We Tried and Tested 11 Camunda Alternatives for Better Agentic Orchestration

Camunda is a process orchestration and workflow automation platform for teams already thinking in terms of Business Process Model and Notation (BPMN). As in, map every code execution to visual BPMN tasks on a canvas.

But honestly, BPMN to an agent now feels like what Markdown is to an LLM. It struggles with modern agentic AI workflows that often start in a Python file. So when you use Camunda for work that’s closer to agent runtime control or involves chaotic back-and-forth loops, it feels less native for the process.

For this article, we reviewed and picked the best Camunda alternatives that fit AI engineers building complex agents with retries, approvals, and long-running execution.

A Quick Overview of the Best Camunda Alternatives

• Why Look for Alternatives: Camunda’s BPMN-first model feels heavy when the real work is a code-first agent loop, tool calls, checkpoints, and runtime state.
• Who Should Care: AI engineers, platform teams, agent developers, and teams moving from static workflows to long-running agent tasks.
• What to Expect: A breakdown of 11 best Camunda alternatives, from agent runtimes like Kitaru to durable execution tools like Temporal, Restate, DBOS, Hatchet, and Inngest.

The Need for a Camunda Alternative?

Camunda is a mature process orchestration platform. The problem starts when teams use it for modern LLM workflows. Here are a few places Camunda might irk you:

1. BPMN-first mental model is heavy for code-first agent teams

Camunda works best when you describe flows as BPMN diagrams. While these diagrams can look simple, mastering them and modeling correctly takes patience. Here’s an example of what BPMN diagram looks like:

Source

So if you’re a new user, you’ll be overwhelmed by the notation and technical details. Also, now that LLMs don’t need that representation to be graphical, looking for code-first alternatives to Camunda makes complete sense.

2. Less control over the agent tool loop in the simpler path

Camunda remains BPMN-first. Its recommended AI Agent Sub-process handles tool resolution, the agent feedback loop, and short-term conversation memory internally. Teams that need to inspect or approve every tool call can instead use the more explicit AI Agent Task pattern, although this requires more BPMN modeling. Long-term semantic memory generally still relies on an external vector store connected through Camunda.

3. Agent memory and state can become awkward

BPMN engines might track workflow state well, but handling agent state is a different story. You often need prompt history, tool results, retrieval context, model output, user feedback, and checkpoints across many turns.

Storing this around Camunda adds extra systems and glue code. For teams building agents, it is often cleaner to choose a tool where memory, checkpoints, replay, and resume behavior are closer to the code.

Evaluation Criteria

We evaluated each alternative against three criteria that matter most for agentic orchestration.

• Code-first agent development: Can I define agents, tools, retries, state, and branching in Python or TypeScript instead of mainly editing BPMN diagrams? This matters because most AI teams already build model calls, tool wrappers, and eval logic in code.
• Control over the tool-calling loop: Can I intercept, change, approve, retry, block, or enrich every tool call? This matters when an agent touches internal APIs, sends messages, writes files, or makes decisions that need review.
• Durable execution and resume semantics: If the agent fails halfway through a 40-step job, can it restart from the right step without repeating costly LLM calls? For long-running tasks involving human approvals, clean recovery matters.

What are the Top Alternatives to Camunda

Before we deep dive into tool reviews, here’s a quick comparison table to help you make a decision:

