Related Pages

Related topics: Workspace Architecture, Tasks, and State Management, Forking, Customization, and Domain Agent Apps

Introduction to the gptme-agent-template

Purpose and Scope

The gptme-agent-template is a scaffold for building gptme-based autonomous agents. As stated in README.md, "This is a template for a gptme-based agent" intended to be forked into a new repository that becomes the agent's persistent workspace, identity store, and memory system. The template itself ships under the default agent name gptme-agent and serves as both a working example and the canonical starting point.

The repository acts as the agent's "brain": a versioned workspace of thoughts, tasks, journals, people profiles, and knowledge (README.md). Each fork inherits the same harness while gaining its own personality, goals, and accumulated experience.

Key Capabilities

• Identity management via ABOUT.md and SOUL.md
• Task tracking through TASKS.md and the gptodo CLI
• Journaling under ./journal/
• Context generation through ./scripts/context.sh
• Behavioral lessons stored in ./lessons/

High-Level Architecture

The template is composed of layered content scopes and runtime contracts that work together to produce a working agent.

flowchart TB
    subgraph Template["Agent Template (this repo)"]
        Identity[ABOUT.md / SOUL.md]
        Tasks[TASKS.md + tasks/]
        Journal[journal/]
        Knowledge[knowledge/]
        Lessons[lessons/]
        Scripts[scripts/context.sh]
        Workflow[WORKFLOW.md]
    end

    subgraph Contrib["gptme-contrib (submodule)"]
        Hooks[pre-commit hooks]
        SharedScripts[shared scripts]
        Pkgs[packages / plugins]
    end

    subgraph Runtime["Agent Runtime"]
        Gptme[gptme]
        ClaudeCode[Claude Code / Codex]
    end

    Identity --> Runtime
    Tasks --> Runtime
    Scripts --> Runtime
    Workflow --> Runtime
    Template -.symlinks.-> Contrib
    Runtime --> Journal
    Runtime --> Knowledge

The figure shows three concerns: identity and memory files in the template, shared assets pulled in from the gptme-contrib submodule, and the runtime harness that consumes the contract.

Content Layers

A critical architectural concept documented in ARCHITECTURE.md is the layered nature of agent workspaces. Each layer has a distinct scope and update cadence.

The rule of thumb from the architecture documentation: "if the content is generic and useful across agents, it should live in contrib with the template symlinking to it. If it's workspace structure or identity, it lives in the template directly" (ARCHITECTURE.md). This separation keeps generic assets reusable while preserving agent-specific identity.

Workspace Structure

The template organizes agent state into well-defined directories, each with a clear responsibility:

• state/ — Work queue management with queue-manual.md and queue-generated.md, following a two-queue system (README.md).
• scripts/ — Automation and utilities, including context.sh (main context orchestrator) and gptodo (task management CLI from gptme-contrib).
• lessons/ — Behavioral patterns that prevent known failure modes, documented in lessons/README.md.
• knowledge/ — Long-form technical designs, forking guides, and reusable patterns (e.g. knowledge/forking-workspace.md, knowledge/portable-agent-apps.md).
• people/ — Profiles of collaborators and contacts the agent interacts with.
• projects/ — Symlinks to projects the agent works on, as described in projects/README.md.

The journal follows a date-based layout (journal/YYYY-MM-DD/) and uses templates to ensure consistent entries — a convention introduced in v0.3 (release notes).

Forking and Identity

Forking the template is the canonical way to create a new agent. Two paths are supported per README.md:

1. Programmatic: gptme-agent create <path>
2. Manual: clone the repo, initialize submodules, and run ./scripts/fork.sh <path> [<agent-name>]

The forking process is guided by knowledge/forking-workspace.md, which specifies what to copy (structure, configs), what to clear (personal content, identity files, people profiles), and how to initialize a new identity. After the fork, ABOUT.md is customized with the agent's personality, tools, goals, and values (ABOUT.md), while SOUL.md carries the runtime persona.

Runtime Contracts

The template supports multiple agent runtimes through shared instruction files. AGENTS.md and CLAUDE.md define identical core rules — file operations with absolute paths, conventional commit style, the default master branch, and pre-commit hook expectations.

The runtimes differ in how they load identity:

• gptme auto-loads identity files via gptme.toml and runs scripts/context.sh dynamically (CLAUDE.md).
• Claude Code / Codex auto-loads only the instruction file; agents must manually read bootstrap files (ABOUT.md, ARCHITECTURE.md, TASKS.md) and invoke scripts/context.sh themselves.

