Agentor is live · free · last verified 23 Oct 2026 · viability monitor
Agentor
Agentor is an open-source Python framework for long-running AI agents with durable, forkable runs and A2A serving.
If your agents die mid-run and you've written the same retry-and-resume scaffolding twice, Agentor's durable, forkable runs and one-line agent.serve() for A2A are worth a serious look. The provider list means no lock-in to a single model vendor. Just budget for the 0.1.0 reality: a young API and persistence you operate yourself, not a hosted service.
Verdict generatedVerified 7d ago · liveness 68/100 · cite: rightaichoice.com/tools/agentor
- Python developers building long-running agents that must survive crashes and restarts
- Platform teams connecting multiple specialist agents over the A2A protocol
- Engineers who want MCP tool integration without a proprietary orchestration layer
- Self-hosting teams that need provider-neutral agents on their own infrastructure
- Non-developers or teams without Python experience
- Anyone wanting a no-code agent builder or managed agent dashboard
- Short request-response use cases where durable run state is wasted overhead
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Skip Agentor if you want a no-code builder or a hosted agent dashboard where someone else owns uptime, tracing and crash recovery instead of you operating durable run state on your own infrastructure in Python.
The bundled GetWeatherTool() reads a free WeatherAPI.com key from the WEATHER_API_KEY environment variable and raises if it is not set, so you register for and manage that key yourself
Agentor is an open-source framework you install with `pip install agentor` at no licence cost, so the real budget is engineering time plus your own provider and infrastructure spend. Against hosted options such as LangGraph Cloud, that makes it materially cheaper on subscription line items and materially more expensive on the hours your team spends operating durable run state; the docs' example model string is "gpt-5-mini" pulled through your own provider account.
In short
Agentor — Agentor is an open-source Python framework for long-running AI agents with durable, forkable runs and A2A serving. Best for Python developers building long-running agents that must survive crashes and restarts, Platform teams connecting multiple specialist agents over the A2A protocol, Engineers who want MCP tool integration without a proprietary orchestration layer. Free to use.
What is Agentor?
Agentor — Agentor is an open-source Python framework for long-running AI agents with durable, forkable runs and A2A serving. Best for Python developers building long-running agents that must survive crashes and restarts, Platform teams connecting multiple specialist agents over the A2A protocol, Engineers who want MCP tool integration without a proprietary orchestration layer. Free to use.
Agentor is an open-source Python framework for building, deploying and operating long-running AI agents with tool use, MCP support and the A2A protocol. There's no canvas to drag boxes around on: you construct an Agentor instance with a name, a model string and a list of tools, call agent.run() for a result, and call agent.serve() to publish the agent over A2A so other agents can discover and call it. The docs' quick example wires GetWeatherTool() against a free WeatherAPI.com key read from WEATHER_API_KEY. Durable execution is the center of gravity. Every step of a run is saved, which means a second process can finish the work after a crash instead of starting over, and you can fork a saved run into a new branch rather than replaying history. Model access stays provider-neutral — point it at OpenRouter, Groq, Together, vLLM, Ollama, or your own OpenAI-compatible server, with your own credentials. Beyond the core loop, Agentor covers the plumbing a production agent needs: custom tool authoring, MCP servers including ones with authorization, tool search, streaming responses, a skills system for packaging capabilities into reusable modules, and tracing for monitoring live runs. Agents can be mounted into a FastAPI APIRouter, and there are guides for SuperAuth setup plus Gmail and Google Calendar integration. Agentor 0.1.0 is the current stable release, installed with pip install agentor on the project's own agent engine. Against managed orchestration like LangGraph Cloud, it trades turnkey hosting for a self-hostable core you control. That's a good trade if your agents have to survive restarts and speak across protocols; a poor one if you'd rather someone else own uptime and you want a frozen API surface.
