Sagewai vs. alternatives
This page compares Sagewai to LangChain, LlamaIndex, CrewAI, AutoGen, and Semantic Kernel. The goal is to help you pick the right tool for your situation, not to declare a winner.
LangChain and LlamaIndex are SDK-layer frameworks: they give you building blocks for calling LLMs and building retrieval pipelines. Sagewai covers the SDK layer too, but also includes a worker fleet, an observability stack, multi-tenant cost governance, Sealed security tiers, and a training loop that moves your cloud-model traffic to local inference over time.
If you need a one-off script that calls an LLM with a tool, LangChain's quickstart will get you there faster. If you need an agent running in production this quarter — with cost dashboards, multi-tenant isolation, an audit trail, and a credible path to reduce API spend — that is where Sagewai's additional scope pays off.
Feature comparison
| Feature | Sagewai | LangChain | LlamaIndex | CrewAI | AutoGen | Semantic Kernel |
|---|---|---|---|---|---|---|
| Model Support | 100+ providers built-in | 50+ via integrations | 30+ via integrations | 10+ | 10+ | 20+ via connectors |
| Local Inference | Built-in (Ollama, vLLM, LM Studio, llama.cpp) | Via adapters | Via adapters | Limited | Via config | Via connectors |
| Cost Governance | Harness proxy + per-project budgets | None built-in | None | None | None | None |
| Agent Registry | Built-in (store, version, discover, govern) | None | None | None | None | None |
| MCP Protocol | Native client + server | Community plugin | None | None | None | None |
| Durable Workflows | Built-in (PostgreSQL-backed checkpointing) | Via LangGraph (separate) | Via Workflows (in-memory) | None | None | None |
| Knowledge Graph | NebulaGraph integration | None built-in | KnowledgeGraphIndex | None | None | None |
| Vector Memory | Milvus integration | Via vectorstores | First-class indices | Via embedchain | None | Via memory connectors |
| Fine-Tuning | Unsloth pipeline (capture → train → deploy → cost-down) | None | Limited (data prep only) | None | None | None |
| Self-Hosted | Full stack (server + workers + observability + Sealed) | Partial (LangServe) | Partial (LlamaIndex serve) | No | No | No |
| Fleet Workers | Distributed execution with pool/label routing | None | None | None | None | None |
| Multi-Tenant | Per-project isolation, quotas, encryption | None | None | None | None | None |
| Cost Tracking | Per-token, per-model, per-project spend | None built-in | None | None | None | None |
| OpenTelemetry | Native (admin + worker + agent + LLM call) | Via callbacks | Via callbacks | None | None | None |
| Guardrails | PII, hallucination, budget, schema, content | Via guardrails integration | None | None | None | Via filters |
| Prompt Preprocessing | Directive engine (@context, @memory, @agent) | None | None | None | None | None |
| Context Engine | Document ingestion, 2-scope access, RAG | Via retrievers | First-class indices + query engine | Via embedchain | None | Via memory |
| Workload Identity | Sealed (per-CLI workload identity, JIT secrets) | None | None | None | None | None |
| Sandbox Isolation | Docker / Podman / Kubernetes / AgentCore | None | None | None | None | None |
| Client Libraries | 17 languages (TS, Go, Rust, Java, C#, + 12 more) | Python, JS | Python, TS | Python | Python, .NET | Python, .NET, Java |
| CI/CD Integration | GitHub Actions (run-agent, run-evals, deploy-worker) | None | None | None | None | None |
| Admin Console | Built-in web dashboard | Via LangSmith (paid) | None | None | Via AutoGen Studio | None |
| License | AGPL-3.0 (free) + commercial | MIT | MIT | MIT | CC-BY-4.0 | MIT |
When to use Sagewai
Enterprise cost control — per-project budgets, complexity-based routing, and a full spend audit trail. No other framework tracks costs at the platform level.
Distributed execution — the server + worker architecture lets you run GPU workers on-prem and CPU workers in the cloud, scaling each independently.
Multi-tenant isolation — each team gets its own project with isolated namespaces, quotas, and encryption. Critical when multiple teams are running AI on the same infrastructure.
Local inference at scale — Ollama/vLLM/Unsloth support with auto-discovery. Fine-tune domain-specific models and serve them at $0/token.
Full ownership — self-host the entire stack. No vendor dependency, no data leaving your network.
Polyglot integration — 17 client libraries mean your Go backend, Rust service, and TypeScript frontend all talk to the same agent infrastructure.
When not to use Sagewai
Quick prototyping — if you need a one-off script with minimal setup, LangChain's simpler onboarding may be faster for throwaway experiments.
Notebook-first workflow — if you work primarily in Jupyter and want inline chain visualisation, LangSmith + LangChain may suit your workflow better.
Microsoft ecosystem — if you are deep in Azure and .NET, Semantic Kernel has tighter Azure integration out of the box.
Multi-agent conversations — if your primary use case is autonomous agent debates and conversations (not workflows), AutoGen's conversation patterns are purpose-built for that.
Migration from LangChain
Key conceptual mapping:
| LangChain | Sagewai |
|---|---|
ChatOpenAI("gpt-4o") | UniversalAgent(model="gpt-4o") |
@tool decorator | @tool decorator (same concept) |
AgentExecutor | BaseAgent (built-in tool loop) |
RunnableSequence | SequentialAgent |
VectorStore | MilvusVectorMemory or ContextEngine |
LangGraph | DurableWorkflow |
LangServe | sagewai admin serve or Fleet Gateway |
LangSmith | Admin Console (self-hosted, free) |
Migration from LlamaIndex
| LlamaIndex | Sagewai |
|---|---|
VectorStoreIndex.from_documents(...) | ContextEngine.ingest_documents(...) |
index.as_query_engine() | ContextEngine.query(...) or MemoryAdapter on an agent |
KnowledgeGraphIndex | NebulaGraph integration in sagewai.memory.graph |
ReActAgent.from_tools(...) | UniversalAgent(tools=[...]) (ReAct is the default strategy) |
Workflow class | SequentialAgent / DurableWorkflow |
IngestionPipeline | ContextEngine ingestion + chunking pipeline |
LlamaIndex is well-suited to retrieval-heavy use cases. Sagewai's Context Engine gives you the same retrieval indices wired into a multi-tenant platform with audit, cost tracking, and worker dispatch.
Migration from CrewAI
| CrewAI | Sagewai |
|---|---|
Agent(role=..., goal=...) | UniversalAgent(name=..., system_prompt=...) |
Task(description=...) | Workflow step or directive |
Crew(agents=[...], tasks=[...]) | SequentialAgent or ParallelAgent |
crew.kickoff() | await workflow.run() |
What Sagewai adds beyond the SDK
Most agent frameworks stop at the SDK layer — they help you build agents but leave deployment, cost control, and operations to you. Sagewai adds:
- Registry — store, version, and govern agents
- Harness — proxy, route, and budget all AI calls
- Observatory — OpenTelemetry-native observability
- Fleet — distributed worker execution with pool/label routing
All open-source, all self-hosted.