Agentor
Open-source Python framework for long-running AI agents with MCP and A2A.
Agentor is a strong open-source pick for Python developers who need durable, protocol-native agents (MCP, A2A). The 0.1.0 prerelease status demands comfort with infrastructure—you'll install with `pip install --pre` and manage your own provider keys. If you prioritize customization and reliability over turnkey simplicity, Agentor delivers without vendor lock-in. For teams needing a managed SaaS or no-code builder, look elsewhere (e.g., LangGraph Cloud or AutoGen).
Verified 14d ago · liveness 68/100 · cite: rightaichoice.com/tools/agentor
- Developers building production-grade long-running AI agents with durable execution
- Platform integrators needing multi-agent interoperability via A2A protocol
- Teams requiring MCP support for tool and data integration
- Open-source enthusiasts wanting a customizable, self-hostable agent framework
- Non-technical users without Python experience
- Teams needing a no-code agent builder or managed SaaS
- Use cases requiring pre-built consumer chatbots without customization
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Skip Agentor if you're not a Python developer comfortable with infrastructure management, or if you need a no-code, managed agent platform with a GUI.
You must bring your own API keys for any OpenAI-compatible provider—there's no built-in model access, so costs depend on your provider's usage fees (e.g., OpenRouter per-token).
Agentor is free and open-source—perfect for developers who want to avoid per-seat costs. Compared to managed frameworks like LangGraph Cloud (which charges per execution) or AutoGen in a managed environment, Agentor has zero platform fees. However, you'll pay in infrastructure and your own time.
In short
Agentor — Open-source Python framework for long-running AI agents with MCP and A2A. Best for Developers building production-grade long-running AI agents with durable execution, Platform integrators needing multi-agent interoperability via A2A protocol, Teams requiring MCP support for tool and data integration. Free to use.
What people actually say about Agentor — is it worth it?
We scanned public community sources for Agentor on Jul 24, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Agentor? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Durable execution with run resumption
- Forkable runs from saved state
- Built-in tool use and custom tool creation
- MCP server support
- A2A protocol via agent.serve()
- Streaming responses
- Tracing for production monitoring
- Skills system for modular agent capabilities
- Structured output for predictable results
- Any OpenAI-compatible provider (OpenRouter, Groq, Together, vLLM, Ollama)
- FastAPI integration for serving
- Celesto CLI for cloud deployment
- SuperAuth setup guide
- Gmail and Google Calendar integration guides
- Agent architecture with lifecycle and security boundaries
About Agentor
Agentor is an open-source Python framework for building, deploying, and operating long-running AI agents with tool use, MCP, and the A2A protocol. It packages durable execution, structured output, and support for any OpenAI-compatible provider into a single cohesive framework, so developers can build production-grade agents without juggling multiple libraries. The current stable release is 0.1.0, and you can install it with `pip install agentor`. The core differentiator is durability: every step of a run is saved, allowing another process to resume after a crash or fork the run into a new one. This makes Agentor a strong fit for long-running workflows where reliability matters. You can also connect custom tools, serve agents over A2A with a single `agent.serve()` call, and build MCP servers to integrate with broader systems. Structured output keeps results predictable, and streaming responses are supported for interactive use cases. For deployment, Agentor integrates with FastAPI, and the Celesto CLI handles cloud deployment. Documentation includes guides for SuperAuth setup and Gmail and Google Calendar integration, giving a clear path to real-world workflows. The framework also includes tracing for production monitoring and a skills system for modular agent capabilities. Agentor targets Python developers who value control and reliability over turnkey simplicity. It's not a no-code platform—you manage infrastructure and write code. If you're building agents that must run confidently and communicate across protocols, it's worth comparing against managed frameworks like LangGraph or AutoGen.
Behind the Verdict
When does Agentor earn its keep? When your agent runs for hours and a crash mid-run isn't an option. Durable execution — saving every step and resuming from the last checkpoint — is the differentiator here, not the LLM wrapper. If you're orchestrating long-running workflows, that alone justifies the setup overhead. Who should pass? Teams without Python expertise, or anyone who wants a managed SaaS with a GUI. Agentor is a developer tool, full stop. You're on the hook for infrastructure, provider keys, and deployment. The 0.1.0 version is early; expect rough edges like documentation gaps and API changes. Compared to LangGraph and AutoGen, Agentor's edge is protocol-native interop. The `agent.serve()` call flips on A2A, so your agent talks to other agents out of the box. MCP support means you plug into a growing ecosystem of tools. LangGraph has a larger community, but Agentor's durability story is more explicit. A concrete caveat: the quickstart uses `gpt-5-mini`, but you can point Agentor at OpenRouter, Groq, Together, vLLM, or Ollama. That's a plus — no vendor lock-in. Just don't expect to call yourself a no-code platform; you'll write code, manage keys, and handle ops. In practice, we'd reach for Agentor when we're building a production agent that must survive failures and interoperate via standards — and when we're willing to trade some convenience for control. For quick prototypes or non-technical stakeholders, a managed service is faster to ship.