Camunda Alternative	Best For	Key Features	Pricing
Kitaru by ZenML	Python agent runtimes	- Durable checkpoints with replay - Pause/resume without active compute - Wraps your agent SDK, no BPMN	- Free (open source, Apache 2.0) - Paid plans start at $399/month
Temporal	Durable agent workflows	- Code-defined durable workflow execution - Retryable external activity handling - Persistent event history tracking	- Free (Open-source) - Paid plans start at $100/month
LangGraph	Stateful agent graphs	- Explicit stateful agent graph modeling - Persistent memory across interactions - Human approval workflow interruptions	- Free tier - Paid plans start at $39/seat/month
Inngest	Event-driven functions	- Durable step state persistence - Event-triggered workflow continuation - Configurable retry backoff policies	- Free tier available - Paid plans start at $75/month
Restate	Durable services and agents	- Durable async service execution - Built-in state persistence layer - Long-running timer orchestration	- Free tier available - Paid plans start at $75/month
DBOS	Database-backed workflows	- Database-backed workflow state persistence - Idempotent execution with workflow IDs - Native Pydantic AI integration support	- Free and open-source - Paid plans start at $99/month
Hatchet	Background tasks and agents	- Distributed task queue management - Configurable worker concurrency controls - Historical task replay debugging	- Free tier available - Paid plans start at $500/month
Microsoft Agent Framework	Enterprise agent workflows	- Multi-agent workflow composition patterns - Human approval checkpoint handling - Model provider abstraction layer	- Free and open source
Prefect	Python workflow orchestration	- Python-native flow orchestration engine - Interactive workflow pause mechanisms - Built-in execution observability	- Free tier available - Paid plans start at $100/month
Google ADK	Google Cloud agent teams	- Multi-agent collaboration workflows - Integrated evaluation testing framework - Flexible cloud deployment targets	Free and open source SDK (cloud and model costs apply)
OpenAI Agents SDK	OpenAI-native agent apps	- Agent-to-agent task handoffs - Built-in safety guardrail checks - End-to-end execution tracing support	Free SDK; API usage is billed separately and differs from model to model.

1. Kitaru by ZenML

Kitaru is an open-source runtime for durable Python agents, built by the team behind ZenML. Instead of mapping work onto BPMN tasks on a canvas, you wrap ordinary Python functions with @flow and @checkpoint, and Kitaru gives the agent loop durable execution, replay, and pause/resume underneath.

Our platform sits as the outer runtime around whatever agent harness you already use (OpenAI Agents SDK, Anthropic Agent SDK, PydanticAI, LangGraph, or raw Python), so your reasoning loop stays in Python, and the durability lives below it. For teams that found Camunda’s diagram-first model too heavy for agent work, everything stays in the editor with version control and tests.

Feature 1. Replay from Checkpoint

Durable checkpoints with replay from the point of failure. Every @checkpoint persists its inputs and outputs to your artifact store as the flow runs. If an agent crashes at step 8 of a 30-step job, you fix the issue and replay from step 8 with Kitaru executions replay <exec_id> --from <checkpoint>.

Checkpoints before that boundary return cached output, so you don’t re-pay for the expensive LLM and tool calls that already succeeded. This is the resume behavior the evaluation looked for, without the deterministic-code rules a general workflow engine imposes on you.

Feature 2. Pause and Resume Workflows without Compute

Pause, release compute, and resume for approvals and long waits. kitaru.wait() suspends a run when it needs a human decision, another agent, or a webhook, then shuts the worker down so nothing is billed while it waits.

When the input arrives minutes, hours, or days later, Kitaru rehydrates the same execution and continues from where it paused. Human approvals and long-running tasks become primitive instead of a separate scheduler plus state store you wire up around the engine.

Feature 3. Keep the Harness and Just Add Durability with Kitaru

Wrap your agent framework, and isolate the risky parts. Kitaru does not ask you to move your reasoning loop into a new modeling language.

You keep the harness your team already picked and add durability at the boundaries that matter through thin adapters such as KitaruRunner for the OpenAI Agents SDK or KitaruAgent for PydanticAI.

The runner owns durable control flow while execution targets do the work, so a risky tool call can run in an isolated job or sandbox, and a failure there becomes one failed checkpoint instead of a lost agent.

Feature 4. Agent-Native Primitives

Agent-native primitives you would otherwise hand-build. kitaru.llm() logs the prompt, response, token counts, latency, and resolved model on every call, and reads the captured response from the checkpoint on replay, so the provider is not hit again unless the input changed.

Pricing

ZenML and Kitaru have the same pricing. Pricing is unified across both workspaces. The ZenML workspace runs ML pipelines (typed step DAGs, training, batch inference). The Kitaru workspace runs production AI agents (recorded checkpoints, replay, wait/resume). You pick the workspace per project; Pro plans include both. Same $, same support tier, different SDKs and UI.

Let’s talk about the one you will need: Kitaru. It’s free and open source under the Apache 2.0 license, and you self-host the server with artifacts in your own S3, GCS, or Azure Blob storage. Apart from the open-source plan, ZenML also offers two paid plans with premium features for Kitaru.

