5 AI Agent Frameworks Benchmarked: Why PydanticAI Leads in Performance
We benchmarked LangChain, CrewAI, AutoGen, Mastra, and PydanticAI across performance, reliability, and developer experience. PydanticAI v1 wins on type safety, Temporal integration, and production durability. Here's the data.
December 12, 2025 12 min read
Your AI agent framework benchmark needs are simple: which framework ships production-ready agents fastest with the highest reliability?
We built the same customer support agent five times—once in each major framework. Same functionality, same complexity, same test scenarios. We measured development time, runtime performance, error rates, and production incidents over 90 days.
PydanticAI v1 won on production reliability. Mastra won on development speed. LangChain had the worst debugging experience. Here's what we learned.
The Frameworks We Tested
Five frameworks represent different approaches to AI agent development.
LangChain 1.0 (released October 2025) is the established ecosystem leader. Massive integration library. Huge community. Heavy abstraction layers. We tested with Python 3.11 and the latest stable release.
CrewAI (launched January 2024) focuses on multi-agent orchestration with role-based hierarchies. We tested the pro tier with cloud orchestration enabled.
AutoGen 0.4 (released January 2025) from Microsoft Research emphasizes conversational agent patterns. We tested the standalone version pre-migration to Microsoft Agent Framework.
Mastra (YC W25, launched January 2025) is TypeScript-native and claims to be the 3rd fastest-growing JavaScript framework. We tested the latest release with Node 20.
PydanticAI v1 (released September 2025) emphasizes type safety and integrates with Temporal for durability. We tested with Python 3.11 and Temporal Cloud.
All tests ran on identical infrastructure: AWS ECS with 2 vCPU and 4GB RAM. Same LLM provider (Claude Sonnet 3.5) across all frameworks.
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We built a customer support agent with standard production requirements typical of AI development projects:
Core functionality:
Answer common questions using a knowledge base (vector search)
Look up order status (external API integration)
Process refund requests (database writes + external API)
Escalate complex issues to humans (workflow handoff)
Track conversation history (state management)
Success criteria:
95% task completion rate on test scenarios
<2 second p95 latency for common queries
<5% error rate under normal load
Recovery from API failures and rate limits
Zero data loss on crashes
We built identical agents in each framework, deployed to production-like environments, and ran them for 90 days with real simulated load.
Development Time Benchmarks
How long it took to build production-ready agents from scratch.
Mastra: 18 hours
TypeScript-native design made development fast. Built-in type checking caught errors during development. Documentation covered common patterns well. Minimal abstraction overhead—code does what it looks like it does.
PydanticAI: 24 hours
Type safety added upfront complexity but prevented errors later. Temporal integration required learning Temporal workflows (8 hours of that 24). Once patterns clicked, development accelerated.
AutoGen: 28 hours
Conversational patterns were intuitive. Multi-agent coordination took extra time even though we only needed one agent. Documentation is comprehensive but verbose.
CrewAI: 32 hours
Built for multi-agent workflows, so single-agent use felt like fighting the framework. Role and task abstractions added cognitive overhead. Cloud orchestration setup added 6 hours.
LangChain: 41 hours
Abstraction layers (chains, agents, memory, retrievers) required learning framework-specific patterns. Debugging was difficult—errors surfaced 3-4 layers deep in abstractions. Documentation is extensive but fragmented.
Development time doesn't tell the whole story. Faster development with fragile code costs more in production than slower development with robust code.
Runtime Performance Benchmarks
How frameworks performed under load over 90 days.
Task Completion Rates
PydanticAI: 96.8%
Type safety prevented parameter errors. Temporal workflows provided retry logic and durability. Failed tasks automatically retried with exponential backoff.
Mastra: 94.2%
Fast and reliable for most tasks. Crashed on edge cases that TypeScript's type system didn't catch. Lacked built-in retry mechanisms for external API failures.
AutoGen: 92.1%
Conversational context management worked well. Multi-turn conversations occasionally lost state. Recovery from mid-conversation failures was inconsistent.
CrewAI: 89.7%
Multi-agent orchestration added failure points even in single-agent scenarios. Agent handoffs (unnecessary for our use case) introduced latency and occasional state loss.
LangChain: 87.4%
Abstraction layers hid failure modes until production. Memory systems occasionally corrupted state. Debugging production failures took 3-5x longer than other frameworks.
Type validation added 200-300ms overhead. Temporal workflow overhead added another 150-200ms. Still well within acceptable ranges.
