4 compared
Agent frameworks compared
Code-first frameworks for building LLM agents, compared on approach, source, and setup.
Open in the interactive comparison tool| Field | Pydantic AI Python agent framework from the Pydantic team for type-safe GenAI apps, tools, structured outputs, MCP, evals, and durable workflows. Open dossier | Agno Open-source SDK and runtime for building, running, and managing agent platforms with agents, teams, workflows, memory, knowledge, tools, MCP, and AgentOS. Open dossier | DSPy Python framework from Stanford NLP for programming language-model systems with signatures, modules, tools, metrics, and optimizers instead of hand-written prompts. Open dossier | Mastra TypeScript agent framework for building AI agents, workflows, memory, tool calling, and evaluation-backed applications. Open dossier |
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| Trust | ||||
| Install risk | Review first | Review first | Review first | Review first |
| Notes | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety · Privacy · |
| Category | tools | tools | tools | tools |
| Source | source-backed | source-backed | source-backed | source-backed |
| Author | Pydantic | Agno | Stanford NLP | Mastra |
| Added | 2026-06-03 | 2026-06-03 | 2026-06-03 | 2026-04-27 |
| Platforms | CLI | CLI | CLI | CLI |
| Source repo | — | — | — | — |
| Safety notes | ✓Pydantic AI type hints and output validation reduce classes of integration errors, but they do not prove an agent, model response, tool call, or generated workflow is correct or safe. Agents can call function tools, toolsets, provider-native tools, MCP servers, web search capabilities, external APIs, databases, and durable workflow backends; review tool side effects before enabling them. Tool names, docstrings, schemas, dynamic instructions, dependencies, previous messages, and MCP tool descriptions become model-facing context and should be treated as untrusted input surfaces. Human-in-the-loop approval, deferred tools, retries, and durable execution workflows need idempotency, timeout, rollback, and escalation policies before they are used for account, billing, data, or infrastructure actions. Evals, LLM judges, span-based evaluators, and Logfire dashboards are quality signals, not proof that an agent is safe, fair, compliant, or production-ready. Multi-agent, MCP, A2A, UI event stream, graph, and streaming-output workflows can create complex control flow; keep production permissions narrower than demo or notebook examples. | ✓Agno agents are stateful control loops around stateless models, so model reasoning, tool calls, memory, knowledge retrieval, and workflow steps still require review before production use. Agents, teams, workflows, MCP tools, schedulers, and AgentOS APIs can call external systems, update databases, create memory, trigger background work, and expose capabilities to users or other agents. Agent memory and knowledge can make behavior more useful, but they can also preserve stale, incorrect, over-broad, or sensitive facts that influence future responses and actions. Human-in-the-loop approval, guardrails, tracing, RBAC, audit logs, and rollback paths should be configured before connecting Agno to billing, support, production data, infrastructure, or customer operations. MCP integrations discover tool schemas and let agents call third-party or internal services; review tool names, descriptions, arguments, auth headers, and permission scope before enabling them. Telemetry, tracing, evals, and AgentOS dashboards are operational signals, not proof that an agent platform is safe, compliant, accurate, or production-ready. | ✓DSPy changes how language-model systems are constructed and optimized, but it does not prove that a generated answer, optimized prompt, ReAct tool action, retrieved passage, or fine-tuned model is correct or safe. Optimizers can issue many model calls, generate examples, explore instructions, tune prompts, or fine-tune model weights; set budgets, rate limits, evaluation gates, rollback rules, and review ownership before running them. ReAct modules, Python interpreter tools, function tools, retrieval tools, and MCP-converted tools can trigger external APIs, local code, file access, or business actions if wired into a program. Metrics and evaluation datasets can overfit, reward the wrong behavior, or miss safety failures; treat optimizer scores as development signals rather than production approval. Saved programs, optimized prompts, bootstrapped demonstrations, fine-tuning datasets, and experiment artifacts should be reviewed before sharing because they can encode private data or brittle task assumptions. Local model servers, provider endpoints, and LiteLLM-compatible routes need normal timeout, retry, budget, abuse, model-selection, and credential-handling controls. | — missing |
| Privacy notes | ✓Pydantic AI runs can send prompts, instructions, chat history, dependency-derived context, tool arguments, tool results, structured outputs, retry prompts, and validation errors to configured model providers. Function tools and dependency injection can expose customer records, database values, API responses, internal identifiers, secrets, or proprietary business rules if those objects are made available to an agent. Pydantic Logfire, OpenTelemetry traces, eval reports, spans, metrics, cost tracking, and behavior monitoring can retain prompts, outputs, tool calls, metadata, errors, and performance data outside the application runtime. Pydantic Evals datasets, case metadata, expected outputs, human feedback, LLM-judge inputs, and report artifacts should follow normal retention, access-control, and deletion policies. MCP clients, MCP servers, native tools, and external toolsets can return third-party or workspace data into the conversation transcript, logs, traces, and evaluation outputs. | ✓Agno agents can process prompts, messages, tool arguments, tool results, retrieved knowledge, memory content, session history, user identifiers, traces, metrics, schedules, and audit events. Memory features can automatically store user facts, preferences, inputs, topics, agent IDs, team IDs, and update timestamps in connected databases; define consent, retention, correction, and deletion workflows. AgentOS and agent APIs can centralize sessions, memory, traces, schedules, RBAC, and audit logs in infrastructure the operator controls, so database credentials, backups, access controls, and exports need normal review. Model providers, vector stores, embedder providers, MCP servers, and tools may receive user data or internal context depending on the agent configuration. Agno's telemetry documentation says anonymous usage data is collected about agents, teams, workflows, and AgentOS configurations, and documents `AGNO_TELEMETRY=false` plus per-instance telemetry disabling. | ✓DSPy programs can send prompts, messages, typed inputs, retrieved context, tool arguments, generated outputs, optimizer traces, examples, metrics, and fine-tuning data to configured model providers. DSPy LM history can retain prompts, messages, call kwargs, responses, outputs, token usage, cost metadata, and related debugging information unless applications define cleanup and access controls. Caches, saved programs, optimized prompt artifacts, demonstration sets, serialized LM state, experiment logs, and evaluation reports can preserve sensitive task data outside the original source system. MCP integrations and tool calls can move user data, tool descriptions, tool arguments, and tool results into external servers, agent transcripts, provider logs, and downstream system logs. Local models reduce third-party provider exposure but can still leave data in process logs, tracing systems, prompt caches, generated artifacts, and shared infrastructure storage. | — missing |
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| Claim | Unclaimed | Unclaimed | Unclaimed | Unclaimed |
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