{"id":92596,"date":"2026-05-24T16:59:06","date_gmt":"2026-05-24T16:59:06","guid":{"rendered":"https:\/\/youzum.net\/tencent-open-sources-tencentdb-agent-memory-a-4-tier-local-memory-pipeline-for-ai-agents\/"},"modified":"2026-05-24T16:59:06","modified_gmt":"2026-05-24T16:59:06","slug":"tencent-open-sources-tencentdb-agent-memory-a-4-tier-local-memory-pipeline-for-ai-agents","status":"publish","type":"post","link":"https:\/\/youzum.net\/ja\/tencent-open-sources-tencentdb-agent-memory-a-4-tier-local-memory-pipeline-for-ai-agents\/","title":{"rendered":"Tencent Open-Sources TencentDB Agent Memory: A 4-Tier Local Memory Pipeline for AI Agents"},"content":{"rendered":"<p class=\"wp-block-paragraph\">Tencent has released <a href=\"https:\/\/github.com\/Tencent\/TencentDB-Agent-Memory\/tree\/main\" target=\"_blank\" rel=\"noreferrer noopener\">TencentDB Agent Memory<\/a>, an open-source memory system for AI agents. The project ships under the MIT license. It targets a problem familiar to anyone shipping long-horizon agents: context bloat and recall failure.<\/p>\n<p class=\"wp-block-paragraph\">It is symbolic short-term memory along with layered long-term memory. It integrates with OpenClaw as a plugin and with the Hermes Agent through a Gateway adapter. The default backend is local SQLite with the sqlite-vec extension, so no external API is required.<\/p>\n<h2 class=\"wp-block-heading\"><strong>Why agent memory is hard<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">Most current memory stacks shred data into fragments and dump them into a flat vector store. Recall then becomes a blind similarity search across disconnected fragments, with no macro-level guidance. The architecture rests on two pillars: memory layering and symbolic memory.<\/p>\n<h2 class=\"wp-block-heading\"><strong>A 4-tier semantic pyramid<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">For long-term personalization, TencentDB Agent Memory builds a four-level pyramid instead of a flat log. The layers are L0 Conversation, L1 Atom, L2 Scenario, and L3 Persona. These correspond to raw dialogue, atomic facts, scene blocks, and a user profile.<\/p>\n<p class=\"wp-block-paragraph\">The Persona layer carries day-to-day user preferences and is queried first. The system drills down to Atoms or raw Conversations only when finer detail is needed. Lower layers preserve evidence; upper layers preserve structure.<\/p>\n<p class=\"wp-block-paragraph\">Storage is heterogeneous. Facts, logs, and traces are persisted in databases for full-text retrieval. Personas, scenes, and canvases are stored as human-readable Markdown files. Layered memory artifacts live under <code>~\/.openclaw\/memory-tdai\/<\/code>.<\/p>\n<h2 class=\"wp-block-heading\"><strong>Symbolic short-term memory via Mermaid<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">Long-running agent tasks consume tokens through verbose tool logs, search results, code, and error traces. TencentDB Agent Memory addresses this through context offloading combined with symbolic memory.<\/p>\n<p class=\"wp-block-paragraph\">Full tool logs are offloaded to external files under <code>refs\/*.md<\/code>. State transitions are encoded in Mermaid syntax inside a lightweight task canvas. The agent reasons over the symbol graph in its context window.<\/p>\n<p class=\"wp-block-paragraph\">When it needs the raw text, it greps for a <code>node_id<\/code> and retrieves the corresponding file. The Tencent dev team describes this as a deterministic drill-down from top-layer symbol to mid-layer index to bottom-layer raw text.