Aside vs GLIA — Persistent Memory for AI Coding Agents: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Aside and GLIA — Persistent Memory for AI Coding Agents — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Aside
Aside Computer Inc.
A Chromium desktop browser with a built-in agent that signs in and completes real work across your logged-in sites.
Key features
- Agentic Browsing: The agent operates your logged-in websites directly - clicking, typing and navigating - so tasks that need no public API still get done.
- Local Memory: Browsing history is distilled into on-device memory files so the agent already knows which tools and accounts a recurring task involves.
- Agent Password Manager: Credentials are autofilled into pages through hardware-backed encryption and Secure Enclave storage, never handed to the model.
- Human Approval Gates: Sensitive steps such as payments, posts and outbound messages pause for your confirmation before the agent proceeds.
- Access Audit Log: Every credential use and scoped permission grant is recorded so you can see exactly what the agent touched and when.
- Routines: Scheduled recurring tasks, such as a 9am daily briefing, run on their own and drop results back into the browser.
- Bring Your Own Model: Connect an existing ChatGPT or Claude subscription or your own API key rather than paying twice for inference.
- Sandboxed Execution: Filesystem and network access are isolated with guardrails so agent runs cannot reach beyond what the task needs.
Best for
- Operations Backfill: Push the same record update through several internal dashboards that have no shared API.
- Recruiting Prep: Reopen a candidate profile viewed yesterday and assemble interview notes from the sites already visited.
- Inbox and Comment Triage: Draft replies, follow-ups and comment responses across email and social accounts under review.
- Daily Briefing: Schedule a routine that gathers overnight metrics and trending topics into one morning summary.
- Sales Research: Work through prospect sites and CRM screens to collect context before an outreach sequence.
- Spreadsheet and Document Work: Have the agent edit local files and web spreadsheets as part of a longer task.
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