Key features
- Durable execution: every run step saved so another process can finish it after a crash
- Fork a saved run into a new branch instead of replaying from the start
- agent.serve() enables the A2A protocol for agent discovery and interoperability
- agent.run() returns structured output
- Streaming responses for interactive agent use cases
- Custom tool authoring, including the bundled GetWeatherTool example
- MCP server support, including MCP servers with authorization
- Tool search across available tools
- Model providers: OpenRouter, Groq, Together, vLLM, Ollama, or your own OpenAI-compatible server
- Skills system for modular, reusable agent capabilities
- Tracing and monitoring for production agents
- Integrate agents with a FastAPI APIRouter
- Gmail and Google Calendar integration guides
- SuperAuth setup guide
Behind the verdict
Most Python agent frameworks assume a request-response shape: prompt in, answer out, forget. Agentor assumes the opposite — that runs are long, that processes crash, and that you'll want to branch from step 47 rather than pay for steps 1 through 46 again. We'd reach for it specifically when that assumption matches your workload: research agents that grind for hours, multi-stage pipelines that call external APIs with side effects, anything where a restart shouldn't mean a re-bill. Pick it if you're a Python team comfortable owning infrastructure and you want agent-to-agent interoperability without adopting a vendor's runtime. The A2A story is unusually cheap here — agent.serve() makes an agent discoverable, which matters if you're building a mesh of specialists rather than one monolith. Provider-neutrality is the other draw: swap OpenRouter for a local vLLM box in an afternoon, credentials stay yours. Pass if you want a dashboard, a managed control plane, or a support contract. Also pass for short request-response bots: durable run state is overhead you'll pay for and never use. Non-Python teams and anyone expecting a no-code builder are outside the target. Where it bites in practice: 0.1.0 means breaking changes are a when, not an if — pin your version and read changelogs before upgrading. Persistence is a primitive you operate, so plan for where run state lives and how you clean it up; a crash-safe run store you configure badly is just a slower failure. Docs are thorough but Mintlify-hosted and moving, with an August 25, 2026 revision. The closest alternative is LangGraph Cloud — same durable-graph instinct, but managed, with a polished UI and support behind it. Choose Agentor when self-hosting and protocol openness beat convenience; choose managed orchestration
Models under the hood
as of 2026-10-22
Limitations
- Agentor is an open-source Python framework, so building and deploying agents requires programming skill and running your own environment via `pip install agentor`.
- The current stable release is 0.1.0, which means a young API surface; durable run persistence and crash recovery are primitives you operate, not a hosted service.
- It runs on Agentor's own agent engine and supports any OpenAI-compatible provider, so you supply your own provider credentials and infrastructure.
as of 2026-10-13
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Is Agentor still active?
Agentor is live · free · last verified 23 Oct 2026 · viability monitor.
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: October 2026
How we score →What's new in Agentor
Checked 9 days agoAcross the latest 1 update: 1 changelog entry.
What do users say about Agentor?
What people actually say about Agentor — is it worth it?
Our community sentiment research written Jul 24, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about Agentor— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 23 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Agentor 11 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.
- — changed pricing page
- — changed our verdict, who should skip it, description, pricing tiers, who it suits, summary, features
- — changed pricing page, limitations
- — changed setup time, who should skip it, switching in, summary, switching out, our verdict, limitations, pricing fit, features, description, hidden costs
- — changed summary, our verdict, integrations, who should skip it, features, description, pricing tiers
- — changed limitations, pricing page
Showing the 6 most recent of 11 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Agentor cost in 2026?
Agentor: Free to use. Pricing last verified 23 Oct 2026.
Plans compared
Every published Agentor tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Open Source | Free | — | Python developers and platform teams who will run agents on their own infrastructure and supply their own model provider credentials.Adds: Starting tier: the full Agentor framework at no licence cost, with durable forkable runs, agent.serve() for A2A, MCP server support and provider-neutral model access. |
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.
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 an open-source framework you install with `pip install agentor` at no licence cost, so the real budget is engineering time plus your own provider and infrastructure spend. Against hosted options such as LangGraph Cloud, that makes it materially cheaper on subscription line items and materially more expensive on the hours your team spends operating durable run state; the docs' example model string is "gpt-5-mini" pulled through your own provider account.
Has Agentor's pricing changed?
No plan price for Agentor has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Agentor?
Pick Agentor if you are Python developers building long-running agents that must survive crashes and restarts, Platform teams connecting multiple specialist agents over the A2A protocol, Engineers who want MCP tool integration without a proprietary orchestration layer, Self-hosting teams that need provider-neutral agents on their own infrastructure.
Look elsewhere if you are Non-developers or teams without Python experience, Anyone wanting a no-code agent builder or managed agent dashboard, Short request-response use cases where durable run state is wasted overhead, Teams that need a vendor support SLA and a frozen 1.0 API.
Skip it if Skip Agentor if you want a no-code builder or a hosted agent dashboard where someone else owns uptime, tracing and crash recovery instead of you operating durable run state on your own infrastructure in Python.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Agentor actually fits — and what changes day-one when you adopt it.
