{ "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": "LlamaIndex Framework scores 70 (BB) on agent readiness against TradingAgents's 61.4 (C), and leads in 4 of 7 scored categories. TradingAgents leads on maintenance \u0026 community.", "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", "dns": "tradingagents.grade.anchorterminal.com", "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": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-02" }, "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": "2026-10-27", "graded": true, "anchor": { "graded": true, "score": 61.4, "grade": "C", "agentReady": false, "rank": 617, "ranked": true, "rankOf": 1510, "categoryRank": 16, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 61, "maintenance": 78, "payments": 60, "reliability": 52, "schema": 61, "security": 62, "transparency": 68 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-02" }, "negative": 0, "verdict": "An Apache 2.0 LangGraph framework at 0.7.1 with seven 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 seven releases between 31 August and 27 October 2026 and \"Upgrading from\" notes in a dated changelog", "CI runs about 1,009 test functions on Python 3.11 to 3.14, a clean-install import check and Ruff on every push and pull request", "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": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61.4 } ], "editorialScores": { "ergonomics": 61, "maintenance": 78, "payments": 60, "reliability": 52, "schema": 61, "security": 62, "transparency": 73 }, "provenanceScore": 62, "checked": { "categories": { "ergonomics": "2026-11-02", "maintenance": "2026-11-02", "payments": "2026-11-02", "reliability": "2026-11-02", "schema": "2026-11-02", "security": "2026-11-02", "transparency": "2026-11-02" }, "last": "2026-11-02", "lastMoved": "2026-11-02", "oldest": "2026-11-02", "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": "2026-11-02", "notes": [ "The terms and privacy policy, both effective 31 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 answered 404 on 2 November 2026 (on 25 October it returned the site's HTML page). 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.", "tauric.ai's robots.txt carries Content-Signal: ai-train=yes, search=yes, ai-input=yes." ], "score": 62 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/tradingagents.json", "live": { "slug": "tradingagents", "versions": [ { "registry": "github", "name": "TauricResearch/TradingAgents", "version": "v0.7.1", "released": "2026-10-27", "seenAt": "2026-11-07T00:12:38.886880905Z" } ], "githubStars": 121015, "securityTxt": { "url": "https://tauric.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T16:01:04.508537721Z" }, "domain": { "domain": "tauric.ai", "registered": "2025-03-26", "source": "https://rdap.identitydigital.services/rdap/domain/tauric.ai", "checkedAt": "2026-11-01T14:17:50.711505753Z" }, "pages": [ { "url": "https://raw.githubusercontent.com/TauricResearch/TradingAgents/main/CHANGELOG.md", "kind": "deprecations", "status": 304, "checkedAt": "2026-11-07T14:19:27.182748345Z", "changedAt": "2026-10-28T04:42:43.2803853Z", "fingerprint": "17477e48440b" }, { "url": "https://tauric.ai/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:21:51.458749311Z", "changedAt": "2026-11-06T13:43:19.647848599Z", "fingerprint": "d55215742695" }, { "url": "https://tauric.ai/terms", "kind": "terms", "status": 200, "checkedAt": "2026-11-07T14:21:53.696550753Z", "changedAt": "2026-11-06T13:43:21.904160871Z", "fingerprint": "9a81673b9bce" } ], "github": { "repo": "tauricresearch/tradingagents", "checkedAt": "2026-11-02T08:27:02.133359759Z", "stars": 119716, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-27T08:43:04Z", "openIssues": 38, "openPRs": 68, "issuesOpened90d": 100, "issuesClosed90d": 228, "prsMerged90d": 15, "ci": { "branch": "main", "runs": 43, "success": 43, "failure": 0, "other": 0, "latest": "success", "latestAt": "2026-10-27T08:39:50Z" }, "replies": { "sampled": 25, "unanswered": 3, "medianDays": 4.6 } }, "updatedAt": "2026-11-07T16:01:04.508537721Z" } }, "edges": [ { "by": 24, "category": "schema", "leads": "llamaindex", "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": "Framework reading. No API reference or docs site. Typed interfaces exist in the source, Pydantic models for the research manager's, trader's and