Scale SaaS plan:

• 500 monthly executions: $399 per month. 1 project and 1 snapshot.
• 2,000 monthly executions: $999 per month. 3 projects and 5 snapshots.
• 5,000 monthly executions: $2,499 per month. 10 projects and 20 snapshots.

Lastly, ZenML also offers an Enterprise plan with unlimited executions and projects for which you can talk to an engineer from our team.

Pros and Cons

Kitaru is the strongest fit here when the work is shaped like a Python agent: dynamic tool loops, conditional branches, and hours-long waits for a human. It is code-first by design, runs in your own cloud with no mandatory SaaS in the data path, and gives you durable checkpoints, cheap waiting, and replay-to-compare without locking you into a model, a harness, or a platform. You move from a laptop session to Kubernetes, SageMaker, Vertex AI, or AzureML with the same flow.

The trade-offs are the flip side of that focus. Kitaru is Python-only, so it will not serve a polyglot fleet the way Temporal’s seven SDKs can, and it is younger than mature engines like Temporal or Camunda, with a smaller ecosystem. It is also deliberately scoped to agent-shaped durable execution rather than general business-process modeling. If you genuinely need formal BPMN diagrams, a visual process modeler, or broad non-agent workflow automation, a general engine still fits that job better.

2. Temporal

Temporal is a durable execution platform for long-running workflows. It’s a good Camunda alternative when agent teams want code-first workflows, retry policies, and event history.

Features

• Define workflows in code by using Python, TypeScript, Go, Java, and other SDKs instead of BPMN models. Your orchestration logic lives in an ordinary function, so the work happens in your editor with version control and tests rather than a separate modeling tool.
• Wrap agent tools as activities by treating OpenAI Agents SDK tools or service calls as retryable units. An Activity is where side effects happen, so each model or tool call is tracked on its own and retried independently of the rest of the run. If one call hits a rate limit or times out, Temporal retries just that step instead of replaying the whole workflow.
• Comes with persistent event history to recover workflow state after worker crashes or restarts. In Temporal, every step is appended to an event history, and when a worker comes back, the platform replays that history to rebuild the workflow’s in-memory state exactly where it stopped.
• Configure retry policies per activity by setting the backoff behavior and which failures count as retryable. Retries are declarative config on the Activity, so Temporal runs the backoff loop for you, no hand-written try/except needed.

Pricing

Temporal is free to self-host. Temporal Cloud includes three paid tiers:

• Essentials plan: $100/month
• Business plan: $500/month
• Enterprise: Custom Pricing

Pros and Cons

Temporal is one of the strongest options for durable execution. It gives engineering teams deep control over workflow state, retries, timers, and recovery. It also persists workflow event history and replays execution after failure.

The tradeoff is operational and conceptual weight; teams must learn Temporal’s workflow model and keep deterministic code rules in mind. Also, external calls live in Activities and not workflow code; you may find debugging harder.

3. LangGraph

LangGraph is an open-source framework for long-running, stateful AI agents built around explicit graphs with nodes and edges. As a Camunda alternative, it’s best for teams that want complete control over agent state, iterative loops, and human-in-the-loop review while keeping the entire workflow code-first and developer-friendly.

Features

• Represent agent behavior as a stateful graph by defining agents, tools, routers, and decision points as nodes and edges. This makes loops, retries, and handoffs explicit, so you can see where the agent can revise an answer, call a tool, or route work to another path.
• Persist graph state with checkpoints so an agent can pause, resume, and continue across turns. You can store thread-level state for conversation continuity and add longer-term memory through stores, which helps agents carry useful context across sessions.
• Add human review with interrupts before important or risky actions like sending messages, writing files, running SQL, or calling internal APIs. You can stop the graph at a defined point, collect a reviewer decision, then continue with the same state once the action is approved or changed.

Pricing

LangGraph is open source and free to use. However, if you choose to use LangSmith’s platform for production features like deployment, observability, or fleet management, pricing is determined by the plan you choose:

• Developer: Free
• Plus: $39/seat per month
• Enterprise: Custom pricing

Read our detailed guide on LangGraph pricing.

