LangChain vs Agno vs Calljmp: Compare agent frameworks and runtimes for production
Compare leading options for building and operating AI agents, see how they differ on durable state, long-running workflows, human-in-the-loop approvals, and production-grade observability.
What LangChain, Agno, 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 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 built-in observability (traces/logs/costs) so you're not assembling a fragile "framework + infra + tracing + approvals" stack.
02
LangChain
LangChain is an open-source framework/library for building LLM-powered apps and agents by composing models, prompts, tools, retrievers, and agent patterns in code (Python and JS/TS). You embed it in your own services and own the surrounding production platform (state, workers, monitoring, security).
03
Agno
Agno positions itself as a "language" / cohesive architecture for agentic software: an SDK (agents/teams/workflows/memory/tools/approval flows), an execution engine (model + tool loop, structured outputs, context/runtime checks), and AgentOS as the production runtime (streaming APIs, auth/isolation, approval enforcement, tracing, control plane).
TL;DR — which agent stack should you choose?
Which platform best aligns with your project's technical requirements and scale?
Best for custom agent logic & ecosystem flexibility
LangChain
Choose LangChain if you want maximum flexibility and ecosystem breadth (Python + JS) and you're comfortable owning the production "kit" around it.
Best for building + running agents in your infrastructure
Agno
Choose Agno if you like a more opinionated, cohesive agent architecture (SDK + engine + AgentOS) but still want to run it in your own cloud/VPC and keep full ownership of data and ops.
Best for production agents without infra glue
Calljmp
Pick Calljmp if you need production execution with durable state, long-running runs, HITL pause/resume, and first-class observability, without stitching queues, persistence, streaming, and ops tooling together yourself.
LangChain
Best for:
- Custom agent code
- Max control
- DIY infrastructure
You'll build: state + queues + monitoring
Agno
Best for:
- Self-hosted runtime
- Inside your stack
- Database & ops control
You'll manage: state + service scaling
Calljmp
Best for:
- Managed runtimes
- State + HITL
- Built-in observability
All set: run + state + approvals
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 | Agno | Calljmp |
|---|---|---|---|
| What it is | OSS framework/library for LLM apps & agent logic | SDK + engine + "AgentOS" runtime architecture for agentic software | Managed agent runtime/backend to run TypeScript agents & workflows |
| Best for | Custom logic inside your services | Cohesive multi-agent architecture you run yourself | Production execution with durability + ops controls |
| Who uses it | Developers | Developers/AI teams running their own infra | Dev + ops/product collaboration |
| How you build | Code-first (Python/JS) | Code-first (Python) | Code-first (TypeScript) |
| Execution model | Runs inside your app/services | Runs in your infrastructure (AgentOS runtime) | Managed execution model with run identity + lifecycle |
| Long-running work | Possible, but you add workers + persistence | Supported via runtime + your DB; you operate it | First-class long execution + orchestration patterns |
| Real-time interaction (progress/events) | You implement workflow/run status streaming | Streaming APIs are part of the runtime | Built-in run status + progress emission patterns |
| HITL (pause/approve/resume) | Patterns exist; full approval loop is on you | Approval flows + approval enforcement in runtime | Native suspend/resume + resume-by-API patterns |
| State management | You bring DB/queues/checkpointing | Sessions/memory/traces stored in your DB | Persisted state is part of the runtime contract |
| Observability & evals | Typically via additional tooling (often LangSmith) | Native tracing, but you still integrate/operate the stack | Built-in traces/logs/cost + shared visibility for teams |
| Hosting/ops | You own infra + reliability | You own infra + reliability | Managed infra; 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 "agent OS" runtime vs managed runtime)
Understanding the core purpose and design of each tool
LangChain
LangChain is a toolkit for composing agent behavior: prompts, tool calling, retrieval, and patterns. It's ideal for iterating on logic and integrating with the ecosystem, but production concerns (durable state, workers, approval UX, monitoring) typically live outside the library.
Agno
Agno tries to be a coherent agent architecture end-to-end: you define behavior with the SDK primitives, run execution through the engine, and deploy via AgentOS, which includes concepts like streaming APIs, auth/isolation, approval enforcement, background execution, and native tracing. Their docs frame this as running in your infrastructure with your database as the system of record.
Calljmp
Calljmp is a hosted execution layer for TypeScript agents and workflows. It's built for durable, long-running runs with built-in traces/logs/costs and suspend/resume for approvals. So you don't bolt observability and control onto a brittle pipeline.
Execution Model: How work gets done
Short-lived vs long-running, batched vs real-time
LangChain
Runs inside your app/services (wherever you deploy it). Great for request/response agent work. Once you go beyond quick calls, teams typically add state storage, async workers/queues/cron, and deeper observability (often LangSmith).
