{ "data": { "a": { "slug": "pydantic-ai", "name": "Pydantic AI", "vendor": "Pydantic", "vendorUrl": "https://pydantic.dev", "kind": "framework", "category": "frameworks", "summary": "Typed Python agent framework for 25+ model providers, with MCP, A2A and durable execution.", "url": "https://www.anchorterminal.com/tools/pydantic-ai", "markdownUrl": "https://www.anchorterminal.com/tools/pydantic-ai.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/pydantic-ai.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/pydantic-ai.json", "dns": "pydantic-ai.grade.anchorterminal.com", "repo": "https://github.com/pydantic/pydantic-ai", "license": "MIT", "transports": [], "packages": [ { "registry": "pypi", "name": "pydantic-ai" } ], "auth": "none", "authNotes": "A library. Credentials are for the models and tools you use.", "pricing": "free", "pricingNotes": "Free and open source. You pay for the model calls it makes. Logfire for tracing is free for personal use, $49 a month for teams.", "priceSummary": "Free · OSS", "where": "library", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 20192, "npmWeekly": null, "pypiWeekly": 1276452, "asOf": "2026-09-26" }, "docsUrl": "https://pydantic.dev/docs/ai/overview/", "llmsTxt": "https://pydantic.dev/docs/ai/llms.txt", "capabilities": [ "agent.framework", "agent.multi-agent", "agent.durable", "agent.mcp-client" ], "tags": [ "framework", "python", "open-source", "typed", "no-telemetry" ], "lastRelease": "2026-10-30", "graded": true, "anchor": { "graded": true, "score": 81.9, "grade": "A", "agentReady": true, "rank": 7, "ranked": true, "rankOf": 1510, "categoryRank": 3, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 95, "maintenance": 90, "payments": 60, "reliability": 83, "schema": 100, "security": 95, "transparency": 87 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-01" }, "negative": -6, "negativeNotes": [ "2026-02-06. Two high-severity advisories, server-side request forgery in URL download handling (GHSA-2jrp-274c-jhv3, CVE-2026-25580) and stored XSS through path traversal in the web UI's CDN URL (GHSA-wjp5-868j-wqv7). Both fixed and published, and more than eight months old, so 1 point each. Nine moderate and two low advisories from May to October 2026, three of them bypasses of its cloud-metadata blocklist, weren't deducted. https://github.com/pydantic/pydantic-ai/security", "2026-08-12. High-severity advisory GHSA-h4xc-3qfq-jf93 (CVSS 7.6). A website the developer visited could trigger agent runs and server-side tool execution through the local web chat UI (`Agent.to_web()`, `clai web`). Fixed in 2.28.0 and 1.107.4 and published, under two months old, so 2 points. It wasn't in the 1 October dossier. https://github.com/pydantic/pydantic-ai/security/advisories/GHSA-h4xc-3qfq-jf93", "2026-10-08. High-severity advisory GHSA-6fqq-452j-qhrp (CVE-2026-107286, CVSS 7.5, availability only). With `ConcurrencyLimitedModel` or `limit_model_concurrency`, a streamed request that ended early could keep its slot, so a network client could exhaust a shared limiter by starting streams and disconnecting. Fixed in 2.53.0 on 2 October and added to the GitHub Advisory Database on 8 October, after the 4 October check, so 2 points as for the other fixed High under two months old. https://osv.dev/vulnerability/GHSA-6fqq-452j-qhrp" ], "verdict": "Typed outputs and tools, validated by Pydantic, with failed validations sent back to the model. Python only.", "bestFor": "Python teams that want typed, validated outputs and tools, many model providers and durable runs on an engine they already use.", "strengths": [ "Typed outputs and tools, validated by Pydantic, with failed validations sent back to the model", "No telemetry until you configure OpenTelemetry or Logfire, and the docs say nothing is sent otherwise", "Durable execution on nine engines, including Temporal, DBOS, Prefect, Restate and AWS Lambda", "Read-only workspaces in core, and Harness sandboxes for commands and model-written code", "A written version policy, with deprecations kept until the next major and V1 security releases still shipping" ], "weaknesses": [ "Python only, and 3.11 or newer since 2.55.0", "1.1k open issues and 469 open pull requests on 1 November 2026, roughly double the 1 October counts", "Fifteen advisories in 2026, four high, including SSRF, three cloud-metadata blocklist bypasses and a cross-site flaw in the web chat UI", "Sandboxes, guardrails and the injection defender live in the Harness, a separate 0.x package classed Alpha", "The Prompt Injection Defender passes results through unchanged unless `block_high_risk=True` is set" ], "agentNotes": [ "Define the output type first. Validation retries fix most malformed answers without a prompt change, and the test model checks the wiring with no key", "Use streamable HTTP for MCP, since SSE is deprecated. Call `.defer_loading()` on large MCP servers so their tools stay out of context until searched", "Set usage limits on every run that calls paid models", "Give model-run commands `LocalWorkspace(read_only=True)` or a Harness sandbox. A plain `LocalWorkspace` runs them on your machine as you", "Stay on 2.53.0 or later, and the Harness on 0.59.0 or later. 