portfolio manager's outputs and Annotated signatures on the twelve data tools, with no py.typed marker (10 of 25). The README and changelog are Markdown in the repository; no llms.txt (4 of 10). The README states limits plainly, that runs vary, which statements past-dated runs withhold and why, and that it is a research tool, not advice (15 of 20). Configuration is a plain dict of string keys, though environment overrides are coerced to the default's type and fail loudly on a bad value (8 of 15). Runnable Python and CLI examples throughout the README; the vendor error hierarchy is documented only in source docstrings (9 of 15). Dated changelog for every release from 0.1.0 with upgrade notes (15)." }, "scores": { "a": 85, "b": 61 } }, { "by": 18, "category": "reliability", "leads": "llamaindex", "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": "Read with the local-software lines, because the owner runs it. Python 3.11 or later is stated and CI tests 3.11 to 3.14, but the project publishes no PyPI package. No workflow uploads one, and the README installs from a clone or Docker Compose. From 0.7.0 a release workflow builds a wheel and source archive from each tag and attaches them to the GitHub release (12 of 20). Public CI on every push and pull request runs the test suite (about 1,009 test functions at v0.7.1) on four Python versions and once in America/New_York, a clean-install import check and Ruff. The 25 October check read the pass state from shields.io, a third-party copy of GitHub data this run doesn't use, so that reading is struck, and the pass state on main is unread (15, as berth and agentvfs). Issue counts came from the same source and are struck too. On the clone, 0.7.1 (27 October) fixes 22 numbered issues, 17 of them above #1518, the newest fixed in 0.7.0 on 16 October, and credits 15 outside contributors; open crash or regression issues and reply times are unread (15, as berth on the same pattern). The changelog follows Keep a Changelog and Semantic Versioning and lists breaking changes under \"Upgrading from\", but 0.5.1, a patch release, moved import paths and removed classes (10 of 15). Version 0.7.1 with no stability statement (0). 12+15+15+10+0 = 52." }, "scores": { "a": 70, "b": 52 } }, { "by": 12, "category": "ergonomics", "leads": "llamaindex", "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": "Framework reading, scored on what an outside agent needs to call it. No MCP client or server, so it can't be attached as tools; a run is four lines of Python (`TradingAgentsGraph(config=...).propagate(ticker, date)`) or one headless CLI command with flags (10 of 25). Analyst selection, debate and risk rounds (default 1), `max_tool_rounds` (20), `max_recur_limit` (100), article limits and `--no-html` size a run (14 of 20). A typed vendor error hierarchy separates unavailable from absent data, the headless CLI names the missing flag before a run starts, and an unparseable decision returns REVIEW rather than Hold (15 of 20). Checkpoint resume is opt-in, a backtest rerun with the same `run_id` skips finished cells, and settlement writes each decision once; nothing writes to an outside system (15 of 20). Sensible defaults, but the default provider is OpenAI's GPT-6 models with a key, and Python only (7 of 15)." }, "scores": { "a": 73, "b": 61 } }, { "by": 12, "category": "security", "leads": "llamaindex", "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": "Framework reading. 30 for what leaves the machine and how credentials are held, 20 for least privilege and approvals, 15 for prompt injection, 15 for audit and 20 for the security programme. Keys are read from the environment, and the package loads the nearest .env on import by walking up from the working directory. The ChatGPT sign-in uses PKCE and dynamic client registration, keeps tokens in a 0600 file and revokes them on logout. The only calls of its own are the interactive CLI's daily release and notice check, with no run data, off without a terminal or with TRADINGAGENTS_NO_UPDATE_CHECK=1. SEC EDGAR gets a placeholder contact address unless one is set (24 of 30). It places no orders and holds no broker credentials, the data tools only read, and every ticker is validated before it is used in a file path. No approval hooks, which nothing in it needs (14 of 20). News, Reddit and StockTwits text goes into prompts. The source names prompt injection as the reason for path validation, but no guidance on injection was found in the README (5 of 15). Saved reports record the run's settings and each agent's report, the memory log keeps every decision, and `debug=True` streams the graph (11 of 15). Security reports go to GitHub private vulnerability reporting per the issue template, and the changelog has Security sections for a langchain-core patch and a dependency removed over CVE-2026-22218. No SECURITY.md, no security.txt on tauric.ai (404 on 2 November), no bounty, and the repository's advisories page wasn't read (8 of 20). 