<\/p>\n<h2 class=\"wp-block-heading\"><strong>Benchmark numbers<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">Results are measured over continuous long-horizon sessions, not isolated turns. SWE-bench, for example, runs 50 consecutive tasks per session to simulate context-accumulation pressure.<\/p>\n<p class=\"wp-block-paragraph\">On WideSearch, integrating the plugin with OpenClaw raises pass rate from 33% to 50%, a 51.52% relative improvement. Token usage drops from 221.31M to 85.64M, a 61.38% reduction.<\/p>\n<p class=\"wp-block-paragraph\">On SWE-bench, success climbs from 58.4% to 64.2% while tokens fall from 3474.1M to 2375.4M, a 33.09% reduction. On AA-LCR, the success rate moves from 44.0% to 47.5%. Tokens drop from 112.0M to 77.3M, a 30.98% reduction.<\/p>\n<p class=\"wp-block-paragraph\">For long-term memory, PersonaMem accuracy rises from 48% to 76%. Note: these numbers come from Tencent\u2019s own evaluations.<\/p>\n<h2 class=\"wp-block-heading\"><strong>Recall and retrieval<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">Retrieval defaults to a hybrid strategy. The system combines BM25 keyword search with vector embeddings, fused using Reciprocal Rank Fusion (RRF). Developers can switch to pure <code>keyword<\/code> or <code>embedding<\/code> mode through a config field. The BM25 tokenizer supports both Chinese (jieba) and English.<\/p>\n<p class=\"wp-block-paragraph\">Default settings trigger an L1 memory extraction every five turns. A user persona is generated every 50 new memories. Recall returns five items by default with a 5-second timeout. On timeout, the system skips injection rather than blocking the conversation.<\/p>\n<h2 class=\"wp-block-heading\"><strong>Installation and developer surface<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">The OpenClaw integration ships as a single npm package: <code>@tencentdb-agent-memory\/memory-tencentdb<\/code>. The project requires Node.js 22.16 or higher. Enabling it takes one config flag. The plugin then handles conversation capture, memory extraction, scene aggregation, persona generation, and recall.<\/p>\n<p class=\"wp-block-paragraph\">For Hermes, a Docker image bundles the agent, the plugin, and the TDAI Memory Gateway. The default model is Tencent Cloud\u2019s DeepSeek-V3.2. Any OpenAI-compatible endpoint works through the <code>MODEL_PROVIDER=custom<\/code> flag.<\/p>\n<p class=\"wp-block-paragraph\">Two tools are exposed to agents during a session: <code>tdai_memory_search<\/code> and <code>tdai_conversation_search<\/code>. Both return references with <code>node_id<\/code> and <code>result_ref<\/code> fields for traceback. A Tencent Cloud Vector Database (TCVDB) backend is also available as an alternative to local SQLite.<\/p>\n<h2 class=\"wp-block-heading\"><strong>Marktechpost\u2019s Visual Explainer<\/strong><\/h2>\n<p><title>TencentDB Agent Memory \u2014 Preview<\/title><\/p>\n<div>\n<div class=\"tg-header\">\n<p class=\"tg-eyebrow\">Open Source \u00a0\/\u00a0 Tencent<\/p>\n<h2 class=\"tg-title\">TencentDB Agent Memory<\/h2>\n<p class=\"tg-subtitle\">A quick-start guide to fully local, 4-tier long-term memory for AI agents.<\/p>\n<\/div>\n<p><span class=\"tg-step-line\"><\/span><\/p>\n<div class=\"tg-stage\">\n<div class=\"tg-slides\">\n<div class=\"tg-slide active\" data-slide=\"1\">\n<p class=\"tg-slide-num\">01 \u00a0\/\u00a0 OVERVIEW<\/p>\n<h3 class=\"tg-slide-title\">What is TencentDB Agent Memory?