For autonomous operation, WORKFLOW.md provides a repo-versioned workflow contract with YAML front-matter for runtime configuration and Markdown body for instructions. Placeholders like {{WORKSPACE}}, {{DATE}}, and {{SESSION_HASH}} are injected at run time by scripts/workflow-render.py.

Common Failure Modes and Lessons

The lessons/README.md documentation emphasizes that lessons use a two-file format: a concise primary file (30–50 lines) plus an optional companion in knowledge/lessons/ for deep context. Keywords must be precise multi-word phrases — single generic words like git or python pollute the context window and should be avoided.

Known portability pitfalls are catalogued in knowledge/portable-agent-apps.md, which warns against "fork traps": apps that cannot state what survives an update. The Update Preservation Rule requires every reusable app to (1) mark generated files, (2) keep user-owned files out of generated paths, (3) preserve tasks/, journal/, secrets, and profiles, (4) detect drift before overwriting, and (5) run validation after every update.

See Also

• gptme Documentation — Background on gptme agents
• ARCHITECTURE.md — Detailed system design
• WORKFLOW.md — Autonomous workflow contract
• Release Notes v0.4 — Latest template updates

Related Pages

Related topics: Introduction to the gptme-agent-template, Autonomous Operation, Multi-Backend Support, and Infrastructure

Workspace Architecture, Tasks, and State Management

Overview

The gptme-agent-template repository provides a forkable agent workspace structure used as the foundation for autonomous AI agents built with gptme. The workspace combines a layered content model, a task-tracking system, journal logging, a knowledge base, a people directory, and a session-level state machine for autonomous runs. According to ARCHITECTURE.md, the design goal is to keep identity, work, and long-term knowledge cleanly separated so the template can be forked and customized for new agents without losing shared best practices.

The workspace is intentionally "repo-versioned": every long-lived artifact (tasks, journal entries, knowledge articles, queue state) lives as a Markdown file in the repository, making changes diffable, reviewable, and recoverable. The release notes for v0.3 highlight the move toward a simplified task directory and a CI workflow, which is why tasks now live directly under tasks/ rather than under CURRENT_TASK.md or tasks/all/ as in earlier revisions.

Workspace Architecture and Content Layers

The repository is organized into several content layers with different scopes and update cadences. ARCHITECTURE.md defines four layers; the table below summarizes how each contributes to the workspace.

The template uses git submodules so that shared content can be updated by pulling a new submodule pointer rather than rewriting files locally. Most files in the template are symlinks into gptme-contrib, while agent-specific files such as ABOUT.md, SOUL.md, gptme.toml, and README.md are kept under local control. Source: ARCHITECTURE.md.

Beyond content layers, the workspace has five functional areas:

• Tasks — single-source-of-truth work items under tasks/, validated by pre-commit hooks.
• Journal — append-only daily logs under journal/YYYY-MM-DD/HHMMSS-topic.md capturing progress, decisions, and reflections.
• Knowledge base — long-term reference material under knowledge/, including forking instructions and tooling guides.
• People directory — stable profiles under people/ for collaborators the agent interacts with.
• State — runtime queue and tracker files under state/ consumed by autonomous-runner scripts.

Each functional area is documented in its own file: TASKS.md, journal/templates/daily.md, and people/templates/person.md all ship templates that define the schema and expected sections for their respective content types.

Task System

The task system tracks work across sessions using YAML-frontmatter Markdown files. Each task carries an identifier (slug), a state, a created timestamp, optional priority, tags, and depends fields, followed by a Markdown body describing the work. Source: TASKS.md.

Tasks move through five lifecycle stages:

1. Creation — author a new file in tasks/ with frontmatter and body.
2. Activation — set state: active in the frontmatter and announce it in a journal entry.
3. Progress tracking — record updates in journal entries and tick subtasks.
4. Completion/cancellation — set state: done or state: cancelled with a final journal entry.
5. Pausing — set state: paused, document progress, and capture the reason.

The gptodo CLI (installed from gptme-contrib) is the canonical interface for listing, showing, and editing tasks without hand-editing frontmatter. Common commands documented in TASKS.md include gptodo status, gptodo list, gptodo show <id>, and gptodo edit <id> --set state active. Pre-commit hooks validate metadata format and values, ensuring that downstream tools can trust the fields. The v0.3 release notes confirm the task directory was simplified (no more CURRENT_TASK.md or tasks/all/) and CI was added to enforce these checks.