24+14+5+11+8 = 62." }, "scores": { "a": 74, "b": 62 } }, { "by": 9, "category": "maintenance", "leads": "tradingagents", "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": "0.7.1 on 27 October 2026 (30). Seven releases in the 90 days to 2 November, 0.4.0 (31 August), 0.5.0, 0.5.1, 0.5.2, 0.6.0, 0.7.0 and 0.7.1 (20). The earlier issue and pull request counts came from shields.io and are struck. On the clone, fixes cite issue numbers and each release credits the people who reported or fixed them (15 outside contributors in 0.7.1), but 251 of the 258 commits since 4 August are by one maintainer's accounts and reply times couldn't be read (13 of 25, as pipecat on clone evidence). No PyPI package; versions ship as GitHub releases with a wheel since 0.7.0 (8 of 15). CI with tests, lint and a clean-install check; dependencies set lower bounds only and no Dependabot configuration is in the repository (7 of 10). 30+20+13+8+7 = 78." }, "scores": { "a": 69, "b": 78 } } ], "facts": [ { "a": "Agent framework", "b": "Agent framework", "name": "Kind" }, { "a": "LlamaIndex", "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": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-09-21", "b": "2026-10-27", "name": "Last release" }, { "a": "no document linked", "b": "2026-10-23", "name": "Terms last updated" }, { "a": "2026-08-24", "b": "2026-10-23", "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": "not found in the text", "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": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "53k stars, 1.3M PyPI/wk", "b": "none", "name": "Popularity" } ], "faq": [ { "answer": "LlamaIndex Framework scores 70 (BB) on agent readiness against TradingAgents's 61.4 (C), and leads in 4 of 7 scored categories. TradingAgents leads on maintenance \u0026 community.", "question": "Which is better for AI agents, LlamaIndex Framework or TradingAgents?" }, { "answer": "Yes. LlamaIndex Framework is open source (MIT). TradingAgents is open source (Apache-2.0).", "question": "Are LlamaIndex Framework and TradingAgents open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 70 against 52", "Schema \u0026 documentation, 85 against 61", "Agent ergonomics, 73 against 61", "Security \u0026 auth, 74 against 62" ], "also": [ "Agent-ready, a grade of BB or better", "No key needed to call it" ], "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": [ "Maintenance \u0026 community, 78 against 69" ], "also": null, "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" } ], "history": { "llamaindex": null, "tradingagents": [ { "causes": [ "correction" ], "date": "2026-11-02", "delta": -3.3, "from": 64.7, "fromGrade": "B", "to": 61.4, "toGrade": "C" }, { "causes": [ "method" ], "date": "2026-10-30", "delta": -0.1, "from": 64.8, "fromGrade": "B", "to": 64.7, "toGrade": "B" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.1, "from": 64.7, "fromGrade": "B", "to": 64.8, "toGrade": "B" } ] }, "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-tradingagents.json", "title": "AgentOS vs TradingAgents", "url": "https://www.anchorterminal.com/compare/agentos-vs-tradingagents" }, { "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-tradingagents.json", "title": "Agno vs TradingAgents", "url": "https://www.anchorterminal.com/compare/agno-vs-tradingagents" }, { "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-tradingagents.json", "title": "BoxLang AI vs TradingAgents", "url": "https://www.anchorterminal.com/compare/boxlang-ai-vs-tradingagents" }, { "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-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-llamaindex.json", "title": "CrewAI vs LlamaIndex