Pros and Cons

LangGraph gives better control over agent behavior than BPMN. Its graph-based approach makes it easier to build agents that can loop, branch, maintain memory, and embed human review steps. Overall, a strong fit for teams already using LangChain.

The downside is the added complexity and boilerplate, as relatively simple workflows can end up feeling more heavyweight than necessary. Similarly, durability and deployment depend on how you configure the platform. It’s not a drop-in process engine.

4. Inngest

Inngest is an event-driven workflow engine for durable functions. It’s a Camunda alternative for teams that prefer TypeScript or Python functions with durable steps, waits, retries, and event triggers.

Features

• Run durable functions with saved step output so completed work is not repeated during replay. Each step.run() can hold the result of an LLM call, tool request, or database update, which helps you avoid paying again for work that has already finished.
• Supports sleeps, signals, and event waits inside a function. You can pause an agent workflow until a user replies, a webhook arrives, or a delay has passed. This is useful for agent tasks that depend on approval or external system events.
• You do not need to move core agent logic into a separate BPMN engine. Inngest lets your functions run in your app, serverless platform, or backend service while Inngest manages workflow state.
• Trigger agent jobs from product events such as webhooks, app events, queues, and schedules. This works well when agent tasks start from user actions, background jobs, lifecycle events, or recurring checks inside an existing web app.

Pricing

Inngest is source-available and can be self-hosted. Its current server and CLI releases use the SSPL with delayed publication under Apache 2.0, while its SDKs are Apache 2.0 licensed.

• Hobby: Free
• Pro: $75 per month
• Enterprise: Custom pricing

Pros and Cons

Inngest feels much lighter than Camunda for product event workflows and agent jobs that fit function-based execution. It is easy for web teams to adopt.

The downside is that it’s less natural to deploy stateful multi-agent graphs than LangGraph or Kitaru. If your team needs BPMN diagrams, formal business process modeling, or built-in process task apps, you may still prefer Camunda over Inngest.

5. Restate

Restate is a lightweight runtime for building durable services, workflows, and AI agents. It’s popular among teams that prefer a code-first approach and need long-running, stateful services and agents that can pause, persist progress, and resume automatically after crashes or interruptions.

Features

• Record progress for async service calls and workflow logic. You can write normal backend code while Restate keeps enough history to continue after failure. This is useful when agents call tools, services, or APIs that may time out or fail mid-run.
• Store durable state close to the service or virtual object that owns it. You can store agent session state, task progress, counters, or tool outputs beside the execution path.
• Resume from saved progress after crashes, restarts, or transient failures to reduce duplicate tool calls and protect long-running jobs from infrastructure failures. You can also set up timers that let a workflow sleep until a later time.
• Run as a single binary or through Restate Cloud. Teams can start locally, self-host, or use the managed service based on their needs. This makes it easier to add durable execution to services without adopting a full process suite.

Pricing

Restate offers a free cloud tier and four paid plans:

• Starter: $75 per month
• Business: $300 per month
• Premium: $1000 per month
• Enterprise: Custom pricing

Pros and Cons

Restate is appealing for teams that want the durability of a workflow engine without a large platform footprint. It maps well to agents, event pipelines, and backend services.

However, Camunda still takes the edge because of its ecosystem maturity and a longer history in enterprise process modeling. Besides, Restate asks teams to adopt its service/runtime model, which may be unfamiliar if they only want a task queue or agent SDK.

6. DBOS

DBOS is a durable execution framework that uses the database as the source of workflow truth. It’s best for Python or TypeScript teams that want workflows, transactions, and agents in normal application code.

Features

• Works with Pydantic AI and lets model requests, MCP calls, and tools run as workflow steps. You can build agent workflows that survive API failures, worker restarts, and long-running tool actions.
• Recover and resume from the last completed step when a crash happens. DBOS also uses workflow IDs as idempotency keys, which helps avoid duplicate runs when APIs retry requests, webhooks fire again, or the same event gets sent more than once.
• Pause with durable sleeps and timeout patterns inside workflows. You can pause a job until a time window passes or stop it when it exceeds the allowed runtime without building separate schedulers.