State Management and Autonomous Workflow

Runtime state for an autonomous agent lives under state/ and is driven by WORKFLOW.md, a repo-versioned contract consisting of YAML front matter (configuration) and a Markdown body (instructions with {{PLACEHOLDER}} substitution). Source: WORKFLOW.md.

Two queue files orchestrate which work is picked up next:

• state/queue-manual.md — the *primary* source. Maintained by the agent or operator, containing session reasoning, dependencies, and strategic notes for the next planned work items. Source: state/queue-manual.md.
• state/queue-generated.md — the *fallback* source. Auto-generated from tasks/ and GitHub issues to provide objective, fresh priorities when the manual queue is empty. Source: state/queue-generated.md.

The WORKFLOW front matter defines harness (allowed runtimes such as gptme and claude-code), tracker (currently gptodo with tasks_dir), session defaults (timeout, commit style, pre-commit requirement), and context.prebuilt (always-loaded files such as README.md, ABOUT.md, SOUL.md, ARCHITECTURE.md, and TASKS.md). Dynamic context is built by scripts/context.sh. Source: WORKFLOW.md.

A typical autonomous session follows four phases, as documented in the workflow body: (1) Setup — refresh the workspace and load context; (2) Planning — pick work from the manual queue or generated fallback, run make typecheck and make test; (3) Execution — make commits using conventional commit messages and update task state when complete; (4) Completion — log progress under journal/{{DATE}}/autonomous-session-{{SESSION_HASH}}.md, commit, and git push origin HEAD. The four-phase loop is invoked by runner scripts such as scripts/runs/autonomous/ described in the autonomous-run README.

See Also

• ARCHITECTURE.md — overall workspace design and content layers.
• TASKS.md — task frontmatter schema, lifecycle, and gptodo reference.
• WORKFLOW.md — runtime contract for autonomous sessions.
• knowledge/forking-workspace.md — how to fork the template into a new agent.
• Release v0.3 — note on simplified task structure and added CI.

Related Pages

Related topics: Workspace Architecture, Tasks, and State Management, Forking, Customization, and Domain Agent Apps

Autonomous Operation, Multi-Backend Support, and Infrastructure

Overview

The gptme-agent-template ships a self-contained infrastructure for running an agent autonomously on a schedule, while remaining agent-harness agnostic. Two parallel entry points — autonomous-run.sh for gptme and autonomous-run-cc.sh for Claude Code — execute the same CASCADE workflow contract declared in WORKFLOW.md, and are wired into a systemd user timer for unattended operation. Source: scripts/runs/autonomous/README.md.

This design decouples three concerns that are normally tangled in autonomous agents:

Architecture

flowchart LR
    Timer["systemd timer<br/>(agent-autonomous.timer)"]
    Service["systemd service<br/>(agent-autonomous.service)"]
    Script["autonomous-run.sh<br/>or autonomous-run-cc.sh"]
    Contract["WORKFLOW.md<br/>(contract)"]
    Queue["state/queue-manual.md<br/>fallback: queue-generated.md"]
    Tasks["tasks/*.md"]
    Journal["journal/YYYY-MM-DD/"]
    Git["git push origin HEAD"]

    Timer -->|OnCalendar| Service
    Service -->|ExecStart| Script
    Script -->|reads| Contract
    Script -->|reads| Queue
    Queue -.->|refreshed from| Tasks
    Script -->|writes| Journal
    Script -->|commits &| Git

Source: scripts/runs/autonomous/README.md, WORKFLOW.md.

The CASCADE Workflow Contract

WORKFLOW.md is the repo-versioned contract between the harness and the agent. Its YAML front matter is consumed by scripts/workflow-render.py at runtime, while the Markdown body carries the agent instructions. Source: WORKFLOW.md.

The front matter declares:

• harness.allowed — the list of supported runtimes, defaulting to gptme and including claude-code. Source: WORKFLOW.md:1-15.
• tracker.kind — the task system in use (gptodo).
• session.default_timeout — wall-clock cap per run (45 minutes by default).
• session.commit_style — conventional commits enforced via require_precommit.
• hooks.post_commit — automatic git push origin HEAD after each successful commit.
• context.prebuilt and context.dynamic_cmd — the set of files always preloaded into the model context, plus a shell command that injects dynamic context before each run.