<\/h3>\n<p class=\"tg-slide-lead\">An MIT-licensed memory system for AI agents that combines symbolic short-term memory with a 4-tier long-term memory pipeline. Runs fully local with zero external API dependencies.<\/p>\n<div class=\"tg-grid-2\">\n<div class=\"tg-card\">\n<h5>Short-term memory<\/h5>\n<p>Offloads verbose tool logs to files and keeps a compact Mermaid task canvas in context.<\/p>\n<\/div>\n<div class=\"tg-card\">\n<h5>Long-term memory<\/h5>\n<p>Distills conversations into a 4-tier semantic pyramid: L0 \u2192 L1 \u2192 L2 \u2192 L3.<\/p>\n<\/div>\n<div class=\"tg-card\">\n<h5>Local backend<\/h5>\n<p>Defaults to SQLite + sqlite-vec. Tencent Cloud Vector Database (TCVDB) is optional.<\/p>\n<\/div>\n<div class=\"tg-card\">\n<h5>Integrations<\/h5>\n<p>Ships as an OpenClaw plugin and a Hermes Agent Docker image.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"tg-slide\" data-slide=\"2\">\n<p class=\"tg-slide-num\">02 \u00a0\/\u00a0 ARCHITECTURE<\/p>\n<h3 class=\"tg-slide-title\">The 4-Tier Semantic Pyramid<\/h3>\n<p class=\"tg-slide-lead\">Long-term memory is layered, not flat. Upper layers carry structure; lower layers preserve evidence.<\/p>\n<div class=\"tg-pyramid\">\n<div class=\"tg-layer tg-layer-l3\"><span class=\"tg-layer-name\">L3 \u00b7 Persona<\/span><span class=\"tg-layer-desc\">User profile (persona.md)<\/span><\/div>\n<div class=\"tg-layer tg-layer-l2\"><span class=\"tg-layer-name\">L2 \u00b7 Scenario<\/span><span class=\"tg-layer-desc\">Scene blocks (Markdown)<\/span><\/div>\n<div class=\"tg-layer tg-layer-l1\"><span class=\"tg-layer-name\">L1 \u00b7 Atom<\/span><span class=\"tg-layer-desc\">Atomic facts (JSONL)<\/span><\/div>\n<div class=\"tg-layer tg-layer-l0\"><span class=\"tg-layer-name\">L0 \u00b7 Conversation<\/span><span class=\"tg-layer-desc\">Raw dialogue<\/span><\/div>\n<\/div>\n<div class=\"tg-callout\">Drill-down path: Persona \u2192 Scenario \u2192 Atom \u2192 Conversation. References use <code>node_id<\/code> and <code>result_ref<\/code> for deterministic traceback.<\/div>\n<\/div>\n<div class=\"tg-slide\" data-slide=\"3\">\n<p class=\"tg-slide-num\">03 \u00a0\/\u00a0 SYMBOLIC SHORT-TERM<\/p>\n<h3 class=\"tg-slide-title\">Mermaid task canvas + context offloading<\/h3>\n<p class=\"tg-slide-lead\">Verbose intermediate logs are the largest token consumers in long tasks. The plugin offloads them to disk and keeps a high-density symbol graph in context.<\/p>\n<div class=\"tg-section\">\n<h4>How it works<\/h4>\n<ul>\n<li>Full tool logs are offloaded to <code>refs\/*.md<\/code> under the data directory.<\/li>\n<li>State transitions are encoded in Mermaid syntax inside a lightweight task canvas.<\/li>\n<li>The agent reasons over the symbol graph, then greps a <code>node_id<\/code> to pull raw text.<\/li>\n<\/ul>\n<\/div>\n<div class=\"tg-callout\">Storage path on disk: <code>~\/.openclaw\/memory-tdai\/<\/code>. All artifacts are human-readable for white-box debugging.<\/div>\n<\/div>\n<div class=\"tg-slide\" data-slide=\"4\">\n<p class=\"tg-slide-num\">04 \u00a0\/\u00a0 INSTALL<\/p>\n<h3 class=\"tg-slide-title\">Install the OpenClaw plugin<\/h3>\n<p class=\"tg-slide-lead\">Requires Node.js 22.16 or higher and an OpenClaw installation.<\/p>\n<pre><code><span class=\"tg-comment\"># Install the npm package as an OpenClaw plugin<\/span>\nopenclaw plugins install @tencentdb-agent-memory\/memory-tencentdb\nopenclaw gateway restart<\/code><\/pre>\n<div class=\"tg-section\">\n<h4>Zero-config enable<\/h4>\n<p>Add the following to <code>~\/.openclaw\/openclaw.json<\/code> to turn it on with default SQLite + sqlite-vec.