2026 advisories hit URL downloads, the metadata blocklist, the web chat UI, streamed requests and nested CodeMode calls" ], "metrics": { "kind": "library", "measured": false }, "reviewCount": 8, "avgRating": 4, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "A", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 81.9 } ], "editorialScores": { "ergonomics": 95, "maintenance": 90, "payments": 60, "reliability": 83, "schema": 100, "security": 95, "transparency": 95 }, "provenanceScore": 79, "checked": { "categories": { "ergonomics": "2026-11-01", "maintenance": "2026-11-01", "payments": "2026-11-01", "reliability": "2026-11-01", "schema": "2026-11-01", "security": "2026-11-01", "transparency": "2026-11-01" }, "last": "2026-11-01", "lastMoved": "2026-10-27", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install pydantic-ai" }, "letme": { "capability": "https://letme.dev/agent.framework", "tool": "https://letme.dev/pydantic-ai" }, "sameCompany": [ "pydantic-logfire" ], "area": "frameworks", "unitPrices": [ { "item": "Logfire Team", "unit": "month", "usd": 49, "note": "tracing, personal use free" } ], "provenance": { "legalEntity": "Pydantic Services Inc.", "domain": "pydantic.dev", "domainRegistered": "2022-04-24", "endpointOnVendorDomain": null, "terms": "https://pydantic.dev/legal/terms-of-service", "privacy": "https://pydantic.dev/legal/privacy-policy", "statusPage": "", "changelog": "https://pydantic.dev/docs/ai/project/changelog/", "securityTxt": "valid", "checked": "2026-10-04", "score": 79 }, "requirements": { "setupChecked": true, "complianceChecked": true, "card": "unknown", "freeTier": true, "minutes": 5, "credential": "api-key", "hosting": [ "self-hosted" ], "euResidency": "unknown", "trainsOnData": "no", "zeroRetention": "unknown", "selfHost": "yes" }, "pageJsonUrl": "https://www.anchorterminal.com/tools/pydantic-ai.json", "live": { "slug": "pydantic-ai", "versions": [ { "registry": "github", "name": "pydantic/pydantic-ai", "version": "v2.66.0", "released": "2026-11-04", "seenAt": "2026-11-06T23:59:23.439768111Z" }, { "registry": "pypi", "name": "pydantic-ai", "version": "2.66.0", "released": "2026-11-04", "seenAt": "2026-11-06T23:59:23.247531163Z" } ], "githubStars": 21063, "pypiWeekly": 1645833, "securityTxt": { "url": "https://pydantic.dev/.well-known/security.txt", "state": "valid", "expires": "2027-09-17T00:00:00.000Z", "checkedAt": "2026-11-07T16:00:15.851020004Z" }, "llmsTxt": { "url": "https://pydantic.dev/docs/ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:33:42.264619636Z" }, "domain": { "domain": "pydantic.dev", "registered": "2022-04-24", "source": "https://pubapi.registry.google/rdap/domain/pydantic.dev", "checkedAt": "2026-11-01T14:14:17.651909972Z" }, "pages": [ { "url": "https://pydantic.dev/docs/ai/project/changelog/", "kind": "changelog", "status": 200, "checkedAt": "2026-11-07T14:17:43.972167702Z", "changedAt": "2026-11-05T13:02:19.667276376Z", "fingerprint": "6ed49e274192" }, { "url": "https://pydantic.dev/articles/pydantic-ai-v2", "kind": "deprecations", "status": 304, "checkedAt": "2026-11-07T14:17:39.932317322Z", "changedAt": "2026-11-03T07:56:50.821656233Z", "fingerprint": "aa0521ac1c73" }, { "url": "https://pydantic.dev/docs/ai/project/version-policy/", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T14:17:46.190611995Z", "changedAt": "2026-11-05T13:02:21.791946069Z", "fingerprint": "733a76ffc807" }, { "url": "https://pydantic.dev/legal/privacy-policy", "kind": "privacy", "status": 304, "checkedAt": "2026-11-07T14:17:48.096204676Z", "changedAt": "2026-11-03T07:56:58.894018256Z", "fingerprint": "cbabf663e2ad" }, { "url": "https://pydantic.dev/legal/terms-of-service", "kind": "terms", "status": 304, "checkedAt": "2026-11-07T14:17:49.987811128Z", "changedAt": "2026-11-03T07:57:00.973475979Z", "fingerprint": "12c8a2fee1c9" } ], "github": { "repo": "pydantic/pydantic-ai", "checkedAt": "2026-11-02T09:19:36.175721096Z", "stars": 20995, "archived": false, "issuesEnabled": true, "pushedAt": "2026-11-02T08:25:21Z", "openIssues": 1241, "openPRs": 494, "issuesOpened90d": 1736, "issuesClosed90d": 1049, "prsMerged90d": 2157, "ci": { "branch": "main", "runs": 50, "success": 46, "failure": 4, "other": 0, "latest": "success", "latestAt": "2026-11-02T04:04:10Z" }, "advisories": [ { "ghsa": "GHSA-4whf-38hw-pxxh", "severity": "high", "published": "2026-10-21T05:06:29Z" }, { "ghsa": "GHSA-xv4x-g8mx-ch56", "severity": "medium", "published": "2026-10-20T05:42:04Z" }, { "ghsa": "GHSA-5pfm-2g3g-gh85", "severity": "medium", "published": "2026-10-20T05:31:48Z" }, { "ghsa": "GHSA-6fqq-452j-qhrp", "cve": "CVE-2026-107286", "severity": "high", "published": "2026-10-02T03:19:39Z" }, { "ghsa": "GHSA-v36g-jcw9-x7cw", "cve": "CVE-2026-107287", "severity": "medium", "published": "2026-09-30T01:31:43Z" }, { "ghsa": "GHSA-4x9p-g9wm-8q7f", "cve": "CVE-2026-107291", "severity": "low", "published": "2026-09-17T04:14:30Z" }, { "ghsa": "GHSA-fpf4-vwcp-v4hp", "cve": "CVE-2026-107290", "severity": "medium", "published": "2026-09-17T04:14:29Z" }, { "ghsa": "GHSA-22h6-qm39-v87j", "cve": "CVE-2026-107288", "severity": "low", "published": "2026-09-17T04:14:28Z" }, { "ghsa": "GHSA-vmxc-h2x2-jmf3", "cve": "CVE-2026-107289", "severity": "medium", "published": "2026-09-17T04:14:27Z" }, { "ghsa": "GHSA-q2xc-rrxj-58x9", "cve": "CVE-2026-107292", "severity": "medium", "published": "2026-08-14T02:58:39Z" }, { "ghsa": "GHSA-3gh4-cghq-f8v4", "cve": "CVE-2026-107293", "severity": "low", "published": "2026-08-12T04:02:44Z" }, { "ghsa": "GHSA-h4xc-3qfq-jf93", "cve": "CVE-2026-107295", "severity": "high", "published": "2026-08-12T04:02:43Z" }, { "ghsa": "GHSA-v2xh-2vp8-57h8", "cve": "CVE-2026-107294", "severity": "medium", "published": "2026-08-08T03:33:55Z" }, { "ghsa": "GHSA-jpr8-2v3g-wgf9", "cve": "CVE-2026-65975", "severity": "medium", "published": "2026-07-11T03:28:12Z" }, { "ghsa": "GHSA-h7p7-w5gc-xj3w", "cve": "CVE-2026-54249", "severity": "medium", "published": "2026-06-10T14:29:58Z" }, { "ghsa": "GHSA-cg7w-rg45-pc59", "cve": "CVE-2026-48782", "severity": "medium", "published": "2026-05-23T01:16:52Z" }, { "ghsa": "GHSA-cqp8-fcvh-x7r3", "cve": "CVE-2026-46678", "severity": "medium", "published": "2026-05-20T17:20:04Z" }, { "ghsa": "GHSA-2jrp-274c-jhv3", "cve": "CVE-2026-25580", "severity": "high", "published": "2026-02-06T01:14:08Z" }, { "ghsa": "GHSA-wjp5-868j-wqv7", "cve": "CVE-2026-25640", "severity": "high", "published": "2026-02-06T01:13:49Z" } ] }, "updatedAt": "2026-11-07T16:00:15.851020004Z" } }, "answer": "Pydantic AI scores 81.9 (A) on agent readiness against Strands Agents's 80 (A), and leads in 5 of 7 scored categories.", "b": { "slug": "strands-agents", "name": "Strands Agents", "vendor": "Amazon Web Services", "vendorUrl": "https://strandsagents.com", "kind": "framework", "category": "frameworks", "summary": "Strands Agents is AWS's open-source agent SDK for Python and TypeScript. A model-driven loop runs tools, MCP servers, multi-agent patterns, sessions, interrupts for human approval and OpenTelemetry tracing, with Amazon Bedrock as the default model provider.", "url": "https://www.anchorterminal.com/tools/strands-agents", "markdownUrl": "https://www.anchorterminal.com/tools/strands-agents.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/strands-agents.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/strands-agents.json", "dns": "strands-agents.grade.anchorterminal.com", "repo": "https://github.com/strands-agents/harness-sdk", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "pypi", "name": "strands-agents" }, { "registry": "npm", "name": "@strands-agents/sdk" }, { "registry": "pypi", "name": "strands-agents-tools" } ], "auth": "none", "authNotes": "A library with no credentials of its own. The default Bedrock provider uses the caller's AWS credentials; other providers take their own API keys.", "pricing": "free", "pricingNotes": "Free and open source under Apache-2.0. You pay for model calls, on Bedrock by default or with any supported provider.", "priceSummary": "Free · OSS", "where": "library", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs or the repository (checked 2026-11-06).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 9066, "npmWeekly": 697548, "pypiWeekly": 8902370, "asOf": "2026-11-06" }, "docsUrl": "https://strandsagents.com/docs", "llmsTxt": "https://strandsagents.com/llms.txt", "capabilities": [ "agent.framework", "agent.mcp-client", "agent.multi-agent", "agent.durable" ], "tags": [ "framework", "python", "typescript", "open-source", "llms-txt", "mcp", "otel", "a2a", "aws" ], "lastRelease": "2026-11-04", "graded": true, "anchor": { "graded": true, "score": 80, "grade": "A", "agentReady": true, "rank": 15, "ranked": true, "rankOf": 1510, "categoryRank": 4, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 87, "maintenance": 87, "payments": 60, "reliability": 87, "schema": 95, "security": 89, "transparency": 72 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": -4, "negativeNotes": [ "2026-10-23 and 2026-10-27. Three GitHub advisories for strands-agents. GHSA-4v6f-7vjh-6x4m (high) says the A2A server shared conversation history across callers with different context IDs, fixed in Python 1.43.0 and TypeScript 1.5.0. GHSA-3w55-933p-8pv5 (high) and GHSA-763v-5pjm-fx36 (moderate) say the default FileSessionManager stored sessions in a world-readable /tmp path that followed symlinks, fixed in 1.48.0. All published with fixes and credited, so 4 points. https://github.com/strands-agents/harness-sdk/security/advisories/GHSA-3w55-933p-8pv5" ], "verdict": "An Apache-2.0 agent SDK for Python and TypeScript with a managed MCP client, multi-agent patterns, interrupts and Cedar policies for approval, opt-in OpenTelemetry and no usage analytics. Bedrock is the default model, the default sandbox is the local host, and three session-isolation advisories were fixed in October 2026.", "bestFor": "Python or TypeScript teams, especially on AWS, who want a model-driven loop with MCP, multi-agent patterns and approval gates.", "strengths": [ "No usage analytics found, with OpenTelemetry tracing opt-in", "Interrupts and interventions pause tool calls for human approval or a Cedar policy", "MCP servers passed straight to the agent, with lifecycle handled", "Python and TypeScript SDKs released weekly, 1.0 since July 2025, under a written versioning policy", "llms.txt with a Markdown twin of every docs page" ], "weaknesses": [ "Three advisories in October 2026 on A2A session sharing and world-readable default session files, fixed in 1.43.0 and 1.48.0", "The default sandbox for shell and file tools is the local host", "Bedrock is the default provider, so a bare agent needs AWS credentials and model access", "Experimental modules and fast-moving standards (MCP, A2A, OTel