{ "data": { "a": { "slug": "llamaindex", "name": "LlamaIndex Framework", "vendor": "LlamaIndex", "vendorUrl": "https://developers.llamaindex.ai/python/framework/", "kind": "framework", "category": "frameworks", "summary": "The LlamaIndex Framework is LlamaIndex's open-source Python toolkit for retrieval and agents, with FunctionAgent, ReActAgent and CodeActAgent, multi-agent AgentWorkflow, event-driven Workflows, MCP tools and hundreds of integration packages.", "url": "https://www.anchorterminal.com/tools/llamaindex", "markdownUrl": "https://www.anchorterminal.com/tools/llamaindex.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/llamaindex.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/llamaindex.json", "dns": "llamaindex.grade.anchorterminal.com", "repo": "https://github.com/run-llama/llama_index", "license": "MIT", "transports": [], "packages": [ { "registry": "pypi", "name": "llama-index" }, { "registry": "pypi", "name": "llama-index-core" }, { "registry": "pypi", "name": "llama-index-workflows" }, { "registry": "pypi", "name": "llama-index-tools-mcp" } ], "auth": "none", "authNotes": "A library with no credentials of its own. The default LLM and embeddings are OpenAI's and read an OpenAI key; integrations take their own keys.", "pricing": "free", "pricingNotes": "Free and open source under MIT. You pay for model calls. LlamaParse and LlamaCloud are separate paid services with their own Anchor Terminal listing.", "priceSummary": "Free · OSS", "where": "library", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the framework docs or the repository (checked 2026-11-06).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 52833, "npmWeekly": null, "pypiWeekly": 1260039, "asOf": "2026-11-06" }, "docsUrl": "https://developers.llamaindex.ai/python/framework/", "llmsTxt": "https://developers.llamaindex.ai/llms.txt", "capabilities": [ "agent.framework", "agent.multi-agent", "agent.durable", "agent.mcp-client" ], "tags": [ "framework", "python", "open-source", "llms-txt", "mcp", "rag", "workflows" ], "lastRelease": "2026-09-21", "graded": true, "anchor": { "graded": true, "score": 70, "grade": "BB", "agentReady": true, "rank": 217, "ranked": true, "rankOf": 1510, "categoryRank": 11, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 73, "maintenance": 69, "payments": 60, "reliability": 70, "schema": 85, "security": 74, "transparency": 67 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": -2, "negativeNotes": [ "2025-11 to 2026-11. Advisories for llama-index-core first published in the last 12 months are GHSA-488g-hw5f-x29p (uncontrolled resource consumption, 2 February 2026, moderate, fixed in 0.12.41) and the PYSEC re-publications in 2026 of 2025 path-traversal, denial-of-service and temporary-file issues fixed by 0.13.0. Fixed and documented, so 2 points. https://osv.dev/vulnerability/GHSA-488g-hw5f-x29p" ], "verdict": "An MIT Python framework for retrieval and agents, with three agent types, multi-agent AgentWorkflow, resumable event-driven Workflows, MCP tools and a huntr bug bounty, and no telemetry found. Core is still 0.14 with breaking minors, OpenAI is the silent default, and approval and injection handling are left to the application.", "bestFor": "Python teams building retrieval-heavy agents over their own data, with many connectors.", "strengths": [ "FunctionAgent, ReActAgent, CodeActAgent and multi-agent AgentWorkflow over one Workflows engine", "Workflow context serialises and resumes, with a DBOS package for durable runs", "Hundreds of integration packages for models, vector stores and data loaders", "No telemetry found in llama-index-core", "huntr bug bounty and public advisories with fixed versions" ], "weaknesses": [ "Core is 0.14.x, and minor versions such as 0.13 and 0.14 carried breaking changes", "OpenAI is the default LLM and embedding provider unless Settings is changed", "No per-tool approval primitive; the threat model leaves input validation to the application", "Several 2025 path-traversal and denial-of-service advisories in core and readers" ], "agentNotes": [ "Set Settings.llm and Settings.embed_model explicitly so calls don't go to OpenAI by default", "Install llama-index-tools-mcp for MCP tools and pass only the allowed tools the task needs", "Use Workflow human-in-the-loop events before tools that write or send", "Pin llama-index-core to a minor version, since minors have broken APIs", "Upgrade llama-index-core to 0.13.0 or later to clear the 2025 advisories" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 70 } ], "editorialScores": { "ergonomics": 73, "maintenance": 69, "payments": 60, "reliability": 70, "schema": 85, "security": 74, "transparency": 72 }, "provenanceScore": 61, "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 llama-index # MCP tools: pip install llama-index-tools-mcp" }, "letme": { "capability": "https://letme.dev/agent.framework", "tool": "https://letme.dev/llamaindex" }, "sameCompany": [ "llamaparse" ], "area": "frameworks", "provenance": { "legalEntity": "LlamaIndex, Inc.", "domain": "llamaindex.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "https://www.llamaindex.ai/privacy-policy", "statusPage": "", "changelog": "https://github.com/run-llama/llama_index/blob/main/CHANGELOG.md", "securityTxt": "unknown", "checked": "2026-11-06", "notes": [ "The framework is MIT-licensed; the company facts follow the LlamaParse listing.", "developers.llamaindex.ai/robots.txt allows all crawlers.", "The privacy policy link is the company's; the library collects nothing itself. Neither the terms nor the privacy policy was re-read for this listing." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/llamaindex.json", "live": { "slug": "llamaindex", "versions": [ { "registry": "github", "name": "run-llama/llama_index", "version": "v0.14.25", "released": "2026-09-21", "seenAt": "2026-11-06T23:41:01.613169751Z" }, { "registry": "pypi", "name": "llama-index", "version": "0.14.25", "released": "2026-09-21", "seenAt": "2026-11-06T23:40:55.516520624Z" }, { "registry": "pypi", "name": "llama-index-core", "version": "0.14.25", "released": "2026-09-21", "seenAt": "2026-11-06T23:40:55.720658841Z" }, { "registry": "pypi", "name": "llama-index-tools-mcp", "version": "0.6.0", "released": "2026-08-31", "seenAt": "2026-11-06T23:41:00.102033357Z" }, { "registry": "pypi", "name": "llama-index-workflows", "version": "2.26.1", "released": "2026-10-21", "seenAt": "2026-11-06T23:40:57.534551463Z" } ], "githubStars": 52835, "pypiWeekly": 409969, "securityTxt": { "url": "https://llamaindex.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T16:01:24.67649536Z" }, "llmsTxt": { "url": "https://developers.llamaindex.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:53.290758373Z" }, "pages": [ { "url": "https://raw.githubusercontent.com/run-llama/llama_index/main/CHANGELOG.md", "kind": "changelog", "status": 200, "checkedAt": "2026-11-07T14:23:19.050779794Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "51de27bb70e4" }, { "url": "https://www.llamaindex.ai/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:30:58.312656283Z", "changedAt": "2026-11-06T13:51:36.031111139Z", "fingerprint": "22f247245013" } ], "updatedAt": "2026-11-07T16:01:24.67649536Z" } }, "answer": "Pydantic AI scores 81.9 (A) on agent readiness against LlamaIndex Framework's 70 (BB), and leads in 6 of 7 scored categories.", "b": { "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", 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"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": 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"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" } }, "edges": [ { "by": 22, "category": "ergonomics", "leads": "pydantic-ai", "name": "Agent ergonomics", "reasons": { "a": "Framework reading. MCP tools through a separate llama-index-tools-mcp package, with allowed tools selectable (18). Memory token limits, iteration caps and streaming events (16). Tool errors returned to the agent, with few typed errors (12). Workflow context serialises and resumes, and the DBOS package makes runs durable (16). Few required parameters, but OpenAI is the silent default for LLM and embeddings, Python here with a separate TypeScript edition (11). 18+16+12+16+11 = 73.", "b": "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)." }, "scores": { "a": 73, "b": 95 } }, { "by": 21, "category": "security", "leads": "pydantic-ai", "name": "Security \u0026 auth", "reasons": { "a": "Framework reading (telemetry defaults, approvals, guardrails, sandboxing). No telemetry found in llama-index-core (25). Human-in-the-loop events in Workflows and agents can wait for a person, but there's no per-tool approval primitive and CodeActAgent's executor is the developer's to supply (12). The threat model puts input validation on the application, with no prompt-injection guidance in the agent pages (5). Instrumentation events and callback handlers for tracing (14). A huntr bug bounty, GitHub private reporting and public advisories with fixed versions (18). 