LangChain vs n8n vs Calljmp: Which AI Agent Runtime Should You Choose?
Compare top alternatives for building and running AI agents. Understand the differences in state management, long-running workflows, HITL approvals, and production observability.
What LangChain, n8n, and Calljmp actually are
Before comparing features, it's important to understand the core mission and architectural philosophy of each platform.
01Target Solution
Calljmp
Calljmp is a managed agentic backend/runtime where teams define AI agents and multi-step workflows as TypeScript code, and run them next to their existing backend with state, retries/timeouts, HITL, and secure tool/data access built in. It's built for both developers and ops/product teams: devs can ship and debug with traces/logs/costs + evals, while ops can track regressions and keep quality/risk under control from one place ("Build in code. Run by business.")
02
LangChain
LangChain is an open-source framework (mainly a library) for building LLM-powered apps by composing prompts, models, tools, retrievers, and chains/agents in code. It's strongest as an application-layer toolkit you embed into your own backend and infrastructure.
03
n8n
n8n is a workflow automation platform that lets you connect apps and APIs using visual "nodes" and triggers (webhooks, schedules, events). It's best for orchestrating business processes and integrations, and you can add AI steps, but it's not primarily an in-product agent runtime.
TL;DR — which agent builder should you choose?
Which platform best aligns with your project's technical requirements and scale?
Best for automation workflows
n8n
Choose n8n if your priority is internal automation: connect lots of SaaS tools fast, set up triggers, and run repeatable workflows without a big engineering lift (visual builder).
Best for custom agent logic
LangChain
Choose LangChain if you want maximum flexibility in custom AI app logic and you have time and resources to build/operate the surrounding production setup (hosting, monitoring, reliability).
Best for production agents
Calljmp
Pick Calljmp if you’re running production AI agents/workflows that need a reliable runtime: stateful long-running execution, pause/resume for approvals (HITL), and built-in observability (traces/logs/costs) without stitching those pieces together yourself.
LangChain
Best for:
- Custom agent logic
- Maximum flexibility
- Framework in your codebase
You'll need to add: runtime + state + ops
n8n
Best for:
- Automation workflows
- SaaS connectors
- Ops-friendly builder
You'll need to add: complex agent runtime
Calljmp
Best for:
- Production agents
- State + real-time
- HITL + observability
All-in-one: agent logic + runtime
Typical outcomes
Fast prototyping
Quickly validate ideas and build initial versions
Fast automation
Deploy workflows and workflows at scale
Reliable production agents
Deploy production-grade agents with confidence
Feature Matrix
LangChain is a dev framework for building LLM apps in code. n8n is a workflow automation platform with visual builders. Calljmp is a managed agent runtime with built-in state, long-running execution, HITL approvals, and observability.
| Capability | LangChain | n8n | Calljmp |
|---|---|---|---|
| What it is | Dev framework/library for building LLM apps/agents in code | Workflow automation platform (triggers + integrations + nodes) | Agent runtime/backend to run agents/workflows as TypeScript code |
| Best for | Custom AI logic inside your services | Cross-tool automation (ops workflows, integrations) | Running production agents/workflows with state + control |
| Who uses it | Developers | Ops + devs (often ops-led) | Built for dev + ops collaboration in one place |
| How you build | Code-first (JS/Python) | Visual workflow builder (low-code) + custom code nodes | Code-first (TypeScript) |
| Execution model | Runs wherever you run it (your app/server) | Runs workflows on triggers/schedules/webhooks | Runs workflows/agents in a managed execution model |
| Long-running work | Possible, but you wire persistence/jobs | Supported (e.g., wait steps), depends on setup | First-class long execution + orchestration patterns |
| Real-time interaction (live state + progress updates) | Possible, but you build it | Execution/log visibility; interactive UX requires extra plumbing | Built-in runtime behavior (stateful runs + progress emission) |
| HITL (pause/approve/resume) | Via LangGraph patterns + persistence you add | Wait/approval flows via wait + resume webhook | Native suspend/resume in workflows + resume via API |
| State management | You implement durable state (DB/queues) | Workflow state stored by n8n | Persisted workflow/agent state as a primitive |
| Observability | Basic logs; requires LangSmith for traces/evals | Execution history per workflow; limited AI-specific cost tracing | Built-in traces/logs/cost visibility (runtime-level) |
| Security & access | You implement auth/tenancy/permissions | Platform users/roles; integration credentials | Runtime-level security model + controlled tool/data access |
| Typical "hidden work" | Hosting, queues, retries, HITL UI, ops tooling | Enterprise governance, complex agent logic, custom reliability | Less infra glue; you still design approval UX + policies |
Start building agents on Calljmp
Create your first TypeScript agent in minutes and get state, real-time progress, HITL approvals, and traces/logs/costs out of the box.
