Related Pages

Related topics: Src

Overview

LlamaIndex is a modular data framework for building Large Language Model (LLM) applications, with a particular focus on Retrieval-Augmented Generation (RAG), agentic workflows, and tool-augmented reasoning. The repository is structured as a monorepo that splits the core library from a large, independently versioned set of integration packages, allowing contributors to add or remove connectors without forcing breaking changes in the core.

Repository Structure

The repository is organized into two top-level areas. The llama-index-core package contains the foundational abstractions such as Document, BaseReader, indices, query engines, and the agent workflow runtime. The llama-index-integrations/ directory contains separately published packages grouped by category, including readers/, tools/, vector_stores/, llms/, embeddings/, callbacks/, and agent/. Each integration follows the same BaseReader or BaseToolSpec contract defined in core, which keeps the surface area uniform and lets users swap implementations without changing application code.

flowchart TB
    A[Application / Agent] --> B[llama-index-core]
    B --> C[Readers]
    B --> D[Tools]
    B --> E[Vector Stores]
    B --> F[LLMs / Embeddings]
    C --> C1[Apify]
    C --> C2[Docling]
    C --> C3[Confluence]
    C --> C4[Wikipedia]
    D --> D1[Wikipedia Tool]
    D --> D2[Seltz]
    D --> D3[AgentQL]
    E --> E1[WordLift]
    F --> F1[OpenAI]
    F --> F2[Bedrock]
    F --> F3[Ollama]

Reader Integrations

Readers are the most numerous category and are responsible for converting external data sources into Document objects. They implement BaseReader.load_data(...) and return a List[Document] that downstream indexers and retrievers can consume.

Document-style readers focus on converting files into structured text:

• Docling Reader exports PDF, DOCX, and HTML to Markdown or Docling's native JSON schema; JSON output requires pairing it with a Docling Node Parser. Source: llama-index-integrations/readers/llama-index-readers-docling/README.md
• LayoutIR Reader uses an Intermediate Representation layer to preserve multi-column layouts, tables, and page structure with stable hash-based block IDs. Source: llama-index-integrations/readers/llama-index-readers-layoutir/README.md
• Docugami Reader produces a hierarchical semantic XML tree of chunks annotated with structural and semantic tags, which is useful for long-form documents such as contracts. Source: llama-index-integrations/readers/llama-index-readers-docugami/README.md
• MarkItDown Reader wraps Microsoft's MarkItDown library to handle .pptx, .docx, .pdf, .html, .csv, .xml, .json, .zip, and plain text. Source: llama-index-integrations/readers/llama-index-readers-markitdown/README.md
• Docstring Walker parses Python source with the ast module to extract only docstrings, enabling code-buddy chat with minimal token usage. Source: llama-index-integrations/readers/llama-index-readers-docstring-walker/README.md

Service- and vault-style readers target external systems:

• Apify Reader consumes datasets from the Apify platform and lets users map dataset items to Document objects via a callable. Source: llama-index-integrations/readers/llama-index-readers-apify/README.md
• Confluence Reader supports four query modes (page_ids, space_key, label, cql) and an event system that emits TotalPagesToProcessEvent, PageDataFetchCompletedEvent, and attachment lifecycle events; custom parsers are required for processing attachments. Source: llama-index-integrations/readers/llama-index-readers-confluence/README.md
• ServiceNow Reader retrieves KB articles by article_sys_id or numbers and requires user-supplied custom parsers inheriting from BaseReader for at least HTML body content. Source: llama-index-integrations/readers/llama-index-readers-service-now/README.md
• Obsidian Reader walks an Obsidian vault, splits markdown by header, and exposes wikilink and backlink metadata per note. Source: llama-index-integrations/readers/llama-index-readers-obsidian/README.md
• Wikipedia Reader fetches specific pages by title; the companion Wikipedia Tool exposes load_data and search_data for use inside agents. Source: llama-index-integrations/readers/llama-index-readers-wikipedia/README.md
• Lilac Reader bridges Lilac's data labeling and cleaning pipelines back into LlamaIndex Document objects. Source: llama-index-integrations/readers/llama-index-readers-lilac/README.md

