PydanticAI
PydanticAI is a Python agent framework built on Pydantic’s type validation system, emphasizing typed inputs/outputs, tool contract validation, and operational rigor. With 15K+ GitHub stars, it offers dependency injection, structured outputs with validation and retry loops, durable execution integrations (Temporal, DBOS), explicit human-in-the-loop approval patterns, and a strong eval framework via pydantic-evals. PydanticAI targets teams that prioritize correctness, testability, and type safety in their agent systems.
Jido
Jido is an Elixir framework that models each agent as a BEAM process with deterministic state transitions, explicit side effects, and OTP supervision. It separates pure business logic from LLM interactions, giving you reproducible testing and fault isolation at the runtime level. Jido targets teams building production agent systems that need to stay up, recover from failures, and scale predictably.
Side-by-side comparison
| Dimension | Jido | PydanticAI |
|---|---|---|
| Language and runtime | Elixir on the BEAM VM with preemptive scheduling, lightweight processes, and built-in distribution | Python with Pydantic’s type system, IDE-friendly APIs, and broad ML ecosystem access |
| Agent model | Each agent is a supervised OTP process with explicit state schema and deterministic transitions | Agents with typed dependencies, structured outputs, and validator-driven retry behavior |
| Tool calling | Actions as composable, typed Elixir modules with schema validation; MCP client support | Typed tool contracts with Pydantic validation; reflection/retry on validation failure; MCP and A2A support |
| Multi-agent coordination | OTP-native process communication via GenServer calls, PubSub, and dynamic supervision trees | Delegation and programmatic handoff patterns; graph-based control flow for multi-agent workflows |
| Failure handling | OTP supervisors restart failed agents automatically; process isolation prevents cascading failures | Durable execution via Temporal/DBOS integrations; validation retry loops for output correctness |
| Observability | BEAM introspection (process info, message queues), Telemetry events, LiveDashboard integration | Deep Logfire integration with OpenTelemetry compatibility; token and cost visibility |
| LLM provider support |
Provider-agnostic through jido_ai, supporting OpenAI, Anthropic, Google, and others via unified interface | Broad provider/model support via adapters with custom model options |
When to choose PydanticAI
PydanticAI is a strong choice when type safety and validation rigor are central to your agent design. Specific scenarios where PydanticAI fits well:
- Type-safety-first teams that want compile-time and runtime validation guarantees on every tool call, agent output, and dependency contract.
- Python teams with Pydantic experience where the familiar validation model and IDE integration reduce the learning curve for building agents.
- Regulated or risk-sensitive domains where deferred tool approval patterns and explicit HITL workflows provide auditable control over dangerous operations.
- Eval-driven development where
pydantic-evalsprovides datasets, evaluators, and repeatable experiment reporting as a core development workflow.
When to choose Jido
Jido is a strong choice when you need runtime reliability and fault isolation alongside typed contracts. Specific scenarios where Jido fits well:
- Elixir/Erlang teams that want agents built on the same concurrency and fault-tolerance model they use for the rest of their system.
- Runtime-level durability where OTP supervision provides automatic restart and failure recovery as a runtime primitive, without integrating external durable execution services.
- Deterministic testing needs where separating pure state logic from side effects lets you unit test agent decisions without mocking validation layers.
- Concurrency-intensive workloads where the BEAM’s preemptive scheduler and lightweight processes handle thousands of simultaneous agents without GIL constraints or thread-pool tuning.
Get started
Ready to try Jido? Follow the getting started guide to build your first agent in under ten minutes.