LangChain vs Mastra vs Calljmp: How to choose the right agent stack for production?
If you are comparing LangChain vs Mastra (or adding Calljmp into the mix), you are probably not just picking "an agent framework." You are deciding where agent logic lives, who owns reliability, and how much production plumbing your team wants to maintain: state, retries, approvals, observability, and safe access to internal systems.
What LangChain, Mastra, 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 hosted execution layer for AI agents and multi-step workflows written as TypeScript code. It is designed for production runs that need durability and control: persisted state across steps, retries/timeouts, suspend/resume for approvals (HITL), and secure access to tools/data—plus built-in traces/logs/cost visibility so you are not bolting observability onto a fragile pipeline.
02
LangChain
LangChain is an open-source toolkit for composing LLM apps: prompts, models, tool calling, retrieval, and agent patterns. You embed it inside your own services (Node/Python) and it runs wherever you run it. It is excellent for iterating on custom logic and integrating with the wider ecosystem—but production concerns (durable state, job workers, approvals, monitoring) are typically things you assemble around it.
03
Mastra
Mastra is a TypeScript-first agent framework aimed at "batteries included" development: agents, workflows, tools, memory/RAG patterns, evaluations, and modern TS ergonomics. You can integrate it into common web stacks (e.g., Next/Node) or run it as a standalone service. It helps you move fast with a cohesive TS developer experience—while still keeping hosting, scaling, and ops as your responsibility.
TL;DR — which agent builder should you choose?
Which platform best aligns with your project's technical requirements and scale?
Best for TS-native development
Mastra
Choose Mastra if you want a modern TypeScript framework to build agents/workflows quickly in your own stack, with lots of agent building blocks included out of the box.
Best for ecosystem flexibility
LangChain
Choose LangChain if you want maximum ecosystem flexibility (especially across Python + JS) and you are comfortable owning the production platform around it.
Best for production execution
Calljmp
Pick Calljmp if you are shipping production agents/workflows that need a managed runtime: long-running stateful execution, pause/resume approvals, and first-class observability—without stitching together queues, persistence, streaming, and ops tooling 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
Compare execution model, long-running behavior, HITL approvals, state handling, and what it takes to get real observability (not just logs).
| Capability | LangChain | Mastra | Calljmp |
|---|---|---|---|
| What it is | Open-source library/toolkit for building LLM apps & agent logic | TypeScript agent framework for agents, workflows, tools, memory/RAG, evals | Managed agent runtime/backend to run TypeScript agents & workflows |
| Best for | Custom agent logic inside your services (fine-grained control) | TS-first building with a cohesive "framework feel" | Production execution with durability + ops controls |
| Who uses it | Developers | Developers (TS-focused teams) | Dev + ops/product collaboration |
| How you build | Code-first (JS/Python) | Code-first (TypeScript) | Code-first (TypeScript) |
| Execution model | Runs inside your app/services | Runs in your stack (web app/server/service) | Runs in a managed execution model with run identity + lifecycle |
| Long-running work | Possible, but you add workers + persistence | Possible; you still design persistence/workers as needed | First-class: long execution, retries/timeouts, orchestration patterns |
| Real-time interaction (progress/events) | You implement streaming + run status | You implement UI streaming + run status | Built-in run status + progress emission patterns |
| HITL (pause/approve/resume) | Patterns available; you wire persistence + approvals | You implement pause/resume semantics in your stack | Native suspend/resume with clean resume-by-API patterns |
| State management | You bring DB/queues/checkpointing | You bring durability (DB/queues) depending on your needs | Persisted state is part of the runtime contract |
| Observability & evals | Typically via additional tooling (often LangChain ecosystem tools) | Framework-level primitives; ops visibility depends on what you deploy | Built-in traces/logs/cost + shared visibility for teams |
| Hosting/ops | You own infra + reliability | You own infra + reliability | Managed infra; you focus on agent logic + integrations |
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.
What each tool actually does (framework vs TypeScript framework vs managed runtime)
Understanding the core purpose and design of each tool
LangChain
LangChain is an open-source toolkit for composing LLM apps: prompts, models, tool calling, retrieval, and agent patterns. You embed it inside your own services (Node/Python) and it runs wherever you run it. It's excellent for iterating on custom logic and integrating with the wider ecosystem—but production concerns (durable state, job workers, approvals, monitoring) are typically things you assemble around it.
Mastra
Mastra is a TypeScript-first agent framework aimed at "batteries included" development: agents, workflows, tools, memory/RAG patterns, evaluations, and modern TS ergonomics. You can integrate it into common web stacks (e.g., Next/Node) or run it as a standalone service. It helps you move fast with a cohesive TS developer experience—while still keeping hosting, scaling, and ops as your responsibility.
Calljmp
Calljmp is a hosted execution layer for AI agents and multi-step workflows written as TypeScript code. It's designed for production runs that need durability and control: persisted state across steps, retries/timeouts, suspend/resume for approvals (HITL), and secure access to tools/data—plus built-in traces/logs/cost visibility so you're not bolting observability onto a fragile pipeline. It's built for developer + operator collaboration. Engineers implement agent logic in code, while product/ops can monitor runs, spot regressions, and keep quality/cost/risk in check from one place (the goal: build in code, run with the business).
Execution Model: How Work Gets Done
Short-lived vs long-running, batched vs real-time
LangChain
Runs inside your own app/services (wherever you deploy it). It's great for request/response style agent work. Once you move beyond quick calls, teams typically add the surrounding "production kit" themselves: state storage, async workers/queues/cron, and observability (often LangSmith for traces/evals).
Mastra
Runs in your stack (your server/service). You model agents/workflows in TypeScript and ship them like any other backend component. Execution behavior (retries, background jobs, streaming, scaling) is largely defined by how you host and instrument the service.