Tool and Vector Store Integrations

Tools expose capabilities to FunctionAgent via to_tool_list() and are designed for agentic loops where the LLM selects which tool to invoke. The Wikipedia Tool provides load_data and search_data for agents. Source: llama-index-integrations/tools/llama-index-tools-wikipedia/README.md. The Seltz Tool returns curated web documents and supports query, max_documents, context, and profile parameters. Source: llama-index-integrations/tools/llama-index-tools-seltz/README.md. The AgentQL Tool ships three async tools: extract_web_data_with_rest_api, extract_web_data_from_browser, and get_web_element_from_browser, the latter two requiring a Playwright browser instance. Source: llama-index-integrations/tools/llama-index-tools-agentql/README.md.

Vector stores persist embeddings and serve nearest-neighbor queries. WordLift extends a vector store with a Knowledge Graph, where every node is assigned a permanent dereferentiable URI; loading documents into an existing graph requires an entity_id in the metadata. Source: llama-index-integrations/vector_stores/llama-index-vector-stores-wordlift/README.md.

Release Cadence and Community Patterns

Releases follow a rolling-cadence monorepo model where each integration package is versioned independently, but the core package (llama-index-core) and many integrations ship in coordinated minor versions such as the v0.14.x series (v0.14.12 through v0.14.22). Recent core additions highlighted in release notes include a token-bucket rate limiter for LLM and embedding API calls (v0.14.16), multimodal type support (v0.14.15), an early_stopping_method parameter on agent workflows and token-based code splitting (v0.14.13), and async tool spec support (v0.14.12).

Community discussions cluster around three recurring themes that the architecture above helps address:

• Local-only execution (issue #928) is enabled by selecting local LLM and embedding integrations such as Ollama, avoiding any data transfer to external services.
• Vendor-native model support (issues #7507 for Amazon Bedrock and #16681 for Claude 3.5 Sonnet V2) is delivered through the dedicated LLM packages under llama-index-integrations/llms/.
• External integrations are encouraged through the CONTRIBUTING.md external-integration guidance, as exemplified by the Bocha Web Search ToolSpec (issue #21883), and recent additions such as llama-index-agent-agentmesh and the AgentMesh trust layer introduced in v0.14.15.

See Also

• Getting Started
• Readers
• Tools
• Vector Stores
• Agent Workflows
• Release Notes

Related Pages

Related topics: Overview, Models

Src — LlamaIndex Integrations Source Tree

Overview

llama-index-integrations/ is the monorepo directory that houses every first-party extension package shipped alongside llama-index-core. Each integration is an independently installable Python package (e.g. pip install llama-index-readers-confluence) that plugs into a stable extension point defined in core. The tree groups packages by extension kind, with the largest families being readers (data connectors that produce Document objects), tools (function-calling tools for agents), and vector stores (backends for similarity search). The community evidence (Bocha, TealTiger, Agent Magnet) shows the project is intentionally steering many new integrations toward *external* repositories, with this tree reserved for vetted, commonly used connectors.

Source: llama-index-integrations/readers/llama-index-readers-apify/README.md Source: llama-index-integrations/readers/llama-index-readers-markitdown/README.md

Integration Categories

Data Readers (Loaders)

Readers implement BaseReader.load_data() and return List[Document]. The tree covers three sub-patterns:

For example, the Confluence reader exposes four mutually exclusive page selection modes — page_ids, space_key, label, and cql — plus attachment processing and a callback-driven event system for monitoring.

Source: llama-index-integrations/readers/llama-index-readers-confluence/README.md

The Docling reader defaults to Markdown export but can be switched to JSON via export_type=DoclingReader.ExportType.JSON, and the README explicitly notes that JSON output must be paired with a Docling Node Parser downstream.

Source: llama-index-integrations/readers/llama-index-readers-docling/README.md

The ServiceNow KB reader requires users to provide their own custom_parsers mapping (at minimum an HTMLParser); the package ships no built-in parsers.