Framework", "url": "https://www.anchorterminal.com/compare/crewai-vs-llamaindex" }, { "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/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-tradingagents.json", "title": "Cursor Agent SDK vs TradingAgents", "url": "https://www.anchorterminal.com/compare/cursor-agent-sdk-vs-tradingagents" }, { "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-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-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-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-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-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-llamaindex.json", "title": "LangGraph vs LlamaIndex Framework", "url": "https://www.anchorterminal.com/compare/langgraph-vs-llamaindex" }, { "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/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-pydantic-ai.json", "title": "LlamaIndex Framework vs Pydantic AI", "url": "https://www.anchorterminal.com/compare/llamaindex-vs-pydantic-ai" }, { "json": "https://www.anchorterminal.com/compare/llamaindex-vs-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-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-tradingagents.json", "title": "Mastra vs TradingAgents", "url": "https://www.anchorterminal.com/compare/mastra-vs-tradingagents" }, { "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-tradingagents.json", "title": "OpenAI Agents SDK vs TradingAgents", "url": "https://www.anchorterminal.com/compare/openai-agents-sdk-vs-tradingagents" }, { "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/smolagents-vs-tradingagents.json", "title": "smolagents vs TradingAgents", "url": "https://www.anchorterminal.com/compare/smolagents-vs-tradingagents" }, { "json": "https://www.anchorterminal.com/compare/strands-agents-vs-tradingagents.json", "title": "Strands Agents vs TradingAgents", "url": "https://www.anchorterminal.com/compare/strands-agents-vs-tradingagents" }, { "json": "https://www.anchorterminal.com/compare/tradingagents-vs-vercel-ai-sdk.json", "title": "TradingAgents vs AI SDK", "url": "https://www.anchorterminal.com/compare/tradingagents-vs-vercel-ai-sdk" } ], "panel": null, "scores": [ { "by": 18, "edge": "llamaindex", "key": "reliability", "llamaindex": 70, "name": "Reliability", "tradingagents": 52, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 24, "edge": "llamaindex", "key": "schema", "llamaindex": 85, "name": "Schema \u0026 documentation", "tradingagents": 61, "weight": 13 }, { "by": 12, "edge": "llamaindex", "key": "ergonomics", "llamaindex": 73, "name": "Agent ergonomics", "tradingagents": 61, "weight": 13 }, { "by": 12, "edge": "llamaindex", "key": "security", "llamaindex": 74, "name": "Security \u0026 auth", "tradingagents": 62, "weight": 14 }, { "by": 0, "edge": "", "key": "payments", "llamaindex": 60, "name": "Payments \u0026 pricing", "tradingagents": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 9, "edge": "tradingagents", "key": "maintenance", "llamaindex": 69, "name": "Maintenance \u0026 community", "tradingagents": 78, "weight": 7 }, { "by": 1, "edge": "tradingagents", "key": "transparency", "llamaindex": 67, "name": "Transparency \u0026 trust", "tradingagents": 68, "weight": 7 } ], "summary": "LlamaIndex Framework scores 70 (BB) on agent readiness against TradingAgents's 61.4 (C), and leads in 4 of 7 scored categories. TradingAgents leads on maintenance \u0026 community. 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.", "tradingagents": "An Apache 2.0 LangGraph framework at 0.7.1 with seven 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/llamaindex-vs-tradingagents", "json": "https://www.anchorterminal.com/compare/llamaindex-vs-tradingagents.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/llamaindex-vs-tradingagents.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/llamaindex-vs-tradingagents.min.md" }, "markdown": "LlamaIndex Framework scores 70 (BB) on agent readiness against TradingAgents's 61.4 (C), and leads in 4 of 7 scored categories. TradingAgents leads on maintenance \u0026 community. 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- TradingAgents: grade C, 61.4/100, rank #617 of 1510. 