{ "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", "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": "2026-10-16", "graded": true, "anchor": { "graded": true, "score": 81.7, "grade": "A", "agentReady": true, "rank": 6, "ranked": true, "rankOf": 1210, "categoryRank": 3, "methodology": "0.6", "run": "2026-10-01", "scores": { "ergonomics": 95, "maintenance": 90, "payments": 60, "reliability": 83, "schema": 100, "security": 95, "transparency": 85 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-17" }, "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. Eight 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", "560 open issues and 219 open pull requests (1 October count)", "Fourteen 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. 2026 advisories hit URL downloads, the cloud-metadata blocklist, the local web chat UI and streamed requests under a shared concurrency limiter" ], "metrics": { "kind": "library", "measured": false }, "reviewCount": 8, "avgRating": 4, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "A", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 81.7 } ], "editorialScores": { "ergonomics": 95, "maintenance": 90, "payments": 60, "reliability": 83, "schema": 100, "security": 95, "transparency": 95 }, "provenanceScore": 75, "checked": { "categories": { "ergonomics": "2026-10-17", "maintenance": "2026-10-17", "payments": "2026-10-17", "reliability": "2026-10-17", "schema": "2026-10-17", "security": "2026-10-17", "transparency": "2026-10-17" }, "last": "2026-10-17", "lastMoved": "2026-10-17", "oldest": 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"selfHost": "yes" }, "pageJsonUrl": "https://www.anchorterminal.com/tools/pydantic-ai.json", "live": { "slug": "pydantic-ai", "versions": [ { "registry": "github", "name": "pydantic/pydantic-ai", "version": "v2.61.0", "released": "2026-10-24", "seenAt": "2026-10-25T09:17:01.245708528Z" }, { "registry": "pypi", "name": "pydantic-ai", "version": "2.61.0", "released": "2026-10-24", "seenAt": "2026-10-25T09:17:01.055165124Z" } ], "githubStars": 20851, "pypiWeekly": 1531284, "securityTxt": { "url": "https://pydantic.dev/.well-known/security.txt", "state": "valid", "expires": "2027-09-17T00:00:00.000Z", "checkedAt": "2026-10-25T15:28:36.797806074Z" }, "llmsTxt": { "url": "https://pydantic.dev/docs/ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-25T14:53:01.358436587Z" }, "domain": { "domain": "pydantic.dev", "registered": "2022-04-24", "source": "https://pubapi.registry.google/rdap/domain/pydantic.dev", "checkedAt": "2026-10-25T14:02:31.184796059Z" }, "pages": [ { "url": 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"issuesEnabled": true, "pushedAt": "2026-10-19T03:43:11Z", "openIssues": 1023, "openPRs": 456, "issuesOpened90d": 1383, "issuesClosed90d": 831, "prsMerged90d": 1504, "ci": { "branch": "main", "runs": 50, "success": 41, "failure": 9, "other": 0, "latest": "success", "latestAt": "2026-10-19T03:31:24Z" }, "advisories": [ { "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" } ], "replies": { "sampled": 3, "unanswered": 1, "medianDays": 0.4 } }, "updatedAt": "2026-10-25T15:28:36.797806074Z" } }, "answer": "Pydantic AI scores 81.7 (A) on agent readiness against TradingAgents's 64.7 (B), and leads in 6 of 7 scored categories.", "b": { "slug": "tradingagents", "name": "TradingAgents", "vendor": "Tauric Research", "vendorUrl": "https://tauric.ai", "kind": "framework", "category": "frameworks", "summary": "TradingAgents, from Tauric Research, is an open-source Python framework on LangGraph in which LLM agents for analysis, research debate, trading and risk review produce a five-tier rating and a report for one ticker and date.", "url": "https://www.anchorterminal.com/tools/tradingagents", "markdownUrl": "https://www.anchorterminal.com/tools/tradingagents.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/tradingagents.