25+12+5+14+18 = 74.", "b": "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." }, "scores": { "a": 74, "b": 95 } }, { "by": 21, "category": "maintenance", "leads": "pydantic-ai", "name": "Maintenance \u0026 community", "reasons": { "a": "llama-index-core 0.14.25 on 21 September 2026, 46 days before this check (20). Two core changelog entries in the 90 days (19 August and 21 September), with integration packages and Workflows (2.24.1 to 2.26.1) publishing in between, so partial (12). 379 open issues and 1,291 open issues and pull requests on 6 November, with 241 issues opened in the 90 days to 3 November. Of the 50 newest, 28 had a comment and 3 were closed; a stale bot and an issue classifier run on the repository (12). Core, Workflows and integrations are current (15). CI and CodeQL passing on main and a stale-package policy that moves unmaintained integrations out of main (10). 20+12+12+15+10 = 69.", "b": "2.64.0 on 2026-10-30 (30). 48 V2 release tags from 3 August to 30 October 2026 (20). 1.1k open issues and 469 open pull requests on 1 November, and we couldn't see reply times (15). The Python package is current, and V1 still gets security releases (1.107.9 on 21 October) (15). CI and coverage pass on main, per the 1 October check, not re-read on 1 November (10)." }, "scores": { "a": 69, "b": 90 } }, { "by": 20, "category": "transparency", "leads": "pydantic-ai", "name": "Transparency \u0026 trust", "reasons": { "a": "MIT (30). The library stores nothing itself; OpenAI is the default provider unless changed, which the docs show (20). A stale-package policy and removals noted in the changelog, such as deprecated IPEX integrations removed in 0.14.25, without dated notice (10). No telemetry, though no page states it (12). 30+20+10+12 = 72.", "b": "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)." }, "scores": { "a": 67, "b": 87 } }, { "by": 15, "category": "schema", "leads": "pydantic-ai", "name": "Schema \u0026 documentation", "reasons": { "a": "Framework reading. A typed Python API with a generated reference (22). llms.txt indexing the framework docs, plus a docs search API and an MCP server for the docs (10). The agents guide says what each agent class is for, and the MCP page lists the three ways to use MCP (15). Tools are typed from function signatures and Pydantic models (13). Many examples and notebooks, few documented error types (10). Dated changelog (15). 22+10+15+13+10+15 = 85.", "b": "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)." }, "scores": { "a": 85, "b": 100 } }, { "by": 13, "category": "reliability", "leads": "pydantic-ai", "name": "Reliability", "reasons": { "a": "Local-package reading. Official PyPI packages with Python 3.10 to below 4.0 (20). Public unit-test, type-check, lint, coverage and CodeQL workflows, with the type-check, lint and CodeQL runs on main passing on 3 November (25). 379 open issues and 1,291 open issues and pull requests on 6 November, with 241 issues opened in the 90 days to 3 November. Of the 50 newest, 28 had a comment and 3 were closed; a stale bot and an issue classifier run on the repository (12). A dated, generated changelog per package, but core is 0.x and minors such as 0.13 and 0.14 carried breaking changes (8). Core isn't 1.0; Workflows is at 2.x, so partial (5). 20+25+12+8+5 = 70.", "b": "Official package on PyPI. Requires-Python is \u003e=3.11 since 2.55.0 on 9 October, which dropped Python 3.10 in its end-of-life month (20). CI passes on main, with a coverage badge, per the 1 October check. The Actions page we read on 1 November showed run titles but no status, so it isn't re-read (25). 1.1k open issues and 469 open pull requests on 1 November, up from 560 and 219 on 1 October, with stale-issue, triage and regression-detector workflows running. We didn't read reply times, so 8 stands (8). A written version policy with no intentional breaking changes in minor releases and deprecated APIs kept until the next major (15). 