Installs Agentor with `pip install agentor`, sets provider credentials, builds an Agentor instance with the model string "gpt-5-mini" and GetWeatherTool(), then calls agent.run() for a structured result before calling agent.serve() to expose the agent over A2A.
Outcome: A working, discoverable agent in a single file, with no orchestration vendor in the request path.
Writes an MCP server with authorization, wires the agent into a FastAPI APIRouter, and lets a crashed run be finished by a second process or forked into a new branch to try a different tool path.
Outcome: Multi-system workflows that keep their state across restarts instead of replaying prior steps.
Points the same agent code at a local Ollama or vLLM instance rather than a hosted provider, then follows the tracing guide to monitor production runs for slow or failing tool calls.
Outcome: Model traffic stays inside the network while run-level observability still works.
Use cases
- Build a long-running support agent that calls MCP tools to reach databases and survives a process crash mid-run
- Deploy several agents that discover and call each other over the A2A protocol for multi-system workflows
- Create a personal assistant that schedules meetings through the documented Gmail and Google Calendar guides
- Embed an Agentor agent in a FastAPI app to stream responses to end users
- Fork a partially completed run into a new branch to try a different tool path without redoing prior steps
- Run an agent against a local Ollama or vLLM model when data cannot leave your network
- Package a repeatable capability as a skill and reuse it across multiple agents
- Trace agent runs in production to see which tool calls are slow or failing
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.
A developer with Python and provider credentials in hand can follow the Quickstart — `pip install agentor`, configure credentials, run the Weather Agent example — and have a working agent in minutes, or less than an hour including a weather API key. Getting durable run persistence and a FastAPI-hosted, A2A-served agent production-ready is a day or more of your own work, since none of that is a
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 or a custom Python agent loop: port the graph nodes into Agentor tools and use durable runs for checkpointing instead of your own persistence layer.
- →From a no-code agent builder: rewrite the assembled flow as Python using custom tools and the skills system to package reusable capabilities.
- →From raw OpenAI-compatible client code: wrap the calls in an Agentor instance and keep the same provider endpoint, since Agentor accepts any OpenAI-compatible server.
- →From a single-agent script: add agent.serve() to expose the existing agent over A2A so other agents can discover and call it.
- ↗To LangGraph Cloud: rebuild the orchestration graph and hand durable run state and uptime to a managed control plane.
- ↗To AutoGen: re-express your agents as conversational agents if multi-agent chat is a better fit than A2A discovery.
- ↗To a narrower OpenAI-compatible client setup: drop the framework and call your provider directly if you no longer need durable runs or agent-to-agent serving.
- ↗To a hosted agent platform: move tracing, persistence and crash recovery off your own infrastructure if operating them is no longer worth the engineering time.
Integrations
Resources & guides
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
What are the best Agentor alternatives?
The closest alternatives to Agentor we list are AutoGen, LangGraph and TaskWeaver. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| AutoGen | Free | Free | — |
| LangGraph | Freemium | Pricing not published | — |
| TaskWeaver | Free | Free | — |
Alternatives to Agentor
View all
AutoGen
AutoGen is Microsoft's open-source framework for building conversational and event-driven multi-agent AI apps in Python and .NET.
LangGraph
LangGraph is an MIT-licensed, low-level agent orchestration framework for building stateful, controllable AI agents as graphs of states and transitions.
TaskWeaver
Microsoft's open-source, code-first Python framework for building stateful, data-intensive conversational AI agents.
Tools that pair well with Agentor
Common stack mates teams adopt alongside Agentor, with the specific reason each pairing earns its keep.
AutoGen
AutoGen is Microsoft's open-source framework for building conversational and event-driven multi-agent AI apps in Python and .NET.
LangGraph
LangGraph is an MIT-licensed, low-level agent orchestration framework for building stateful, controllable AI agents as graphs of states and transitions.
TaskWeaver
Microsoft's open-source, code-first Python framework for building stateful, data-intensive conversational AI agents.
Agentor vs its closest rival
Agentor's closest head-to-head is with Locus Robotics — Locus Robotics and Agentor solve completely different problems: Locus automates physical warehouse workflows with AMRs, while Agentor is a developer framework for software agents. Your choice depends on whether you need to move boxes or automate code. Verdict generated
Head-to-head comparisons
Agentor vs Locus Robotics
Locus Robotics and Agentor solve completely different problems: Locus automates physical warehouse workflows with AMRs, while Agentor is a developer framework for software agents. Your choice depends on whether you need to move boxes or automate code.
GeneratedAgentor 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.
GeneratedAgentor 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.
GeneratedFrequently asked questions
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