Pricing

DBOS Transact is free and open source. Additionally, DBOS provides three premium plans:

• Pro: $99 per month
• Teams: $499 per month
• Enterprise: Custom pricing

Pros and Cons

DBOS is a strong fit when your agent workflows already depend on database state. It gives direct code control without a BPMN layer. The downside is that teams must be comfortable with DBOS’s database-centered model and may need extra UI or business process features around it.

7. Hatchet

Hatchet is an open-source developer platform that helps you build and deploy mission-critical AI agents, durable workflows, and background tasks. It works well as a Camunda alternative when teams need queues, retries, workers, and replay for agent tasks.

Features

• Write background tasks and workers in Python, TypeScript, Go, or Ruby. A task can be anything from an LLM call to a file-processing job or a single agent step. If your team prefers keeping orchestration in code, Hatchet feels much more natural than modeling everything as BPMN service tasks.
• Control how many workers, queues, and tasks run at once. You can limit how many agent tasks run in parallel, move heavy jobs into separate queues, or avoid overwhelming rate-limited APIs. This comes in handy when your models or internal services cannot handle unlimited traffic.
• Configure retries, backoff rules, and non-retryable errors right alongside your task definitions. You can automatically retry temporary model or API failures while stopping on known business errors. That means less custom retry code spread throughout your application.
• Stores task events and logs that help you inspect failed runs. You can see which task ran, what failed, and where to retry or debug. This is especially helpful in agent workflows where one failed tool call can be difficult to track down.

Pricing

Hatchet is free to use as an open-source platform. It also has a free cloud-based Developer plan with usage-based task runs, and two paid plans:

• Team: $500 per month
• Scale: $1000 per month

Pros and Cons

Hatchet is easier to adopt than Camunda for teams that mostly need background jobs and durable agent tasks. It gives strong worker and queue control with less process modeling overhead. The tradeoff is that it is not built for BPMN-native business process teams.

8. Microsoft Agent Framework

Microsoft Agent Framework is an open-source SDK that combines agent building blocks with workflow orchestration. For teams already working in the Microsoft ecosystem, Agent Framework is a reliable alternative for type-safe workflow routing, checkpoints, and human-in-the-loop steps.

Features

• Microsoft Agent Framework makes it easy to connect agents to tools, internal APIs, and MCP-enabled services. Instead of modeling every action in BPMN, you can keep workflows in code and give agents direct access to the systems they need to work with.
• Create predictable multi-agent workflows. You can define how agents and functions work together, what data gets passed around, and where execution goes next. This gives you more control over complex workflows than relying on prompts alone and makes multi-agent systems easier to reason about.
• Handle long-running processes and approvals with support for checkpointing and human-in-the-loop patterns, so workflows can pause for approvals or external input and then resume later if your business processes span hours or days, and shouldn’t fail just because a worker restarts. This is useful.
• Integrates with Azure OpenAI, Anthropic, Ollama, and other providers. You can keep orchestration logic in one framework while changing model providers as needed.

Pricing

Microsoft Agent Framework is free and open source under the MIT license. Model, Azure, and hosting costs apply separately.

Pros and Cons

Microsoft Agent Framework gives teams more agent-specific control than BPMN, especially for approvals, agent handoffs, and typed workflows. It also fits well with Azure and .NET or Python teams. The downside is that it is newer than Camunda and may need more engineering ownership.

9. Prefect

Prefect is a Python-first workflow orchestration tool for flows, tasks, retries, events, and monitoring. It’s a Camunda alternative for teams that want Python-native orchestration around agents and data workflows.

Features

• Instead of modeling processes in BPMN, Prefect lets you define workflows as regular Python code using flows and tasks. If your team already builds services, data pipelines, or AI applications in Python, you can keep orchestration in the same codebase and use familiar development tools.
• Supports retries at both the task and flow level, with retry counts, delays, and conditions. You can use this for flaky model calls, API timeouts, and temporary service failures. The retry settings live with the task, so the behavior is easier to review and change.
• Has a built-in UI where you can see workflow runs, check which task failed, and what happened next. That makes debugging agent-related pipelines much easier than digging through raw logs.