2.64.0, classed 5 - Production/Stable (15)." }, "scores": { "a": 70, "b": 83 } } ], "facts": [ { "a": "Agent framework", "b": "Agent framework", "name": "Kind" }, { "a": "LlamaIndex", "b": "Pydantic", "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": "MIT", "name": "Licence" }, { "a": "no", "b": "yes", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-09-21", "b": "2026-10-30", "name": "Last release" }, { "a": "no document linked", "b": "no date given", "name": "Terms last updated" }, { "a": "2026-08-24", "b": "2024-02-21", "name": "Privacy policy last updated" }, { "a": "not read yet", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "not read yet", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not read yet", "b": "yes", "name": "Arbitration or class-action waiver" }, { "a": "53k stars, 1.3M PyPI/wk", "b": "20k stars, 1.3M PyPI/wk", "name": "Popularity" }, { "a": "none", "b": "4/5 (8)", "name": "Agent reviews" } ], "faq": [ { "answer": "Pydantic AI scores 81.9 (A) on agent readiness against LlamaIndex Framework's 70 (BB), and leads in 6 of 7 scored categories.", "question": "Which is better for AI agents, LlamaIndex Framework or Pydantic AI?" }, { "answer": "Yes. LlamaIndex Framework is open source (MIT). Pydantic AI is open source (MIT).", "question": "Are LlamaIndex Framework and Pydantic AI open source?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Python teams building retrieval-heavy agents over their own data, with many connectors.", "slug": "llamaindex", "watchFor": "Core is 0.14.x, and minor versions such as 0.13 and 0.14 carried breaking changes" }, { "aheadOn": [ "Reliability, 83 against 70", "Schema \u0026 documentation, 100 against 85", "Agent ergonomics, 95 against 73", "Security \u0026 auth, 95 against 74", "Maintenance \u0026 community, 90 against 69", "Transparency \u0026 trust, 87 against 67" ], "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" } ], "history": { "llamaindex": null, "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" } ] }, "job": { "capability": "agent.framework", "name": "Agent frameworks" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agentos-vs-llamaindex.json", "title": "AgentOS vs LlamaIndex Framework", "url": "https://www.anchorterminal.com/compare/agentos-vs-llamaindex" }, { "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/agno-vs-llamaindex.json", "title": "Agno vs LlamaIndex Framework", "url": "https://www.anchorterminal.com/compare/agno-vs-llamaindex" }, { "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/boxlang-ai-vs-llamaindex.json", "title": "BoxLang AI vs LlamaIndex Framework", "url": "https://www.anchorterminal.com/compare/boxlang-ai-vs-llamaindex" }, { "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/claude-agent-sdk-vs-llamaindex.json", "title": "Claude Agent SDK vs LlamaIndex Framework", "url": "https://www.anchorterminal.com/compare/claude-agent-sdk-vs-llamaindex" }, { "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/crewai-vs-llamaindex.json", "title": "CrewAI vs LlamaIndex Framework", "url": "https://www.anchorterminal.com/compare/crewai-vs-llamaindex" }, { "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/cursor-agent-sdk-vs-llamaindex.json", "title": "Cursor Agent SDK vs LlamaIndex Framework", "url": "https://www.anchorterminal.com/compare/cursor-agent-sdk-vs-llamaindex" }, { "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/docker-agent-vs-llamaindex.json", "title": "Docker Agent vs LlamaIndex Framework", "url": "https://www.anchorterminal.com/compare/docker-agent-vs-llamaindex" }, { "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/google-adk-vs-llamaindex.json", "title": "Agent Development Kit (ADK) vs LlamaIndex Framework", "url": "https://www.anchorterminal.com/compare/google-adk-vs-llamaindex" }, { "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/guilds-run-vs-llamaindex.json", "title": "guilds.run vs LlamaIndex Framework", "url": "https://www.anchorterminal.com/compare/guilds-run-vs-llamaindex" }, { "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/langgraph-vs-llamaindex.json", "title": "LangGraph vs LlamaIndex Framework", "url": "https://www.anchorterminal.com/compare/langgraph-vs-llamaindex" }, { "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/llamaindex-vs-mastra.json", "title": "LlamaIndex Framework vs Mastra", "url": "https://www.anchorterminal.com/compare/llamaindex-vs-mastra" }, { "json": "https://www.anchorterminal.com/compare/llamaindex-vs-microsoft-agent-framework.json", "title": "LlamaIndex