surface summaries; lower layers are queried on demand. Markdown at the top, SQLite full-text + sqlite-vec below — inspectable all the way down from a node_id to the original log.

What developers praise

60%+ token cut with better recall
Traceable — readable in Markdown down to logs
Auto-capture convenient; retrieval "not random"
Local-first, MIT-licensed, no external APIs

Open questions

Capture can be "too reactive," needs explicit prompts
Benchmarks are Tencent's own evaluations
Calls for independent reproducibility
Little time since release — limited outside verification

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