<\/p>\n<\/div>\n<pre><code>{\n  \"memory-tencentdb\": {\n    \"enabled\": true\n  }\n}<\/code><\/pre>\n<\/div>\n<div class=\"tg-slide\" data-slide=\"5\">\n<p class=\"tg-slide-num\">05 \u00a0\/\u00a0 CONFIGURATION<\/p>\n<h3 class=\"tg-slide-title\">Daily-tuning parameters<\/h3>\n<p class=\"tg-slide-lead\">Every field has a sensible default. The most common knobs are listed below.<\/p>\n<div class=\"tg-table-wrap\">\n<table>\n<thead>\n<tr>\n<th>Field<\/th>\n<th>Default<\/th>\n<th>Description<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><code>storeBackend<\/code><\/td>\n<td>sqlite<\/td>\n<td>Storage backend<\/td>\n<\/tr>\n<tr>\n<td><code>recall.strategy<\/code><\/td>\n<td>hybrid<\/td>\n<td>keyword \/ embedding \/ hybrid (RRF)<\/td>\n<\/tr>\n<tr>\n<td><code>recall.maxResults<\/code><\/td>\n<td>5<\/td>\n<td>Items returned per recall<\/td>\n<\/tr>\n<tr>\n<td><code>recall.timeoutMs<\/code><\/td>\n<td>5000<\/td>\n<td>Skip injection on timeout<\/td>\n<\/tr>\n<tr>\n<td><code>pipeline.everyNConversations<\/code><\/td>\n<td>5<\/td>\n<td>L1 extraction every N turns<\/td>\n<\/tr>\n<tr>\n<td><code>persona.triggerEveryN<\/code><\/td>\n<td>50<\/td>\n<td>Generate persona every N memories<\/td>\n<\/tr>\n<tr>\n<td><code>offload.enabled<\/code><\/td>\n<td>false<\/td>\n<td>Short-term compression toggle<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<div class=\"tg-slide\" data-slide=\"6\">\n<p class=\"tg-slide-num\">06 \u00a0\/\u00a0 SHORT-TERM COMPRESSION<\/p>\n<h3 class=\"tg-slide-title\">Enable Mermaid offloading (v0.3.4+)<\/h3>\n<p class=\"tg-slide-lead\">Three steps to turn on context offload for long-horizon tasks.<\/p>\n<div class=\"tg-section\">\n<h4>Step 1 \u00b7 Enable offload in plugin config<\/h4>\n<\/div>\n<pre><code>{\n  \"memory-tencentdb\": {\n    \"config\": {\n      \"offload\": { \"enabled\": true }\n    }\n  }\n}<\/code><\/pre>\n<div class=\"tg-section\">\n<h4>Step 2 \u00b7 Register the slot so OpenClaw routes offload requests<\/h4>\n<\/div>\n<pre><code>{\n  \"plugins\": {\n    \"slots\": {\n      \"contextEngine\": \"openclaw-context-offload\"\n    }\n  }\n}<\/code><\/pre>\n<div class=\"tg-section\">\n<h4>Step 3 \u00b7 Apply the runtime patch (once per OpenClaw install)<\/h4>\n<\/div>\n<pre><code>bash scripts\/openclaw-after-tool-call-messages.patch.sh<\/code><\/pre>\n<\/div>\n<div class=\"tg-slide\" data-slide=\"7\">\n<p class=\"tg-slide-num\">07 \u00a0\/\u00a0 HERMES DOCKER<\/p>\n<h3 class=\"tg-slide-title\">Run memory-enabled Hermes in one container<\/h3>\n<p class=\"tg-slide-lead\">A single Docker image bundles Hermes Agent, the memory_tencentdb plugin, and the TDAI Memory Gateway.<\/p>\n<pre><code><span class=\"tg-comment\"># Build the image<\/span>\ndocker build -f Dockerfile.hermes -t hermes-memory .\n\n<span class=\"tg-comment\"># Run the container (default model: DeepSeek-V3.2 on Tencent Cloud LKE)<\/span>\ndocker run -d \n  --name hermes-memory \n  --restart unless-stopped \n  -p 8420:8420 \n  -e MODEL_API_KEY=\"your-api-key\" \n  -e MODEL_BASE_URL=\"https:\/\/api.lkeap.cloud.tencent.com\/v1\" \n  -e MODEL_NAME=\"deepseek-v3.2\" \n  -e MODEL_PROVIDER=\"custom\" \n  -v hermes_data:\/opt\/data \n  hermes-memory\n\n<span class=\"tg-comment\"># Health check<\/span>\ncurl http:\/\/localhost:8420\/health<\/code><\/pre>\n<div class=\"tg-callout\">Any OpenAI-compatible endpoint works through <code>MODEL_PROVIDER=custom<\/code>. Memory data persists in the <code>hermes_data<\/code> volume.