Report: Mastra vs LangGraph for AI Orchestration

Overview

Mastra and LangGraph both target the "AI agent" space, but they sit in different ecosystems and make different trade-offs:

• Mastra: TypeScript-first framework for building AI agents and apps with a modern JS/TS stack (React / Next.js / serverless, etc.). Focus: developer experience, web-app integration, and an "all‑in‑one" stack (agents, workflows, RAG, memory, MCP, evals, deployment). (Mastra)
• LangGraph: Python-first, low-level orchestration runtime from the LangChain team for building long-running, stateful agents and multi-agent systems with robust control flows and durability. (LangGraph)

They overlap (both can run agent workflows), but if you treat them as interchangeable you’ll get burned. LangGraph is closer to an agent runtime; Mastra is closer to an application framework for JS/TS teams.

The table below summarizes the key, verified differences.

Feature / capability comparison

Feature / Capability	Mastra	LangGraph
Primary language & ecosystem	TypeScript; integrates with React, Next.js, Astro, Vercel, Netlify, serverless and Mastra Cloud. (Mastra About, Deployment)	Python; tightly integrated with LangChain ecosystem, common in ML / data / backend teams. (LangGraph overview)
Core abstraction	Framework for agents + workflows + RAG + memory + MCP + evals with JS/TS ergonomics. Marketed as "all-in-one". (mastra.ai)	Graph-based runtime: you define a stateful graph of nodes (LLM/tool/human steps), with edges controlling transitions, loops, branches, retries. (LangGraph)
Control-flow sophistication	Supports workflows and multi-agent setups in TS, but orchestration is framed as higher-level workflows and deterministic graphs rather than a dedicated orchestration engine. (workflows overview, architectural analysis)	Designed specifically for single- and multi-agent, hierarchical, sequential, and cyclic graphs; strong support for complex control flows (routing, retry, guards, branches, loops). (LangGraph overview)
State & durable execution	Provides durable workflows and memory; persistence is usually wired through your own infra (DBs, vector stores) or Mastra Cloud. RAG and memory have observability hooks but long-running, resumable jobs depend on your deployment choices. (RAG, memory, deployment)	Explicit durable execution and checkpointing: the graph runtime can save state and resume after crashes/API failures or human approvals. Used in long-running agents (fintech, customer support, AML, etc.). (durable execution docs)
Memory model	Three-tier memory (working, conversation history, semantic recall) plus RAG-based memory; stored via pluggable storage (LibSQL, Turso, etc.). Designed for conversational / agent memory in TS apps. (memory overview, Turso example)	Rich agent memory integrated with LangMem: working, episodic, semantic, procedural, with long-term recall across conversations and workflows. Tuned for Python agent systems. (LangGraph memory, LangGraph long-term memory)
Model routing / multi-provider support	Strong multi-model router: “one API for any model” with 800+ models from ~48 providers; integrates with Google, OpenAI, Anthropic, etc.; issues exist for some V2 Gemini models in older versions (e.g. gemini-2.0-flash-lite not supported in Mastra core 0.14.1 generate API). (Models page, Gemini issue)	Primarily leverages LangChain’s model ecosystem; easy to plug in OpenAI, Anthropic, Gemini, Bedrock, etc. Model routing is possible but less of a marquee marketing feature than in Mastra.
RAG capabilities	First-class RAG: standardized APIs to chunk, embed, and store docs; supports multiple vector DBs (pgvector, Pinecone, Qdrant, MongoDB, Couchbase, etc.) with observability for chunk quality and retrieval relevance. (RAG overview, research assistant guide)	RAG supported through LangChain retrievers and memory; LangGraph focuses on orchestrating where RAG fits in the agent loop rather than providing its own RAG engine. Great if you’re already using LangChain’s RAG stack. LangGraph vs dedicated RAG frameworks
Human-in-the-loop	Supports MCP and tools; human-in-the-loop is typically implemented via surrounding app (UI, workflow logic). Some case studies mention real-time human workflows (e.g. Kestral using Mastra to turn company knowledge into tasks) but there is no dedicated “approval runtime" comparable to LangGraph’s. (Kestral case study)	Built-in human-in-the-loop hooks: you can design nodes that pause for review/approval, then resume via durable execution. Klarna and others combine this with internal tooling for supervised actions. (LangGraph overview)
Observability & evals	Shipping AI Tracing, evals (LLM + NLP metrics), RAG observability (chunk quality, retrieval performance), and workflow logging. Uses OpenTelemetry and custom tracing; strong focus on developer-centric observability in TS apps. (AI tracing announcement, evals)	Integrated with LangSmith for tracing, evaluation, dataset runs, and production monitoring; battle‑tested in large enterprises. Klarna reportedly used LangGraph + LangSmith to cut resolution times by ~80%. (LangGraph + LangSmith)
Production references	Case studies: Kestral (multi-agent task generation linking sales to product) (case study), Index (AI‑first analytics platform; moved from LangChain to Mastra for TS type-safety and multi-agent orchestration) (Index case study), Plaid (microservice AI foundation) (Plaid blog). Ecosystem is newer and smaller but growing.	References: Klarna (AI assistant handling 2.3M conversations, 85M users, ~80% faster resolution) (Klarna press + LangGraph, Klarna’s LangGraph architecture), Morningstar Mo research assistant (~3000 internal users, 20% less research time, 50% faster writing) (LangChain LinkedIn), AppFolio, AirTop, Cisco CX, Elastic, various banks and healthcare systems.
Security & stability considerations	Actively developed; some rough edges (e.g. Gemini V2 model support, MCP security considerations). MCP itself has well‑documented security risks (prompt injection, tool abuse, data exfiltration) that you must mitigate at the app level. (MCP security)	LangGraph has had at least one notable security issue in JsonPlusSerializer pre‑3.0 allowing potential RCE if used with untrusted data. (vuln report) Also, some users report intermittent freezes / deadlocks in certain versions and setups. You must stay current on versions and harden your runtime like any Python service.
DX & learning curve	Very attractive to JS/TS and frontend-heavy teams: npm create mastra@latest, templates, strong Next.js / Astro integration, Mastra Cloud, and serverless-friendly footprint. Good when agents are tightly coupled to web apps/edge functions. (installation, deployment)	Steeper learning curve: you design explicit state graphs and reason about state transitions, checkpoints, and edge conditions. Feels natural to backend / ML engineers comfortable with Python, distributed systems, and long-running workflows.

