AgentLoop vs Weavable – Persistent work context for AI agents: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of AgentLoop and Weavable – Persistent work context for AI 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.
W
Weavable – Persistent work context for AI agents
Weavable
Persistent, structured work-context layer that ingests, scopes, and serves live context from business tools to AI agents via a unified endpoint.
Key features
- Pre-built Connectors: Ingests updates and records from HubSpot, Jira, Slack, Zendesk, Notion and other common business systems to centralize source data.
- Preprocessing & Structuring: Extracts entities, relationships, timelines and summaries from raw updates to convert scattered signals into structured, machine-friendly context.
- Context Scoping: Filters and scopes context per agent, workflow, or role to deliver only the relevant slice of data and reduce prompt size and noise.
- Single MCP Endpoint: Serves scoped, maintained context to any agent or orchestration layer through a unified endpoint, simplifying integration and routing.
- Persistence & Live Updates: Maintains continuous, up-to-date work context so agents retain continuity across sessions and reflect recent system changes.
- Policy & Maintenance Controls: Configurable rules for retention, update frequency, and scoping to keep context accurate, curated, and privacy-compliant.
- Single MCP endpoint to serve scoped context to any agent
- Pre-processes and scopes context from external tools (HubSpot, Jira, Slack, Zendesk, Notion, etc.)
- Persistent, live work context with relationship extraction and maintenance
- Structured context delivery suitable for agent workflows (reduces ad-hoc plumbing)
- Real-time or near-real-time synchronization of updates from integrated systems
- Designed to be stack-agnostic — can integrate with multiple SaaS platforms
- Scoping and filtering to provide only relevant context per task or workflow
Best for
- Customer Support Assistants: Provide chat agents with live, consolidated context from Zendesk, Slack, and CRM history so they resolve tickets faster with up-to-date background.
- Sales Personalization: Equip outreach or proposal-generation agents with scoped HubSpot CRM timelines and contact relationships for tailored messaging and accurate follow-ups.
- Incident Response Automation: Feed Jira issues, Slack incident channels, and change logs into responder agents to accelerate diagnosis and remediation with relevant recent events.
- Knowledge Worker Augmentation: Supply drafting or summarization agents with curated project context and documents from Notion and other sources to produce coherent reports.
- Cross-System Workflows: Orchestrate multi-step automations that require synchronized state across tools by giving orchestration agents a single source of scoped truth.
- Agent Testing & Development: Let developers iterate on agent behavior using stable, replayable context slices instead of rebuilding environment plumbing for each test.
- Supplying customer history and ticket context to conversational support agents
- Feeding scoped CRM and deal context to sales automation agents
- Providing developer or ops agents with up-to-date issue and deployment context from Jira/Slack
- Orchestrating multi-tool workflows where agents need consolidated, persistent state
- Enabling knowledge retrieval and action-taking agents with curated, live document/context slices