conventions) sit outside the semantic versioning guarantee" ], "agentNotes": [ "Upgrade to strands-agents 1.48.0 or later, and set a private session directory rather than the /tmp default", "Pass a model explicitly when AWS credentials aren't configured, since Bedrock is the default", "Use interrupts or the harness's `interventions` for tools that write, send or pay", "Run shell and file tools in the Docker or SSH sandbox when the agent runs untrusted code", "Pin a minor version when using A2A, MCP or experimental modules" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "A", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 80 } ], "editorialScores": { "ergonomics": 87, "maintenance": 87, "payments": 60, "reliability": 87, "schema": 95, "security": 89, "transparency": 86 }, "provenanceScore": 58, "checked": { "categories": { "ergonomics": "2026-11-06", "maintenance": "2026-11-06", "payments": "2026-11-06", "reliability": "2026-11-06", "schema": "2026-11-06", "security": "2026-11-06", "transparency": "2026-11-06" }, "last": "2026-11-06", "oldest": "2026-11-06", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install strands-agents strands-agents-tools # TypeScript: npm i @strands-agents/sdk" }, "letme": { "capability": "https://letme.dev/agent.framework", "tool": "https://letme.dev/strands-agents" }, "sameCompany": [ "strands-decider" ], "area": "frameworks", "provenance": { "legalEntity": "Amazon Web Services, Inc.", "domain": "strandsagents.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/strands-agents/harness-sdk/releases", "securityTxt": "none", "checked": "2026-11-06", "notes": [ "The licence file and NOTICE attribute the project to Amazon.com, Inc. or its affiliates; SECURITY.md sends reports to the AWS Vulnerability Disclosure Program on HackerOne.", "strandsagents.com/robots.txt allows all crawlers with Content-Signal ai-train=no, search=yes, ai-input=yes.", "strandsagents.com/.well-known/security.txt returns 404." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/strands-agents.json", "live": { "slug": "strands-agents", "versions": [ { "registry": "github", "name": "strands-agents/harness-sdk", "version": "typescript/v1.24.0", "released": "2026-11-04", "seenAt": "2026-11-07T00:08:42.12303393Z" }, { "registry": "npm", "name": "@strands-agents/sdk", "version": "1.24.0", "seenAt": "2026-11-07T00:08:40.21989722Z" }, { "registry": "pypi", "name": "strands-agents", "version": "1.64.0", "released": "2026-11-04", "seenAt": "2026-11-07T00:08:40.035164104Z" }, { "registry": "pypi", "name": "strands-agents-tools", "version": "0.9.0", "released": "2026-11-04", "seenAt": "2026-11-07T00:08:41.063215915Z" } ], "githubStars": 9066, "npmWeekly": 697548, "pypiWeekly": 8902370, "securityTxt": { "url": "https://strandsagents.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T16:00:32.408705195Z" }, "llmsTxt": { "url": "https://strandsagents.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:34:02.911668247Z" }, "updatedAt": "2026-11-07T16:00:32.408705195Z" } }, "edges": [ { "by": 15, "category": "transparency", "leads": "pydantic-ai", "name": "Transparency \u0026 trust", "reasons": { "a": "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).", "b": "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." }, "scores": { "a": 87, "b": 72 } }, { "by": 8, "category": "ergonomics", "leads": "pydantic-ai", "name": "Agent ergonomics", "reasons": { "a": "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).", "b": "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." }, "scores": { "a": 95, "b": 87 } }, { "by": 6, "category": "security", "leads": "pydantic-ai", "name": "Security \u0026 auth", "reasons": { "a": "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.", "b": "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." }, "scores": { "a": 95, "b": 89 } }, { "by": 5, "category": "schema", "leads": "pydantic-ai", "name": "Schema \u0026 documentation", "reasons": { "a": "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).", "b": "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." }, "scores": { "a": 100, "b": 95 } } ], "facts": [ { "a": "Agent framework", "b": "Agent framework", "name": "Kind" }, { "a": "Pydantic", "b": "Amazon Web Services", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Apache-2.0", "name": "Licence" }, { "a": "yes", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-30", "b": "2026-11-04", "name": "Last release" }, { "a": "no date given", "b": "no document linked", "name": "Terms last updated" }, { "a": "2024-02-21", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "not read yet", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "not read yet", "name": "Terms restrict automated access" }, { "a": "yes", "b": "not read yet", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not read yet", "name": "Terms or service can change without notice" }, { "a": "yes", "b": "not read yet", "name": "Arbitration or class-action waiver" }, { "a": "20k stars, 1.3M PyPI/wk", "b": "9.1k stars, 698k npm/wk, 8.9M PyPI/wk", "name": "Popularity" }, { "a": "4/5 (8)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Pydantic AI scores 81.9 (A) on agent readiness against Strands Agents's 80 (A), and leads in 5 of 7 scored categories.", "question": "Which is better for AI agents, Pydantic AI or Strands