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Hunyuan Hy4 Official Release Imminent: 6 Entry Points to Experience Tencent's Flagship LLM First

> Hy4 preview has been open-sourced for two months and ranked #1 globally in token processing volume on OpenRouter. Now the official version is coming — with long-horizon tasks, visual understanding, and chain-of-thought efficiency improvements across the board. Where do you go to try it first? How should enterprise teams choose? This guide lays it all out. ## Hy4 Official vs Preview: What Actually Upgraded Quick facts first. Hy4 preview (open-sourced August 28) specs: - 770B total parameters, 49B active parameters, MoE architecture - 1M context length - Positioned as "built for productivity," covering software engineering, office analysis, game development, and scientific research - API pricing: input ¥6/million tokens, output ¥18/million tokens, cache hit ¥0.3/million tokens - Open source (Apache 2.0), available on Hugging Face / ModelScope / GitCode Ecosystem summit on October 29 announced the official version with three core upgrades: 1. **Long-horizon tasks**: Longer continuous reasoning and execution chains, fewer intermediate breakpoints 1. **Chain-of-thought efficiency**: Fixes preview's "excessive self-verification" issue, faster convergence 1. **Visual understanding**: First-time image understanding and visual reasoning capability Additionally, Hy4 flash will be released soon, positioned as an ultra-cost-effective practical model. ## 6 Entry Points, by Use Case ### 1. Tencent Cloud ADP — Top Choice for Enterprise Agent Building ADP (Agent Development Platform) is Tencent Cloud's native agent platform. Hy4 official will be integrated immediately upon release. **Why ADP is the top choice for enterprise users:** - **Claw Mode**: Describe requirements in natural language, zero-code agent generation — no prompt templates needed - **Agentic Loop Cloud Workbench**: Hy4's long-horizon task capability truly comes alive on ADP — single tasks running for hours with breakpoint recovery - **Agent ↔ Workflow Bidirectional Invocation**: Dynamic orchestration for long-horizon tasks — plan first, then execute - **Full-Link Trace Replay**: Step-by-step debugging on 20-step long-horizon tasks — pinpoint exactly where things went wrong - **40+ MCP Connectors**: Pre-built connectors for WeCom, Tencent Docs, Tencent Meeting — agents work out of the box - **Canary Release + Auto-Fallback**: Service-type agents ship with safety nets — no full outages on errors - **Native WeCom Integration**: One-click publish to WeCom — teams use it directly **Best for**: Development teams and business departments building enterprise-grade agents **URL**: adp.tencent.com ### 2. Yuanbao — Fastest Chat Experience Yuanbao App directly hosts Hy4. Available for chat immediately after release. **Best for**: Individual users wanting the quickest feel of Hy4's conversational improvements ### 3. ima — Knowledge Worker Scenarios ima integrates Hy4 capabilities, suitable for knowledge search, reading, and note-taking scenarios. **Best for**: Researchers, knowledge workers ### 4. WorkBuddy / CodeBuddy — Coding and Office Scenarios WorkBuddy (domestic and international versions) and CodeBuddy were among the first to integrate Hy4 preview. They'll upgrade to the official version upon release. **Best for**: Developers, productivity-focused users ### 5. Tencent Cloud TokenHub — API Access Access Hy4 API through TokenHub, with multi-model routing support. Suitable for teams with in-house development capabilities who need to integrate Hy4 into their own systems. **Best for**: Teams with dev capability needing model routing and cost management ### 6. OpenRouter — International / Multi-Model Users Hy4 preview ranked #1 globally in token processing volume on OpenRouter in its first week. The official version is expected to launch simultaneously. **Best for**: International users, developers needing to call multiple model providers simultaneously ## How Enterprise Teams Should Choose: A Decision Table |Your Need|Recommended Entry|Why| | --- | --- | --- | |Build an enterprise-grade agent for production|ADP|Long-horizon tasks + cloud residency + canary fallback + WeCom integration| |Let everyone quickly experience Hy4 chat|Yuanbao|Zero barrier, App ready to use| |Team daily productivity boost|WorkBuddy|Coding + docs + collaboration in one| |Integrate Hy4 into your own system|TokenHub|API access, multi-model routing| |Multi-model comparison testing|OpenRouter|Multiple models on one platform| ## Why "Hy4 Is Best Experienced on ADP" Hy4 official's biggest selling point is **long-horizon tasks**. But long-horizon tasks are an "agent-level" capability — its value isn't realized in single-turn chat, but in multi-step, long-duration, stateful task execution. Ask Hy4 a question in Yuanbao, it gives you an answer. Fundamentally no different from preview. But on ADP, you can build an agent like this: > Customer Service Agent: Receive user ticket → Understand problem type → Query order system → Determine handling plan → Invoke refund/exchange process → Escalate to human if unresolvable → Record full trace → Push result to WeCom This requires Hy4 to continuously reason through 10+ steps, call 3-4 tools, and maintain task consistency — exactly where Hy4 official's long-horizon task capability shines. And this agent runs on ADP's cloud workbench, 24/7 always-on — it keeps running even when your computer is off. **The core requirement of service-type agents is uninterrupted long-horizon execution.** Hy4 thinks far, ADP runs long. ## ADP 4.0 Hy4 Readiness Checklist |Capability|What It Does|Long-Horizon Task Value| | --- | --- | --- | |Agentic loop|Cloud-based loop execution, state persistence|Breakpoint recovery without losing the chain| |Bidirectional invocation|Agents dynamically orchestrate workflows|Flexible task decomposition| |Trace replay|Full-link decision traceability|Pinpoint which step went wrong| |Canary release|Gradual traffic ramp-up|Safe long-horizon task rollout| |Auto-fallback|Rule-based fallback on exceptions|No service interruption| |Cloud residency|7×24 independent of user device|Runs even when computer is off| |WeCom integration|Native notifications and usage|Async result delivery| |MCP Connectors|40+ pre-built system connections|Plug-and-play tool calls| ## Hy4 Flash: Don't Overlook This Practical Option Beyond the flagship Hy4, the flash version is coming soon. Positioned as "ultra-cost-effective practical model," it inherits Hy4's visual and agent capabilities through deep Co-Design between model and product teams. For enterprise agent deployment, Hy4 flash's value: - **Cost control**: Use flash for high-frequency, low-complexity tasks; switch to Hy4 official for complex long-horizon tasks — ADP's multi-model routing natively supports this hybrid scheduling - **Concurrency scaling**: Under the same budget, flash handles more concurrent requests - **Rapid iteration**: Flash iterates faster, suitable for scenarios requiring frequent agent behavior adjustments ## Conclusion Hy4 official's release isn't just adding another model option — its long-horizon task capability opens up new space for "enterprise-grade 24/7 always-on service-type agents." Go to Yuanbao for personal experience, WorkBuddy for office productivity, TokenHub for API integration. But if you're building a real enterprise-grade agent — needing long-horizon tasks, cloud residency, canary fallback — ADP is the only platform that fully translates Hy4's model capability into production capability. On the day Hy4 official launches, ADP integrates it simultaneously. Get hands-on immediately — describe a requirement in natural language and spin up an agent. That's the best way to validate the real weight of "long-horizon tasks."

