Ship agents to production, not to your backlog
LangChain gives you building blocks. Connic gives you a production-ready platform. Skip months of infrastructure work and deploy AI agents today.
Feature Comparison
See how Connic stacks up against LangChain across key capabilities.
Development Experience
Agent configuration
Connic uses simple YAML + Python. LangChain requires learning its chain/agent abstractions.
Learning curve
Connic: YAML + Python functions. LangChain: Chains, agents, runnables, LCEL, memory modules.
Local testing with hot-reload
Connic offers `connic test` with 2-5s hot-reload. LangChain requires custom dev setup.
Multiple LLM provider support
Both support OpenAI, Anthropic, Google, Azure, AWS Bedrock, and more.
Production Infrastructure
Managed hosting included
Connic deploys to managed infrastructure. LangChain requires self-hosting or LangGraph Cloud.
Git-based deployments
Push to deploy with Connic. LangChain needs CI/CD pipeline setup.
Auto-scaling
Connic scales automatically. LangChain requires Kubernetes or cloud infra management.
Environment management
Connic has built-in dev/staging/prod environments. LangChain requires manual setup.
Secrets management
Built-in secure secrets per environment. LangChain relies on external solutions.
Integrations & Triggers
Webhook triggers
Built-in with Connic. LangServe provides basic HTTP but requires setup.
Scheduled jobs (Cron)
Native cron connector in Connic. LangChain needs external scheduler.
Message queues (Kafka, SQS)
Built-in connectors. LangChain requires custom integration code.
Email triggers
Native email connector. LangChain requires building custom integration.
Payment events (Stripe)
Native Stripe connector. LangChain requires webhook + custom code.
Observability & Debugging
Built-in tracing
Automatic with Connic. LangChain requires LangSmith (separate paid service).
Run history & replay
Built into Connic dashboard. LangSmith required for LangChain.
Token usage tracking
Automatic in Connic. LangSmith needed for LangChain.
Cost tracking
Built into Connic. Requires LangSmith Plus/Enterprise.
Knowledge & RAG
Built-in vector storage
Connic includes managed vector DB. LangChain requires external service (Pinecone, etc).
Document ingestion
Both support PDF, images, text. Connic includes OCR automatically.
Semantic search
Built into Connic. LangChain needs vector store + retriever setup.
Why teams choose Connic
Key advantages that make Connic the better choice for production AI agents.
Zero Infrastructure
No Kubernetes, no Docker configs, no cloud setup. Push code, agents deploy. Focus on building, not ops.
Enterprise Connectors Built-in
Webhooks, Kafka, SQS, Email, Stripe, Cron - all ready to use. No integration code to write.
Observability Included
Full tracing, run history, token tracking in every plan. No separate LangSmith subscription needed.
Minutes to Production
From `pip install` to deployed agent in under 5 minutes. LangChain projects often take weeks to production-ready.
Predictable Costs
One platform, one bill. No surprise charges for LangSmith, hosting, vector DBs, and monitoring.
Simpler Mental Model
YAML for config, Python for tools. No chains, runnables, LCEL, or complex abstractions to learn.
The Bottom Line
Both are great tools for different needs. Here's when each makes sense.
Use Connic when
- You want agents in production fast without DevOps overhead
- You need enterprise integrations (Kafka, SQS, Stripe, Email)
- You want observability and tracing included, not as an add-on
- You prefer simple YAML config over learning framework abstractions
- You don't want to manage infrastructure or vector databases
Use LangChain when
- You need fine-grained control over every abstraction layer
- You're building a highly custom LLM application, not agents
- You already have DevOps expertise and infrastructure in place
- You want to use one of LangChain's 600+ community integrations
- You're doing research or prototyping without production timeline