Agents?" }, { "answer": "Yes. Pydantic AI is open source (MIT). Strands Agents is open source (Apache-2.0).", "question": "Are Pydantic AI and Strands Agents open source?" } ], "goodFor": [ { "aheadOn": [ "Schema \u0026 documentation, 100 against 95", "Agent ergonomics, 95 against 87", "Security \u0026 auth, 95 against 89", "Transparency \u0026 trust, 87 against 72" ], "also": null, "goodFor": "Python teams that want typed, validated outputs and tools, many model providers and durable runs on an engine they already use.", "slug": "pydantic-ai", "watchFor": "Python only, and 3.11 or newer since 2.55.0" }, { "aheadOn": null, "also": null, "goodFor": "Python or TypeScript teams, especially on AWS, who want a model-driven loop with MCP, multi-agent patterns and approval gates.", "slug": "strands-agents", "watchFor": "Three advisories in October 2026 on A2A session sharing and world-readable default session files, fixed in 1.43.0 and 1.48.0" } ], "history": { "pydantic-ai": [ { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.2, "from": 81.7, "fromGrade": "A", "to": 81.9, "toGrade": "A" }, { "causes": [ "event" ], "date": "2026-10-17", "delta": -2, "from": 83.7, "fromGrade": "A", "to": 81.7, "toGrade": "A" }, { "causes": [ "method" ], "date": "2026-10-08", "delta": -0.2, "from": 83.9, "fromGrade": "A", "to": 83.7, "toGrade": "A" } ], "strands-agents": null }, "job": { "capability": "agent.framework", "name": "Agent frameworks" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agentos-vs-pydantic-ai.json", "title": "AgentOS vs Pydantic AI", "url": "https://www.anchorterminal.com/compare/agentos-vs-pydantic-ai" }, { "json": "https://www.anchorterminal.com/compare/agentos-vs-strands-agents.json", "title": "AgentOS vs Strands Agents", "url": "https://www.anchorterminal.com/compare/agentos-vs-strands-agents" }, { "json": "https://www.anchorterminal.com/compare/agno-vs-pydantic-ai.json", "title": "Agno vs Pydantic AI", "url": "https://www.anchorterminal.com/compare/agno-vs-pydantic-ai" }, { "json": "https://www.anchorterminal.com/compare/agno-vs-strands-agents.json", "title": "Agno vs Strands Agents", "url": "https://www.anchorterminal.com/compare/agno-vs-strands-agents" }, { "json": "https://www.anchorterminal.com/compare/boxlang-ai-vs-pydantic-ai.json", "title": "BoxLang AI vs Pydantic AI", "url": "https://www.anchorterminal.com/compare/boxlang-ai-vs-pydantic-ai" }, { "json": "https://www.anchorterminal.com/compare/boxlang-ai-vs-strands-agents.json", "title": "BoxLang AI vs Strands Agents", "url": "https://www.anchorterminal.com/compare/boxlang-ai-vs-strands-agents" }, { "json": "https://www.anchorterminal.com/compare/claude-agent-sdk-vs-pydantic-ai.json", "title": "Claude Agent SDK vs Pydantic AI", "url": "https://www.anchorterminal.com/compare/claude-agent-sdk-vs-pydantic-ai" }, { "json": "https://www.anchorterminal.com/compare/claude-agent-sdk-vs-strands-agents.json", "title": "Claude Agent SDK vs Strands Agents", "url": "https://www.anchorterminal.com/compare/claude-agent-sdk-vs-strands-agents" }, { "json": "https://www.anchorterminal.com/compare/crewai-vs-pydantic-ai.json", "title": "CrewAI vs Pydantic AI", "url": "https://www.anchorterminal.com/compare/crewai-vs-pydantic-ai" }, { "json": "https://www.anchorterminal.com/compare/crewai-vs-strands-agents.json", "title": "CrewAI vs Strands Agents", "url": "https://www.anchorterminal.com/compare/crewai-vs-strands-agents" }, { "json": "https://www.anchorterminal.com/compare/cursor-agent-sdk-vs-pydantic-ai.json", "title": "Cursor Agent SDK vs Pydantic AI", "url": "https://www.anchorterminal.com/compare/cursor-agent-sdk-vs-pydantic-ai" }, { "json": "https://www.anchorterminal.com/compare/cursor-agent-sdk-vs-strands-agents.json", "title": "Cursor Agent SDK vs Strands Agents", "url": "https://www.anchorterminal.com/compare/cursor-agent-sdk-vs-strands-agents" }, { "json": "https://www.anchorterminal.com/compare/docker-agent-vs-pydantic-ai.json", "title": "Docker Agent vs Pydantic AI", "url": "https://www.anchorterminal.com/compare/docker-agent-vs-pydantic-ai" }, { "json": "https://www.anchorterminal.com/compare/docker-agent-vs-strands-agents.json", "title": "Docker Agent vs Strands Agents", "url": "https://www.anchorterminal.com/compare/docker-agent-vs-strands-agents" }, { "json": "https://www.anchorterminal.com/compare/google-adk-vs-pydantic-ai.json", "title": "Agent Development Kit (ADK) vs Pydantic AI", "url": "https://www.anchorterminal.com/compare/google-adk-vs-pydantic-ai" }, { "json": "https://www.anchorterminal.com/compare/google-adk-vs-strands-agents.json", "title": "Agent Development Kit (ADK) vs Strands Agents", "url": "https://www.anchorterminal.com/compare/google-adk-vs-strands-agents" }, { "json": "https://www.anchorterminal.com/compare/guilds-run-vs-pydantic-ai.json", "title": "guilds.run vs Pydantic AI", "url": "https://www.anchorterminal.com/compare/guilds-run-vs-pydantic-ai" }, { "json": "https://www.anchorterminal.com/compare/guilds-run-vs-strands-agents.json", "title": "guilds.run vs Strands Agents", "url": "https://www.anchorterminal.com/compare/guilds-run-vs-strands-agents" }, { "json": "https://www.anchorterminal.com/compare/langgraph-vs-pydantic-ai.json", "title": "LangGraph vs Pydantic