Why people choose Promptise to build AI applications.
Most AI is still a chatbot. It waits for a message and answers it. Promptise is for AI that does real work. Build the agents. Open your product to other AI. Run it all in production.
The first tab is for the people who decide. The second is for the people who build. Every claim about another framework links to its source.
Agents that run your operations at 2 a.m.
A chatbot waits for someone to type. A Promptise agent starts on a cron schedule, a webhook, a new file or a message from another agent. It works inside your systems while your team sleeps. Long jobs get a goal and a check. The agent keeps working until the check passes.
At 02:00 the agent reconciles yesterday's invoices. When a payment fails, a Stripe webhook wakes it to email the customer.
Elsewhere this usually needs a paid platform such as LangSmith Deployment or CrewAI AMP. The other route is a separate workflow engine such as Temporal.
MCP servers you can put in front of customers.
Your customers are starting to work through AI agents. Those agents reach your product through MCP, the standard way AI calls software. Promptise builds MCP servers you can trust with that traffic. Every agent signs in with OAuth, a JWT or an API key. Each tool decides which roles and scopes may call it. Risky calls wait for a person. Every call lands in an audit log that shows any tampering. Already have an API? MCPcast turns your OpenAPI spec into a server like this.
A SaaS company opens its product to its customers' agents. Each agent sees only its own account. Every delete asks a person first. A leaked key is switched off without a restart.
Every framework here can call MCP servers. None of the six ships an SDK to build production ones. FastMCP is a strong standalone option for that.
Sell one agent to a thousand customers.
You build the agent once. Every customer gets their own memory, cache, tools and rate limits. Acme's data never reaches Globex. One deployment serves every account.
A SaaS company adds an assistant to every workspace. A heavy customer hits its own rate limit. Everyone else keeps their speed.
Per-user memory is common elsewhere. A cache separated per customer is not in the docs of the six frameworks we checked. Per-customer rate limits usually need a paid gateway.
Your rules, enforced in code.
You decide what an agent may do alone. Three refunds per run. A daily spending cap. A delete that always needs a person. At the limit, the agent stops and asks. Each agent signs in with its own identity from Entra ID, AWS, Google Cloud or SPIFFE. You can switch off one agent and leave the others running.
A billing agent issues three refunds in a run. The fourth waits for a person's approval.
Limits on the number of calls are common. A limit on irreversible actions is not in the docs of the other six. Neither is a built-in loop detector.
Replay any decision your agent made.
A customer asks why the agent refunded them. You open that run. Every model call, tool call, token and cost is there. Passwords and keys are removed before anything is stored. The traces flow into the monitoring you already run through OpenTelemetry or Prometheus.
Support answers the customer with the exact steps. Engineering finds the slow tool call in the same trace.
Tracing elsewhere often runs through a hosted product such as LangSmith, Logfire or OpenAI's dashboard. Promptise keeps it on your servers.
Agents that learn from their mistakes.
When a tool call fails, the agent records why. After a few failures it writes itself a lesson. It uses that lesson on the next run. A reviewer's reason for rejecting an action becomes a lesson too. Each customer's lessons stay with that customer.
An agent books a room with the city name and fails. It learns that the tool wants a city code. The next booking works.
Your servers. Your data. Your model.
Promptise is open source under Apache 2.0. It runs on your own infrastructure. There is no Promptise cloud in between. The agent works with OpenAI, Anthropic, Google, Azure, AWS Bedrock or a local model. Switching models is one line.
A bank runs its agents on a local model inside its own network. No prompt leaves the building.
In other frameworks, deployment and identity often sit in a paid platform. Examples are LangSmith and CrewAI AMP.
One framework instead of five.
Most teams glue together an agent framework, an MCP server SDK, a workflow engine, an identity layer and a tracing service. In Promptise they are one framework. Agents, MCP servers and clients, the runtime, identity and observability share one config. You upgrade one version.
A new developer clones the project. One command starts the agent with its MCP servers and live traces.
Agents that start on their own.
Most agents wait for a chat message. A Promptise agent starts when something happens. That can be a schedule, a webhook from Stripe or GitHub, a new file, an event or another agent. It works inside your operations day and night.
Every night the agent reconciles yesterday's invoices. When a payment fails, Stripe wakes it to email the customer. When a contract arrives, it flags the risky clauses.
Elsewhere · In other frameworks, agents that start on their own usually need a paid platform. Examples are LangSmith Deployment and CrewAI AMP. The other option is a separate workflow engine such as Temporal. Sources
Turn your API into an MCP server.
Point MCPcast at your OpenAPI spec. It builds a small set of tools that an agent can use well. Risky calls need a person's approval. Tests come with it.