Execution Model: How Work Gets Done
Short-lived vs long-running, batched vs real-time
LangChain
Runs wherever you run it (your app/server). For anything beyond short requests, you usually add supporting infrastructure: a database for state, a queue/cron for async work, and your own monitoring plus (often) LangSmith for traces/evals.
n8n
Runs workflows on triggers/schedules/webhooks. Supported (e.g., wait steps), depends on setup. Teams deploy n8n as a central automation server (self-hosted or cloud) that runs workflows based on triggers (schedule, webhook, app event). It excels when the "glue" is the product: you connect SaaS tools, route data, run steps (including AI nodes), and use wait/approval patterns via resume links/webhooks.
Calljmp
Runs workflows/agents in a managed execution model with state as a built-in primitive. Teams define agents/workflows as TypeScript and run them on Calljmp as a dedicated runtime that manages execution across steps (including long runs). Typical setup is: your app/API/Slack triggers an agent run → Calljmp handles state + suspend/resume for approvals → results are pushed back to your UI, database, or tools, with traces/logs/cost visible for both dev and ops.
State & Long-Running Work
Keeping agents alive across minutes, hours, or days
LangChain
You implement durable state (DB/queues). Possible, but you wire persistence/jobs. For long-running work, you need to build or integrate a state store (database or message queue), handle checkpointing, and manage resumption. This is flexible but requires significant engineering effort.
n8n
Workflow state stored by n8n. Supported (e.g., wait steps), depends on setup. n8n workflows can pause and resume, but interactive "agent UX" (live progress bar, multi-user approvals inside your product) often means extra custom plumbing around n8n.
Calljmp
Persisted workflow/agent state as a primitive. First-class long execution + orchestration patterns. Calljmp is designed as a stateful agent runtime (Durable Objects-style execution model): a run has an identity, keeps state as it progresses, and can emit events/status as it goes. That makes these patterns natural: "Agent is working…" with live step updates, long-running multi-step runs that stay interactive, and suspend/resume for approvals (HITL) without losing context.
HITL & Approval Workflows
Pausing for human feedback and approvals
LangChain
Via LangGraph patterns + persistence you add. Possible, but you build it. You can design approval mechanics using LangGraph checkpointers and custom state management, but building a full approval UX (UI, notifications, resumption) is a system you assemble.
n8n
Wait/approval flows via wait + resume webhook. n8n has wait/approval features, but building a "live agent run" experience (progress bar, step-by-step UI updates, multi-user approvals inside your product) often means extra custom plumbing around n8n.
Calljmp
Native suspend/resume in workflows + resume via API. Suspend/resume for approvals (HITL) without losing context. Calljmp is designed as a stateful agent runtime: a run has an identity, keeps state as it progresses, and can emit events/status as it goes. Operational monitoring where dev + ops can see what is happening now, not only after the fact.
Real-time execution & interactive workflows
When we say real-time in agent systems, we don't mean "the agent watches the internet instantly." We mean: a running agent can keep live state and stream progress (steps, partial results, "waiting for approval", errors) back to your UI/Slack/API while it's executing, even if it takes minutes or hours.
LangChain
Real-time is possible, but you build the plumbing. LangChain can power interactive experiences (streaming tokens, tool calls), but real-time workflow progress usually requires you to wire up: a state store (DB/Redis) to persist progress, a queue/worker model for long-running jobs, a streaming channel (WebSockets/SSE) for status updates, and your own pause/resume + approval mechanics (often via LangGraph + a checkpointer). Result: very flexible, but real-time interactivity is a system you assemble, not a default.
n8n
Great execution visibility, limited interactive "agent UX" by default. n8n workflows can run quickly and you can see execution history/logs, and you can create wait/approval patterns. But "real-time" here typically looks like: a workflow starts on a trigger, steps execute, you inspect run logs/history, or send notifications. It's excellent for automation control flow, but building a "live agent run" experience (progress bar, step-by-step UI updates, multi-user approvals inside your product) often means extra custom plumbing around n8n.
Calljmp
Real-time is a first-class runtime behavior. Calljmp is designed as a stateful agent runtime (Durable Objects-style execution model): a run has an identity, keeps state as it progresses, and can emit events/status as it goes. That makes these patterns natural: "Agent is working…" with live step updates, long-running multi-step runs that stay interactive, suspend/resume for approvals (HITL) without losing context, and operational monitoring where dev + ops can see what's happening now, not only after the fact. You focus on the agent logic and the UI/ops workflow, instead of building the real-time execution layer.
Observability & Debugging
Seeing what happened, and why
LangChain
Basic logs; requires LangSmith for traces/evals. LangChain itself offers basic logging. For production visibility, you usually add LangSmith (paid) to track traces, token usage, evals, and performance. This requires additional cost and setup.
n8n
Execution history per workflow; limited AI-specific cost tracing. n8n provides execution history per workflow and logs, but it does not provide deep AI-specific observability (token usage, cost per model call, eval metrics). Building a comprehensive monitoring stack requires external tools.