The Markdown body defines four phases — Loose Ends → Task Selection → Execution → Completion — and explicitly states that "there is always Tier 3 work available", discouraging empty NOOP sessions. Source: WORKFLOW.md.

Multi-Backend Harness Support

Multi-backend support is implemented as two parallel shell scripts rather than a single script with branches:

Both scripts implement the same CASCADE phases and read the same WORKFLOW.md contract; only the final gptme / claude-code invocation differs. This keeps each backend's quirks (argument shape, tool-call protocol) localized to one file. Source: scripts/README.md.

The harness.allowed list in WORKFLOW.md is the single source of truth for which backends are valid; forks should add new harnesses there and ship a matching autonomous-run-<harness>.sh plus a *.service.example unit.

Scheduling with systemd

The template ships example unit files in dotfiles/.config/systemd/user/:

• agent-autonomous.service.example — runs autonomous-run.sh once per activation.
• agent-autonomous.timer.example — triggers the service on a OnCalendar schedule.
• agent-autonomous-cc.service.example — Claude Code counterpart.

The README notes the default schedule is hourly, with common variants being every 15 minutes (*:0/15) or three times a day (*-*-* 06,10,14:00:00). Source: scripts/runs/autonomous/README.md.

Operators inspect state with:

systemctl --user status agent-autonomous.timer
journalctl --user -u agent-autonomous.service --since "1 hour ago"
journalctl --user -u agent-autonomous.service -f

Source: scripts/runs/autonomous/README.md.

Queue System & Task Selection

The autonomous run resolves its next action through a two-queue priority scheme:

1. state/queue-manual.md — PRIMARY source. Manually curated, contains rich session reasoning, dependencies, and strategic notes. If populated, it overrides everything.
2. state/queue-generated.md — FALLBACK source. Auto-generated from tasks/*.md and open GitHub issues before each run. The template for this file is a plain Markdown stub that lists a "Current Run" header and a "Planned Next" section. Source: state/queue-generated.md.

Tasks themselves live directly under tasks/ as individual Markdown files with YAML frontmatter (state, created, priority, tags, depends). A pre-commit hook validates the metadata, and the gptodo CLI surfaces them via gptodo status, gptodo list --sort state, etc. Source: TASKS.md.

Safety Classifications

The autonomous-run script tags each operation class with a traffic light so the agent (or its operator) can decide without re-deriving policy:

• GREEN — code, tests, docs, refactoring: execute autonomously.
• YELLOW — social media, email: follow established patterns.
• RED — financial, major decisions: escalate to a human.

These classifications are intentionally templated and must be customized per fork to reflect the operator's risk tolerance. Source: scripts/runs/autonomous/README.md.

Common Failure Modes

The README enumerates the three most frequent breakages:

1. Script exits immediately — usually a missing WORKSPACE path or gptme not on PATH.
2. git pull fails — SSH keys not loaded for the systemd user, or no network.
3. Timeouts — SCRIPT_TIMEOUT is too short for the chosen schedule; the README recommends 3000s for hourly runs and 6000s for every-two-hour runs.

Source: scripts/runs/autonomous/README.md.

Additional failure surfaces referenced by WORKFLOW.md:

• Pre-commit regressions — require_precommit: true causes the run to abort if make typecheck / make test fail; agents must fix regressions, not bypass hooks.
• Template drift — agents forked from this template will diverge as the template evolves; the v0.2 release notes emphasize running template-comparison checks before each run. Source: ARCHITECTURE.md.

Customization Checklist

Before going live on a fork, the README and WORKFLOW.md front matter recommend adjusting:

• harness.default and per-harness model_routing in WORKFLOW.md.
• The timer's OnCalendar and SCRIPT_TIMEOUT.
• Pre-run validation hooks (e.g. scripts/validate-workspace.sh).
• Safety classifications to match your domain.
• Manual queue content — an empty queue-manual.md will silently fall through to auto-generation.

See Also

• ARCHITECTURE.md — overall workspace architecture and content layers.
• TASKS.md — task lifecycle, metadata, and gptodo CLI reference.
• scripts/README.md — full script catalog including journal migration and search utilities.
• knowledge/forking-workspace.md — fork hygiene so template updates land cleanly.

