Feature matrix
This page compares Harn with adjacent orchestration systems for teams building event-driven LLM systems. It focuses on platform shape, not broad product quality: the question is where the primitive lives and what portability, governance, and replay guarantees the team gets by default.
The comparison is current as of April 2026.
At a glance
| Capability | Harn | Inngest / AgentKit | Temporal | LangGraph | Cursor Automations |
|---|---|---|---|---|---|
| Typed DSL? | Yes. Harn programs are a typed orchestration language. | No. Workflows are written through language SDKs and AgentKit libraries. | No. Workflows are general-purpose-language code with SDK constraints. | No. Graphs are Python or JavaScript objects and functions. | No. Automations are trigger plus instruction configurations for Cursor agents. |
| Deterministic replay? | Yes. Replay is a runtime contract around the EventLog, step.run memoized effects, VM effects, transcripts, and trigger delivery. | Partial. Durable steps memoize work and resume runs, but LLM replay depends on how the application wraps model calls. | Yes for workflow state. Workflow code must remain deterministic and side effects move into Activities. | Partial. Checkpoints resume stateful graph runs; node code and model calls still need app-level discipline. | No public deterministic replay contract for arbitrary automations. |
| LLM-native trigger predicates? | Yes. Flow predicates and trigger budgets are part of the runtime surface. | Partial. AI calls can be durable steps, but trigger classification is application code. | No. Temporal can orchestrate an LLM classifier Activity, but predicates are not a workflow-language primitive. | Partial. Conditional edges can use model output, but this is graph logic rather than a trigger substrate. | No. Event and schedule triggers launch instruction-driven agents; predicates are not typed public runtime objects. |
| OSS and self-hostable? | Yes. The language, runtime, orchestrator, connectors, and docs are open and self-hostable. | Partial. SDKs are Apache-2.0, while the server/CLI use SSPL plus delayed Apache publication and can be self-hosted. | Yes. Temporal is open source and can be self-hosted, with Temporal Cloud available. | Partial. LangGraph is open source; the full managed platform/self-hosted control plane is tied to LangSmith plans. | Partial. Cursor announced self-hosted cloud agents for code/tool execution, but Automations remain a Cursor product surface. |
| Same program, any deploy? | Yes. The same .harn program can run locally, in CI, in a self-hosted orchestrator, or behind Harn Cloud. | Partial. Functions deploy to app infrastructure but depend on Inngest's event/executor contract. | Partial. Workflow code is portable across Temporal deployments, but Activities, workers, and task queues are deployment-shaped. | Partial. Graph code can move between local and platform deployments, with production behavior depending on checkpointers and LangSmith deployment choices. | No. Automations are Cursor-managed agent workflows. |
| Cost governance? | Yes. Trigger budgets, model routing, and runtime context make cost limits a first-class policy concern. | Partial. Flow-control primitives cover concurrency, throttling, rate limits, and queues; model spend policy is app-level. | Partial. Temporal controls retries, schedules, workers, and task queues; LLM cost policy is app-level. | Partial. Model selection and limits are usually app or LangSmith configuration. | Partial. Usage controls are product-level rather than per-program model-routing policy. |
| Trust graph and HITL primitives? | Yes. Harn has explicit HITL stdlib, approval patterns, trust graph records, and transcript lineage. | Partial. Human-in-the-loop can be modeled through durable waits and functions. | Partial. Signals, Updates, and Activities can implement approval workflows, but not as LLM-specific trust graph primitives. | Partial. Interrupts support human review and state edits; trust graph semantics are app-level. | Partial. Cursor has review-oriented agent UX, but no public trust graph data model. |
| BYO model and sovereign deploy? | Yes. Provider configuration covers hosted APIs, OpenAI-compatible endpoints, Ollama, local servers, and self-hosted deployment. | Partial. AgentKit supports multiple model providers; sovereign deployment depends on the app, Inngest deployment, and provider setup. | Partial. Temporal can run in sovereign environments; LLM provider choice is entirely in Activities. | Partial. LangGraph can call any model the app integrates; deployment sovereignty depends on the chosen runtime and LangSmith tier. | Partial. Cursor supports configured models and self-hosted cloud agents, but the automation surface is still Cursor-specific. |
How to read the table
Yes means the capability is an explicit platform contract or first-class runtime primitive.
