AgentLoop vs GLIA — Persistent Memory for AI Coding Agents: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of AgentLoop and GLIA — Persistent Memory for AI Coding Agents — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
AgentLoop
Edward Yi
AgentLoop turns a single ChatGPT plan into unattended Codex worker + independent-critic cycles that build against your local rubric until the work passes.
Key features
- Fresh Worker Per Cycle: Each build cycle spawns a clean Codex worker with fresh context so long-running loops do not accumulate stale state or memory drift.
- Independent Critic Process: A separate fresh process grades every result against your rubric so passing tests never become permission to stop looking.
- Rubric in GUIDELINES.md: Definition-of-done lives as plain Markdown in your repo and is read on every cycle, so standards persist while prompts do not.
- Evidence Carried in Files: Worker output, critic verdicts, and fixes are written to project files so the next cycle inherits the actual state of the work.
- Bounded Goal + Cycle Budget: You cap the loop with a goal.md and cycle budget so unattended runs stop at a predictable ceiling.
- MCP Status Interface: Ask ChatGPT for status through MCP so you can monitor cycles, verdicts, transcripts, and cost without opening the dashboard.
- Local-first Install: git clone the pinned v1.1.0 release and run node src/daemon.js — no npm install, no hosted workspace, MIT licensed.
Best for
- Shipping a bounded feature: Add a CSV export across UI, API, and regression suite while the critic enforces end-to-end behavior and edge cases.
- Migration work: Run an unattended migration where fresh workers apply the change and the critic verifies each step against a rubric.
- Hardening pass: Give AgentLoop a hardening goal so it iterates on defects the existing test suite misses, like malformed input handling.
- Product polish loop: Point AgentLoop at a polish goal with clear acceptance criteria and let it converge to VERDICT: PASS.
- Unattended overnight runs: Kick off a long loop, monitor cycle verdicts, and cancel from the dashboard or via MCP when the receipt looks right.
- Enforcing team standards: Codify team engineering standards in GUIDELINES.md so every worker builds against the same definition of done.
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