Agentor
FreeOpen-source Python framework for long-running AI agents with MCP & A2A
By Tanmay Verma, Founder · Last verified 05 Jul 2026
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Added 7d ago
69/100MonitorIn short
Agentor — Open-source Python framework for long-running AI agents with MCP & A2A. Best for Developers building production-grade long-running AI agents, Platform integrators needing multi-agent interoperability via A2A, Teams requiring durable agent workflows with failure recovery. Free to use.
Is Agentor actually worth it?
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Editorial Verdict
Best for
Developers building production-grade long-running AI agentsPlatform integrators needing multi-agent interoperability via A2ATeams requiring durable agent workflows with failure recoveryOpen-source enthusiasts wanting a customizable agent framework
Not ideal for
Non-technical users without Python experienceTeams needing a no-code agent builderUse cases requiring pre-built consumer chatbots without customizationRapid prototyping where a managed service would be faster
Agentor is a solid open-source choice for developers building production-grade, long-running agents with MCP and A2A support. Its durable execution and interoperability stand out, but it requires strong Python skills. Best for teams that need reliability and customization over ease of use.
Compare with: Agentor vs Marvin, Agentor vs Zhipu GLM, Agentor vs MetaGPT
Last verified: July 2026
What independent users actually report about Agentor
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
24 mentions across 5 sources (Hacker News, YouTube, Bluesky, GitHub, Lemmy).
28% positive72% critical
Recurring strengths
- +Open-source and free to use for any project.
- +Built-in support for MCP and A2A protocols out of the box.
- +Designed for durable, long-running agent execution.
- +Integrates with FastAPI for easy serving.
- +Cloud deployment via Celesto CLI simplifies operations.
Recurring frustrations
- −Virtually no community feedback or user reviews exist.
- −Majority of online mentions refer to an unrelated real estate template.
- −No evidence of production reliability or scalability.
- −Learning curve unclear due to sparse documentation feedback.
- −Only 190 GitHub stars—very early stage project.
Patterns worth knowing
Name collision with real estate template causes confusion and noise.
Seen on Bluesky
Lack of real user feedback makes reliability claims unverifiable.
Seen on Hacker News, GitHub
Promising as an open-source framework for long-running agents with built-in protocols.
Seen on GitHub, Hacker News
Learning curve
beginnerProductive in ~A few hours
Hidden costs people mention
- • Cloud tracing and monitoring may require paid Celesto service (unclear pricing).
Viability Score
69/100
MonitorHow likely is Agentor to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
40
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- Long-running agent execution with durability
- Built-in tool use with custom tool creation
- Model Context Protocol (MCP) support
- Agent-to-Agent (A2A) protocol for interoperability
- Streaming responses from agents
- Tracing and monitoring for production
- Skill system for modular agent capabilities
- Cloud deployment via Celesto CLI
- FastAPI integration for serving agents
- Authorization in MCP servers
- SuperAuth setup guide
- Gmail and Google Calendar integration guides
- Open-source with extensible architecture
About Agentor
FreeAdvancedAPI availableAPI · CLI
Agentor is an open-source Python framework for building, deploying, and operating long-running AI agents. It provides built-in support for tool use, the Model Context Protocol (MCP), and the Agent-to-Agent (A2A) protocol, enabling agents to call external tools and communicate with other agents. The framework is designed for durable execution and production reliability, with features like streaming responses, tracing, and cloud deployment via the Celesto CLI. Targeted at developers and platform integrators, Agentor simplifies agent construction with a clean API. Users can define agents with custom models (e.g., GPT-5-mini) and tools, then serve them over A2A for multi-agent interoperability. The framework also supports skill systems, MCP server creation, FastAPI integration, and authorization in MCP servers. Agentor's standout feature is its focus on durability and interoperability. Agents are long-running by default, and the built-in A2A protocol makes them discoverable and collaborative. The framework is open source and can be deployed on-prem or in the cloud, with optional cloud tracing and monitoring. Compared to other agent frameworks like LangChain or CrewAI, Agentor prioritizes durable execution and protocol-based communication (MCP/A2A) over abstraction layers. It's a good fit for developers who need fine-grained control and production reliability, but it requires Python expertise and is not suited for non-technical users.
Behind the Verdict
Agentor fills a specific niche: developers who want to build durable, long-running agents that can interoperate via standard protocols. If your team is comfortable with Python and needs agents that survive failures and collaborate, this is a strong pick. Where it falls short is accessibility. There's no no-code builder, and the learning curve is steep for those unfamiliar with agent frameworks. The documentation, while comprehensive, assumes technical proficiency. For quick prototypes or consumer-facing chatbots, you'd be better off with something like OpenAI's Assistants API or a managed platform. Compared to LangChain, Agentor feels more opinionated about architecture—it pushes you toward MCP and A2A from the start, which is great for standardization but might feel restrictive if you want to experiment with different patterns. On the plus side, the built-in tracing and cloud CLI make deployment and monitoring straightforward. One caveat: the example code references a 'gpt-5-mini' model, which suggests ties to a specific provider. Make sure your preferred model fits. Also, the framework is open source but leans on the Celesto ecosystem for cloud features—worth checking the licensing. In practice, we'd recommend Agentor for teams building multi-agent systems where reliability and protocol compliance matter more than rapid iteration. For solo experiments or one-off tasks, lighter frameworks may suffice.
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Use Cases
- Build a long-running customer support agent that uses MCP tools to access databases.
- Deploy multiple agents that communicate via A2A to handle complex workflows.
- Create a personal assistant agent capable of scheduling meetings via Gmail and Calendar.
- Integrate an agent into a FastAPI app for real-time streaming responses.
- Develop a research agent that searches the web and summarizes findings with tool use.
Models Under the Hood
gpt-5-mini
Limitations
- Agentor is a framework requiring Python programming skills.
- There is no graphical interface, and users must manage their own LLM API keys and infrastructure.
- The framework is open-source with community support; there is no official enterprise support tier mentioned.
- Cloud deployment and tracing rely on Celesto's platform, which may have additional costs.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
—
—
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Integrations
OpenAI GPTMCP serversFastAPICelesto CloudGmailGoogle Calendar
Resources & Guides
Frequently Asked Questions
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