Daemons by Charlie Labs vs…: Comparison (2026) | linkgo
Daemons by Charlie Labs vs GLIA — Persistent Memory for AI Coding Agents: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Daemons by Charlie Labs and GLIA — Persistent Memory for AI Coding Agents — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Daemons by Charlie Labs
Charlie Labs
Freemium
Always-on AI agents defined in markdown that work 24/7 across Slack, Linear, and GitHub without prompts.
Key features
Markdown-Defined Daemons: Author agent behavior in simple .md files that are easy to read, version, and customize.
Always-On Operation: Daemons run 24/7 and act proactively without requiring explicit prompts each time.
Multi-Tool Integration: Works across Slack, Linear, GitHub, and more to coordinate tasks where teams already operate.
Completed-Work Billing: A credit system charges only for finished work like bugfixes, features, or refactors, with PR reviews always free.
Engineering Automation: Keeps pull requests, issues, CI, and documentation moving so engineers focus on novel problems.
Best for
PR Maintenance: Keeping pull requests reviewed and moving without manual chasing.
Issue Triage: Proactively managing Linear and GitHub issues across the backlog.
Routine Refactors: Shipping small fixes and refactors automatically so engineers focus on harder work.
Docs Upkeep: Keeping documentation in sync as code and issues change.
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
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