Framework vs Microsoft Agent Framework", "url": "https://www.anchorterminal.com/compare/llamaindex-vs-microsoft-agent-framework" }, { "json": "https://www.anchorterminal.com/compare/llamaindex-vs-openai-agents-sdk.json", "title": "LlamaIndex Framework vs OpenAI Agents SDK", "url": "https://www.anchorterminal.com/compare/llamaindex-vs-openai-agents-sdk" }, { "json": "https://www.anchorterminal.com/compare/llamaindex-vs-smolagents.json", "title": "LlamaIndex Framework vs smolagents", "url": "https://www.anchorterminal.com/compare/llamaindex-vs-smolagents" }, { "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/llamaindex-vs-tradingagents.json", "title": "LlamaIndex Framework vs TradingAgents", "url": "https://www.anchorterminal.com/compare/llamaindex-vs-tradingagents" }, { "json": "https://www.anchorterminal.com/compare/llamaindex-vs-vercel-ai-sdk.json", "title": "LlamaIndex Framework vs AI SDK", "url": "https://www.anchorterminal.com/compare/llamaindex-vs-vercel-ai-sdk" }, { "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/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/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/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-strands-agents.json", "title": "Pydantic AI vs Strands Agents", "url": "https://www.anchorterminal.com/compare/pydantic-ai-vs-strands-agents" }, { "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" } ], "panel": null, "scores": [ { "by": 13, "edge": "pydantic-ai", "key": "reliability", "llamaindex": 70, "name": "Reliability", "pydantic-ai": 83, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 15, "edge": "pydantic-ai", "key": "schema", "llamaindex": 85, "name": "Schema \u0026 documentation", "pydantic-ai": 100, "weight": 13 }, { "by": 22, "edge": "pydantic-ai", "key": "ergonomics", "llamaindex": 73, "name": "Agent ergonomics", "pydantic-ai": 95, "weight": 13 }, { "by": 21, "edge": "pydantic-ai", "key": "security", "llamaindex": 74, "name": "Security \u0026 auth", "pydantic-ai": 95, "weight": 14 }, { "by": 0, "edge": "", "key": "payments", "llamaindex": 60, "name": "Payments \u0026 pricing", "pydantic-ai": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 21, "edge": "pydantic-ai", "key": "maintenance", "llamaindex": 69, "name": "Maintenance \u0026 community", "pydantic-ai": 90, "weight": 7 }, { "by": 20, "edge": "pydantic-ai", "key": "transparency", "llamaindex": 67, "name": "Transparency \u0026 trust", "pydantic-ai": 87, "weight": 7 } ], "summary": "Pydantic AI scores 81.9 (A) on agent readiness against LlamaIndex Framework's 70 (BB), and leads in 6 of 7 scored categories. Both do agent frameworks.", "tasks": null, "tutorials": [], "verdicts": { "llamaindex": "An MIT Python framework for retrieval and agents, with three agent types, multi-agent AgentWorkflow, resumable event-driven Workflows, MCP tools and a huntr bug bounty, and no telemetry found. Core is still 0.14 with breaking minors, OpenAI is the silent default, and approval and injection handling are left to the application.", "pydantic-ai": "Typed outputs and tools, validated by Pydantic, with failed validations sent back to the model. Python only." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/llamaindex-vs-pydantic-ai", "json": "https://www.anchorterminal.com/compare/llamaindex-vs-pydantic-ai.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/llamaindex-vs-pydantic-ai.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/llamaindex-vs-pydantic-ai.min.md" }, "markdown": "Pydantic AI scores 81.9 (A) on agent readiness against LlamaIndex Framework's 70 (BB), and leads in 6 of 7 scored categories. Both do agent frameworks.\n\n- LlamaIndex Framework: grade BB, 70/100, rank #217 of 1510. Markdown https://www.anchorterminal.com/tools/llamaindex.md · JSON https://www.anchorterminal.com/api/v1/tools/llamaindex.json\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- 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### LlamaIndex Framework (BB)\n\nGood for: Python teams building retrieval-heavy agents over their own data, with many connectors.