Related Pages

Related topics: Introduction to the gptme-agent-template, Autonomous Operation, Multi-Backend Support, and Infrastructure

Forking, Customization, and Domain Agent Apps

Overview and Purpose

The gptme-agent-template is designed as a forkable agent architecture: a foundation for creating new agents that share a robust infrastructure while developing their own identity, goals, and workflows. Forking is the primary mechanism for distributing the template, and v0.2 specifically called out "improved forking" and "more pre-commit checks to make sure new forks are good" as release highlights (v0.2 release).

The template is intentionally split into three concerns that the forking process must respect:

1. Forking — the mechanical act of copying the workspace and resetting identity-bearing content.
2. Customization — the design of *what* should be customized (identity, tasks, journals) versus *what* must remain generic (architecture, tools, content layers).
3. Domain Agent Apps — a higher-level concept of packaging a workflow as a reusable app with a single source of truth for the workflow, separated into system and user layers.

Source: knowledge/agent-forking.md:1-14

Forking an Agent

The Forking Concept

Forking in this template is not a one-shot code fork on GitHub. It is a workspace copy operation in which the new repository inherits the template's directory structure, scripts, configurations, and documentation templates, but starts fresh on personal, identity-bearing state. The original template repository (gptme-agent-template) is treated as a parent that new agents can sync updates from.

Source: knowledge/agent-forking.md:1-14

What Stays and What Gets Cleared

The forking process distinguishes carefully between content that defines the system and content that defines the individual agent. The table below summarizes the policy described in the template's forking documentation.

Source: knowledge/forking-workspace.md:13-60

Fork Creation Steps

The documented procedure is intentionally simple:

1. Copy the workspace structure.
2. Clear personal content as described above.
3. Initialize a new identity (name, personality, goals, visual identity, social presence).
4. Update configurations (paths in gptme.toml, ABOUT.md, etc.).
5. Create the first task to give the agent a starting direction.

Source: knowledge/forking-workspace.md:42-49

For a customization checklist after the copy, the forking guide recommends defining identity (personality, communication style, boundaries, visual identity), purpose (goals, focus areas, success metrics), and relationships (creator, peers, boundaries).

Source: knowledge/agent-forking.md:17-40

Customization and Content Layers

The template composes shared content via git submodules. Each layer has a different scope, and the forking process must respect these boundaries to prevent duplication and staleness.

graph TD
    T["gptme-agent-template<br/>(All agents)"] -->|symlinks| C["gptme-contrib<br/>(All gptme users)"]
    T -->|copy+fork| W["Agent workspace<br/>(Single agent)"]
    O["Org-shared repo<br/>(e.g. gptme-superuser)"] -->|submodule| W
    C -.->|updates| T
    O -.->|updates| W

The relationship between the template and contrib repos is critical: most files in the agent template should be symlinks into gptme-contrib so that agents pick up updates by simply updating the contrib submodule. Examples include pre-commit hooks, lesson files, shared scripts, and validators.

Some files cannot be symlinks because they are agent-specific or require local customization: ABOUT.md, SOUL.md, gptme.toml, README.md, and task/journal content. The rule of thumb stated in the architecture doc is: *if the content is generic and useful across agents, it should live in contrib with the template symlinking to it; if it is workspace structure or identity, it lives in the template directly.*

Source: ARCHITECTURE.md:54-77

Forked agents will drift from the template over time. The architecture document recommends adding the template as a remote and diffing against it to detect new shared content that should be pulled in.

Source: ARCHITECTURE.md:78-83

Task System Customization

The task system was simplified in v0.3 to remove CURRENT_TASK.md and the tasks/all/ directory; tasks are now stored directly under tasks/ and validated by pre-commit hooks (v0.3 release). The optional gptodo CLI, installed from gptme-contrib, provides status, list, show, and edit operations. Task files use YAML frontmatter for state, created, priority, tags, and depends fields, and follow a lifecycle of creation → activation → progress tracking → completion/pause.

Source: TASKS.md:1-90

Domain Agent Apps (Portable Apps)

The template extends the forking concept with a higher-level abstraction: portable agent apps. An app is a repository that packages a domain workflow for an agent to operate — "more than a prompt and less than a bespoke runtime." Examples include a release manager, support triage operator, research assistant, personal finance operator, or self-monitoring agent.

