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Code Graph RAG vs TencentDB Agent Memory: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Code Graph RAG and TencentDB Agent Memory — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

C

Code Graph RAG

vitali87

Freemium

Multi-language monorepo RAG: Tree-sitter parses your codebase into a Memgraph knowledge graph so you can query, edit, and refactor in plain English.

Key features

  • Multi-Language Graph Ingest: Tree-sitter parses Python, TypeScript, TSX, JavaScript, Rust, Go, Java, C, C++, C#, PHP, Lua, and Dart into a single language-agnostic Memgraph schema.
  • Natural-Language Cypher: The interactive CLI turns plain-English questions into Cypher queries and answers grounded in the real code structure, not vector-only guesses.
  • AST-Based Surgical Editing: The agent edits code through structural patches with a diff preview before any change is applied.
  • Structural Search & Replace: ast-grep is exposed as an agent tool, so you match and rewrite by AST pattern across the whole codebase instead of regex.
  • Pluggable ast-grep Tier: Add a new language from a single YAML pattern file — Ruby was added this way with Module/Function/Class nodes plus import edges.
  • Data-Flow Tracing: FLOWS_TO taint edges follow values through assignments, function calls, and I/O sinks across C, Java, C#, and Go.
  • Dead-Code Detection: Walk call and reference edges from entry points to find functions and modules nothing reaches.
  • Shared Graph Across Projects: Index many repos into one shared graph and query across them; a `clean` subcommand resets from scratch with confirmation.

Best for

  • Monorepo Q&A: Ask 'where is refund logic in this monorepo?' and get grounded answers from a graph of the real code, not stale docs.
  • AI-Assisted Refactoring: Rename or restructure APIs across languages with AST patches and a diff preview before commit.
  • Cross-Language Data-Flow Audits: Trace a value through assignments and function calls to see where sensitive data ends up.
  • Dead-Code Cleanup: Find unreachable functions and modules by walking call edges from entry points.
  • Codebase Onboarding: Give a new engineer or agent a queryable graph they can explore in natural language.
  • Structural Migrations: Use ast-grep to rewrite deprecated patterns (imports, error handling, config lookups) across a polyglot codebase.
View Code Graph RAG details
T

TencentDB Agent Memory

Tencent Cloud

Free

Team-level memory hub for AI agents — layered long-term memory + symbolic short-term memory that cuts tokens 61% and lifts task success 51%.

Key features

  • Symbolic short-term memory: Offloads heavy tool logs and condenses task state into compact Mermaid symbol graphs, cutting in-context tokens dramatically.
  • Layered long-term memory: L0 Conversation → L1 Atom → L2 Scenario → L3 Persona semantic pyramid instead of flat vector storage.
  • Four reusable memory assets: Chat Memory, Skill, LLM-Wiki, and Code-Graph — governed, shared, and equipped across agents and frameworks.
  • Drill-down traceability: Deterministic path from every high-level abstraction back to raw evidence via node id — no irreversible lossy summarization.
  • Heterogeneous storage: Raw facts/logs in a database for full-text retrieval, top-layer personas and canvases as human-readable Markdown for inspection.
  • Benchmarked gains: -61.38% tokens and +51.52% relative pass rate on WideSearch with OpenClaw; +59% on PersonaMem accuracy across long-horizon sessions.
  • Zero-config with OpenClaw: Local SQLite + sqlite-vec backend by default; automatic conversation capture, memory extraction, and recall before each turn.
  • Hermes Gateway integration: Works with the Nous Research Hermes agent gateway for hosted agent deployments.

Best for

  • AI engineering teams running long-horizon coding agents (SWE-bench-style workloads) who need to cut input tokens and lift task success across a session.
  • Product teams building personal assistants that must remember user preferences across weeks of conversation without shipping the whole chat history to the model.
  • Agent framework authors who want a drop-in memory layer for OpenClaw or Hermes Gateway with symbolic + layered storage rather than a flat vector store.
  • Enterprise teams building a shared memory hub so multiple agents (support, dev, analyst) reuse the same personas, SOPs, and Code-Graph facts.
  • Research groups benchmarking agent memory approaches who need a reproducible open-source baseline with published PersonaMem and WideSearch numbers.
  • Cost-sensitive operators of long-running agents who want a traceable, auditable memory system that avoids lossy summarization.
View TencentDB Agent Memory details