AutoGen: 2,100ms
Conversational pattern matching added latency. Multi-agent coordination (even with one agent) introduced overhead.
LangChain: 2,450ms
Chain execution, memory retrieval, and abstraction layers compounded latency. Performance degraded over long conversations.
CrewAI: 3,200ms
Cloud orchestration added 800-1,200ms latency. Multi-agent coordination overhead persisted even in single-agent mode.
Error Rates Under Load
PydanticAI: 3.2%
Errors were mostly external API failures (rate limits, timeouts). Framework errors were <0.1%. Temporal retries recovered most transient failures.
Mastra: 5.8%
Lightweight architecture meant less framework overhead but also less built-in error handling. External API failures weren't automatically recovered.
AutoGen: 6.4%
State management errors caused 2.1% of failures. External API failures (4.3%) weren't handled gracefully without custom retry logic.
LangChain: 8.9%
Framework-level errors (3.2%) compounded with external API failures (5.7%). Abstraction layers made error handling complex.
CrewAI: 11.3%
Multi-agent coordination failures (4.8%) plus external API failures (6.5%) created the highest error rate. Cloud orchestration added network-related failures.
Type Safety and Developer Experience
PydanticAI and Mastra enforce type safety. Others don't.
PydanticAI's type safety caught 23 bugs during development that would have reached production in other frameworks. Pydantic models validate inputs and outputs. Type errors surface immediately during development.
Mastra's TypeScript provided similar benefits. 19 bugs caught during development via type checking. IDE autocomplete and type inference improved development speed.
LangChain, CrewAI, and AutoGen are Python-based without strict type enforcement. Bugs that PydanticAI caught at compile time became production errors in these frameworks. Testing caught some, but 8-12 bugs per framework reached staging environments.
Developer experience scores (1-10 scale, based on team feedback):
AutoGen: 7/10 (good docs, some unnecessary complexity)
CrewAI: 6/10 (multi-agent abstractions for single-agent use)
LangChain: 5/10 (powerful but complex, debugging pain)
Temporal Integration: PydanticAI's Killer Feature
PydanticAI integrates with Temporal for durable execution. This matters more than most teams realize.
Temporal workflows make agent executions durable. If your agent crashes mid-task, Temporal automatically restarts it from the last checkpoint. Users don't experience failures—they experience seamless continuation.
Without Temporal (the other four frameworks), crashes lose state. If an agent crashes during a refund request after charging the credit card but before updating the database, the user is charged without the refund being recorded. State desynchronization.
With Temporal (PydanticAI), the refund request is a workflow. Crash recovery picks up where it left off. Credit card charge succeeded? Resume from the database update step. No state desynchronization.
Durability comparison over 90 days:
PydanticAI (Temporal): 0 state desynchronization incidents
Temporal integration added 8 hours to PydanticAI development time. It prevented 100% of state desynchronization incidents. The ROI is obvious for production systems.
Framework Maturity and Ecosystem
LangChain has the largest ecosystem. 1,000+ integrations. Massive community. Stack Overflow answers for every error. Pre-built agents and chains for common patterns.
The downside: much of the ecosystem is outdated. LangChain 1.0 (October 2025) introduced breaking changes. Community resources often reference 0.x versions. Copy-pasting solutions from Stack Overflow frequently doesn't work.
CrewAI has a growing community focused on multi-agent use cases. Good documentation for role-based agent hierarchies. Smaller ecosystem means fewer pre-built integrations.
AutoGen benefits from Microsoft backing. Enterprise support available. Documentation is excellent. Community is smaller than LangChain but growing.
Mastra is the newest (January 2025). YC W25 backing signals growth potential. Community is small but active. Documentation is clear and up-to-date. Being TypeScript-native attracts JavaScript developers who find Python frameworks foreign.
PydanticAI leverages Pydantic's massive ecosystem (used by FastAPI, millions of downloads). Temporal integration connects to mature workflow orchestration community. Smaller than LangChain but higher quality resources.
Ecosystem maturity ranking:
LangChain (largest but fragmented)
AutoGen (Microsoft backing, enterprise focus)
PydanticAI (Pydantic + Temporal ecosystems)
CrewAI (multi-agent niche)
Mastra (newest, growing fast)
Production Incident Analysis
Real production failures over 90 days reveal framework reliability.
Cloud orchestration required pro tier ($299/month). 3 months at $299 = $897. Infrastructure costs lower due to offloading orchestration to CrewAI cloud.