<\/div>\n<\/div>\n<div class=\"tg-slide\" data-slide=\"8\">\n<p class=\"tg-slide-num\">08 \u00a0\/\u00a0 AGENT TOOLS &amp; RECALL<\/p>\n<h3 class=\"tg-slide-title\">What the agent sees<\/h3>\n<p class=\"tg-slide-lead\">Two tools are exposed to the agent during a session. Recall uses BM25 + vector + RRF fusion by default.<\/p>\n<div class=\"tg-grid-2\">\n<div class=\"tg-card\">\n<h5>tdai_memory_search<\/h5>\n<p>Search across L1 Atoms, L2 Scenarios, and L3 Persona.<\/p>\n<\/div>\n<div class=\"tg-card\">\n<h5>tdai_conversation_search<\/h5>\n<p>Search raw L0 Conversation history.<\/p>\n<\/div>\n<\/div>\n<div class=\"tg-section\">\n<h4>Retrieval defaults<\/h4>\n<ul>\n<li>Hybrid strategy: BM25 keyword + vector embedding, fused via Reciprocal Rank Fusion.<\/li>\n<li>BM25 tokenizer supports Chinese (jieba) and English.<\/li>\n<li>Returns 5 items per recall; 5000 ms timeout; on timeout it skips injection.<\/li>\n<li>References include <code>node_id<\/code> and <code>result_ref<\/code> for traceback.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<div class=\"tg-slide\" data-slide=\"9\">\n<p class=\"tg-slide-num\">09 \u00a0\/\u00a0 BENCHMARKS<\/p>\n<h3 class=\"tg-slide-title\">Reported gains with OpenClaw<\/h3>\n<p class=\"tg-slide-lead\">Measured over continuous long-horizon sessions, not isolated turns. SWE-bench runs 50 consecutive tasks per session.<\/p>\n<div class=\"tg-table-wrap\">\n<table>\n<thead>\n<tr>\n<th>Benchmark<\/th>\n<th>Baseline<\/th>\n<th>With Plugin<\/th>\n<th>\u0394 Pass<\/th>\n<th>\u0394 Tokens<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>WideSearch<\/td>\n<td>33%<\/td>\n<td>50%<\/td>\n<td>+51.52%<\/td>\n<td>\u221261.38%<\/td>\n<\/tr>\n<tr>\n<td>SWE-bench<\/td>\n<td>58.4%<\/td>\n<td>64.2%<\/td>\n<td>+9.93%<\/td>\n<td>\u221233.09%<\/td>\n<\/tr>\n<tr>\n<td>AA-LCR<\/td>\n<td>44.0%<\/td>\n<td>47.5%<\/td>\n<td>+7.95%<\/td>\n<td>\u221230.98%<\/td>\n<\/tr>\n<tr>\n<td>PersonaMem<\/td>\n<td>48%<\/td>\n<td>76%<\/td>\n<td>+59%<\/td>\n<td>\u2014<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class=\"tg-callout\">Numbers come from Tencent\u2019s own evaluations and reflect the integration with OpenClaw.<\/div>\n<\/div>\n<div class=\"tg-slide\" data-slide=\"10\">\n<p class=\"tg-slide-num\">10 \u00a0\/\u00a0 RESOURCES<\/p>\n<h3 class=\"tg-slide-title\">Where to go next<\/h3>\n<p class=\"tg-slide-lead\">Documentation, source code, and community channels.<\/p>\n<div class=\"tg-grid-2\">\n<div class=\"tg-card\">\n<h5>Source code<\/h5>\n<p>github.com\/Tencent\/TencentDB-Agent-Memory<\/p>\n<\/div>\n<div class=\"tg-card\">\n<h5>npm package<\/h5>\n<p>@tencentdb-agent-memory\/memory-tencentdb<\/p>\n<\/div>\n<div class=\"tg-card\">\n<h5>License<\/h5>\n<p>MIT<\/p>\n<\/div>\n<div class=\"tg-card\">\n<h5>Roadmap<\/h5>\n<p>Portable memory, automatic Skill generation, visual debugging dashboard.