Calljmp
Built-in traces/logs/cost visibility (runtime-level). Calljmp includes traces, logs, and token/cost tracking at the runtime level. Dev teams can track performance, debug issues, and see exactly what happened during a run. Ops teams can monitor quality, detect regressions, and track costs without extra tooling.
Security & Access Control
Protecting data, secrets, and agent capabilities
LangChain
You implement auth/tenancy/permissions. LangChain itself does not define a security model. You implement authentication, multi-tenancy, API key management, and role-based access control in your own application layer. This offers flexibility but requires careful design.
n8n
Platform users/roles; integration credentials. n8n provides platform-level users/roles and secret management for integration credentials. For stricter governance (API key rotation, audit logs, per-agent access control), you often build extra layers.
Calljmp
Runtime-level security model + controlled tool/data access. Calljmp enforces security at the runtime level: agents can access only specific tools and data based on roles/tags. API keys are managed centrally, and every agent run is audited. This means devs can ship faster—auth is not their problem—and ops keeps quality under control.
Launch an agent you can share
Build once, then let teammates or clients run it from a portal/workspace — while you keep full visibility into runs and performance.
Architecture Patterns: How Teams Deploy
Framework inside your stack vs automation hub vs agent runtime
LangChain
Teams typically embed LangChain inside an existing backend service (API, worker, or monolith) where it orchestrates LLM calls, tool calls, and retrieval. For anything beyond short requests, you usually add supporting infrastructure: a database for state, a queue/cron for async work, and your own monitoring plus (often) LangSmith for traces/evals.
n8n
Teams deploy n8n as a central automation server (self-hosted or cloud) that runs workflows based on triggers (schedule, webhook, app event). It excels when the "glue" is the product: you connect SaaS tools, route data, run steps (including AI nodes), and use wait/approval patterns via resume links/webhooks.
Calljmp
Teams define agents/workflows as TypeScript and run them on Calljmp as a dedicated runtime that manages execution across steps (including long runs). Typical setup is: your app/API/Slack triggers an agent run → Calljmp handles state + suspend/resume for approvals → results are pushed back to your UI, database, or tools, with traces/logs/cost visible for both dev and ops.
DX + UX for Teams
Collaboration, reviews, and day-2 ops
LangChain
Great developer DX for writing agent logic in code, but team collaboration isn't built-in—you typically add tools (e.g., LangSmith) and your own review/rollout process.
n8n
Very accessible UX for automation and cross-tool workflows. Collaboration is solid at the workflow level, but AI-specific governance (prompt/version discipline, eval-driven iteration, per-agent quality/cost) usually needs conventions.
Calljmp
Developer-built, operator-run: devs ship code, while ops/product collaborate via shared visibility into runs, outcomes, and performance—so you don't have to glue execution + observability + team workflows together.
Best choice by scenario (LangChain vs n8n vs Calljmp)
Match your requirements to the right tool
LangChain
You want maximum flexibility in code to design custom agent behavior (tool calling, RAG patterns, routing logic). You're fine assembling the production system around it: state storage, async jobs/queues, retries, auth/tenancy, monitoring/evals, and approval flows. Your team prefers an OSS library approach and wants to stay close to the metal.
n8n
Your goal is automation across tools (Slack, Gmail, HubSpot, Zendesk, Sheets, internal APIs) with fast setup. You want a visual workflow builder that ops and non-platform engineers can maintain. "Good enough AI steps" inside workflows are fine, and you don't need a fully interactive agent runtime.
Calljmp
You're running production agents/workflows that need stateful execution across multiple steps (including long runs). You need pause/resume for approvals (HITL) and a clean way to resume runs from your UI/Slack/API. You want the runtime pieces pre-integrated (execution control, persisted state, retries, observability, secure tool/data access) so you're not gluing a framework + queues + DB + tracing + custom approval plumbing into a fragile stack.
Pricing & Total Cost
Sticker price is rarely the real cost
LangChain
"Free library" + you fund the production stack. LangChain itself isn't the main expense. Costs come from what you add around it: compute, queues/workers, a database for state, monitoring/tracing (often LangSmith), and the ongoing effort to maintain retries, timeouts, and incident debugging.
n8n
Platform cost + complexity as automation spreads. n8n gives fast time-to-value with a platform pricing/self-host tradeoff. Costs rise with workflow volume, execution frequency, and governance needs across teams. It's great for automations, but complex AI logic or strict reliability/security can push you into custom work.
Calljmp
Pay for the runtime, save on "glue" cost. Calljmp bundles the runtime pieces (stateful execution, suspend/resume for approvals, observability, retries, secure tool/data access). That can lower total cost by reducing the extra systems and time you'd otherwise spend stitching and operating a framework + infra stack.
Run your first workflow end-to-end
Follow a guided example to connect tools, add an approval step, and ship a production-ready agent without gluing infra together.
Common Questions
Answers to the most frequent inquiries about these agent orchestration platforms.