The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
These results are measured over continuous long-horizon sessions, not isolated turns. For example, SWE-bench runs 50 consecutive tasks per session to simulate the context-accumulation pressure of real-world long-horizon agents.
Overview
Memory is not about hoarding everything in the AI — it is about sparing humans from having to repeat themselves.
In practice, we constantly re-explain the same SOPs, project background, tool conventions, and output formats to the Agent. Such information should not require repetition, nor should it be indiscriminately dumped into the context.
TencentDB Agent Memory helps the Agent learn your workflows, retain task context, and reuse past experience. We reject both brute-force history accumulation and irreversible lossy summarization. Instead, we design memory as a layered system: symbolic memory for in-task information overload, and memory layering for cross-session experience.
Let the Agent remember what should be remembered, so people can focus on judgment, creation, and work that truly matters.
Core Technology: Reject Flat Storage, Embrace Layering and Symbolization
Our architecture rests on two pillars: memory layering and symbolic memory. Together they ensure Agents do not merely "remember more", but "reason better".
1. Memory Layering: Progressive Disclosure with Heterogeneous Storage
Traditional memory systems shred data into fragments and dump them into a flat vector store. Recall degenerates into a blind search across disconnected fragments, with no macro-level guidance.
Whether it is long-term knowledge, short-term tasks, or future skill capabilities, memory should never be flat — both its formation and its recall must be hierarchical. TencentDB Agent Memory adopts layering as its unified architectural paradigm:
Short-term context layering. The bottom layer archives raw tool outputs (refs/*.md); the middle layer extracts step-level summaries (jsonl); the top layer condenses state into a lightweight Mermaid canvas. The Agent only needs to attend to the top-layer structure in context, and drills down to the lower layers via node_id when an error occurs.
Long-term personalization layering. In place of flat logs, we build a semantic pyramid: L0 Conversation (raw dialogue) → L1 Atom (atomic facts) → L2 Scenario (scene blocks) → L3 Persona (user profile). The Persona layer carries day-to-day preferences; the system drills down to Atoms only when details matter.
Skill generation layering. Layering also applies to actions. The middle layer derives common solution patterns (Scenario) from bottom-layer execution traces (Conversation), and the top layer distills reusable Skills or standard SOPs (Persona).
Heterogeneous storage and progressive disclosure. A dual-layer storage strategy underpins this architecture. The bottom layer (facts, logs, traces) is persisted in databases for robust full-text retrieval; the top layer (personas, scenes, canvases) is stored as human-readable Markdown files for high information density and white-box inspection. Lower layers preserve evidence; upper layers preserve structure.
Full traceability and lossless recovery. Compression often sacrifices traceability. TencentDB Agent Memory avoids irreversible compression by maintaining a deterministic path from high-level abstractions back to ground-truth evidence. Whether it is an offloaded error log or a distilled user preference, the system guarantees a complete drill-down path: "top-layer symbol (Persona / canvas) → mid-layer index (Scenario / jsonl) → bottom-layer raw text (L0 Conversation / refs)".
2. Symbolic Memory: Maximum Semantics in Minimum Symbols (Mermaid Canvas)
In long tasks, the largest token consumers are verbose intermediate logs (search results, code, error traces). To address this, we combine context offloading with symbolic memory:
Mermaid symbol graph. Instead of verbose prose or flat JSON, we encode task state transitions in high-density Mermaid syntax — precise enough for LLMs to parse, concise enough for humans to read.
History offloading. Full tool logs are offloaded to external files; only a lightweight Mermaid task map remains in context.
node_id tracing. The Agent reasons over the symbol graph; to verify a detail, it greps for the node_id and instantly retrieves the full raw text — cutting token cost while preserving full traceability.
graph LR
Log["Verbose Logs<br/>(hundreds of thousands of tokens)"] -->|"1. Offload full text"| FS[("External FS<br/>(refs/*.md)")]
Log -->|"2. Extract relations"| MMD["Mermaid Canvas<br/>(with node_id)"]
MMD -->|"3. Light injection"| Agent(("Agent Context<br/>(a few hundred tokens)"))
Agent -. "4. Recall via node_id" .-> FS
style Log fill:#f1f5f9,stroke:#94a3b8,stroke-dasharray: 5 5,color:#475569
style FS fill:#f8fafc,stroke:#cbd5e1,stroke-width:2px,color:#334155
style MMD fill:#eff6ff,stroke:#3b82f6,stroke-width:2px,color:#1e3a8a
style Agent fill:#fffbeb,stroke:#f59e0b,stroke-width:2px,color:#92400e
Once enabled, TencentDB Agent Memory automatically handles conversation capture, memory extraction, scene aggregation, persona generation, and recall before the next turn.
1.3 Enable short-term compression (optional, requires version ≥ 0.3.4)
Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows - all through natural language commands.
Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More
