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AI Tool Comparison
Agno vs Letta
A side-by-side breakdown to help you pick the right tool for your workflow.
Agno
Build multi-modal agents in plain Python — text, image, audio, and video inputs handled natively. Agno's tool library and memory system handle the infrastructure.
Agents
free
Letta
Build agents that actually remember — facts, preferences, and past interactions persist across every session without manual context management.
Agents
freemium
Bottom Line
Agno and Letta are rated evenly — the right pick comes down to which workflow you're running.
Choose Agno if…
Coding
Choose Letta if…
Coding
| Attribute | Agno | Letta |
|---|---|---|
| Category | Agents | Agents |
| Pricing | free | freemium |
| Pricing Detail | Open source / Free (Agno Cloud in beta) | Open source / Free (Letta Cloud from $0.02/step) |
| Rating |
Key Features
Agno
- Multi-modal agents with native text, image, audio, and video reasoning
- Plain Python class definitions — no framework-specific DSL
- 30+ built-in tools: web search, SQL, file ops, APIs
- Pluggable memory backends including PostgreSQL, MongoDB, and SQLite
- Agent Teams for orchestrating multiple specialized sub-agents
- Structured output support via Pydantic models
Letta
- Three-tier memory architecture: working, recall, and archival
- Self-directed memory management — agents decide what to store
- Cross-session persistence with no manual prompt engineering
- REST API and Python SDK for embedding agents in applications
- Agent builder UI for configuring memory and persona
- Open-source core with managed Letta Cloud option
Pros
Agno
- •Multi-modal by default — no special handling for image or audio inputs
- •Pythonic API makes agents readable to anyone who knows Python
- •Built-in tool library means less boilerplate for common tasks
Letta
- •Solves long-term memory without external memory databases or prompting tricks
- •Open-source core means full control over data and deployment
- •Framework-level memory is more reliable than ad-hoc RAG approaches
Cons
Agno
- Python-only — no TypeScript SDK unlike some competitors
- Cloud observability platform is still early-stage
- Less community content than LangChain or CrewAI at the same maturity
Letta
- Memory management adds latency compared to stateless agents
- Cloud pricing is per-step — complex agents with many memory reads get expensive
- Steeper learning curve than simpler stateless frameworks