A company's internal API has 300 endpoints. Its agents get a handful of clear tools instead. Every delete asks a person first.
Elsewhere · Google ADK and LangChain can also turn a spec into tools. FastMCP can generate servers from OpenAPI. The review step, the tests and the readiness score are what MCPcast adds. Sources
Make your platform ready for AI agents.
AI agents need a way into your product. Promptise builds it as an MCP server. MCP is the standard interface AI agents use. Sign-in, permissions per tool, approvals and an audit trail come with it. The audit log is HMAC-chained, so any edit shows. A test client checks every tool before you ship. Your own agents and your customers' agents can then use your platform safely.
A SaaS company opens its product to its customers' AI agents. Each agent signs in and sees only its own account. Every action is recorded.
Elsewhere · Every framework here can call MCP servers. None of the six ships an SDK to build production ones. FastMCP is a strong standalone option for that. Sources
Serve every customer with one agent.
Each customer gets their own memory, data, permissions and limits. One customer never sees another's data. You can sell one agent to hundreds of companies.
A SaaS company adds an assistant to every customer workspace. Acme's files and answers never reach Globex.
Elsewhere · Per-user memory is common in other frameworks. A cache separated per customer is not documented in them. Neither are per-customer rate limits outside paid gateways. Sources
Give your agent a goal.
Some work takes days. You give the agent the result you want and a way to check it. It keeps working until the check passes.
A migration of 200 tables runs for several days. It finishes when every table passes validation.
Elsewhere · The other frameworks have parts of this. They work within a single run. Sources
Design how your agent thinks.
Most agents think in one fixed loop. Promptise lets you design the thinking itself. Your agent can plan before it acts. It can check its own work. It can justify every answer.
A claims agent plans each case. It checks every figure against the policy. A person signs off on the decision.
Elsewhere · Graph engines and ready-made patterns are common. LangGraph is built around one. Microsoft Agent Framework, Pydantic AI, Google ADK and CrewAI have them too. Promptise gives you reasoning steps as building blocks. The agent can choose its own path through them. Sources
Set limits your agent can't cross.
You decide what the agent may do alone. For example, three refunds per run or a daily spending cap. At the limit, it stops and asks a person.
A billing agent issues up to three refunds per run. The fourth refund waits for a person.
Elsewhere · Limits on the number of calls are common. A limit on irreversible actions is not documented in the other six. Neither is a built-in loop detector. Sources
Give every agent its own identity.
Each agent signs in through your existing identity provider. It gets only the access it needs. You can switch off one agent without touching the others.
A bank switches off one misbehaving agent in its directory. The other agents keep working.
Elsewhere · Google ADK uses Google service accounts and OAuth. Identity features in CrewAI AMP and LangSmith are paid. SPIFFE is not documented in the other six. Sources
See everything your agent did.
Turn it on with one setting. Every run, model call, tool call, token and cost is recorded. You can watch it live in the terminal or open a report. You can also send it to your own monitoring through OpenTelemetry or Prometheus. Passwords and keys are removed before anything is stored.
A customer asks why the agent refunded them. Support opens that run. They see each step, each tool call and what it cost.
Elsewhere · Other frameworks offer tracing too, often through a hosted product such as LangSmith, Logfire or OpenAI's tracing dashboard. Promptise records it on your own servers and sends it to the tools you already use.
Write prompts like code.
Your prompts decide how the agent behaves. In Promptise you build them from shared parts. You test them before they ship. You can roll back a bad change in seconds.
Fifty support prompts share one refund rule. Legal changes the rule once. The tests show that every prompt still works.
Elsewhere · Prompt templates exist in other frameworks. LangChain's are the closest. Most others offer instruction templates, not a full prompt module. Sources
Run your agent as a real project.
An agent is software. In Promptise it lives in one project folder with its prompts and tools. You watch every step and its cost while you build. The same project runs in production.
A new developer clones the repo. One command starts the agent. They see every tool call and what it costs.
Elsewhere · LangGraph, CrewAI and Google ADK have project CLIs too. ADK and LangGraph Studio also have browser dev UIs. The OpenAI Agents SDK has none. Promptise covers the agent, its MCP servers, dev tools and production in one CLI. Sources
Checked against each framework's own docs.
Each mark links to its source. We read every source on 2026-10-23. "Not in the docs" means we searched the docs and the code and found nothing. It does not mean it can't be built.
Hover a mark for the note, click it for the source. Versions checked: LangGraph 1.2, CrewAI 1.15, OpenAI Agents SDK 0.24, Microsoft Agent Framework 1.23, Pydantic AI 2.60, Google ADK 2.12.