AI", "url": "https://www.anchorterminal.com/compare/langgraph-vs-pydantic-ai" }, { "json": "https://www.anchorterminal.com/compare/langgraph-vs-strands-agents.json", "title": "LangGraph vs Strands Agents", "url": "https://www.anchorterminal.com/compare/langgraph-vs-strands-agents" }, { "json": "https://www.anchorterminal.com/compare/llamaindex-vs-pydantic-ai.json", "title": "LlamaIndex Framework vs Pydantic AI", "url": "https://www.anchorterminal.com/compare/llamaindex-vs-pydantic-ai" }, { "json": "https://www.anchorterminal.com/compare/llamaindex-vs-strands-agents.json", "title": "LlamaIndex Framework vs Strands Agents", "url": "https://www.anchorterminal.com/compare/llamaindex-vs-strands-agents" }, { "json": "https://www.anchorterminal.com/compare/mastra-vs-pydantic-ai.json", "title": "Mastra vs Pydantic AI", "url": "https://www.anchorterminal.com/compare/mastra-vs-pydantic-ai" }, { "json": "https://www.anchorterminal.com/compare/mastra-vs-strands-agents.json", "title": "Mastra vs Strands Agents", "url": "https://www.anchorterminal.com/compare/mastra-vs-strands-agents" }, { "json": "https://www.anchorterminal.com/compare/microsoft-agent-framework-vs-pydantic-ai.json", "title": "Microsoft Agent Framework vs Pydantic AI", "url": "https://www.anchorterminal.com/compare/microsoft-agent-framework-vs-pydantic-ai" }, { "json": "https://www.anchorterminal.com/compare/microsoft-agent-framework-vs-strands-agents.json", "title": "Microsoft Agent Framework vs Strands Agents", "url": "https://www.anchorterminal.com/compare/microsoft-agent-framework-vs-strands-agents" }, { "json": "https://www.anchorterminal.com/compare/openai-agents-sdk-vs-pydantic-ai.json", "title": "OpenAI Agents SDK vs Pydantic AI", "url": "https://www.anchorterminal.com/compare/openai-agents-sdk-vs-pydantic-ai" }, { "json": "https://www.anchorterminal.com/compare/openai-agents-sdk-vs-strands-agents.json", "title": "OpenAI Agents SDK vs Strands Agents", "url": "https://www.anchorterminal.com/compare/openai-agents-sdk-vs-strands-agents" }, { "json": "https://www.anchorterminal.com/compare/pydantic-ai-vs-smolagents.json", "title": "Pydantic AI vs smolagents", "url": "https://www.anchorterminal.com/compare/pydantic-ai-vs-smolagents" }, { "json": "https://www.anchorterminal.com/compare/pydantic-ai-vs-tradingagents.json", "title": "Pydantic AI vs TradingAgents", "url": "https://www.anchorterminal.com/compare/pydantic-ai-vs-tradingagents" }, { "json": "https://www.anchorterminal.com/compare/pydantic-ai-vs-vercel-ai-sdk.json", "title": "Pydantic AI vs AI SDK", "url": "https://www.anchorterminal.com/compare/pydantic-ai-vs-vercel-ai-sdk" }, { "json": "https://www.anchorterminal.com/compare/smolagents-vs-strands-agents.json", "title": "smolagents vs Strands Agents", "url": "https://www.anchorterminal.com/compare/smolagents-vs-strands-agents" }, { "json": "https://www.anchorterminal.com/compare/strands-agents-vs-tradingagents.json", "title": "Strands Agents vs TradingAgents", "url": "https://www.anchorterminal.com/compare/strands-agents-vs-tradingagents" }, { "json": "https://www.anchorterminal.com/compare/strands-agents-vs-vercel-ai-sdk.json", "title": "Strands Agents vs AI SDK", "url": "https://www.anchorterminal.com/compare/strands-agents-vs-vercel-ai-sdk" } ], "panel": null, "scores": [ { "by": 4, "edge": "strands-agents", "key": "reliability", "name": "Reliability", "pydantic-ai": 83, "strands-agents": 87, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 5, "edge": "pydantic-ai", "key": "schema", "name": "Schema \u0026 documentation", "pydantic-ai": 100, "strands-agents": 95, "weight": 13 }, { "by": 8, "edge": "pydantic-ai", "key": "ergonomics", "name": "Agent ergonomics", "pydantic-ai": 95, "strands-agents": 87, "weight": 13 }, { "by": 6, "edge": "pydantic-ai", "key": "security", "name": "Security \u0026 auth", "pydantic-ai": 95, "strands-agents": 89, "weight": 14 }, { "by": 0, "edge": "", "key": "payments", "name": "Payments \u0026 pricing", "pydantic-ai": 60, "strands-agents": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 3, "edge": "pydantic-ai", "key": "maintenance", "name": "Maintenance \u0026 community", "pydantic-ai": 90, "strands-agents": 87, "weight": 7 }, { "by": 15, "edge": "pydantic-ai", "key": "transparency", "name": "Transparency \u0026 trust", "pydantic-ai": 87, "strands-agents": 72, "weight": 7 } ], "summary": "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.", "tasks": null, "tutorials": [], "verdicts": { "pydantic-ai": "Typed outputs and tools, validated by Pydantic, with failed validations sent back to the model. Python only.", "strands-agents": "An Apache-2.0 agent SDK for Python and TypeScript with a managed MCP client, multi-agent patterns, interrupts and Cedar policies for approval, opt-in OpenTelemetry and no usage analytics. Bedrock is the default model, the default sandbox is the local host, and three session-isolation advisories were fixed in October 2026." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/pydantic-ai-vs-strands-agents", "json": "https://www.anchorterminal.com/compare/pydantic-ai-vs-strands-agents.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/pydantic-ai-vs-strands-agents.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/pydantic-ai-vs-strands-agents.min.md" }, "markdown": "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.