Sagewai vs. Alternatives
How Sagewai compares to other agent frameworks. Sagewai is not just an SDK — it is a complete platform with deployment infrastructure, cost governance, and enterprise-grade fleet management.
Feature Comparison
| Feature | Sagewai | LangChain | CrewAI | AutoGen | Semantic Kernel |
|---|---|---|---|---|---|
| Model Support | 100+ via LiteLLM | 50+ via integrations | 10+ | 10+ | 20+ via connectors |
| Local Inference | Built-in (Ollama, vLLM, LM Studio, llama.cpp) | Via adapters | Limited | Via config | Via connectors |
| Cost Governance | Harness proxy + per-project budgets | None built-in | None | None | None |
| Agent Registry | Built-in (store, version, discover, govern) | None | None | None | None |
| MCP Protocol | Native client + server | Community plugin | None | None | None |
| Durable Workflows | Built-in (PostgreSQL-backed checkpointing) | Via LangGraph (separate) | None | None | None |
| Knowledge Graph | NebulaGraph integration | None built-in | None | None | None |
| Vector Memory | Milvus integration | Via vectorstores | Via embedchain | None | Via memory connectors |
| Fine-Tuning | Unsloth pipeline (train, serve, $0/token) | None | None | None | None |
| Self-Hosted | Full stack (server + workers + observability) | Partial (LangServe) | No | No | No |
| Fleet Workers | Distributed execution with pool/label routing | None | None | None | None |
| Multi-Tenant | Per-project isolation, quotas, encryption | None | None | None | None |
| Cost Tracking | Per-token, per-model, per-project spend | None built-in | None | None | None |
| Guardrails | PII, hallucination, budget, schema, content | Via guardrails integration | None | None | Via filters |
| Prompt Preprocessing | Directive engine (@context, @memory, @agent) | None | None | None | None |
| Context Engine | Document ingestion, 2-scope access, RAG | Via retrievers | Via embedchain | None | Via memory |
| Client Libraries | 17 languages (TS, Go, Rust, Java, C#, + 12 more) | Python, JS | Python | Python, .NET | Python, .NET, Java |
| CI/CD Integration | GitHub Actions (run-agent, run-evals, deploy-worker) | None | None | None | None |
| Admin Console | Built-in web dashboard | Via LangSmith (paid) | None | Via AutoGen Studio | None |
| License | AGPL-3.0 (free) + commercial | MIT | MIT | CC-BY-4.0 | MIT |
When to Choose Sagewai
Enterprise cost control — Per-project budgets, complexity-based routing, full spend audit trail. No other framework tracks costs at the platform level.
Distributed execution — Server + worker architecture with pool/label routing. Run GPU workers on-prem and CPU workers in the cloud. Scale independently.
Multi-tenant isolation — Each team gets their own project with isolated namespaces, quotas, and encryption. Critical for organizations with multiple AI initiatives.
Local inference at scale — Built-in Ollama/vLLM/Unsloth support with auto-discovery. Fine-tune domain models and serve at $0/token.
Full ownership — Self-host everything. No vendor dependency, no data leaving your network, no per-seat pricing.
Polyglot integration — 17 client libraries mean your Go backend, Rust service, and TypeScript frontend all talk to the same agent infrastructure.
When NOT to Choose Sagewai
Quick prototyping — If you need a one-off script with minimal setup, LangChain's simpler getting-started may be faster for throwaway experiments.
Notebook-first workflow — If you work primarily in Jupyter notebooks and want inline chain visualization, 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 this.
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 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() |
The Sagewai Advantage
Most frameworks stop at the SDK layer — they help you build agents but leave deployment, cost control, and operations to you. Sagewai provides the complete stack:
- Build agents with the SDK
- Govern them with the Registry
- Control costs with the Harness
- Monitor with the Observatory
- Deploy with the Fleet
All open-source, all self-hosted, all yours.