Source: llama-index-integrations/readers/llama-index-readers-service-now/README.md

Tools (Agent Function Specs)

Tools implement BaseToolSpec and expose .to_tool_list() for FunctionAgent. Examples in the tree include:

• WikipediaToolSpec — load_data (page by title) and search_data (multi-page search).
• SeltzToolSpec — a single search() function returning Document objects with query, max_documents, context, and profile parameters.
• AgentQL — three tools: extract_web_data_with_rest_api, extract_web_data_from_browser, and get_web_element_from_browser. The browser-bound tools require a Playwright instance and the package is async-only (a known limitation tracked in PR #17808).

Source: llama-index-integrations/tools/llama-index-tools-wikipedia/README.md Source: llama-index-integrations/tools/llama-index-tools-seltz/README.md Source: llama-index-integrations/tools/llama-index-tools-agentql/README.md

Vector Stores

The vector_stores/ family follows the BaseVectorStore contract. The WordLift vector store, for example, is built around a Knowledge Graph and requires an entity_id in the metadata of every node added to an existing graph; the README frames this in the context of W3C Linked Data / 5-star open data.

Source: llama-index-integrations/vector_stores/llama-index-vector-stores-wordlift/README.md

Architecture and Conventions

Every package under llama-index-integrations/ follows the same conventions so users can reason about them uniformly:

1. Subclass a core base class — readers subclass BaseReader, tools subclass BaseToolSpec, vector stores subclass BaseVectorStore.
2. Single pip distribution per integration — installed separately so dependencies stay minimal.
3. README first — each package has a README showing install command, a minimal usage snippet, and the available methods.
4. External integration guidance — community contributions such as Bocha Web Search now live in standalone repos and are advertised through LlamaHub rather than being merged into the monorepo, per the project's CONTRIBUTING.md.

A typical reader call looks like:

from llama_index.readers.confluence import ConfluenceReader

reader = ConfluenceReader(
    base_url="https://yoursite.atlassian.com/wiki",
    oauth2={"client_id": "...", "token": {"access_token": "..."}},
)
documents = reader.load_data(space_key="DEV", include_attachments=True)

Source: llama-index-integrations/readers/llama-index-readers-confluence/README.md

Community Trends and Common Failure Modes

The community context for this repository highlights three patterns worth noting when working with llama-index-integrations/:

• Long-tail maintenance burden — recent release notes (v0.14.12 → v0.14.22) are dominated by chore(deps) and uv lock --upgrade PRs sweeping 50–87 directories at a time, reflecting the cost of maintaining hundreds of packages. The project is pushing more contributors toward external packages to keep this sustainable.
• Pydantic v1 default — issue #13477 (top-engagement) requests flipping the default to Pydantic v2 because FastAPI and other modern frameworks already use v2, and v1 models break interop.
• Local-only LLM usage — issue #928 (44 comments) asks how to run LlamaIndex with local models (e.g. Alpaca), which is increasingly addressed by llama-index-llms-* packages in a sibling integrations tree not covered by the source files in this page.
• Reliance on external services — readers like Apify, Confluence, and ServiceNow require user-supplied credentials; omitting them raises authentication errors at call time rather than import time.

Source: llama-index-integrations/readers/llama-index-readers-apify/README.md Source: llama-index-integrations/readers/llama-index-readers-obsidian/README.md

See Also

• LlamaIndex main repository: run-llama/llama_index
• llama-index-core package and BaseReader / BaseToolSpec / BaseVectorStore contracts
• LlamaHub integration directory (for community/external integrations such as Bocha Web Search and TealTiger governance)

Related Pages

Related topics: Src, Agent

Models

Overview and Scope

The Models module in LlamaIndex lives under llama_index/core/chat_ui/models/ and defines the Pydantic-based data structures that underpin the Chat UI feature. It is a small, focused namespace that exposes schema objects (such as Artifact) used to serialize, validate, and transport state between a LlamaIndex pipeline and a front-end chat surface. The module is the canonical place where chat-UI-specific domain types are declared, and it is imported by the rest of the chat_ui package.