Calljmp
Runs agents/workflows in a managed execution environment where a run has identity + lifecycle by default. Your app/API/Slack triggers a run → Calljmp manages execution across steps (including long runs), supports suspend/resume for approvals, and pushes outcomes back to your UI/tools with traces/logs/cost visible to both dev and ops.
State & Long-Running Work
Keeping agents alive across minutes, hours, or days
LangChain
Long-running is possible, but durability is on you. You'll design checkpointing, persist state in a DB/queue, and implement resumption patterns. Very flexible—also more engineering and more edge cases.
Mastra
You can build long-running flows, but you still need a durable backbone: where state is stored, how jobs resume after restarts, and how concurrency is handled. In practice, this becomes "your workflow service + your persistence + your job runner."
Calljmp
Durable state is a first-class primitive. Runs persist across steps, can wait for external events or approvals, and continue without losing context. It's built for long execution patterns where "pause, resume, and observe" are normal—not bolt-ons.
HITL & Approval Workflows
Pausing for human feedback and approvals
LangChain
You can implement HITL using graph/checkpoint patterns, but the full system (approval UI, notifications, secure resume, audit trail) is something you assemble and maintain.
Mastra
You can design approval checkpoints in your workflow logic, but you still need to build the approval loop: who approves, how they're notified, how the run resumes safely, and how actions are audited.
Calljmp
HITL is built into the runtime via native suspend/resume. A run can pause, wait for approval from UI/Slack/API, and continue from the same point with full context. Operational visibility while it's waiting—not only after it completes.
Real-time execution & interactive workflows
"Real-time" here means: a running agent can keep live state and stream progress (step updates, partial results, "waiting for approval", errors) to your UI/Slack/API while the run is active—even if it lasts minutes or hours.
LangChain
Streaming tokens is easy; streaming workflow status is extra work. Teams usually wire up: durable progress storage, a worker model for long tasks, a push channel (SSE/WebSockets), and custom pause/resume mechanics.
Mastra
You can build a live "agent run" UX, but it's defined by your app architecture: how you emit events, how you store run state, and how you handle reconnects and resumes. Great control—more plumbing.
Calljmp
Interactive execution is a runtime behavior: runs have identity, maintain state, and can emit status/events across long workflows. You spend time on agent logic + UX, not on building a separate "real-time execution layer."
Observability & Debugging
Seeing what happened, and why
LangChain
You'll get logs from your app, but deeper visibility (step traces, token usage, evals) typically comes from additional tooling—many teams use LangSmith for that layer.
Mastra
You can instrument it like any service (logs/traces/metrics). The quality of observability depends on your deployment and what you integrate (OpenTelemetry, dashboards, cost tracking, eval harnesses, etc.).
Calljmp
Observability is built into execution: traces, logs, and cost/tokens are captured at the runtime level so teams can debug runs, compare behavior, and monitor drift/regressions without assembling separate tracing systems.
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.
Security & Access Control
Protecting data, secrets, and agent capabilities
LangChain
Security is defined by your app. You implement auth, tenancy boundaries, secret handling, and permissioning around whatever tools the agent can call.
Mastra
Same model: your app owns the security perimeter. You decide how tools are exposed, how permissions are enforced, and how secrets/tokens are managed.
Calljmp
Designed around controlled tool/data access + centralized secret handling (Vault) + audited runs. You still apply least-privilege in your integrations, but the runtime provides a consistent execution + audit surface across agents.
Architecture Patterns: How Teams Deploy
Framework in your stack vs TS workflow service vs managed agent runtime
LangChain
Embedded in an existing backend service. As workflows become more complex, teams add persistence, workers, and an observability/evals layer on top.
Mastra
Deployed as part of your TypeScript backend (or its own service). It becomes another production component you operate: deployments, scaling, state, and instrumentation are yours.
Calljmp
Your app triggers runs; Calljmp executes them durably and observably. Results are delivered back to your systems/UI/tools, with state + approvals handled as part of the runtime lifecycle.
DX + UX for Teams
Collaboration, reviews, and day-2 ops
LangChain
Strong for developers writing logic, but cross-team operation (reviews, rollouts, regression control) typically depends on your internal process + extra tooling.
Mastra
Good developer ergonomics for TypeScript teams. Collaboration is still "software collaboration" (repo/CI/conventions), and ops maturity depends on what you build around it.
Calljmp
Developer-built, operator-visible: devs ship code, and ops/product can monitor runs and outcomes in a shared surface—without building a separate ops console for agent behavior.
Best choice by scenario (LangChain vs Mastra vs Calljmp)
Match your requirements to the right tool
LangChain
Pick it if you want maximum freedom to design agent behavior in code and you're okay owning the surrounding production stack: durability, workers, monitoring/evals, approvals, and governance.
Mastra
Pick it if you're TypeScript-first and want a cohesive framework to build agents/workflows inside your own stack, with the understanding that reliability, scaling, and ops are still yours to run.
Calljmp
Pick it if you need production execution with durable state, suspend/resume approvals, and built-in observability—so you're not stitching together framework + queues + persistence + tracing + approval plumbing into a brittle system.
Pricing & Total Cost
Sticker price is rarely the real cost
LangChain
The library is "free," but the real spend is the platform work around it: compute, queues/workers, state storage, observability/evals (often LangSmith), and ongoing maintenance for retries/timeouts/incidents.
Mastra
Framework costs are rarely the main line item; it's the operational footprint: hosting, scaling, durable state, instrumentation, and the engineering time to keep workflows reliable as usage grows.
Calljmp
You pay for the managed runtime, but can save on "glue costs": fewer components to build and operate, and less time spent rebuilding execution + observability + approvals from scratch.
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.