Agenta Agent Workspace
Build conversational agents, share them with a team, and run recurring work in the background.
- Source repo
- Agenta-AI/agenta
- Stars
- ★ 4.8k
- Last updated
- today
- License
- NOASSERTION
- Primary language
- TypeScript
- FA score
- 52/100 · Major gaps
At a glance
- Works with
- Universal · cross-platformClaude Code · OpenAI API · Claude API
- You'll need
- Typical use
- A knowledge-work team that regularly organizes research or maintains a shared wiki can use a background agent in the shared workspace.
- Main limitation
- The supplied material lacks complete self-host installation commands, environment variables, ports, credentials, and runtime prerequisites, so deployment still requires further documentation.
- Source review
- 52/100 · Major gaps
What does this agent do, and when should you use it?
Agenta is an open-source workspace for building and running specialized agents. Users describe work in chat, connect the applications an agent needs, and improve the agent through feedback. It combines shared file workspaces, role-based team access, scheduled background work, and triggers from connected-app events. The platform traces model and tool calls and retains agent-configuration history for failure analysis, change comparison, usage tracking, and estimated-cost review. Its self-hosted path is described as usable with existing Claude or ChatGPT subscriptions, and the currently supported harnesses are Claude Code and Pi.
A user describes a task in chat, connects applications, and works with an agent on files, documents, research, or a wiki in a shared workspace. Agenta can run the agent interactively in chat, on a schedule, or after an event in a connected app. It connects applications through MCP servers and, through Composio, lists access to more than 1,000 apps including Gmail, Slack, Notion, and GitHub. It traces every model and tool call, tracks token usage and estimated costs, and retains version history for each agent configuration. Agent definitions may use AGENTS.md, skills, and MCP servers; the README also lists Local runtime, Daytona sandboxes, and Docker sandboxes.
- A knowledge-work team that regularly organizes research or maintains a shared wiki can use a background agent in the shared workspace.
- An individual or team with an existing Claude or ChatGPT subscription that wants to self-host local agent work can use Agenta as the operating workspace.
- A team that needs work to begin after events in connected Gmail, Slack, Notion, or GitHub can use event-triggered background agents.
- An engineering or operations team investigating agent failures, model requests, tool calls, token usage, or estimated cost can inspect traces and configuration history.
- A team that wants one agent to collaborate in chat and also perform recurring work can combine the interactive and scheduled execution modes.
How do you install or deploy this agent?
The supplied README does not provide a standalone, reproducible self-host installation command, runtime version, environment variables, ports, or credential list. Its documented bootstrap route is npx skills add Agenta-AI/agenta-skills, followed by asking a skills-capable agent to self-host and test Agenta; the supplied material does not specify the configuration needed to complete deployment.
How do you use this agent?
Describe the work to automate or augment in chat, connect the applications the agent needs, and improve it through feedback. Work with the agent directly in the shared workspace, schedule recurring work, or have it run when an event occurs in a connected application. Set tool permissions to distinguish actions that may run automatically, require human approval, or are blocked; then use traces, usage, costs, and configuration history to review results.
What are this agent's strengths and limitations?
- Combines interactive chat, scheduled execution, and connected-app event triggers in one workspace rather than only predefined workflow steps.
- Traces every model and tool call while preserving configuration history, enabling failure investigation, change comparison, and token and estimated-cost review.
- Supports Claude Code and Pi harnesses and lists model paths including OpenAI, Anthropic, OpenRouter, and Ollama.
- Provides shared file workspaces, role-based team access, and per-tool human approval or blocking controls.
- The supplied material lacks complete self-host installation commands, environment variables, ports, credentials, and runtime prerequisites, so deployment still requires further documentation.
- Only Claude Code and Pi are marked as completed harnesses; Codex, Gemini, and OpenCode remain roadmap items.
- Several runtimes and trigger capabilities remain planned, including E2B, Vercel, Cloudflare, generic webhook triggers, and OAuth MCP transport.
- Repository metadata says NOASSERTION for the license while the README says MIT; adopters should verify the governing license files and terms.
How does this agent compare with similar options?
The README distinguishes Agenta from n8n, Activepieces, and Zapier by positioning those products around predictable, predefined workflows and Agenta around work where an agent plans, uses tools, and adapts. Against Claude Cowork, it emphasizes open source, selectable harnesses and models, inspectable agent components, and both interactive and background execution. Against Claude Code, Codex, Pi, and OpenCode, Agenta is positioned as the shared workspace around the execution layer, adding files, team access, triggers, versions, and traces.
Key facts side by side with the most closely related agents.
| Agent | Source review | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|
| Agenta Agent Workspace This agent | 52 · Major gaps | ★ 4.8k | today | TypeScript | Claude Code · OpenAI API · Claude API |
| Okou | 56 · Major gaps | ★ 1.2k | 4d ago | TypeScript | OpenAI API · Claude API |
| CowAgent | 52 · Major gaps | ★ 47k | today | Python | OpenAI API · Claude API |
| holaOS | 52 · Major gaps | ★ 11k | 1mo ago | TypeScript | Codex · Claude Code · OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows: README mentions human approval and permission control, tracking, usage, cost, and team access. SECURITY.md provides vulnerability reporting process. But no implementation details, such as permission model, approval mechanism, data flow diagram, sensitive data handling, dependency security audit, external effect control, rollback mechanism, source attribution. Therefore each criterion gets 1 because present but thin.
Evidence shows: README describes features consistently, no contradictions. But no dependency availability guarantees (e.g., lock files, images), failure message design. So self_consistency gets 2, others get 1.
Evidence shows: README clearly identifies target users (teams), use cases (building agents, background running), supports many models and frameworks, provides self-hosting and cloud. But no detailed capability boundaries, trigger precision, environment fit details. So give 2.
Evidence shows: README is well-structured, provides installation guide (self-hosting), roadmap, contribution guide, license (MIT but with EE parts). But lacks changelog, known limitations, maintenance responsibility. So give 2.
Evidence shows: README emphasizes output usability (tracking, version history), marginal value (comparison with other tools), cost benefit (using existing subscriptions). But no specific data or case studies. So give 2.
Evidence shows: Claims in README (e.g., supported models, features) lack specific evidence or sources. SECURITY.md provides process but not verified. So give 1.
- No specific security implementation details, such as permission model, approval flow, data flow diagram, need further code review.
- Dependency security not mentioned, need to check dependency locking and vulnerability scanning.
- No rollback mechanism and source attribution, need to confirm version control and audit capabilities.