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 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\nAhead on:\n- Reliability, 70 against 52\n- Schema \u0026 documentation, 85 against 61\n- Agent ergonomics, 73 against 61\n- Security \u0026 auth, 74 against 62\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- No key needed to call it\n\nWatch for: Core is 0.14.x, and minor versions such as 0.13 and 0.14 carried breaking changes\n\n### TradingAgents (C)\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\nAhead on:\n- Maintenance \u0026 community, 78 against 69\n\nWatch for: No MCP client or MCP server, so an agent drives it only through Python or the CLI\n\n\n## Why the edge\n\n### Schema \u0026 documentation: LlamaIndex Framework +24\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- TradingAgents (61): Framework reading. No API reference or docs site. Typed interfaces exist in the source, Pydantic models for the research manager's, trader's and portfolio manager's outputs and Annotated signatures on the twelve data tools, with no py.typed marker (10 of 25). The README and changelog are Markdown in the repository; no llms.txt (4 of 10). The README states limits plainly, that runs vary, which statements past-dated runs withhold and why, and that it is a research tool, not advice (15 of 20). Configuration is a plain dict of string keys, though environment overrides are coerced to the default's type and fail loudly on a bad value (8 of 15). Runnable Python and CLI examples throughout the README; the vendor error hierarchy is documented only in source docstrings (9 of 15). Dated changelog for every release from 0.1.0 with upgrade notes (15).\n\n### Reliability: LlamaIndex Framework +18\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- TradingAgents (52): Read with the local-software lines, because the owner runs it. Python 3.11 or later is stated and CI tests 3.11 to 3.14, but the project publishes no PyPI package. No workflow uploads one, and the README installs from a clone or Docker Compose. From 0.7.0 a release workflow builds a wheel and source archive from each tag and attaches them to the GitHub release (12 of 20). Public CI on every push and pull request runs the test suite (about 1,009 test functions at v0.7.1) on four Python versions and once in America/New_York, a clean-install import check and Ruff. The 25 October check read the pass state from shields.io, a third-party copy of GitHub data this run doesn't use, so that reading is struck, and the pass state on main is unread (15, as berth and agentvfs). Issue counts came from the same source and are struck too. On the clone, 0.7.1 (27 October) fixes 22 numbered issues, 17 of them above #1518, the newest fixed in 0.7.0 on 16 October, and credits 15 outside contributors; open crash or regression issues and reply times are unread (15, as berth on the same pattern). The changelog follows Keep a Changelog and Semantic Versioning and lists breaking changes under \"Upgrading from\", but 0.5.1, a patch release, moved import paths and removed classes (10 of 15). Version 0.7.1 with no stability statement (0). 12+15+15+10+0 = 52.\n\n### Agent ergonomics: LlamaIndex Framework +12\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- TradingAgents (61): Framework reading, scored on what an outside agent needs to call it. No MCP client or server, so it can't be attached as tools; a run is four lines of Python (`TradingAgentsGraph(config=...).propagate(ticker, date)`) or one headless CLI command with flags (10 of 25). Analyst selection, debate and risk rounds (default 1), `max_tool_rounds` (20), `max_recur_limit` (100), article limits and `--no-html` size a run (14 of 20). A typed vendor error hierarchy separates unavailable from absent data, the headless CLI names the missing flag before a run starts, and an unparseable decision returns REVIEW rather than Hold (15 of 20). Checkpoint resume is opt-in, a backtest rerun with the same `run_id` skips finished cells, and settlement writes each decision once; nothing writes to an outside system (15 of 20). Sensible defaults, but the default provider is OpenAI's GPT-6 models with a key, and Python only (7 of 15).\n\n### Security \u0026 auth: LlamaIndex Framework +12\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- TradingAgents (62): Framework reading. 