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<p><span class=\"tg-step-line\"><\/span><\/p>\n<div class=\"tg-controls\">\n<div class=\"tg-dots\"><\/div>\n<div class=\"tg-nav\">\n<button class=\"tg-btn\" aria-label=\"Previous slide\">\u2039<\/button><br \/>\n<span class=\"tg-counter\">01 \/ 10<\/span><br \/>\n<button class=\"tg-btn\" aria-label=\"Next slide\">\u203a<\/button>\n<\/div>\n<\/div>\n<p><span class=\"tg-step-line\"><\/span><\/p>\n<div class=\"tg-tagline\">\n<p class=\"tg-tagline-text\">Curated by <strong>MARKTECHPOST<\/strong> \u00a0\u00b7\u00a0 AI Research, Engineered for Builders<\/p>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Tencent has released TencentDB Agent Memory, an open-source memory system for AI agents. The project ships under the MIT license. It targets a problem familiar to anyone shipping long-horizon agents: context bloat and recall failure. It is symbolic short-term memory along with layered long-term memory. It integrates with OpenClaw as a plugin and with the Hermes Agent through a Gateway adapter. The default backend is local SQLite with the sqlite-vec extension, so no external API is required. Why agent memory is hard Most current memory stacks shred data into fragments and dump them into a flat vector store. Recall then becomes a blind similarity search across disconnected fragments, with no macro-level guidance. The architecture rests on two pillars: memory layering and symbolic memory. A 4-tier semantic pyramid For long-term personalization, TencentDB Agent Memory builds a four-level pyramid instead of a flat log. The layers are L0 Conversation, L1 Atom, L2 Scenario, and L3 Persona. These correspond to raw dialogue, atomic facts, scene blocks, and a user profile. The Persona layer carries day-to-day user preferences and is queried first. The system drills down to Atoms or raw Conversations only when finer detail is needed. Lower layers preserve evidence; upper layers preserve structure. Storage is heterogeneous. Facts, logs, and traces are persisted in databases for full-text retrieval. Personas, scenes, and canvases are stored as human-readable Markdown files. Layered memory artifacts live under ~\/.openclaw\/memory-tdai\/. Symbolic short-term memory via Mermaid Long-running agent tasks consume tokens through verbose tool logs, search results, code, and error traces. TencentDB Agent Memory addresses this through context offloading combined with symbolic memory. Full tool logs are offloaded to external files under refs\/*.md. State transitions are encoded in Mermaid syntax inside a lightweight task canvas. The agent reasons over the symbol graph in its context window. When it needs the raw text, it greps for a node_id and retrieves the corresponding file. The Tencent dev team describes this as a deterministic drill-down from top-layer symbol to mid-layer index to bottom-layer raw text. Benchmark numbers Results are measured over continuous long-horizon sessions, not isolated turns. SWE-bench, for example, runs 50 consecutive tasks per session to simulate context-accumulation pressure. On WideSearch, integrating the plugin with OpenClaw raises pass rate from 33% to 50%, a 51.52% relative improvement. Token usage drops from 221.31M to 85.64M, a 61.38% reduction. On SWE-bench, success climbs from 58.4% to 64.2% while tokens fall from 3474.1M to 2375.4M, a 33.09% reduction. On AA-LCR, the success rate moves from 44.0% to 47.5%. Tokens drop from 112.0M to 77.3M, a 30.98% reduction. For long-term memory, PersonaMem accuracy rises from 48% to 76%. Note: these numbers come from Tencent\u2019s own evaluations. Recall and retrieval Retrieval defaults to a hybrid strategy. The system combines BM25 keyword search with vector embeddings, fused using Reciprocal Rank Fusion (RRF). Developers can switch to pure keyword or embedding mode through a config field. The BM25 tokenizer supports both Chinese (jieba) and English. Default settings trigger an L1 memory extraction every five turns. A user persona is