Markdown-defined roles
Daemons are defined with simple `.md` files that live in a repository. The file captures the role, watch conditions, routines, deny rules, schedule, policy, and output format so behavior is explicit and portable.
Charlie Labs
Charlie Labs provides always-on AI daemons defined in Markdown files that work proactively across tools like Slack, Linear, GitHub, and Sentry. It is aimed at teams that want recurring maintenance, triage, and reporting work handled continuously with explicit approval controls.
What Charlie Labs does
Charlie Labs provides daemons: always-on AI processes that run proactively without prompts and are defined in simple Markdown files. The product is positioned for teams that want recurring operational and engineering work handled continuously across systems like Slack, Linear, GitHub, and Sentry.
The core idea is to describe a role once — including what the daemon watches, what it should do, what it must not do, and when it should ask for approval — then let it run with a guarded execution loop. That loop covers intake, context gathering, planning, execution, verification, reporting, and continued monitoring so routine work can move forward without constant manual prompting.
Pricing is organized around workspace usage limits rather than monthly credits. The published plans include a free tier, a Starter plan, and a Team plan, with overage available when a workspace needs more throughput than its included limits allow.
Core capabilities
Signal-driven execution
Daemons can watch for signals from systems such as GitHub, Linear, Slack, and Sentry, then convert those signals into scoped work. The execution model is designed around intake, context building, plan setting, execution, verification, and reporting.
Built-in approval controls
The workflow includes approval gates for high-impact or broader changes. Routine updates can run automatically inside policy boundaries, while riskier actions pause for a clear human decision.
Durable run artifacts
Each run produces durable artifacts such as run records, working context, code updates, checks, linked task IDs, and completion reports. The goal is to leave a visible trail from signal to final state.
Always-on maintenance
Daemons are built for recurring maintenance rather than one-off prompts. The home page describes them as proactive, always-on processes that keep PRs, issues, ownership, priorities, code, dependencies, docs, and runbooks from drifting.
Workspace-wide access
The pricing page notes unlimited team members and no maximum number of daemons on the listed plans. That makes the model suitable for shared workspace usage rather than per-seat workflow gating.
Common ways teams use daemons
PR hygiene and review support
Use a daemon to keep pull requests review-ready by watching for PR activity, suggesting missing context, and posting review-oriented updates without requiring a person to prompt it each time.
Issue tracking maintenance
Use a daemon to watch issue trackers for recurring signals, apply labels, or keep statuses and ownership aligned so triage does not stall between human check-ins.
Ongoing maintenance and drift reduction
Use a daemon to keep dependencies, documentation, runbooks, or other recurring assets current as the codebase and workflow change over time.
Signal-to-update workflows
Use a daemon to turn incoming signals into verified updates, comments, or follow-up tasks while preserving checkpoints and approval gates for riskier changes.
Team-wide background automation
Use a daemon as a shared workspace helper that benefits the whole team after one setup, rather than a one-off assistant for a single person.
Pros and Cons
Pros
- Daemons are defined in straightforward `.md` files, which makes behavior easier to inspect and modify.
- The execution model is explicit about boundaries, verification, and approval, which helps teams control autonomous work.
- Daemons can keep recurring maintenance moving across multiple systems instead of waiting for a person to notice every follow-up.
- Plans include unlimited team members and no cap on the number of daemons, supporting shared workspace use.
- The pricing page offers a free plan and paid tiers, with overage available when demand exceeds included limits.
Cons
- The public pages provide only partial detail on supported integrations and example workflows, so buyers may need to review the docs to confirm fit for their stack.
- Higher-impact or broader changes are not fully autonomous; the daemon pauses and asks for approval when scope or risk increases.
- Usage is governed by workspace limits, so sustained throughput may require overage or a higher plan.
FAQ
What is a Charlie daemon?
Charlie daemons are defined in Markdown files and are designed to run with explicit roles, watch conditions, routines, deny rules, and schedules. The source pages show that they can be a good fit when a team wants recurring maintenance, triage, or reporting work handled continuously rather than as one-off prompts.
Which systems can daemons work with?
The source pages show daemons operating across systems such as Slack, Linear, GitHub, and Sentry. The home page also describes them as working proactively across Slack, Linear, GitHub, and more.
How is Charlie priced?
The pricing page says plans are based on daily and weekly Charlie usage limits, not monthly credits. If a workspace hits its limits, it can buy overage credits or upgrade; otherwise usage pauses until the limits reset.
Can multiple team members use the same workspace?
Yes. The pricing page says unlimited team members are included on the listed plans, and the home page describes a shared, team-wide benefit from adding a daemon once.
When does a daemon ask for human approval?
The how-it-works page shows that daemons can pause for approval on higher-impact, broader, or riskier changes. Routine metadata updates can run automatically, while edits with greater scope or risk require a human decision.
Quick Facts
- Category
- AI agents / developer tool
- Primary format
- Markdown-defined daemons
- Works across
- Slack, Linear, GitHub, and Sentry are named in the source pages
- Pricing model
- Workspace usage limits with overage available
- Team access
- Unlimited team members on listed plans
- Source domain
- charlielabs.ai
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