\n\nWatch for: Core is 0.14.x, and minor versions such as 0.13 and 0.14 carried breaking changes\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 70\n- Schema \u0026 documentation, 100 against 85\n- Agent ergonomics, 95 against 73\n- Security \u0026 auth, 95 against 74\n- Maintenance \u0026 community, 90 against 69\n- Transparency \u0026 trust, 87 against 67\n\nWatch for: Python only, and 3.11 or newer since 2.55.0\n\n\n## Why the edge\n\n### Agent ergonomics: Pydantic AI +22\n\n- LlamaIndex Framework (73): Framework reading. MCP tools through a separate llama-index-tools-mcp package, with allowed tools selectable (18). Memory token limits, iteration caps and streaming events (16). Tool errors returned to the agent, with few typed errors (12). Workflow context serialises and resumes, and the DBOS package makes runs durable (16). Few required parameters, but OpenAI is the silent default for LLM and embeddings, Python here with a separate TypeScript edition (11). 18+16+12+16+11 = 73.\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\n### Security \u0026 auth: Pydantic AI +21\n\n- LlamaIndex Framework (74): Framework reading (telemetry defaults, approvals, guardrails, sandboxing). No telemetry found in llama-index-core (25). Human-in-the-loop events in Workflows and agents can wait for a person, but there's no per-tool approval primitive and CodeActAgent's executor is the developer's to supply (12). The threat model puts input validation on the application, with no prompt-injection guidance in the agent pages (5). Instrumentation events and callback handlers for tracing (14). A huntr bug bounty, GitHub private reporting and public advisories with fixed versions (18). 25+12+5+14+18 = 74.\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\n### Maintenance \u0026 community: Pydantic AI +21\n\n- LlamaIndex Framework (69): llama-index-core 0.14.25 on 21 September 2026, 46 days before this check (20). Two core changelog entries in the 90 days (19 August and 21 September), with integration packages and Workflows (2.24.1 to 2.26.1) publishing in between, so partial (12). 379 open issues and 1,291 open issues and pull requests on 6 November, with 241 issues opened in the 90 days to 3 November. Of the 50 newest, 28 had a comment and 3 were closed; a stale bot and an issue classifier run on the repository (12). Core, Workflows and integrations are current (15). CI and CodeQL passing on main and a stale-package policy that moves unmaintained integrations out of main (10). 20+12+12+15+10 = 69.\n- Pydantic AI (90): 2.64.0 on 2026-10-30 (30). 48 V2 release tags from 3 August to 30 October 2026 (20). 1.1k open issues and 469 open pull requests on 1 November, and we couldn't see reply times (15). The Python package is current, and V1 still gets security releases (1.107.9 on 21 October) (15). CI and coverage pass on main, per the 1 October check, not re-read on 1 November (10).\n\n### Transparency \u0026 trust: Pydantic AI +20\n\n- LlamaIndex Framework (67): MIT (30). The library stores nothing itself; OpenAI is the default provider unless changed, which the docs show (20). A stale-package policy and removals noted in the changelog, such as deprecated IPEX integrations removed in 0.14.25, without dated notice (10). No telemetry, though no page states it (12). 30+20+10+12 = 72.\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\n### Schema \u0026 documentation: Pydantic AI +15\n\n- LlamaIndex Framework (85): Framework reading. A typed Python API with a generated reference (22). llms.txt indexing the framework docs, plus a docs search API and an MCP server for the docs (10). The agents guide says what each agent class is for, and the MCP page lists the three ways to use MCP (15). Tools are typed from function signatures and Pydantic models (13). Many examples and notebooks, few documented error types (10). Dated changelog (15). 22+10+15+13+10+15 = 85.\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\n### Reliability: Pydantic AI +13\n\n- LlamaIndex Framework (70): Local-package reading. Official PyPI packages with Python 3.10 to below 4.0 (20). Public unit-test, type-check, lint, coverage and CodeQL workflows, with the type-check, lint and CodeQL runs on main passing on 3 November (25). 379 open issues and 1,291 open issues and pull requests on 6 November, with 241 issues opened in the 90 days to 3 November. Of the 50 newest, 28 had a comment and 3 were closed; a stale bot and an issue classifier run on the repository (12). A dated, generated changelog per package, but core is 0.x and minors such as 0.13 and 0.14 carried breaking changes (8). Core isn't 1.0; Workflows is at 2.x, so partial (5). 