AgentMesh.help
Freemium
Developers integrating AI agents into task workflows
Agency Agents
FreeOpen SourceSelf-HostedAgentic
Most AI coding sessions start fresh — no context, no role, no process — so you spend the first ten minutes re-explaining what you need before the model does anything useful. Agency Agents exists to stop that reset.
Similar tools
GBrainPaidGBrain lets teams write decisions, processes, and institutional knowledge into a shared…
AgentMesh.helpFreemiumThe platform is a REST-native task market where agents register in three API calls, browse open…
Famulor White-label PlatformFreemiumThe platform runs inbound and outbound phone calls, WhatsApp marketing sequences, booking…
ForkbenchFreemiumForkbench organizes work into Threads: each Thread carries a single objective, a backlog of…
Get This Tool
License: MIT Any use incl. commercial
Local-run terms: Users can clone the repository, copy agent Markdown files into supported tools, or use the provided install scripts and desktop app to add agents locally.
Pricing
- Model
- Free
Summary
The project is a MIT-licensed, self-hostable collection of pre-defined agent definitions organized by domain — engineering, marketing, product, design, and more — built to be activated inside Claude Code, Cursor, and similar AI coding tools. Each agent carries a defined personality, a stated process, and expected deliverables, so the session opens with role context already loaded. The differentiator is breadth plus specificity: you are not configuring a blank agent; you are picking a specialist with an opinionated approach baked in. The ceiling appears when your workflow requires branching between agents at runtime or dynamic handoffs — the repo defines agents, it does not orchestrate them. Teams needing cross-agent coordination wire that logic themselves on top.
Bottom line: Pick this when you want repeatable, domain-specific AI sessions in Claude Code or Cursor without writing your own system prompts — skip it when the task requires agents that coordinate with each other automatically.
Find your fit
Before you choose Agency Agents
More options in AI Agent Apps. Compare pricing, platforms, and features side by side.
adris.tech
Freemium
Agencies and consultancies
Pros
- Pre-defined personality and process per agent, so sessions open with role context already loaded rather than you spending the first exchanges re-establishing what the model should be doing.
- MIT license with self-hosted install options (install.sh and brew command documented in the repo), so the definitions stay on your infrastructure and are not gated behind a vendor's API or auth layer.
- Organized by domain directory — engineering, marketing, product, design, finance, and others — so a team can adopt only the agents relevant to their work without importing unrelated definitions.
- Community fork count and open contribution model mean the agent library grows through pull requests, so domain gaps can be filled without waiting for a vendor roadmap.
- Personality-driven definitions that go beyond bare system prompts, which means the model's tone and decision-making style stays consistent across sessions rather than varying with however the user frames the first message.
Cons
- No built-in runtime coordination between agents: when a task requires one agent to trigger or hand off to another based on output, you write that logic yourself — and at more than two or three agents, you are maintaining a separate orchestration layer that is not part of this repo.
- No API surface is provided, so any team that wants to call these agent definitions programmatically from their own application has to extract and adapt the definition files manually rather than consuming them via an endpoint.
- The definitions are only as current as the last accepted pull request — if a domain evolves (a new framework standard, a changed marketing platform) and the community has not merged an update, the agent's stated process drifts from reality, and you get confidently outdated guidance.
- Teams that need agents to run tasks autonomously across a multi-step pipeline — rather than as session-scoped personas — will exhaust what this repo offers and migrate to a full agent framework (LangGraph, Dify, or similar) that treats coordination and state as first-class concerns, at which point these definitions become input prompts to a larger system rather than the system itself.
About
- Platforms
- macOS, Linux, Windows
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- July 4, 2026
Best For
Who it's for
- Users of Claude Code, Cursor, and similar AI coding tools
- Teams seeking pre-defined agent workflows
- Developers wanting personality-driven AI assistants
What it does well
- Activating specialized agents for frontend development tasks
- Using agents for marketing copy and community engagement
- Employing agents for product management and project tracking
Integrations
Compare Agency Agents
Frequently Asked Questions
- Is Agency Agents free?
- Agency Agents offers a free option. Hosting, model usage, or optional services may have separate costs; check the pricing details and official terms.
- Is Agency Agents open source?
- Yes. Agency Agents is open source.
- Can I self-host Agency Agents?
- Yes. Agency Agents supports self-hosting on your own infrastructure.
- What platforms does Agency Agents support?
- Agency Agents is available on: macOS, Linux, Windows.
People also compared
Curated lists that include this category
Generic AI sessions waste time because the model has no standing context about who it is or what process it follows. Agency Agents is an open-source repository of domain-scoped agent definitions — covering engineering, marketing, product management, project tracking, design, finance, sales, and more — intended to be loaded into AI coding tools like Claude Code and Cursor at the start of a session. Each agent definition ships with a personality profile, a stated workflow, and a list of expected deliverables, which means the model arrives at the task with a role already established rather than requiring the user to construct one from scratch.
The differentiating feature is the combination of personality and process. Most prompt libraries give you a system prompt and leave the process implicit. The agents here, the vendor states via the repository README, are ‘specialists with personality, processes, and proven deliverables’ — the implication being that the deliverable format is defined ahead of time, not negotiated during the session. The repo is organized into directories by domain (engineering, marketing, product, academic, etc.), and the file structure suggests each agent is a standalone definition file that can be adopted or modified independently under the MIT license.
This fits teams that run repeated, domain-specific AI sessions and want consistency across those sessions without maintaining their own prompt library. Where it breaks is the coordination layer: the repo provides agent definitions, not a runtime that connects them. If your workflow requires one agent to hand off to another based on what the first returned — frontend agent detects a design inconsistency and routes to a design agent — you build that routing yourself. Teams with multi-step, cross-domain automation needs will hit this ceiling and end up maintaining a separate orchestration layer alongside the repo, effectively running two systems.
Contribute
- Work on Agency Agents? Claim this listing.
- Embed a badge for your README or site.
Sign in to rate this tool or suggest an edit.