PhidatavsLiteLLM

Full side-by-side comparison — features, pricing, platforms, and which one wins in 2026.

Phidata

AI Agent Frameworks

Build AI agents with memory, knowledge, and tools

Full review →Website ↗

LiteLLM

LLM APIs & Inference

Unified API proxy for 100+ LLM providers — one interface, any model

Full review →Website ↗
FeaturePhidataLiteLLM
CategoryAI Agent FrameworksLLM APIs & Inference
PricingFree (open-source) + CloudFree (open-source), hosted proxy available
GitHub Stars
15k
✓ More stars
16k
PlatformsLinux, macOS, WindowsLinux, macOS, Docker
Key Features
  • ✓ Agent memory
  • ✓ Knowledge base
  • ✓ Tool use
  • ✓ Structured output
  • ✓ Multi-model
  • ✓ Unified API for 100+ LLM providers
  • ✓ Load balancing across multiple API keys/providers
  • ✓ Automatic fallbacks when providers fail
  • ✓ Spend tracking and budget alerts per team/project
  • ✓ Rate limiting and retry logic built-in
  • ✓ OpenAI SDK compatible — zero code changes
  • ✓ Self-hostable proxy server
  • ✓ Supports streaming, function calling, vision
Pros
  • + Clean, Pythonic API
  • + Built-in memory and knowledge
  • + Production-focused
  • + Good documentation
  • + Multi-model support
  • + One API for 100+ providers
  • + Built-in load balancing and fallbacks
  • + Spend tracking and rate limiting
  • + OpenAI SDK compatible
Cons
  • − Rebranding confusion (Phidata→Agno)
  • − Smaller community than LangChain
  • − Some features require cloud
  • − Less flexible for custom setups
  • − Adds a proxy layer (slight latency)
  • − Complex config for advanced features
Tags
agentsmemoryknowledgepython
api-gatewaymulti-providerproxyopen-source

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