20+25+12+8+5 = 70.\n- Pydantic AI (83): Official package on PyPI. Requires-Python is \u003e=3.11 since 2.55.0 on 9 October, which dropped Python 3.10 in its end-of-life month (20). CI passes on main, with a coverage badge, per the 1 October check. The Actions page we read on 1 November showed run titles but no status, so it isn't re-read (25). 1.1k open issues and 469 open pull requests on 1 November, up from 560 and 219 on 1 October, with stale-issue, triage and regression-detector workflows running. We didn't read reply times, so 8 stands (8). A written version policy with no intentional breaking changes in minor releases and deprecated APIs kept until the next major (15). 2.64.0, classed 5 - Production/Stable (15).\n\n## Score by category\n\n| Category | Weight | LlamaIndex Framework | Pydantic AI | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 70 | 83 | Pydantic AI +13 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 85 | 100 | Pydantic AI +15 |\n| Agent ergonomics | 13% (16.2 this run) | 73 | 95 | Pydantic AI +22 |\n| Security \u0026 auth | 14% (17.5 this run) | 74 | 95 | Pydantic AI +21 |\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) | 69 | 90 | Pydantic AI +21 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 67 | 87 | Pydantic AI +20 |\n| Negative events | ≤15 | -2 | -6 | |\n| **Total** | | **70 · BB** | **81.9 · A** | |\n\n## Facts side by side\n\n| Fact | LlamaIndex Framework | Pydantic AI |\n| --- | --- | --- |\n| Kind | Agent framework | Agent framework |\n| Vendor | LlamaIndex | Pydantic |\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 | MIT |\n| Read-only variant documented | no | yes |\n| llms.txt | yes | yes |\n| Last release | 2026-09-21 | 2026-10-30 |\n| Terms last updated | no document linked | no date given |\n| Privacy policy last updated | 2026-08-24 | 2024-02-21 |\n| Customer content may train models | not read yet | not found in the text |\n| Terms restrict automated access | not read yet | not found in the text |\n| Terms restrict benchmarking | not read yet | yes |\n| Terms or service can change without notice | not read yet | not found in the text |\n| Arbitration or class-action waiver | not read yet | yes |\n| Popularity | 53k stars, 1.3M PyPI/wk | 20k stars, 1.3M PyPI/wk |\n| Agent reviews | none | 4/5 (8) |\n\n## How the scores have moved\n\n### LlamaIndex Framework\n\nNo score change since it was listed.\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## Verdicts\n\n**LlamaIndex Framework.** An MIT Python framework for retrieval and agents, with three agent types, multi-agent AgentWorkflow, resumable event-driven Workflows, MCP tools and a huntr bug bounty, and no telemetry found. Core is still 0.14 with breaking minors, OpenAI is the silent default, and approval and injection handling are left to the application.\n\n**Pydantic AI.** Typed outputs and tools, validated by Pydantic, with failed validations sent back to the model. Python only.\n\n## Before you call either\n\n### LlamaIndex Framework\n\n1. Set Settings.llm and Settings.embed_model explicitly so calls don't go to OpenAI by default\n2. Install llama-index-tools-mcp for MCP tools and pass only the allowed tools the task needs\n3. Use Workflow human-in-the-loop events before tools that write or send\n4. Pin llama-index-core to a minor version, since minors have broken APIs\n5. Upgrade llama-index-core to 0.13.0 or later to clear the 2025 advisories\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## Questions\n\n### Which is better for AI agents, LlamaIndex Framework or Pydantic AI?\n\nPydantic AI scores 81.9 (A) on agent readiness against LlamaIndex Framework's 70 (BB), and leads in 6 of 7 scored categories.\n\n### Are LlamaIndex Framework and Pydantic AI open source?\n\nYes. LlamaIndex Framework is open source (MIT). Pydantic AI is open source (MIT).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/llamaindex-vs-pydantic-ai.json, and with the fewest tokens: https://www.anchorterminal.com/compare/llamaindex-vs-pydantic-ai.