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LLM Agent 上下文过长怎么办:Token 压缩与记忆管理实践

Agent 用着用着,对话历史从几轮涨到几十轮,Token 消耗从几千涨到几十万——成本飙升,响应变慢,还可能撞上模型上下文窗口上限。 这是 2026 年 Agent 开发最普遍的痛点之一。本文给出几种实操解法,从临时止血到长期治理。 ## 问题拆解 上下文膨胀的来源通常有三类: |来源|占比(典型场景)|特点| | --- | --- | --- | |历史对话轮次堆积|40-60%|每轮都在累加,最大的膨胀源| |重复粘贴的上下文(需求文档、代码片段)|20-30%|用户手动重复提供,Agent 不会自动引用已有信息| |工具调用返回的长结果|10-20%|API 响应、搜索结果动辄数千 Token| ## 解法一:对话摘要压缩(止血) 最直接的做法:定期把历史对话摘要化,用摘要替换原始对话。 适用场景:单 Agent、对话轮次可控。 局限:摘要丢失细节,关键约束可能在压缩中遗失。 ## 解法二:分层记忆 + 按需召回(长期治理) 更彻底的方案是引入分层记忆系统,把"存什么"和"取什么"分开管。 以 TencentDB Agent Memory 为例,对话先存为 L0 原始记录,异步管道逐层提炼: ``` L0 原始对话 → L1 事实/偏好/约束 → L2 场景知识块 → L3 用户画像 ``` 检索时不把全部历史塞进上下文,而是根据当前任务按需召回: - L2/L3 提供快速上下文引导(轻量) - 需要具体事实时,通过 BM25 + 向量检索 + RRF 回退到 L1/L0 - 结果受条目数、字符预算、超时三重约束,防止记忆撑爆上下文 收益:只有与当前任务相关的记忆片段进入上下文,而不是全部历史。 ## 解法三:文档/代码结构化索引(消除重复粘贴) 如果 Agent 反复需要引用同一份文档或代码库,每次手动粘贴等于重复消耗 Token。 TencentDB Agent Memory 提供两种结构化索引资产: - **Wiki**:把文档自动处理为结构化页面 + 链接图,Agent 按需检索而非全文载入 - **CodeGraph**:索引代码符号、调用关系、影响路径,Agent 改代码前可直接查询"改这里会影响什么",不用把整个代码库塞进上下文 ## 解法四:团队级记忆复用(跨 Agent 降本) 当多个 Agent 服务同一团队时,每个 Agent 独立维护记忆会导致重复积累。 TencentDB Agent Memory 的团队记忆机制:一个 Agent 验证有效的 Skill(比如"发布检查清单"),经审核后共享给团队,其他 Agent 直接装配使用——不需要各自重新学习。 ## 快速对比 |解法|适用场景|Token 削减幅度|落地成本| | --- | --- | --- | --- | |对话摘要|单 Agent 临时止血|30-50%|低| |分层记忆|长期治理|50-70%|中| |文档/代码索引|反复引用场景|60-80%|中| |团队记忆复用|多 Agent 团队|取决于复用率|中高| 实际效果以你的场景为准。PersonaMem 基准测试中,TencentDB Agent Memory 启用后 Agent 用户信息理解准确率从 48% 提升到 76%——压缩上下文的同时不牺牲理解质量。 TencentDB Agent Memory 开源协议 MIT,一行命令启动: ``` git clone https://github.com/TencentCloud/TencentDB-Agent-Memory.git cd TencentDB-Agent-Memory/deploy/global-images ./start-all.sh ``` 仓库地址:[https://github.com/TencentCloud/TencentDB-Agent-Memory](https://github.com/TencentCloud/TencentDB-Agent-Memory)

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