Where Mastra is strong

1. TypeScript-native AI app development
   From their docs: “Mastra is a framework for building AI-powered applications and agents with a modern TypeScript stack… from early prototypes to production-ready applications” (Mastra docs). It integrates directly with React, Next.js, Astro, serverless platforms (Vercel, Netlify), and Mastra Cloud, which gives TS teams a very smooth path from idea to deployment. (deployment overview)

2. Unified model API and rich integrations
   Mastra exposes a single API to 800+ models from 40+ providers, marketed as “one API for any model” (Models page). This reduces provider SDK boilerplate in TS code and plays well with the JS ecosystem. Example: Couchbase RAG tutorial uses Mastra as the orchestration layer plus Couchbase as vector DB and Next.js for UI. (Couchbase tutorial)

3. Workflows, RAG, memory, evals in one TS framework
   You get:

   • Workflows with structured steps and multi-agent orchestration. (workflows overview)
   • RAG system with standardized APIs, multiple vector stores, and observability for chunk quality / retrieval performance. (RAG overview)
   • Memory (working, history, semantic recall) with persistent storage. (memory overview)
   • Evals & tracing using AI Tracing and multiple metrics. (AI tracing)

   For a JS/TS stack, this “all-in-one” approach is compelling: you don’t have to assemble five libraries plus a custom eval stack.

4. Real-world TS case studies

   • Kestral: uses Mastra to ingest sales transcripts, customer conversations and docs to generate objective, cross-team task lists, implemented as multi-agent workflows. (Kestral case study)
   • Index: moved from LangChain to Mastra for type-safety and better multi-agent coordination in a TS analytics product; they highlight significant improvement in dev velocity due to TS-native APIs. (Index case study)
   • Plaid: blogged about using Mastra as the AI foundation in a microservices architecture. (Plaid tech blog)

Where Mastra is weaker / gotchas

1. Ecosystem maturity vs LangGraph
   Mastra is newer, with fewer large-enterprise references and a smaller community. You see strong early adopters, but not the depth of usage you see around LangGraph + LangChain.

2. Model support edge cases
   The “one API for any model” slogan is broadly true, but there are rough edges. Example: a GitHub issue shows gemini-2.0-flash-lite failing with

   “V2 models are not supported for the current version of generate. Please use generateVNext instead.” (issue)

   That’s not catastrophic, but it tells you you’ll occasionally hit version/method mismatches where docs and implementation lag.

3. MCP and security exposure
   Mastra leans into MCP, which is powerful but also opens you to classic tool / data exfiltration threats if not carefully sandboxed. Elastic and others have documented realistic attack patterns against MCP tools (prompt injection, tool orchestration abuse, etc.). (Elastic MCP attacks) You must design your own security controls around MCP.

4. Less specialized as a runtime
   Mastra does provide durable workflows and memory, but the framework’s center of gravity is “TS AI app framework,” not “agent runtime with strong durability semantics.” If you need long-lived background agents with complex failure handling, you’ll be doing more infra work yourself than you would with LangGraph.

Where LangGraph is strong

1. Purpose-built agent runtime & orchestration
   LangGraph is explicitly:

   “a low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents… supporting diverse control flows – single agent, multi-agent, hierarchical, sequential” (LangGraph overview).

   You define a graph of nodes (LLM calls, tools, routers, human approval, etc.) with state passed between them. This makes branching, loops, retries, and guardrails explicit and testable.