\n\n- Pydantic AI: grade A, 81.9/100, rank #7 of 1510. Markdown https://www.anchorterminal.com/tools/pydantic-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/pydantic-ai.json\n- Strands Agents: grade A, 80/100, rank #15 of 1510. Markdown https://www.anchorterminal.com/tools/strands-agents.md · JSON https://www.anchorterminal.com/api/v1/tools/strands-agents.json\n- Best agent frameworks and SDKs: https://www.anchorterminal.com/best/frameworks/index.md\n- All 171 frameworks comparisons: https://www.anchorterminal.com/compare/frameworks/index.md\n\n## Which one, for what\n\n### Pydantic AI (A)\n\nGood for: Python teams that want typed, validated outputs and tools, many model providers and durable runs on an engine they already use.\n\nAhead on:\n- Schema \u0026 documentation, 100 against 95\n- Agent ergonomics, 95 against 87\n- Security \u0026 auth, 95 against 89\n- Transparency \u0026 trust, 87 against 72\n\nWatch for: Python only, and 3.11 or newer since 2.55.0\n\n### Strands Agents (A)\n\nGood for: Python or TypeScript teams, especially on AWS, who want a model-driven loop with MCP, multi-agent patterns and approval gates.\n\nWatch for: Three advisories in October 2026 on A2A session sharing and world-readable default session files, fixed in 1.43.0 and 1.48.0\n\n\n## Why the edge\n\n### Transparency \u0026 trust: Pydantic AI +15\n\n- 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).\n- 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.\n\n### Agent ergonomics: Pydantic AI +8\n\n- 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).\n- 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.\n\n### Security \u0026 auth: Pydantic AI +6\n\n- 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.\n- 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.\n\n### Schema \u0026 documentation: Pydantic AI +5\n\n- 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).\n- 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.\n\n## Score by category\n\n| Category | Weight | Pydantic AI | Strands Agents | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 83 | 87 | Strands Agents +4 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 100 | 95 | Pydantic AI +5 |\n| Agent ergonomics | 13% (16.2 this run) | 95 | 87 | Pydantic AI +8 |\n| Security \u0026 auth | 14% (17.5 this run) | 95 | 89 | Pydantic AI +6 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 60 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 90 | 87 | Pydantic AI +3 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 87 | 72 | Pydantic AI +15 |\n| Negative events | ≤15 | -6 | -4 | |\n| **Total** | | **81.9 · A** | **80 · A** | |\n\n## Facts side by side\n\n| Fact | Pydantic AI | Strands Agents |\n| --- | --- | --- |\n| Kind | Agent framework | Agent framework |\n| Vendor | Pydantic | Amazon Web Services |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 |\n| Read-only variant documented | yes | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-30 | 2026-11-04 |\n| Terms last updated | no date given | no document linked |\n| Privacy policy last updated | 2024-02-21 | no document linked |\n| Customer content may train models | not found in the text | not read yet |\n| Terms restrict automated access | not found in the text | not read yet |\n| Terms restrict benchmarking | yes | not read yet |\n| Terms or service can change without notice | not found in the text | not read yet |\n| Arbitration or class-action waiver | yes | not read yet |\n| Popularity | 20k stars, 1.3M PyPI/wk | 9.1k stars, 698k npm/wk, 8.9M PyPI/wk |\n| Agent reviews | 4/5 (8) | none |\n\n## How the scores have moved\n\n### Pydantic AI\n\n- +0.2 on 27 October 2026: checklist\n- -2.0 on 17 October 2026: negative event\n- -0.2 on 8 October 2026: checklist\n\n### Strands Agents\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Pydantic AI.** Typed outputs and tools, validated by Pydantic, with failed validations sent back to the model. Python only.\n\n**Strands Agents.** An Apache-2.0 agent SDK for Python and TypeScript with a managed MCP client, multi-agent patterns, interrupts and Cedar policies for approval, opt-in OpenTelemetry and no usage analytics. Bedrock is the default model, the default sandbox is the local host, and three session-isolation advisories were fixed in October 2026.\n\n## Before you call either\n\n### Pydantic AI\n\n1. Define the output type first. Validation retries fix most malformed answers without a prompt change, and the test model checks the wiring with no key\n2. Use streamable HTTP for MCP, since SSE is deprecated. Call `.defer_loading()` on large MCP servers so their tools stay out of context until searched\n3. Set usage limits on every run that calls paid models\n4. Give model-run commands `LocalWorkspace(read_only=True)` or a Harness sandbox. A plain `LocalWorkspace` runs them on your machine as you\n5. Stay on 2.53.0 or later, and the Harness on 0.59.0 or later. 2026 advisories hit URL downloads, the metadata blocklist, the web chat UI, streamed requests and nested CodeMode calls\n\n### Strands Agents\n\n1. Upgrade to strands-agents 1.48.0 or later, and set a private session directory rather than the /tmp default\n2. Pass a model explicitly when AWS credentials aren't configured, since Bedrock is the default\n3. Use interrupts or the harness's `interventions` for tools that write, send or pay\n4. Run shell and file tools in the Docker or SSH sandbox when the agent runs untrusted code\n5. Pin a minor version when using A2A, MCP or experimental modules\n\n## Questions\n\n### Which is better for AI agents, Pydantic AI or Strands Agents?