GLIA — Persistent Memory for AI Coding Agents vs jcode: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of GLIA — Persistent Memory for AI Coding Agents and jcode — 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
j
jcode
1jehuang
Open-source, resource-efficient coding agent harness built for multi-session workflows, deep customizability, and high performance.
Key features
- Multi-Session Workflows: Purpose-built to run many concurrent coding-agent sessions on a single machine without resource contention.
- Ultra-Low RAM Footprint: ~28 MB baseline for a single session with local embeddings off — several times leaner than comparable harnesses.
- Cross-Platform: First-class support for Linux, macOS, and Windows via GitHub Releases with Homebrew and source builds.
- Infinite Customizability: Harness internals are exposed for deep tweaking — providers, prompts, memory, and tooling can all be swapped.
- Provider-Agnostic: Configure your own LLM providers rather than being locked into one vendor.
- Benchmarks Included: Public benchmark suite at jcode.sh/bench so users can compare RAM, boot-up, and session performance against alternatives.
- Local Embedding Toggle: Turn local embedding on for retrieval-heavy work or off to minimize resource usage.
- Community Support: Active Discord community and dedicated docs site for onboarding and customization help.
Best for
- Running Ten Agents in Parallel: A developer spins up a coding agent per repo and lets them work in parallel without exhausting RAM.
- Low-Resource Machines: Use jcode on older laptops or cloud VMs where heavier harnesses eat too much memory to be practical.
- Custom Harness for a Specific Stack: Deeply customize prompts, tools, and providers to match a language or company codebase.
- Benchmark-Driven Selection: Teams evaluating agent harnesses use jcode's published metrics to compare performance apples-to-apples.
- Self-Hosted Coding Agents: Bring your own LLM provider (local or cloud) to avoid vendor lock-in on a proprietary harness.