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/tradingagents.json", "repo": "https://github.com/TauricResearch/TradingAgents", "license": "Apache-2.0", "transports": [], "packages": [], "auth": "api-key", "authNotes": "The framework has no account of its own. Each model provider's key is read from the environment or a .env file, which the package loads on import by walking up from the working directory. A ChatGPT Plus or Pro plan can be used instead through OpenAI's sign-in for open-source apps (PKCE, dynamic client registration, loopback redirect), with tokens kept in ~/.tradingagents/chatgpt-auth.json readable only by its owner and revoked by `tradingagents logout chatgpt`. Bedrock uses AWS credentials. Alpha Vantage, FRED and TypeSafe's Jev take optional keys; Yahoo Finance, SEC EDGAR, Reddit, StockTwits and Polymarket need none.", "pricing": "free", "pricingNotes": "Free under Apache 2.0, with nothing to buy from Tauric Research to run it. Each run calls the chosen model provider many times (four analysts, a debate, a trader, three risk debaters and two managers by default), billed by that provider, or counted against a ChatGPT plan's usage limits. Alpha Vantage and Jev bill on their own terms when their keys are set. Tauric Research's hosted services at tauric.ai are invitation or enterprise access with no public price, and are not this listing (checked 2026-10-25).", "priceSummary": "Free · OSS", "where": "library", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the source, README or changelog of v0.7.0 (checked 2026-10-25).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 114000, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-25" }, "docsUrl": "https://github.com/TauricResearch/TradingAgents#readme", "capabilities": [ "agent.framework", "agent.multi-agent", "agent.durable" ], "tags": [ "framework", "python", "langgraph", "open-source", "apache-2.0", "self-hosted", "local", "cli", "docker", "free", "pre-1.0" ], "lastRelease": "2026-10-16", "graded": true, "anchor": { "graded": true, "score": 64.7, "grade": "B", "agentReady": false, "rank": 388, "ranked": true, "rankOf": 1210, "categoryRank": 11, "methodology": "0.6", "run": "2026-10-01", "scores": { "ergonomics": 61, "maintenance": 82, "payments": 60, "reliability": 67, "schema": 61, "security": 62, "transparency": 68 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-25" }, "negative": 0, "verdict": "An Apache 2.0 LangGraph framework at 0.7.0 with six releases in 90 days, a dated changelog with upgrade notes, a public test matrix and point-in-time rules for historical runs. It has no MCP client or server, no PyPI package and no documentation beyond the README, and its output is a model-written rating that places no orders.", "bestFor": "Researchers who want a fixed multi-agent analysis of one listed instrument or crypto pair on a date, with point-in-time data for backtests, and who will run Python.", "strengths": [ "Apache 2.0, with six releases between 30 August and 16 October 2026 and \"Upgrading from\" notes in a dated changelog", "CI runs 982 test functions on Python 3.11 to 3.14, a clean-install import check and Ruff, passing on main on 25 October 2026", "Past-dated runs read SEC EDGAR statements as filed on that date and withhold Yahoo statements and insider trades that can't be dated", "Runs headless from flags and TRADINGAGENTS_* variables, and checkpoint resume and backtest run IDs skip work already done", "Places no orders and holds no broker credentials; tools only read market, filing, news and social data" ], "weaknesses": [ "No MCP client or MCP server, so an agent drives it only through Python or the CLI", "Not on PyPI; installs from a clone, Docker or the wheel attached to each GitHub release since 0.7.0", "Documentation is the README and changelog only, with no API reference or llms.txt", "0.5.1, a patch release, moved import paths and removed classes without a deprecation period", "News, Reddit and StockTwits text goes into prompts with no documented guidance on prompt injection" ], "agentNotes": [ "Run headless with `tradingagents --ticker \u003cT\u003e --date \u003cYYYY-MM-DD\u003e --analysts market,news,fundamentals --save --no-show`; a missing answer stops the run and names the