generated every 50 new memories. Recall returns five items by default with a 5-second timeout. On timeout, the system skips injection rather than blocking the conversation. Installation and developer surface The OpenClaw integration ships as a single npm package: @tencentdb-agent-memory\/memory-tencentdb. The project requires Node.js 22.16 or higher. Enabling it takes one config flag. The plugin then handles conversation capture, memory extraction, scene aggregation, persona generation, and recall. For Hermes, a Docker image bundles the agent, the plugin, and the TDAI Memory Gateway. The default model is Tencent Cloud\u2019s DeepSeek-V3.2. Any OpenAI-compatible endpoint works through the MODEL_PROVIDER=custom flag. Two tools are exposed to agents during a session: tdai_memory_search and tdai_conversation_search. Both return references with node_id and result_ref fields for traceback. A Tencent Cloud Vector Database (TCVDB) backend is also available as an alternative to local SQLite. Marktechpost\u2019s Visual Explainer TencentDB Agent Memory \u2014 Preview Open Source \u00a0\/\u00a0 Tencent TencentDB Agent Memory A quick-start guide to fully local, 4-tier long-term memory for AI agents. 01 \u00a0\/\u00a0 OVERVIEW What is TencentDB Agent Memory? An MIT-licensed memory system for AI agents that combines symbolic short-term memory with a 4-tier long-term memory pipeline. Runs fully local with zero external API dependencies. Short-term memory Offloads verbose tool logs to files and keeps a compact Mermaid task canvas in context. Long-term memory Distills conversations into a 4-tier semantic pyramid: L0 \u2192 L1 \u2192 L2 \u2192 L3. Local backend Defaults to SQLite + sqlite-vec. Tencent Cloud Vector Database (TCVDB) is optional. Integrations Ships as an OpenClaw plugin and a Hermes Agent Docker image. 02 \u00a0\/\u00a0 ARCHITECTURE The 4-Tier Semantic Pyramid Long-term memory is layered, not flat. Upper layers carry structure; lower layers preserve evidence. L3 \u00b7 PersonaUser profile (persona.md) L2 \u00b7 ScenarioScene blocks (Markdown) L1 \u00b7 AtomAtomic facts (JSONL) L0 \u00b7 ConversationRaw dialogue Drill-down path: Persona \u2192 Scenario \u2192 Atom \u2192 Conversation. References use node_id and result_ref for deterministic traceback. 03 \u00a0\/\u00a0 SYMBOLIC SHORT-TERM Mermaid task canvas + context offloading Verbose intermediate logs are the largest token consumers in long tasks. The plugin offloads them to disk and keeps a high-density symbol graph in context. How it works Full tool logs are offloaded to refs\/*.md under the data directory. State transitions are encoded in Mermaid syntax inside a lightweight task canvas. The agent reasons over the symbol graph, then greps a node_id to pull raw text. Storage path on disk: ~\/.openclaw\/memory-tdai\/. All artifacts are human-readable for white-box debugging. 04 \u00a0\/\u00a0 INSTALL Install the OpenClaw plugin Requires Node.js 22.16 or higher and an OpenClaw installation. # Install the npm package as an OpenClaw plugin openclaw plugins install @tencentdb-agent-memory\/memory-tencentdb openclaw gateway restart Zero-config enable Add the following to ~\/.openclaw\/openclaw.json to turn it on with default SQLite + sqlite-vec. { &#8220;memory-tencentdb&#8221;: { &#8220;enabled&#8221;: true } } 05 \u00a0\/\u00a0 CONFIGURATION Daily-tuning parameters Every field has a sensible default. The most common knobs are listed below. 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