Partial means the system can support the outcome, but the guarantee comes from application code, paid/platform configuration, or surrounding deployment choices rather than the core programming model.
No means the capability is not the system's public primitive, even if a team could build something similar around it.
Why these rows matter
Typed DSL
Harn uses a purpose-built language for agent orchestration. That keeps trigger policy, model calls, concurrency, retries, budgets, human review, and trust metadata in the same program instead of scattering them across SDK callbacks, queue handlers, dashboards, and prompt-only automation settings.
See Language basics, Workflow runtime, and Flow predicate language.
Deterministic replay
LLM systems need replay for debugging, evaluation, and incident review. Harn's runtime owns the transcript and event-log boundary so replay can reason about the same model request, tool result, trigger event, approval, and dispatch history.
Temporal has strong deterministic replay for workflow state, but it requires workflow code to obey deterministic constraints and pushes side effects into Activities. Inngest and LangGraph provide durable execution and checkpointing, which are valuable, but they do not by themselves make arbitrary LLM calls replay-identical.
See Durable step stdlib, Transcript architecture, Testing, and Trigger event schema.
LLM-native trigger predicates
Harn treats predicates over events as runtime objects, including model-backed classifiers and budget policy. The point is not that other systems cannot call an LLM before deciding what to do. They can. The distinction is that Harn can make that decision inspectable, typed, budgeted, and replayable through the same trigger machinery that dispatched the work.
See Triggers, Flow predicate language, and Trigger budgets.
Open and self-hostable
Harn's open-source boundary includes the runtime substrate: language, VM, orchestrator, EventLog contracts, connectors, protocols, and self-hostable deployment path. Harn Cloud can add managed tenancy and operations, but the core orchestration model is not reserved for a hosted service.
See Orchestrator, Deploy to Render, Deploy to Fly.io, and Deploy to Railway.
Same program, any deploy
A .harn program should remain the unit of review whether it runs as a local
script, CI job, self-hosted orchestrator workflow, MCP server, ACP backend, or
managed cloud workflow. That portability is the practical payoff of keeping the
workflow in one language and putting host-specific details at the boundary.
See Harn portal, Outbound workflow server, MCP, ACP, and A2A integration, and Host boundary.
Cost governance
Agent systems fail operationally when model calls, retries, and background triggers become invisible. Harn exposes trigger budgets and runtime context so teams can place limits near the workflow rather than relying only on provider billing pages or ad hoc wrapper code.
See Trigger budgets, Runtime context, and LLM providers.
Trust graph and HITL
Harn's supervision model is built around explicit approvals, human review, agent session lineage, and trust graph records. HITL is not only an "approve" button at the end of a run; it is part of the orchestration and audit trail.
See Human in the loop, Trust graph, Sessions, and Agent state.
BYO model and sovereign deploy
Harn is model-neutral by design. Workflows can target hosted providers, OpenAI-compatible endpoints, local model servers, Ollama, or a provider chosen by an enterprise deployment. The same boundary supports sovereign, private, and air-gapped deployments when the model and infrastructure allow it.
See LLM providers, Provider capability matrix, and Orchestrator secrets.
Public references
- Inngest documents SDK-defined AI workflows, AgentKit, durable steps, flow control, and self-hosting in its public docs and repository: https://www.inngest.com/ai and https://github.com/inngest/inngest.
- Temporal describes open-source durable workflows, event histories, and deterministic workflow constraints in its docs: https://docs.temporal.io/ and https://docs.temporal.io/workflows.
- LangGraph documents durable execution, checkpointing, interrupts, and human-in-the-loop patterns: https://docs.langchain.com/oss/python/langgraph/overview and https://docs.langchain.com/oss/python/langgraph/human-in-the-loop.
- Cursor announced Automations and self-hosted cloud agents in its public changelog: https://cursor.com/changelog/03-05-26 and https://cursor.com/changelog/03-25-26.