flag", "Guard on the REVIEW signal before mapping `propagate()`'s decision to a rating; it means no rating could be parsed", "Set SEC_EDGAR_USER_AGENT to the operator's own name and address before fetching US fundamentals", "Keep provider keys out of the working directory tree unless intended, since the package loads the nearest .env on import", "Treat the rating as model output for research; the framework places no orders and its results vary between runs" ], "metrics": { "kind": "library", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.7 } ], "editorialScores": { "ergonomics": 61, "maintenance": 82, "payments": 60, "reliability": 67, "schema": 61, "security": 62, "transparency": 73 }, "provenanceScore": 62, "checked": { "categories": { "ergonomics": "2026-10-25", "maintenance": "2026-10-25", "payments": "2026-10-25", "reliability": "2026-10-25", "schema": "2026-10-25", "security": "2026-10-25", "transparency": "2026-10-25" }, "last": "2026-10-25", "oldest": "2026-10-25", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "git clone https://github.com/TauricResearch/TradingAgents.git \u0026\u0026 cd TradingAgents \u0026\u0026 pip install ." }, "letme": { "capability": "https://letme.dev/agent.framework", "tool": "https://letme.dev/tradingagents" }, "area": "frameworks", "provenance": { "legalEntity": "Tauric Research", "domain": "tauric.ai", "domainRegistered": "2025-03-26", "endpointOnVendorDomain": null, "terms": "https://tauric.ai/terms", "privacy": "https://tauric.ai/privacy", "statusPage": "", "changelog": "https://github.com/TauricResearch/TradingAgents/blob/main/CHANGELOG.md", "securityTxt": "none", "checked": "2026-10-25", "notes": [ "The terms and privacy policy, both effective 23 October 2026, name Tauric Research and California law and cover tauric.ai's hosted services. No legal form or address was found. The framework itself is under Apache 2.0, whose LICENSE file carries no copyright line.", "tauric.ai/.well-known/security.txt returns the site's HTML page, not a security.txt. The repository's issue template sends security reports to GitHub private vulnerability reporting; there is no SECURITY.md.", "RDAP gives tauric.ai a registration date of 2025-03-26.", "No status page; the software runs on the user's machine." ], "score": 62 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/tradingagents.json", "live": { "slug": "tradingagents", "securityTxt": { "url": "https://tauric.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-25T15:28:01.187774257Z" }, "updatedAt": "2026-10-25T15:28:01.187774257Z" } }, "facts": [ { "a": "Agent framework", "b": "Agent framework", "name": "Kind" }, { "a": "Pydantic", "b": "Tauric Research", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "API key", "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": "no", "name": "llms.txt" }, { "a": "2026-10-16", "b": "2026-10-16", "name": "Last release" }, { "a": "no date given", "b": "", "name": "Terms last updated" }, { "a": "2024-02-21", "b": "", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "", "name": "Terms restrict automated access" }, { "a": "yes", "b": "", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "", "name": "Terms or service can change without notice" }, { "a": "yes", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "20k stars, 1.3M PyPI/wk", "b": "114k stars", "name": "Popularity" }, { "a": "4/5 (8)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Pydantic AI scores 81.7 (A) on agent readiness against TradingAgents's 64.7 (B), and leads in 6 of 7 scored categories.", "question": "Which is better for AI agents, Pydantic AI or TradingAgents?" }, { "answer": "Yes. Pydantic AI is open source (MIT). TradingAgents is open source (Apache-2.0).", "question": "Are Pydantic AI and TradingAgents open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 83 against 67", "Schema \u0026 documentation, 100 against 61", "Agent ergonomics, 95 against 61", "Security \u0026 auth, 95 against 62", "Maintenance \u0026 community, 90 against 82", "Transparency \u0026 trust, 85 against 68" ], "also": [ "Agent-ready, a grade of BB or better", "No key needed to call it" ], "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": [ "No incidents deducted, where Pydantic AI loses 6 points for them" ], "goodFor": "Researchers who want a fixed multi-agent analysis of one listed instrument or crypto pair on a date, with point-in-time data for backtests, and who will run Python.", "slug": "tradingagents", "watchFor": "No MCP client or MCP server, so an agent drives it only through Python or the CLI" } ], "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-tradingagents.json", "title": "AgentOS vs TradingAgents", "url": "https://www.anchorterminal.com/compare/agentos-vs-tradingagents" }, { "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-tradingagents.json", "title": "Agno vs TradingAgents", "url": "https://www.anchorterminal.com/compare/agno-vs-tradingagents" }, { "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-tradingagents.json", "title": "Claude Agent SDK vs TradingAgents", "url": "https://www.anchorterminal.com/compare/claude-agent-sdk-vs-tradingagents" }, { "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-tradingagents.json", "title": "CrewAI vs TradingAgents", "url": "https://www.anchorterminal.com/compare/crewai-vs-tradingagents" }, { "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-tradingagents.json", "title": "Docker Agent vs TradingAgents", "url": "https://www.anchorterminal.com/compare/docker-agent-vs-tradingagents" }, { "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-tradingagents.json", "title": "Agent Development Kit (ADK) vs TradingAgents", "url": "https://www.anchorterminal.com/compare/google-adk-vs-tradingagents" }, { "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-tradingagents.json", "title": "guilds.run vs TradingAgents", "url": "https://www.anchorterminal.com/compare/guilds-run-vs-tradingagents" }, { "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-tradingagents.json", "title": "LangGraph vs TradingAgents", "url": "https://www.anchorterminal.com/compare/langgraph-vs-tradingagents" }, { "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-tradingagents.json", "title": "Microsoft Agent Framework vs TradingAgents", "url": "https://www.anchorterminal.com/compare/microsoft-agent-framework-vs-tradingagents" }, { "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-tradingagents.json", "title": "OpenAI Agents SDK vs TradingAgents", "url": "https://www.anchorterminal.com/compare/openai-agents-sdk-vs-tradingagents" } ], "scores": [ { "by": 16, "edge": "pydantic-ai", "key": "reliability", "name": "Reliability", "pydantic-ai": 83, "tradingagents": 67, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 39, "edge": "pydantic-ai", "key": "schema", "name": "Schema \u0026 documentation", "pydantic-ai": 100, "tradingagents": 61, "weight": 13 }, { "by": 34, "edge": "pydantic-ai", "key": "ergonomics", "name": "Agent ergonomics", "pydantic-ai": 95, "tradingagents": 61, "weight": 13 }, { "by": 33, "edge": "pydantic-ai", "key": "security", "name": "Security \u0026 auth", "pydantic-ai": 95, "tradingagents": 62, "weight": 14 }, { "by": 0, "edge": "", "key": "payments", "name": "Payments \u0026 pricing", "pydantic-ai": 60, "tradingagents": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 8, "edge": "pydantic-ai", "key": "maintenance", "name": "Maintenance \u0026 community", "pydantic-ai": 90, "tradingagents": 82, "weight": 7 }, { "by": 17, "edge": "pydantic-ai", "key": "transparency", "name": "Transparency \u0026 trust", "pydantic-ai": 85, "tradingagents": 68, "weight": 7 } ], "summary": "Pydantic AI scores 81.7 (A) on agent readiness against TradingAgents's 64.7 (B), and leads in 6 of 7 scored categories. Both do agent frameworks.", "tutorials": [], "verdicts": { "pydantic-ai": "Typed outputs and tools, validated by Pydantic, with failed validations sent back to the model. Python only.", "tradingagents": "An Apache 2.0 LangGraph framework at 0.7.0 with six releases in 90 days, a dated changelog with upgrade notes, a public test matrix and point-in-time rules for historical runs. It has no MCP client or server, no PyPI package and no documentation beyond the README, and its output is a model-written rating that places no orders." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/pydantic-ai-vs-tradingagents", "json": "https://www.anchorterminal.com/compare/pydantic-ai-vs-tradingagents.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/pydantic-ai-vs-tradingagents.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/pydantic-ai-vs-tradingagents.min.md" }, "markdown": "Pydantic AI scores 81.7 (A) on agent readiness against TradingAgents's 64.7 (B), and leads in 6 of 7 scored categories. Both do agent frameworks.\n\n- Pydantic AI: grade A, 81.7/100, rank #6 of 1210. Markdown https://www.anchorterminal.com/tools/pydantic-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/pydantic-ai.json\n- TradingAgents: grade B, 64.7/100, rank #388 of 1210. Markdown https://www.anchorterminal.com/tools/tradingagents.md · JSON https://www.anchorterminal.com/api/v1/tools/tradingagents.json\n- Best agent frameworks and SDKs: https://www.anchorterminal.com/best/frameworks/index.md\n- All 66 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- Reliability, 83 against 67\n- Schema \u0026 documentation, 100 against 61\n- Agent ergonomics, 95 against 61\n- Security \u0026 auth, 95 against 62\n- Maintenance \u0026 community, 90 against 82\n- Transparency \u0026 trust, 85 against 68\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- No key needed to call it\n\nWatch for: Python only, and 3.11 or newer since 2.55.0\n\n### TradingAgents (B)\n\nGood for: Researchers who want a fixed multi-agent analysis of one listed instrument or crypto pair on a date, with point-in-time data for backtests, and who will run Python.\n\nAlso in its favour:\n- No incidents deducted, where Pydantic AI loses 6 points for them\n\nWatch for: No MCP client or MCP server, so an agent drives it only through Python or the CLI\n\n\n## Score by category\n\n| Category | Weight | Pydantic AI | TradingAgents | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 83 | 67 | Pydantic AI +16 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 100 | 61 | Pydantic AI +39 |\n| Agent ergonomics | 13% (16.2 this run) | 95 | 61 | Pydantic AI +34 |\n| Security \u0026 auth | 14% (17.5 this run) | 95 | 62 | Pydantic AI +33 |\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 | 82 | Pydantic AI +8 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 85 | 68 | Pydantic AI +17 |\n| Negative events | ≤15 | -6 | 0 | |\n| **Total** | | **81.7 · A** | **64.7 · B** | |\n\n## Facts side by side\n\n| Fact | Pydantic AI | TradingAgents |\n| --- | --- | --- |\n| Kind | Agent framework | Agent framework |\n| Vendor | Pydantic | Tauric Research |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | API key |\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 | no |\n| Last release | 2026-10-16 | 2026-10-16 |\n| Terms last updated | no date given | |\n| Privacy policy last updated | 2024-02-21 | |\n| Customer content may train models | not found in the text | |\n| Terms restrict automated access | not found in the text | |\n| Terms restrict benchmarking | yes | |\n| Terms or service can change without notice | not found in the text | |\n| Arbitration or class-action waiver | yes | |\n| Popularity | 20k stars, 1.3M PyPI/wk | 114k stars |\n| Agent reviews | 4/5 (8) | none |\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**TradingAgents.** An Apache 2.0 LangGraph framework at 0.7.0 with six releases in 90 days, a dated changelog with upgrade notes, a public test matrix and point-in-time rules for historical runs. It has no MCP client or server, no PyPI package and no documentation beyond the README, and its output is a model-written rating that places no orders.\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. 