2. Durable execution and human-in-the-loop
   The durable execution docs show a runtime that:

   • Saves state/checkpoints at key points.
   • Resumes workflows after crashes or timeouts.
   • Pauses for human review (e.g., risk officer approving a transaction) then resumes. (durable execution)

   This is what Klarna, Morningstar, and others are using for mission-critical flows.

3. Production proof: Klarna, Morningstar, AppFolio, Elastic, etc.

   • Klarna AI Assistant: handles 2.3M conversations, covering ~2/3 of all customer support; average resolution time reportedly dropped from 11 to ~2 minutes (~80% reduction) and work equivalent to 700 FTEs was automated in a month. (Klarna press, Altar analysis, Klarna AI risks and corrections)
   • Morningstar “Mo”: an internal research assistant used by ~3000 analysts; they report ~20% less research time and ~50% faster writing with fewer edits. (LangChain LinkedIn)
   • AppFolio, AirTop, Cisco CX, Elastic: case studies show LangGraph underpinning domain copilots, DevOps assistants, and browser automation. (LangGraph case studies)

4. Deep observability with LangSmith
   LangGraph is designed to pair with LangSmith:

   • Trace every step in the graph (inputs, tool calls, outputs).
   • Run eval suites against datasets.
   • Compare multi-version runs and track regressions.

   This is central to why enterprise teams trust it for opaque LLM flows.

5. Sophisticated memory & multi-agent workflows
   LangGraph + LangMem give you:

   • Working memory, episodic memory (past events), semantic memory (facts/preferences), procedural memory (how to do tasks). (LangMem conceptual guide)
   • Multi-agent orchestrations used in AML, financial analysis, etc., where agents coordinate over shared context. LangGraph AML blueprint

Where LangGraph is weaker / gotchas

1. Not a "true agentic" framework in the autonomy sense
   Several practitioners argue that LangGraph is fundamentally a controlled orchestration tool, not an autonomous agent framework:

   “LangGraph is not a true agentic framework … the agency lies with the developer who designs the graph, not with the AI system itself.” (critique)

   If you want emergent behavior, self-improvement, or peer‑to‑peer agent swarms, you’ll build that logic yourself on top of LangGraph.

2. Operational rough edges & bugs

   • Some users report intermittent hangs in certain versions/setups (e.g., v0.2.60 + Flask/Gunicorn/Gevent) where flows freeze with no exception, especially in aggregation nodes. (GitHub discussion)
   • As with LangChain, version churn and dependency changes can break code if you don’t pin and test carefully. (community thread)

3. Security vulnerability pre‑3.0
   A flaw in JsonPlusSerializer (pre‑3.0) could, in some patterns, allow remote code execution if you deserialize untrusted data and fall back to JSON mode. (vulnerability writeup)

   This doesn’t make LangGraph “unsafe,” but you must:

   • Upgrade to patched versions.
   • Never deserialize untrusted data without validation.
   • Treat LangGraph like any other backend component from a security perspective.

4. Python-centric
   If your team is primarily JS/TS, LangGraph means:

   • Running a separate Python service.
   • Dealing with infra for that runtime (deployment, scaling, observability).

   For some orgs that’s fine; for a small web team that just wants “agents in Next.js,” it can be friction.

How to choose: Mastra vs LangGraph

You’ll get the best results by optimizing for stack and orchestration complexity.

Choose Mastra if

• Your core stack is TypeScript / Node and your agents are tightly coupled to web backends or serverless (Next.js, Astro, Vercel, Netlify).
• You want a single TS framework covering agents, workflows, RAG, memory, evals, and deployment.
• Your orchestration is moderately complex (multi-step flows, maybe multi-agent), but not “Klarna‑scale” background runtimes with heavy SLA and compliance.
• You value JS/TS DX and type-safety more than having the most battle-tested agent runtime.

Choose LangGraph if

• Your team is comfortable with Python and/or already invested in LangChain + LangSmith.
• You need long-running, stateful, multi-agent workflows with durability, human approvals, and strong observability.
• You’re building something analogous to:
  • An AI customer service assistant at Klarna scale.
  • An internal research copilot like Morningstar’s Mo.
  • Agentic workflows embedded deeply in fintech/healthtech processes.
• You care about formal control over graphs (loops, retries, guardrails) and production‑grade eval + tracing.

When you might use both

Some teams adopt a split architecture:

• LangGraph as the backend agent runtime (stateful graphs, durability, observability).
• Mastra or pure TS for the frontend + API layer, calling LangGraph over HTTP / gRPC.

You might go this way if:

• You already have a Python ML/agent team and a JS product team.
• You want the best orchestration/runtime you can get, but also great TS developer experience for the app-facing part.

Practical recommendations

If you give me more context (Python vs TS team, cloud provider, and your exact use case), I can sketch a concrete architecture:

• “Mastra only” design for a TS-centric product.
• “LangGraph only” design for a data/ML-heavy backend.
• Or a hybrid where LangGraph runs the heavy agent logic and your JS stack consumes it.