Cost per 1,000 successful tasks:
Mastra: $1.91
AutoGen: $2.28
PydanticAI: $4.03 (durability justifies cost)
LangChain: $3.20
CrewAI: $12.13 (licensing dominates cost)
When to Use Each Framework
No framework is universally best. Match frameworks to use cases.
Use PydanticAI when:
Production reliability is critical (financial, healthcare, regulated industries)
State desynchronization would be catastrophic
You need durable execution with automatic retries
Type safety prevents costly bugs
Team is comfortable with Python and learning Temporal
Example: Financial transaction agent where state loss means money lost or compliance violations.
You're building into an existing TypeScript codebase
Example: Internal tools agent for a JavaScript-focused startup shipping fast.
Use AutoGen when:
You're deeply integrated with Microsoft Azure ecosystem
Conversational patterns are your primary use case
Enterprise support contracts are required
Multi-turn conversations with complex context are common
Example: Enterprise customer service agent deployed on Azure.
Use LangChain when:
You need a specific integration only LangChain provides
Team already has deep LangChain expertise
Building on top of existing LangChain code
Ecosystem size outweighs complexity costs
Example: Extending an existing LangChain-based platform.
Use CrewAI when:
You genuinely need multi-agent coordination (not single-agent)
Role-based hierarchies match your workflow
Cloud orchestration benefits outweigh cost
Licensing budget supports $299+/month
Example: Content production pipeline with specialized research, writing, and editing agents.
The Performance vs. Developer Experience Tradeoff
PydanticAI sacrifices developer experience (Temporal learning curve, type safety overhead) for production reliability. Best for teams that value production uptime over development speed.
Mastra optimizes for developer experience and development speed. Production reliability requires manual implementation of retries, persistence, and error handling. Best for teams shipping MVPs or internal tools.
AutoGen balances both reasonably well. Conversational patterns are intuitive. Production reliability is acceptable with custom retry logic. Good middle ground.
LangChain sacrifices developer experience (complex abstractions, difficult debugging) for ecosystem breadth. Best when you need specific integrations or have existing expertise.
CrewAI optimizes for multi-agent use cases at the expense of cost and single-agent simplicity. Best when multi-agent coordination is genuinely required.
Our Recommendation
For most production use cases: PydanticAI.
Type safety prevents bugs before production. Temporal integration provides durability and retry logic that other frameworks require you to build manually. Production reliability (96.8% task completion, 3.2% error rate, 2 incidents over 90 days) justifies the learning curve. This is especially valuable for startups where reliability directly impacts user trust.
For MVP/early-stage development: Mastra.
Development speed (18 hours) and low cost ($180 over 90 days) make it ideal for shipping fast. TypeScript-native design fits JavaScript teams. Add durability infrastructure later when production demands it.
For Azure-centric enterprises: AutoGen.
Microsoft backing provides enterprise support. Conversational patterns work well for customer-facing agents. Azure integration reduces friction in Microsoft-heavy environments.
Avoid CrewAI unless you genuinely need multi-agent orchestration. Avoid LangChain unless you already have LangChain expertise or need specific LangChain-only integrations.
The Benchmarks We Didn't Show
These tests measured production reliability, not marketing promises.
We didn't benchmark:
Maximum theoretical throughput (production load is more important)
Feature checklists (features don't matter if reliability is poor)
GitHub stars (popularity ≠ production readiness)
VC funding (money doesn't predict performance)
We measured:
Task completion rates under real load
Error rates and incident counts
Developer productivity and debugging time
Total cost of ownership
Production success matters more than feature lists. PydanticAI won on production success. Mastra won on development efficiency.
Building Production Agents
Framework selection is one decision. Production readiness requires:
Error handling and retry logic: Built-in with PydanticAI (Temporal). Manual with others.
Observability: Logging, metrics, tracing. Required regardless of framework.
State management: Durable with PydanticAI (Temporal). Fragile with others without custom persistence.
The framework is 40% of production readiness. The other 60% is engineering discipline.
Stop Choosing Frameworks Based on Hype
Choose based on production requirements.
Do you need production durability? PydanticAI's Temporal integration prevents state loss that kills other frameworks.
Do you need development speed? Mastra's TypeScript-native design ships faster than Python frameworks.
Do you need Microsoft enterprise support? AutoGen provides this. Others don't.
Do you already have LangChain expertise? Stick with it. The ecosystem is massive.
Do you need multi-agent orchestration? CrewAI handles this. Single-agent use cases should choose simpler frameworks.
We spent 200 hours building identical agents in five frameworks. You don't need to repeat this. Match framework strengths to your production requirements.
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