Agno
Runs as a runtime you operate (AgentOS), with streaming APIs and runtime behaviors designed for agentic execution. You still decide how it's deployed, scaled, and monitored in your environment.
Calljmp
Runs agents/workflows in a managed execution environment where a run has identity + lifecycle by default: your app/API triggers a run → Calljmp executes across steps (including long runs), supports suspend/resume, and makes traces/logs/cost visible to dev + ops.
State & Long-running work
Keeping agents alive across minutes, hours, or days
LangChain
Long-running is possible, but durability is on you: checkpointing, state storage, resumption, concurrency, and failure handling. Flexible - more engineering and edge cases.
Agno
The docs explicitly describe AgentOS as the "production runtime" with background execution, request isolation, and native tracing, while sessions/memory/traces live in your database—so statefulness is supported, but you still own the operational backbone.
Calljmp
Durable state is a first-class primitive. Runs persist across steps, can wait for external events or approvals, and continue without losing context.
HITL & approval workflows
Pausing for human feedback and approvals
LangChain
You can implement HITL using graph/checkpoint patterns, but the end-to-end system (approval UI, notifications, secure resume, audit trail) is something you assemble and maintain.
Agno
Agno's docs include "approval flows" at the SDK layer and "approval enforcement" in the runtime layer (AgentOS), which helps, but you still integrate it into your product/process and operate the system.
Calljmp
HITL is built into the runtime via native suspend/resume. A run can pause, wait for approval from UI/Slack/API, then continue from the same point with full context and shared operational visibility.
Real-time execution & interactive workflows
"Real-time" here means: a running agent keeps live state and streams progress (step updates, partial results, "waiting for approval", errors) to your UI/Slack/API while it's active even if it lasts minutes or hours.
LangChain
Streaming tokens is easy; streaming workflow status is extra work. Teams wire up durable progress storage, a worker model, and a push channel (SSE/WebSockets), plus custom pause/resume mechanics.
Agno
Agno describes streaming APIs and request isolation as runtime responsibilities inside AgentOS, plus native tracing, so the building blocks exist, but the "live run UX" still depends on your app and deployment.
Calljmp
Interactive execution is a runtime behavior: runs have identity, maintain state, and can emit status/events across long workflows. You focus on agent logic + UX, not 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 like LangSmith.
Agno
Agno highlights native tracing and full auditability in its production/runtime story, with data stored in your DB—so you can get strong visibility, but you still own the observability pipeline end-to-end.
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 agent tool access.
Agno
Agno's runtime layer (AgentOS) explicitly calls out authentication, request isolation, and approval enforcement as runtime responsibilities implemented in your infrastructure.
Calljmp
Security is handled at the runtime layer with controlled tool/data access and auditability, so devs can ship faster while ops keeps risk/quality under control.
Architecture Patterns: How Teams Deploy
Framework inside your stack vs self-hosted agent runtime vs managed 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) a tracing/evals layer.
Agno
Teams deploy AgentOS as a self-hosted runtime (usually a FastAPI service) that exposes production APIs for running agents/teams/workflows, with streaming support and governance primitives (like approvals). Typical setup is: your app triggers AgentOS endpoints → AgentOS reads/writes sessions/memory/knowledge/traces to your database → you manage/monitor via the Control Plane (or your own internal tools). You own deployment, scaling, upgrades, and the data layer.
Calljmp
Teams define agents/workflows as TypeScript and run them on Calljmp as a dedicated managed 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 tooling for traces/evals and define your own review/rollout process (versioning prompts, safety policies, regression checks, cost controls).
Agno
Strong "coherent architecture" DX: agents/teams/workflows share consistent primitives, and AgentOS adds production semantics (APIs, isolation, tracing, approval enforcement). UX exists via the Control Plane and starter UIs, but day-2 ops are still on you: operating the runtime, database, CI/CD, and internal governance conventions.
Calljmp
Developer-built, operator-run: devs ship code, while ops/product collaborate via shared visibility into runs, outcomes, performance, and costs, so you don't have to glue execution + observability + team workflows together.
Best choice by scenario
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.
Agno
You want a cohesive agent architecture plus a self-hosted runtime that turns agents into an internal "agent service" with production APIs, isolation, and governance primitives. You strongly care about data ownership (everything in your DB) and you're comfortable running and maintaining the runtime and data layer.
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 stitching 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, tracing/evals, and the ongoing effort to maintain retries, timeouts, and incident debugging.
Agno
Open-source core + you fund the runtime and data plane. Even if the framework/runtime is "free," you still pay for deployment, scaling, database/storage, observability pipeline, and on-call reliability. The tradeoff is control: you own infra, policies, and data end-to-end.
Calljmp
Pay for the managed 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 engineering 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.