GLIA — Persistent Memory for AI Coding Agents vs Wisry: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of GLIA — Persistent Memory for AI Coding Agents and Wisry — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
GLIA — Persistent Memory for AI Coding Agents
Eshaan Nair (ArcRift / Glia-AI)
Persistent local memory layer and MCP server that syncs browser chat context with coding agents via a shared SQLite knowledge graph.
Key features
- Browser Extension Capture: Chrome extension detects chat sessions, captures conversation content, and can start or isolate project memory to prevent context bleed between new chats and projects.
- MCP Server Tools: Native MCP server exposes agent-callable tools such as recall_context and store_memory so IDE-based coding agents can programmatically fetch relevant project memory at session start and persist decisions at session end.
- Local Knowledge Graph Backend: Uses a local SQLite-backed knowledge graph to store condensed project summaries, decisions, and context, keeping data local, auditable, and fast to query.
- Dual-Mode Operation: Two primary modes — extension-driven (auto-connect/inject) for browser-first workflows and MCP server-driven tool calls for IDE-integrated agents — both read/write the same unified memory store.
- Context Injection & New-Chat Detection: Supports manual 'Inject Context' to paste summaries into chat input and detects new-chat events to start fresh project contexts and avoid unintentional context carryover.
- Shared, Immediate Sync: Memory saved from the browser extension is immediately available to recall_context calls in coding tools (and vice versa), enabling cross-environment continuity and collaborative workflows.
- Durable key-value memory for agent state and decisions
- Memory lifecycle and salience management
- Contradiction detection and safety heuristics
- Multi-agent coordination (Researcher / Supervisor pattern)
- Reference implementation / integrations for coding tools
- Chrome extension that intercepts browser chat sessions and saves context to a local knowledge graph
- Native MCP server exposing tool APIs for coding agents (recall_context, store_memory and other ArcRift tools)
- Local SQLite-backed knowledge graph as the unified backend for extension and MCP server
- New-chat detection to reset active session and prevent context bleed between projects
- Inject Context action to paste knowledge-graph summaries into chat input for one-time context pushes
- Shared memory between browser and IDE agents — saves via extension are immediately available to MCP recall calls
- One-command setup packages (arcrift-setup; legacy installers glia-ai-setup and synq-setup)
- Seven callable ArcRift tools for coding-agent workflows (including recall_context and store_memory)
- Designed to integrate with multiple chat providers (ChatGPT, Claude.ai, Gemini, DeepSeek) and coding agents (Cursor, Claude Code, Windsurf)
Best for
- Rehydrate Project Context: When resuming work after days or switching chats, a coding agent calls recall_context to load prior design choices, architecture notes, and decision history so the agent produces consistent recommendations.
- IDE-Agent State Persistence: An AI coding assistant (e.g., Claude Code or Cursor) stores code-review decisions and rationale via store_memory so future sessions won't contradict earlier architectural constraints.
- Cross-Channel Continuity: Engineers who discuss system design in web chat (ChatGPT/Claude) can sync those conversations to their local agent in the IDE, ensuring knowledge is available where code is written.
- Forensic Decision Logs: Teams can maintain an auditable trail of agent-assisted decisions and context summaries for postmortems or compliance, since the knowledge graph preserves saved memory entries.
- Preventing Repetition and Conflict: By recalling project-specific constraints and prior choices, the tool reduces time spent re-explaining context and prevents agents from suggesting actions that conflict with earlier decisions.
- One-off Context Injection: For ad-hoc assistance, users can inject a summarized knowledge-graph snippet directly into a chat to provide targeted context without enabling continuous sync.
- Maintain project context across coding sessions in IDE agents
- Allow coding agents to recall past design decisions and constraints
- Robust long-running experiments and automated systems design
- Persist and reuse test results and metrics across agent workflows
- Preserve project-specific chat context across multiple chat sessions and tools to avoid re-explaining decisions
- Preload relevant project memory into coding agents at session start (via recall_context) to improve continuity
- Persist decisions and design choices after a coding session (via store_memory) so future agents can reuse them
- Inject summarized project context into web chat inputs for ad-hoc context boosts without enabling auto-connect
- Local/self-hosted setups for teams or individuals prioritizing data privacy and control over memory storage
- Unifying browser chat and IDE agent workflows so conversational discoveries immediately inform coding agents
Wisry
Wisry
Agentic ad platform that reverse-engineers the ads already winning in your market, rebuilds them for your brand, and launches them to Meta and Google.
Key features
- Competitive ad research agents: Analyze the ads currently performing in your market and reverse-engineer the creative patterns behind them
- Evidence-backed angles: Produces a set of six messaging angles per run, each grounded in observed market performance rather than a generic template
- Brand-matched creative: Rebuilds winning concepts as static and video ads in the customer's own brand rather than reusing competitor assets
- Direct campaign launch: Pushes finished creative live to Meta and Google, optimized for return on ad spend
- End-to-end loop: Research, angles, creative and live campaign run as one continuous flow instead of separate tools and handoffs
- Trained on $1B+ ad spend: Creative and targeting models are built on a large base of historical advertising performance data
- Multi-model orchestration: Coordinates several leading foundation models rather than relying on a single provider
Best for
- An ecommerce brand entering a new category and wanting to see which creative angles already convert there before spending
- A performance marketer who needs a steady volume of fresh ad variations to fight creative fatigue
- A small DTC team without an in-house creative department producing static and video ads at agency cadence
- Testing six distinct messaging angles against each other instead of iterating on a single hypothesis
- Launching Meta and Google campaigns directly from the creative step rather than exporting assets to a separate campaign manager
- An agency scaling creative output across multiple ecommerce clients without proportional headcount