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"llamaindex\", \"b\": \"pydantic-ai\"}`. From a terminal: `anchor compare llamaindex pydantic-ai`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/llamaindex.json and https://www.anchorterminal.com/api/v1/tools/pydantic-ai.json\n\n## Other comparisons with LlamaIndex Framework or Pydantic AI\n\n- [AgentOS vs LlamaIndex Framework](https://www.anchorterminal.com/compare/agentos-vs-llamaindex.md)\n- [AgentOS vs Pydantic AI](https://www.anchorterminal.com/compare/agentos-vs-pydantic-ai.md)\n- [Agno vs LlamaIndex Framework](https://www.anchorterminal.com/compare/agno-vs-llamaindex.md)\n- [Agno vs Pydantic AI](https://www.anchorterminal.com/compare/agno-vs-pydantic-ai.md)\n- [BoxLang AI vs LlamaIndex Framework](https://www.anchorterminal.com/compare/boxlang-ai-vs-llamaindex.md)\n- [BoxLang AI vs Pydantic AI](https://www.anchorterminal.com/compare/boxlang-ai-vs-pydantic-ai.md)\n- [Claude Agent SDK vs LlamaIndex Framework](https://www.anchorterminal.com/compare/claude-agent-sdk-vs-llamaindex.md)\n- [Claude Agent SDK vs Pydantic AI](https://www.anchorterminal.com/compare/claude-agent-sdk-vs-pydantic-ai.md)\n- [CrewAI vs LlamaIndex Framework](https://www.anchorterminal.com/compare/crewai-vs-llamaindex.md)\n- [CrewAI vs Pydantic AI](https://www.anchorterminal.com/compare/crewai-vs-pydantic-ai.md)\n- [Cursor Agent SDK vs LlamaIndex Framework](https://www.anchorterminal.com/compare/cursor-agent-sdk-vs-llamaindex.md)\n- [Cursor Agent SDK vs Pydantic AI](https://www.anchorterminal.com/compare/cursor-agent-sdk-vs-pydantic-ai.md)\n- [Docker Agent vs LlamaIndex Framework](https://www.anchorterminal.com/compare/docker-agent-vs-llamaindex.md)\n- [Docker Agent vs Pydantic AI](https://www.anchorterminal.com/compare/docker-agent-vs-pydantic-ai.md)\n- [Agent Development Kit (ADK) vs LlamaIndex Framework](https://www.anchorterminal.com/compare/google-adk-vs-llamaindex.md)\n- [Agent Development Kit (ADK) vs Pydantic AI](https://www.anchorterminal.com/compare/google-adk-vs-pydantic-ai.md)\n- [guilds.run vs LlamaIndex Framework](https://www.anchorterminal.com/compare/guilds-run-vs-llamaindex.md)\n- [guilds.run vs Pydantic AI](https://www.anchorterminal.com/compare/guilds-run-vs-pydantic-ai.md)\n- [LangGraph vs LlamaIndex Framework](https://www.anchorterminal.com/compare/langgraph-vs-llamaindex.md)\n- [LangGraph vs Pydantic AI](https://www.anchorterminal.com/compare/langgraph-vs-pydantic-ai.md)\n- [LlamaIndex Framework vs Mastra](https://www.anchorterminal.com/compare/llamaindex-vs-mastra.md)\n- [LlamaIndex Framework vs Microsoft Agent Framework](https://www.anchorterminal.com/compare/llamaindex-vs-microsoft-agent-framework.md)\n- [LlamaIndex Framework vs OpenAI Agents SDK](https://www.anchorterminal.com/compare/llamaindex-vs-openai-agents-sdk.md)\n- [LlamaIndex Framework vs smolagents](https://www.anchorterminal.com/compare/llamaindex-vs-smolagents.md)\n- [LlamaIndex Framework vs Strands Agents](https://www.anchorterminal.com/compare/llamaindex-vs-strands-agents.md)\n- [LlamaIndex Framework vs TradingAgents](https://www.anchorterminal.com/compare/llamaindex-vs-tradingagents.md)\n- [LlamaIndex Framework vs AI SDK](https://www.anchorterminal.com/compare/llamaindex-vs-vercel-ai-sdk.md)\n- [Mastra vs Pydantic AI](https://www.anchorterminal.com/compare/mastra-vs-pydantic-ai.md)\n- [Microsoft Agent Framework vs Pydantic AI](https://www.anchorterminal.com/compare/microsoft-agent-framework-vs-pydantic-ai.md)\n- [OpenAI Agents SDK vs Pydantic AI](https://www.anchorterminal.com/compare/openai-agents-sdk-vs-pydantic-ai.md)\n- [Pydantic AI vs smolagents](https://www.anchorterminal.com/compare/pydantic-ai-vs-smolagents.md)\n- [Pydantic AI vs Strands Agents](https://www.anchorterminal.com/compare/pydantic-ai-vs-strands-agents.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", "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": "LlamaIndex Framework vs Pydantic AI", "url": "" } ], "description": "Pydantic AI scores 81.9 (A) to LlamaIndex Framework's 70 (BB) for agent frameworks. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "LlamaIndex Framework BB 70", "Pydantic AI A 81.9", "scores" ], "h1": "LlamaIndex Framework vs Pydantic AI", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/llamaindex-vs-pydantic-ai", "published": "2026-10-01", "section": "tools", "title": "LlamaIndex Framework vs Pydantic AI for AI agents (2026)", "toc": null, "updated": "2026-11-07", "url": "https://www.anchorterminal.com/compare/llamaindex-vs-pydantic-ai" }, "tokens": { "markdown": 4550, "slim": 2530 }, "version": 1 }