30 for what leaves the machine and how credentials are held, 20 for least privilege and approvals, 15 for prompt injection, 15 for audit and 20 for the security programme. Keys are read from the environment, and the package loads the nearest .env on import by walking up from the working directory. The ChatGPT sign-in uses PKCE and dynamic client registration, keeps tokens in a 0600 file and revokes them on logout. The only calls of its own are the interactive CLI's daily release and notice check, with no run data, off without a terminal or with TRADINGAGENTS_NO_UPDATE_CHECK=1. SEC EDGAR gets a placeholder contact address unless one is set (24 of 30). It places no orders and holds no broker credentials, the data tools only read, and every ticker is validated before it is used in a file path. No approval hooks, which nothing in it needs (14 of 20). News, Reddit and StockTwits text goes into prompts. The source names prompt injection as the reason for path validation, but no guidance on injection was found in the README (5 of 15). Saved reports record the run's settings and each agent's report, the memory log keeps every decision, and `debug=True` streams the graph (11 of 15). Security reports go to GitHub private vulnerability reporting per the issue template, and the changelog has Security sections for a langchain-core patch and a dependency removed over CVE-2026-22218. No SECURITY.md, no security.txt on tauric.ai (404 on 2 November), no bounty, and the repository's advisories page wasn't read (8 of 20). 24+14+5+11+8 = 62.\n\n### Maintenance \u0026 community: TradingAgents +9\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- TradingAgents (78): 0.7.1 on 27 October 2026 (30). Seven releases in the 90 days to 2 November, 0.4.0 (31 August), 0.5.0, 0.5.1, 0.5.2, 0.6.0, 0.7.0 and 0.7.1 (20). The earlier issue and pull request counts came from shields.io and are struck. On the clone, fixes cite issue numbers and each release credits the people who reported or fixed them (15 outside contributors in 0.7.1), but 251 of the 258 commits since 4 August are by one maintainer's accounts and reply times couldn't be read (13 of 25, as pipecat on clone evidence). No PyPI package; versions ship as GitHub releases with a wheel since 0.7.0 (8 of 15). CI with tests, lint and a clean-install check; dependencies set lower bounds only and no Dependabot configuration is in the repository (7 of 10). 30+20+13+8+7 = 78.\n\n## Score by category\n\n| Category | Weight | LlamaIndex Framework | TradingAgents | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 70 | 52 | LlamaIndex Framework +18 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 85 | 61 | LlamaIndex Framework +24 |\n| Agent ergonomics | 13% (16.2 this run) | 73 | 61 | LlamaIndex Framework +12 |\n| Security \u0026 auth | 14% (17.5 this run) | 74 | 62 | LlamaIndex Framework +12 |\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 | 78 | TradingAgents +9 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 67 | 68 | TradingAgents +1 |\n| Negative events | ≤15 | -2 | 0 | |\n| **Total** | | **70 · BB** | **61.4 · C** | |\n\n## Facts side by side\n\n| Fact | LlamaIndex Framework | TradingAgents |\n| --- | --- | --- |\n| Kind | Agent framework | Agent framework |\n| Vendor | LlamaIndex | 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 | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-09-21 | 2026-10-27 |\n| Terms last updated | no document linked | 2026-10-23 |\n| Privacy policy last updated | 2026-08-24 | 2026-10-23 |\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 | not found in the text |\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 | not found in the text |\n| Popularity | 53k stars, 1.3M PyPI/wk | none |\n\n## How the scores have moved\n\n### LlamaIndex Framework\n\nNo score change since it was listed.\n\n### TradingAgents\n\n- -3.3 on 2 November 2026 (B to C): correction\n- -0.1 on 30 October 2026: checklist\n- +0.1 on 27 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**TradingAgents.** An Apache 2.0 LangGraph framework at 0.7.1 with seven 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### 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### 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, LlamaIndex Framework or TradingAgents?\n\nLlamaIndex Framework scores 70 (BB) on agent readiness against TradingAgents's 61.4 (C), and leads in 4 of 7 scored categories. TradingAgents leads on maintenance \u0026 community.\n\n### Are LlamaIndex Framework and TradingAgents open source?