How does Agent Guild prevent agents from losing track of long-term projects?

Agent Guild functions as a local protocol that manages task state and long-term memory outside of the model's context window. It prevents "context drift" by allowing agents to save their progress to disk and resume specific tasks with all relevant history and rules intact.

Which AI agents or frameworks are compatible with this skill?

This skill is designed to be framework-agnostic and works with any AI agent capable of executing Python 3.9+ CLI commands. Because it relies on a local filesystem protocol rather than specific API integrations, you can use it to coordinate work between different models or platforms.

Where is my task data and memory stored?

All data, including task checkpoints and agent identities, is stored locally on your machine (defaulting to `~/.agent-guild/`). It does not require a cloud subscription or external database, ensuring your work cycles remain private and under your control.

What is included in the purchase and how do I get started?

Installation is straightforward via a Python-based setup, and the package includes the core CLI tool, the handoff protocol documentation, and pre-configured templates for agent identities. Once installed, you can immediately begin checkpointing tasks and managing persistent memory.

How is this different from just asking an agent to remember things in a prompt?

Unlike simple prompts that get buried as a conversation grows, this skill provides a structured storage budget and "preview-first" maintenance. It allows you to load only the specific task data you need, preventing the performance degradation and "hallucinations" caused by overloaded context windows.

agent guild

Local-first shared memory and task handoff protocol for persistent agent work cycles and session isolation.

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agent guild

Example session with this skill installed

I need to step away. Can you save my progress on the React migration task and tell me what the next step is?

Read your context and instructions
Compiled the agent guild
Generated the document

I have created a checkpoint for your current task. The state, including the completed component refactors and the identified dependency blocks, is now saved to the local guild storage.

Task Checkpoint: React Migration

Field	Details
Task ID	`20231027-a1b2c3d4e5f6`
Status	`active`
Project	`frontend-refactor`
Revision	`5`
Summary	Completed `Button` and `Input` components; `AuthContext` is pending.

Progress Report

Done: Converted src/components/ui to TypeScript. Verified styles in Storybook.
Remaining: Refactor src/context/AuthContext.js to use the new hook pattern.
Decisions: Replaced styled-components with Tailwind to match the new design system per your instruction.
Evidence: npm test passed for all modified components. See snapshots/rev_5/test-results.log.

Next steps

Run ag task resume --id 20231027-a1b2c3d4e5f6 when you return to reload this context.
Begin refactoring AuthContext.js following the pattern in src/hooks/useAuth.ts.
Update the provider in App.tsx once the context is stable.

App.tsx

PDF · document

Generated

Example file from a real run - the skill writes it into your workspace.

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About this skill

Context window limits and session resets often cause agents to lose track of multi-step work. You end up repeating instructions or manually copying progress between chats.

What it does

Persistent memory manages long-term facts, identities, and rules across different sessions and agents.
Task checkpointing saves the full state of active work including goals, decisions, and evidence into isolated task packages.
Opt-in resumption prevents context pollution by listing pending tasks and only loading one when explicitly selected.
Cross-agent messaging enables structured communication between different agent identities without duplicating entire contexts.
History management enforces a configurable storage budget (default 1GiB) with preview-first maintenance plans to prevent bloat.

How it works

Bootstrap identity by loading agent-specific rules and universal preferences without injecting old task data.
List pending work using metadata-only queries to show active tasks and their status for user selection.
Resume task context to pull in the specific history, attachments, and next actions for the chosen work item.
Checkpoint progress during the session to save milestones, changed files, and updated implementation details.
Archive on completion to move the entire task package to a long-term storage directory for audit and reference.

Frameworks & tools

The skill is built with a Python 3.9+ CLI and utilizes standard library modules. It operates entirely on local filesystem storage within ~/.agent-guild/ or a custom directory.

Why this beats prompting it yourself

Manual prompts for "remembering" things are fragile and often get truncated or ignored as conversations grow. This skill provides a structured CLI-backed protocol that ensures work state is saved to disk, not just held in volatile model context.

Use cases

Manage long-running software migrations across multiple days and sessions.
Handoff complex research tasks from one specialized agent to another.
Maintain a consistent persona and set of coding standards across all local projects.
Audit past decisions by reviewing archived task packages and implementation evidence.

Known limitations

Does not provide distributed locking for multi-device sync or automated credential management. Requires Python 3.9+ available on the host machine.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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agent guild