\n\nPydantic AI scores 81.9 (A) on agent readiness against Strands Agents's 80 (A), and leads in 5 of 7 scored categories.\n\n### Are Pydantic AI and Strands Agents open source?\n\nYes. Pydantic AI is open source (MIT). Strands Agents is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/pydantic-ai-vs-strands-agents.json, and with the fewest tokens: https://www.anchorterminal.com/compare/pydantic-ai-vs-strands-agents.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"pydantic-ai\", \"b\": \"strands-agents\"}`. From a terminal: `anchor compare pydantic-ai strands-agents`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/pydantic-ai.json and https://www.anchorterminal.com/api/v1/tools/strands-agents.json\n\n## Other comparisons with Pydantic AI or Strands Agents\n\n- [AgentOS vs Pydantic AI](https://www.anchorterminal.com/compare/agentos-vs-pydantic-ai.md)\n- [AgentOS vs Strands Agents](https://www.anchorterminal.com/compare/agentos-vs-strands-agents.md)\n- [Agno vs Pydantic AI](https://www.anchorterminal.com/compare/agno-vs-pydantic-ai.md)\n- [Agno vs Strands Agents](https://www.anchorterminal.com/compare/agno-vs-strands-agents.md)\n- [BoxLang AI vs Pydantic AI](https://www.anchorterminal.com/compare/boxlang-ai-vs-pydantic-ai.md)\n- [BoxLang AI vs Strands Agents](https://www.anchorterminal.com/compare/boxlang-ai-vs-strands-agents.md)\n- [Claude Agent SDK vs Pydantic AI](https://www.anchorterminal.com/compare/claude-agent-sdk-vs-pydantic-ai.md)\n- [Claude Agent SDK vs Strands Agents](https://www.anchorterminal.com/compare/claude-agent-sdk-vs-strands-agents.md)\n- [CrewAI vs Pydantic AI](https://www.anchorterminal.com/compare/crewai-vs-pydantic-ai.md)\n- [CrewAI vs Strands Agents](https://www.anchorterminal.com/compare/crewai-vs-strands-agents.md)\n- [Cursor Agent SDK vs Pydantic AI](https://www.anchorterminal.com/compare/cursor-agent-sdk-vs-pydantic-ai.md)\n- [Cursor Agent SDK vs Strands Agents](https://www.anchorterminal.com/compare/cursor-agent-sdk-vs-strands-agents.md)\n- [Docker Agent vs Pydantic AI](https://www.anchorterminal.com/compare/docker-agent-vs-pydantic-ai.md)\n- [Docker Agent vs Strands Agents](https://www.anchorterminal.com/compare/docker-agent-vs-strands-agents.md)\n- [Agent Development Kit (ADK) vs Pydantic AI](https://www.anchorterminal.com/compare/google-adk-vs-pydantic-ai.md)\n- [Agent Development Kit (ADK) vs Strands Agents](https://www.anchorterminal.com/compare/google-adk-vs-strands-agents.md)\n- [guilds.run vs Pydantic AI](https://www.anchorterminal.com/compare/guilds-run-vs-pydantic-ai.md)\n- [guilds.run vs Strands Agents](https://www.anchorterminal.com/compare/guilds-run-vs-strands-agents.md)\n- [LangGraph vs Pydantic AI](https://www.anchorterminal.com/compare/langgraph-vs-pydantic-ai.md)\n- [LangGraph vs Strands Agents](https://www.anchorterminal.com/compare/langgraph-vs-strands-agents.md)\n- [LlamaIndex Framework vs Pydantic AI](https://www.anchorterminal.com/compare/llamaindex-vs-pydantic-ai.md)\n- [LlamaIndex Framework vs Strands Agents](https://www.anchorterminal.com/compare/llamaindex-vs-strands-agents.md)\n- [Mastra vs Pydantic AI](https://www.anchorterminal.com/compare/mastra-vs-pydantic-ai.md)\n- [Mastra vs Strands Agents](https://www.anchorterminal.com/compare/mastra-vs-strands-agents.md)\n- [Microsoft Agent Framework vs Pydantic AI](https://www.anchorterminal.com/compare/microsoft-agent-framework-vs-pydantic-ai.md)\n- [Microsoft Agent Framework vs Strands Agents](https://www.anchorterminal.com/compare/microsoft-agent-framework-vs-strands-agents.md)\n- [OpenAI Agents SDK vs Pydantic AI](https://www.anchorterminal.com/compare/openai-agents-sdk-vs-pydantic-ai.md)\n- [OpenAI Agents SDK vs Strands Agents](https://www.anchorterminal.com/compare/openai-agents-sdk-vs-strands-agents.md)\n- [Pydantic AI vs smolagents](https://www.anchorterminal.com/compare/pydantic-ai-vs-smolagents.md)\n- [Pydantic AI vs TradingAgents](https://www.anchorterminal.com/compare/pydantic-ai-vs-tradingagents.md)\n- [Pydantic AI vs AI SDK](https://www.anchorterminal.com/compare/pydantic-ai-vs-vercel-ai-sdk.md)\n- [smolagents vs Strands Agents](https://www.anchorterminal.com/compare/smolagents-vs-strands-agents.md)\n- [Strands Agents vs TradingAgents](https://www.anchorterminal.com/compare/strands-agents-vs-tradingagents.md)\n- [Strands Agents vs AI SDK](https://www.anchorterminal.com/compare/strands-agents-vs-vercel-ai-sdk.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-07", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Pydantic AI vs Strands Agents", "url": "" } ], "description": "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.", "facts": [ "Pydantic AI A 81.9", "Strands Agents A 80", "scores" ], "h1": "Pydantic AI vs Strands Agents", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/pydantic-ai-vs-strands-agents", "published": "2026-10-01", "section": "tools", "title": "Pydantic AI vs Strands Agents for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-11-07", "url": "https://www.anchorterminal.com/compare/pydantic-ai-vs-strands-agents" }, "tokens": { "markdown": 3950, "slim": 1930 }, "version": 1 }