\n\nYes. LlamaIndex Framework 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/llamaindex-vs-tradingagents.json, and with the fewest tokens: https://www.anchorterminal.com/compare/llamaindex-vs-tradingagents.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"llamaindex\", \"b\": \"tradingagents\"}`. From a terminal: `anchor compare llamaindex tradingagents`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/llamaindex.json and https://www.anchorterminal.com/api/v1/tools/tradingagents.json\n\n## Other comparisons with LlamaIndex Framework or TradingAgents\n\n- [AgentOS vs LlamaIndex Framework](https://www.anchorterminal.com/compare/agentos-vs-llamaindex.md)\n- [AgentOS vs TradingAgents](https://www.anchorterminal.com/compare/agentos-vs-tradingagents.md)\n- [Agno vs LlamaIndex Framework](https://www.anchorterminal.com/compare/agno-vs-llamaindex.md)\n- [Agno vs TradingAgents](https://www.anchorterminal.com/compare/agno-vs-tradingagents.md)\n- [BoxLang AI vs LlamaIndex Framework](https://www.anchorterminal.com/compare/boxlang-ai-vs-llamaindex.md)\n- [BoxLang AI vs TradingAgents](https://www.anchorterminal.com/compare/boxlang-ai-vs-tradingagents.md)\n- [Claude Agent SDK vs LlamaIndex Framework](https://www.anchorterminal.com/compare/claude-agent-sdk-vs-llamaindex.md)\n- [Claude Agent SDK vs TradingAgents](https://www.anchorterminal.com/compare/claude-agent-sdk-vs-tradingagents.md)\n- [CrewAI vs LlamaIndex Framework](https://www.anchorterminal.com/compare/crewai-vs-llamaindex.md)\n- [CrewAI vs TradingAgents](https://www.anchorterminal.com/compare/crewai-vs-tradingagents.md)\n- [Cursor Agent SDK vs LlamaIndex Framework](https://www.anchorterminal.com/compare/cursor-agent-sdk-vs-llamaindex.md)\n- [Cursor Agent SDK vs TradingAgents](https://www.anchorterminal.com/compare/cursor-agent-sdk-vs-tradingagents.md)\n- [Docker Agent vs LlamaIndex Framework](https://www.anchorterminal.com/compare/docker-agent-vs-llamaindex.md)\n- [Docker Agent vs TradingAgents](https://www.anchorterminal.com/compare/docker-agent-vs-tradingagents.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 TradingAgents](https://www.anchorterminal.com/compare/google-adk-vs-tradingagents.md)\n- [guilds.run vs LlamaIndex Framework](https://www.anchorterminal.com/compare/guilds-run-vs-llamaindex.md)\n- [guilds.run vs TradingAgents](https://www.anchorterminal.com/compare/guilds-run-vs-tradingagents.md)\n- [LangGraph vs LlamaIndex Framework](https://www.anchorterminal.com/compare/langgraph-vs-llamaindex.md)\n- [LangGraph vs TradingAgents](https://www.anchorterminal.com/compare/langgraph-vs-tradingagents.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 Pydantic AI](https://www.anchorterminal.com/compare/llamaindex-vs-pydantic-ai.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 AI SDK](https://www.anchorterminal.com/compare/llamaindex-vs-vercel-ai-sdk.md)\n- [Mastra vs TradingAgents](https://www.anchorterminal.com/compare/mastra-vs-tradingagents.md)\n- [Microsoft Agent Framework vs TradingAgents](https://www.anchorterminal.com/compare/microsoft-agent-framework-vs-tradingagents.md)\n- [OpenAI Agents SDK vs TradingAgents](https://www.anchorterminal.com/compare/openai-agents-sdk-vs-tradingagents.md)\n- [Pydantic AI vs TradingAgents](https://www.anchorterminal.com/compare/pydantic-ai-vs-tradingagents.md)\n- [smolagents vs TradingAgents](https://www.anchorterminal.com/compare/smolagents-vs-tradingagents.md)\n- [Strands Agents vs TradingAgents](https://www.anchorterminal.com/compare/strands-agents-vs-tradingagents.md)\n- [TradingAgents vs AI SDK](https://www.anchorterminal.com/compare/tradingagents-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 TradingAgents", "url": "" } ], "description": "LlamaIndex Framework scores 70 (BB) to TradingAgents's 61.4 (C) for agent frameworks. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "LlamaIndex Framework BB 70", "TradingAgents C 61.4", "scores" ], "h1": "LlamaIndex Framework vs TradingAgents", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/llamaindex-vs-tradingagents", "published": "2026-10-01", "section": "tools", "title": "LlamaIndex Framework vs TradingAgents for AI agents (2026)", "toc": null, "updated": "2026-11-07", "url": "https://www.anchorterminal.com/compare/llamaindex-vs-tradingagents" }, "tokens": { "markdown": 4950, "slim": 2880 }, "version": 1 }