Source: knowledge/portable-agent-apps.md:1-16

The Core Rule: One Source of Truth

The core rule is keep workflow truth in one place. Runtime-specific files (AGENTS.md, WORKFLOW.md, skill wrappers, foreign runtime exports) may point to that workflow, wrap it, or expose it through a schema, but they must not copy the workflow body by hand. The doc is explicit: *"If two files repeat the same domain procedure in full, one of them is already rotting."*

Source: knowledge/portable-agent-apps.md:18-23

System Layer vs. User Layer

Before adding features, the app should be split into two layers:

• System layer (upgradeable product surface): runtime entrypoints, commands, bundles, skills, shared procedures, scripts, templates, validators, and generated compatibility exports.
• User layer (private or identity-bearing state): profiles, preferences, local policy, tasks, journals, generated reports, run history, credentials, and long-lived local data stores.

System updates may replace or regenerate system-layer files; they must preserve user-layer files by default.

Source: knowledge/portable-agent-apps.md:25-45

Update Preservation Rule

Every reusable app needs an explicit update rule:

1. Mark generated files with their source or exporter.
2. Keep user-owned files out of generated paths.
3. Preserve tasks/, journal/, secrets, and local profiles by default.
4. Detect drift before overwriting user-edited system files.
5. Run validation after every update.

The doc states bluntly: *"If an app cannot say what survives an update, it is a fork trap."*

Source: knowledge/portable-agent-apps.md:65-72

Common Pitfalls and Best Practices

• Over-copying workflow content. Duplicating a checklist in AGENTS.md, a skill, and a foreign runtime export leads to rot. Route all of them to a single commands/<name>.md owner.
• Losing template updates by editing symlinked files. Files that are symlinks into gptme-contrib will be overwritten by the next submodule update. Local customization belongs in non-symlinked files.
• Clearing too much during a fork. Stripping the knowledge base wholesale removes generic AI/ML content that should remain. Evaluate files individually rather than wiping directories.
• Forgetting pre-commit validation. v0.2 added pre-commit checks specifically to ensure new forks are good; failing to run them can produce a fork that drifts from the template's quality bar.
• Skipping the update preservation rule on a portable app. Without it, the app becomes a one-shot fork that cannot receive system-layer improvements without losing user state.

Source: knowledge/forking-workspace.md:13-60, knowledge/portable-agent-apps.md:18-72, v0.2 release notes

See Also

• Workspace Architecture
• Task Management System
• Lesson System
• Autonomous Run Infrastructure

Doramagic Pitfall Log

Found 7 structured pitfall item(s), including 0 high/blocking item(s). Top priority: Configuration risk - Configuration risk requires verification.

1. Configuration risk: Configuration risk requires verification

• Severity: medium
• Finding: Project evidence flags a configuration risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: capability.host_targets | github_repo:891962756 | https://github.com/gptme/gptme-agent-template

2. Capability evidence risk: Capability evidence risk requires verification

• Severity: medium
• Finding: README/documentation is current enough for a first validation pass.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: capability.assumptions | github_repo:891962756 | https://github.com/gptme/gptme-agent-template

3. Maintenance risk: Maintenance risk requires verification

• Severity: medium
• Finding: Project evidence flags a maintenance risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: evidence.maintainer_signals | github_repo:891962756 | https://github.com/gptme/gptme-agent-template

4. Security or permission risk: Security or permission risk requires verification

• Severity: medium
• Finding: no_demo
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: downstream_validation.risk_items | github_repo:891962756 | https://github.com/gptme/gptme-agent-template

5. Security or permission risk: Security or permission risk requires verification

• Severity: medium
• Finding: no_demo
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: risks.scoring_risks | github_repo:891962756 | https://github.com/gptme/gptme-agent-template

6. Maintenance risk: Maintenance risk requires verification

• Severity: low
• Finding: issue_or_pr_quality=unknown。
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: evidence.maintainer_signals | github_repo:891962756 | https://github.com/gptme/gptme-agent-template

7. Maintenance risk: Maintenance risk requires verification

• Severity: low
• Finding: release_recency=unknown。
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: evidence.maintainer_signals | github_repo:891962756 | https://github.com/gptme/gptme-agent-template

Community Discussion Evidence

Doramagic exposes project-level community discussion separately from official documentation. Review these links before using gptme-agent-template with real data or production workflows.

• v0.4 - github / github_release
• v0.3: Simplified Task Structure & CI - github / github_release
• First update - github / github_release
• v0.1 - github / github_release
• Configuration risk requires verification - GitHub / issue