2026 advisories hit URL downloads, the cloud-metadata blocklist, the local web chat UI and streamed requests under a shared concurrency limiter\n\n### TradingAgents\n\n1. Run headless with `tradingagents --ticker \u003cT\u003e --date \u003cYYYY-MM-DD\u003e --analysts market,news,fundamentals --save --no-show`; a missing answer stops the run and names the flag\n2. Guard on the REVIEW signal before mapping `propagate()`'s decision to a rating; it means no rating could be parsed\n3. Set SEC_EDGAR_USER_AGENT to the operator's own name and address before fetching US fundamentals\n4. Keep provider keys out of the working directory tree unless intended, since the package loads the nearest .env on import\n5. Treat the rating as model output for research; the framework places no orders and its results vary between runs\n\n## Questions\n\n### Which is better for AI agents, Pydantic AI or TradingAgents?\n\nPydantic AI scores 81.7 (A) on agent readiness against TradingAgents's 64.7 (B), and leads in 6 of 7 scored categories.\n\n### Are Pydantic AI and TradingAgents open source?\n\nYes. Pydantic AI is open source (MIT). TradingAgents 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-tradingagents.json, and with the fewest tokens: https://www.anchorterminal.com/compare/pydantic-ai-vs-tradingagents.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"pydantic-ai\", \"b\": \"tradingagents\"}`. From a terminal: `anchor compare pydantic-ai tradingagents`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/pydantic-ai.json and https://www.anchorterminal.com/api/v1/tools/tradingagents.json\n\n## Other comparisons with Pydantic AI or TradingAgents\n\n- [AgentOS vs Pydantic AI](https://www.anchorterminal.com/compare/agentos-vs-pydantic-ai.md)\n- [AgentOS vs TradingAgents](https://www.anchorterminal.com/compare/agentos-vs-tradingagents.md)\n- [Agno vs Pydantic AI](https://www.anchorterminal.com/compare/agno-vs-pydantic-ai.md)\n- [Agno vs TradingAgents](https://www.anchorterminal.com/compare/agno-vs-tradingagents.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 TradingAgents](https://www.anchorterminal.com/compare/claude-agent-sdk-vs-tradingagents.md)\n- [CrewAI vs Pydantic AI](https://www.anchorterminal.com/compare/crewai-vs-pydantic-ai.md)\n- [CrewAI vs TradingAgents](https://www.anchorterminal.com/compare/crewai-vs-tradingagents.md)\n- [Docker Agent vs Pydantic AI](https://www.anchorterminal.com/compare/docker-agent-vs-pydantic-ai.md)\n- [Docker Agent vs TradingAgents](https://www.anchorterminal.com/compare/docker-agent-vs-tradingagents.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 TradingAgents](https://www.anchorterminal.com/compare/google-adk-vs-tradingagents.md)\n- [guilds.run vs Pydantic AI](https://www.anchorterminal.com/compare/guilds-run-vs-pydantic-ai.md)\n- [guilds.run vs TradingAgents](https://www.anchorterminal.com/compare/guilds-run-vs-tradingagents.md)\n- [LangGraph vs Pydantic AI](https://www.anchorterminal.com/compare/langgraph-vs-pydantic-ai.md)\n- [LangGraph vs TradingAgents](https://www.anchorterminal.com/compare/langgraph-vs-tradingagents.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 TradingAgents](https://www.anchorterminal.com/compare/microsoft-agent-framework-vs-tradingagents.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 TradingAgents](https://www.anchorterminal.com/compare/openai-agents-sdk-vs-tradingagents.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": "2026-10-25", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "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 TradingAgents", "url": "" } ], "description": "Pydantic AI scores 81.7 (A) to TradingAgents's 64.7 (B) for agent frameworks. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Pydantic AI A 81.7", "TradingAgents B 64.7", "scores" ], "h1": "Pydantic AI vs TradingAgents", "image": "https://www.anchorterminal.com/assets/og/compare-pydantic-ai-vs-tradingagents.png", "path": "/compare/pydantic-ai-vs-tradingagents", "published": "2026-10-01", "section": "tools", "title": "Pydantic AI vs TradingAgents for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-25", "url": "https://www.anchorterminal.com/compare/pydantic-ai-vs-tradingagents" }, "tokens": { "markdown": 2250, "slim": 630 }, "version": 1 }