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Real-world workflow fit
Concrete scenarios for the personas Agentor actually fits — and what changes day-one when you adopt it.
You install Agentor with `pip install --pre`, create an agent with `Agentor(name='Support', model='gpt-5-mini')`, add custom tools to query your database, and run it in a FastAPI app.
Outcome: You get a long-running agent that survives crashes and can be resumed, with MCP support to tap into your data.
You build two agents—one for scheduling, one for email—and call `agent.serve()` on each.
Outcome: The agents are discoverable and interoperable via A2A, enabling a multi-agent workflow without extra middleware.
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
as of 2026-09-14
Limitations
- Agentor is an open-source Python framework, so building and deploying agents requires programming skills and managing your own environment via `pip install agentor`.
- The current stable release is version 0.1.0.
- It runs on Agentor's own agent engine and supports any OpenAI-compatible provider, meaning users supply their own provider credentials and infrastructure.
as of 2026-08-26
Verification history
We have re-verified Agentor 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Agentor tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0
Ideal for
Python developers and open-source enthusiasts who want a free, self-hostable agent framework for building custom, long-running agents without per-seat costs.
What this tier adds
The only tier—free and open-source, giving you durable execution, MCP support, A2A protocol, and a full-featured framework at zero cost.
Where the pricing makes sense
The company stage and team size where Agentor's pricing actually pencils out — and where peers do it cheaper.
Agentor is free and open-source—perfect for developers who want to avoid per-seat costs. Compared to managed frameworks like LangGraph Cloud (which charges per execution) or AutoGen in a managed environment, Agentor has zero platform fees. However, you'll pay in infrastructure and your own time.
Setup time & first value
How long it actually takes to get something useful out of Agentor — broken out by persona, not the marketing-page minute.
For a Python developer: under 10 minutes to install (`pip install --pre agentor`) and run the quickstart. Setting up a custom tool or MCP server takes an additional hour or two. Configuring Gmail/Google Calendar integrations may take half a day if you need OAuth.
Switching to or from Agentor
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangGraph: Rewrite your graph logic as Agentor agents, using `agent.run()` calls and tools; incorporate durability and MCP/A2A features gradually.
- ↗To LangGraph: Export your agent logic as Python functions and translate to LangGraph's StateGraph; you'll lose built-in A2A/MCP integration unless you add custom nodes.
Integrations
Resources & Guides
- Quickstartdocs.celesto.ai
Quickstart · Agentor
Get up and running fast from docs.celesto.ai
- Resourcedocs.celesto.ai
Installation · Agentor
Helpful link from docs.celesto.ai
- Resourcedocs.celesto.ai
Durable Runs · Agentor
Helpful link from docs.celesto.ai
- Resourcedocs.celesto.ai
Tracing · Agentor
Helpful link from docs.celesto.ai
- Resourcedocs.celesto.ai
Skills · Agentor
Helpful link from docs.celesto.ai
Tutorials & Learning
YouTube returned 6 videos for “Agentor”, and we withheld 6: 6 could not be judged, because “Agentor” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Agentor.
Official links
Tools that pair well with Agentor
Common stack mates teams adopt alongside Agentor, with the specific reason each pairing earns its keep.
OpenAI Agents SDK
OpenAI Agents SDK: Lightweight Python framework for building multi-agent workflows with handoffs, sandboxing, and voice.
Mastra
Mastra is an open-source TypeScript agent framework for building durable AI agents and workflows that run for days.
Zhipu GLM
Zhipu GLM delivers open-source LLM models, MaaS APIs, and autonomous agents for Chinese enterprises and developers.
Featured Head-to-Head Comparisons
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Agentor vs Presto Voice
Agentor and Presto Voice serve completely different needs. Choose Agentor if you're a developer building custom, production-grade AI agents with multi-agent interoperability and open-source flexibility. Choose Presto Voice if you operate a QSR chain and want a turnkey voice AI solution proven to increase revenue and efficiency at the drive-thru.
Agentor vs Truleo
If you're a law enforcement agency drowning in siloed data, Truleo is purpose-built to automate intelligence gathering, report writing, and jail call analysis. But if you're a developer building custom long-running AI agents with interoperability, Agentor's open-source framework with MCP and A2A is the free, flexible choice. They serve completely different worlds.
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