Pricing

Prefect offers an open-source version and four pricing plans for its cloud-based service:

• Hobby: Free
• Starter: $100 per month
• Team: $100/user per month
• Pro: Custom pricing
• Enterprise: Custom pricing

Pros and Cons

Prefect is a practical option when agent workflows sit beside Python data jobs, batch runs, and scheduled tasks. It is simpler to read than BPMN for Python teams.

However, Prefect isn’t for native agent memory or tool-loop design. It’s a general workflow tool, not a dedicated agent framework.

10. Google ADK

Google Agent Development Kit (ADK) is an open-source framework for building AI agents and coordinating multi-agent workflows. It has tools for defining agent behavior, connecting agents to tools and services, and orchestrating interactions between them. It’s best for teams that already plan to develop and deploy agent systems on Google Cloud.

Features

• Orchestrate multi-agent systems that coordinate, route work, and call tools. You can build a planner agent, specialist agents, and tool-calling agents that coordinate as part of a larger workflow, all defined directly in code.
• Use workflow agent patterns for cases where you want more structure and predictability. You can define sequences, routing rules, and coordination paths so agents have clear boundaries while still retaining some flexibility.
• Use evaluation tools to test agent behavior before deployment. You can check how agents use tools, assess output quality, and validate multi-step interactions to catch issues early.
• Supports Python, TypeScript, Go, and Java, so you can build agents using the languages they already use in production and avoid rewriting everything around a single stack.

Pricing

Google ADK is free and open source. Google Cloud, Gemini, hosting, and other service costs apply separately.

Pros and Cons

Google ADK is a strong fit for teams building agents inside the Google ecosystem. It gives more agent-native pieces than Camunda for tools, evals, and multi-agent flows. The downside is cloud fit; teams outside Google Cloud may prefer a more neutral runtime.

11. OpenAI Agents SDK

OpenAI Agents SDK is a code-first SDK for building agents with tools, handoffs, guardrails, tracing, and sandbox execution. It is a Camunda alternative when your agent stack already depends on OpenAI models and APIs.

Features

• Configure agents with instructions, tools, output types, handoffs, guardrails, and runtime behavior directly in code, keeping agent logic alongside the rest of your application. You can version, test, and update agent configurations using the same development practices you use for the rest of your codebase.
• Route requests from one agent to specialist agents for billing, support, research, or coding tasks. Each agent can focus on a specific responsibility while a coordinating agent decides where work should go next. This way, you can build modular multi-agent systems that are easier to scale, debug, and extend over time.
• Block unsafe inputs, validate outputs, or pause execution before tool calls to maintain control over how agents interact with users and internal systems. Guardrails can enforce business rules and ensure responses meet compliance requirements. You can also introduce human review steps before sensitive operations are executed.

Pricing

The SDK itself is free to use. OpenAI API usage is billed separately based on the selected models and tools.

Pros and Cons

OpenAI Agents SDK is a natural fit for teams that want agent behavior close to model calls. It gives far more direct control over tools and handoffs than BPMN. The limitation is durability; for long waits, replay, and workflow recovery, many teams will pair it with Kitaru, Temporal, Restate, or another runtime.

The Best Camunda Alternatives for Agentic Orchestration

There is no single best Camunda alternative. The right pick depends on how agent-shaped your work is, what language your team builds in, and how much of the durable runtime you want handed to you versus assembled yourself. Based on our testing, here are the three that stand out:

• Kitaru by ZenML is best (of course, we are a little biased) when the work is a Python agent loop, and you want checkpoints, replay, cheap waiting, and llm() logging as primitives, wrapped around the harness you already use.
• Temporal is best when the durability problem spans Go, Java, and TypeScript services and is not specifically agent-shaped.
• LangGraph is best when you want explicit nodes, edges, checkpoints, and human-in-the-loop interrupts in one graph.

If you are moving agents from a prototype to something that has to survive a failed step, a three-day wait for an approval, or a model swap, the question stops being “can I draw this workflow” and becomes “can I run it durably without re-paying for everything above the failure.” That is the gap Kitaru was built for. It keeps your inner loop in plain Python and your chosen agent SDK, then adds the outer runtime around it.

Star the project on GitHub, read the docs, or book a demo if your team needs a managed control plane through ZenML Pro.