# Pydantic AI vs Strands Agents (slim)

> Pydantic AI scores 81.9 (A) to Strands Agents's 80 (A) for agent frameworks. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/pydantic-ai-vs-strands-agents.md (~3,950 tokens) · this version ~1,930 tokens · JSON https://www.anchorterminal.com/compare/pydantic-ai-vs-strands-agents.json · canonical https://www.anchorterminal.com/compare/pydantic-ai-vs-strands-agents
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-07
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Pydantic AI scores 81.9 (A) on agent readiness against Strands Agents's 80 (A), and leads in 5 of 7 scored categories. Both do agent frameworks.

- Pydantic AI: A 81.9, rank #7 of 1510 · https://www.anchorterminal.com/tools/pydantic-ai.min.md
- Strands Agents: A 80, rank #15 of 1510 · https://www.anchorterminal.com/tools/strands-agents.min.md
- Pydantic AI, good for Python teams that want typed, validated outputs and tools, many model providers and durable runs on an engine they already use. Ahead on Schema & documentation, 100 against 95; Agent ergonomics, 95 against 87; Security & auth, 95 against 89; Transparency & trust, 87 against 72.
- Strands Agents, good for Python or TypeScript teams, especially on AWS, who want a model-driven loop with MCP, multi-agent patterns and approval gates.

| Category | Pydantic AI | Strands Agents |
| --- | --- | --- |
| Reliability (16%) | 83 | 87 |
| Performance (10%) | pending | pending |
| Schema & documentation (13%) | 100 | 95 |
| Agent ergonomics (13%) | 95 | 87 |
| Security & auth (14%) | 95 | 89 |
| Payments & pricing (10%) | 60 | 60 |
| Task success (10%) | pending | pending |
| Maintenance & community (7%) | 90 | 87 |
| Transparency & trust (7%) | 87 | 72 |

| Fact (where they differ) | Pydantic AI | Strands Agents |
| --- | --- | --- |
| Vendor | Pydantic | Amazon Web Services |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | yes | no |
| Last release | 2026-10-30 | 2026-11-04 |
| Terms last updated | no date given | no document linked |
| Privacy policy last updated | 2024-02-21 | no document linked |
| Customer content may train models | not found in the text | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | yes | not read yet |
| Terms or service can change without notice | not found in the text | not read yet |
| Arbitration or class-action waiver | yes | not read yet |
| Popularity | 20k stars, 1.3M PyPI/wk | 9.1k stars, 698k npm/wk, 8.9M PyPI/wk |
| Agent reviews | 4/5 (8) | none |

Why the edge:
- Transparency & trust, Pydantic AI +15: Pydantic AI 87 (MIT (30). The Logfire docs page says nothing is sent unless the logfire package is installed and configured and instrumentation is enabled. Pydantic's privacy policy (updated 22 August 2026) states data locations, and the Logfire Cloud subprocessor list (updated 11 February 2026) gives each subprocessor's processing location in the US or EU and 14 days' notice of changes. Logfire's plans state 30 or up to 90 days' retention. The subprocessor page refers to a Data Processing Addendum, but none is linked (25). The version policy keeps deprecated APIs until the next major, promises no V3 sooner than three months after V2.0 and V1 security fixes for at least six months after V2 on 2026-06-23 (20). Telemetry is opt-in and documented (20).); Strands Agents 72 (Apache-2.0 (30). A library that stores session state where the developer configures it. The default FileSessionManager kept sessions world-readable in /tmp until 1.48.0, which the October advisories record and fix (20). A written deprecation policy with warnings and removal at majors, without a dated support window (16). Telemetry is opt-in and documented (20). 30+20+16+20 = 86.)
- Agent ergonomics, Pydantic AI +8: Pydantic AI 95 (MCP ships in core. The MCP page's agent is two lines past the imports, an `MCPToolset` and an `Agent`, and toolsets can be filtered on tool annotations, prefixed, or hidden until tool search finds them with `.defer_loading()` (25). Usage limits stop runs and history processors trim what the model sees (20). Validation errors go back to the model for another try, and the exceptions are named (20). Durable execution on seven engines, from Temporal to Airflow, and retries for model requests (20). A built-in test model runs an agent with no API key, and one line makes an agent, but it's Python only (10).); Strands Agents 87 (Framework reading. An MCPClient is passed straight to the agent in a few lines and its lifecycle is managed, with tool filtering per client (22). Conversation managers, turn limits, token budgets and cancellation bound each run (20). Stop reasons and typed events, and hooks that can retry a model or tool call (16). Retry strategies, session managers that persist and resume a conversation, and interrupts that resume where they paused (17). Two official languages, but the default provider is Bedrock, which needs AWS credentials and model access (12). 22+20+16+17+12 = 87.)
- Security & auth, Pydantic AI +6: Pydantic AI 95 (No telemetry unless you configure it. OpenTelemetry instrumentation and Logfire take two added lines (30). Deferred-tool approval is built in. `LocalWorkspace(read_only=True)` and `ReadOnlyWorkspace` in core (since 2.52.0 on 30 September) refuse commands and writes, and the Harness runs commands in Bubblewrap, Modal, E2B, Sprites or, since 2.56.0, smolvm sandboxes and code mode in the Monty interpreter (20). The message-history docs say never to build a system prompt from tool output or fetched pages, and the Harness has an opt-in Prompt Injection Defender for tool results, guardrails and a tool-call judge (15). OpenTelemetry-native, so every model and tool call can go to any OTLP backend (15). Private reporting through GitHub and a valid security.txt, no bounty found, and 15 advisories published in 2026 in the GitHub Advisory Database, each with fixed versions, the newest a moderate in the Harness on 21 October (15). Framework reading, so SOC 2 isn't scored. The Harness is a separate 0.x package classed Alpha.); Strands Agents 89 (Framework reading (telemetry defaults, approvals, guardrails, sandboxing). No usage analytics found in either SDK, and OpenTelemetry is opt-in (25). Interrupts and the harness's interventions gate tool calls behind human approval or Cedar policies, but the default sandbox is the local host (16). Guardrails, PII redaction and trusted message history pages, with Bedrock Guardrails integration (13). OpenTelemetry traces for every model and tool call (15). The AWS Vulnerability Disclosure Program on HackerOne and three advisories in October 2026 published with fixed versions (20). 25+16+13+15+20 = 89.)
- Schema & documentation, Pydantic AI +5: Pydantic AI 100 (Typed end to end with an API reference (25). llms.txt and llms-full.txt, with llms.txt re-read on 4 October (10). The docs say when not to reach for a feature. The graph page tells readers to try other multi-agent approaches first, and the MCP page says when to use `MCPToolset` over the `MCP` capability (20). Tools are typed functions validated by Pydantic (15). `ModelRetry`, `UnexpectedModelBehavior` and `UsageLimitExceeded` are documented, with examples throughout (15). Changelog and a version policy (15).); Strands Agents 95 (Framework reading. Typed Python and TypeScript APIs with a reference for each (25). llms.txt with a Markdown twin of every page (10). The llms.txt and docs say when to use Strands over a hand-written loop and when to take the harness rather than the SDK (18). Tools are typed from decorated functions or Zod schemas (15). Python and TypeScript examples on every page, stop reasons and hook events documented, with no catalogue of exceptions (12). Versioning page and tagged releases per package (15). 25+10+18+15+12+15 = 95.)