Source: llama_index/core/chat_ui/models/__init__.py

Because the wider ecosystem relies heavily on Pydantic for request/response validation (see issue #13477 — *"[Feature Request]: default to pydantic v2"*), these model classes are the most likely touchpoint when users report validation or serialization mismatches between LlamaIndex and downstream frameworks such as FastAPI.

Core Data Structures

`Artifact`

The Artifact model is the primary type defined in the Models namespace. It represents a discrete piece of content that a chat agent can produce or consume — for example, a retrieved document, a generated code snippet, or an intermediate reasoning trace that the Chat UI needs to render.

Source: llama_index/core/chat_ui/models/artifact.py

The Artifact type follows the same Pydantic conventions used elsewhere in the project, which means it integrates directly with the Document and Node schemas that the loaders in the llama-index-integrations/readers/ tree produce. For example, a Confluence page ingested by ConfluenceReader is exposed as a Document, which the Chat UI can wrap in an Artifact for display alongside the model's natural-language response.

Source: llama-index-integrations/readers/llama-index-readers-confluence/README.md

Package Exports

The __init__.py for the Models module re-exports the public types so they can be imported as from llama_index.core.chat_ui.models import Artifact (or similar), keeping import paths short and stable for downstream consumers.

Source: llama_index/core/chat_ui/models/__init__.py

Role in the LlamaIndex Pipeline

Models in this namespace are designed to be the boundary object between the agentic core and any external chat surface. The flow below illustrates how a reader loads content, the agent reasons over it, and the Chat UI renders the result using the Artifact model.

flowchart LR
    A[Reader / Tool] --> B[Document / Node]
    B --> C[Agent / Workflow]
    C --> D[Artifact Model]
    D --> E[Chat UI Frontend]
    C --> F[LLM Response]
    F --> E

The diagram highlights that Artifact is a side-channel payload: it carries structured context (sources, retrieved passages, tool outputs) in parallel to the streaming LLM tokens, so a front-end can render citations or attachments without having to parse the model's free-form text.

Source: llama-index-integrations/tools/llama-index-tools-wikipedia/README.md, llama-index-integrations/readers/llama-index-readers-wikipedia/README.md

Integration Points and Configuration

The Models module has no runtime configuration of its own — it is a pure schema layer. However, it is consumed by:

Source: llama-index-integrations/readers/llama-index-readers-obsidian/README.md, llama-index-integrations/readers/llama-index-readers-apify/README.md

Because Pydantic is the serialization substrate, any change to a model's field set is a breaking change for downstream JSON consumers. The community request to standardize on Pydantic v2 (see issue #13477) is therefore directly relevant to the Artifact class: when the project migrates, this module is one of the schemas that must be revalidated.

Common Failure Modes

1. Pydantic version drift — Mixing Pydantic v1 and v2 models in the same application can cause ValidationError or TypeError when an Artifact is deserialized by a downstream service built on Pydantic v2. Source: issue #13477.
2. Missing metadata fields — If a reader (e.g., ObsidianReader) is not configured with extract_tasks=True, the resulting Document will not carry the additional fields that the Artifact schema expects for rendering. Source: llama-index-integrations/readers/llama-index-readers-obsidian/README.md.
3. Out-of-corpus answers — When a simple vector index returns text for questions outside the indexed documents, the Artifact payload will be empty or stale, and the Chat UI may render the LLM's hallucinated answer without a grounding source. Source: issue #1321 — *"Answer from outside the documents"*.

See Also

• Chat UI architecture
• Pydantic migration guide
• Reader integrations catalog
• Tool integrations catalog

Related Pages

Related topics: Models

Agent

The Agent in LlamaIndex is the orchestration primitive that turns an LLM into a tool-using, query-driven executor. It binds together a chat model, a set of tool specs (search, retrieval, browser, knowledge bases, custom callables), and an execution loop so that natural language requests can be decomposed, dispatched, and synthesized into a final answer. The community has confirmed this is the dominant integration surface — most readers and tools in the llama-index-integrations tree ship usage examples that wire their components into an Agent.

1. Core Construction Pattern

The canonical construction is FunctionAgent from llama_index.core.agent.workflow, paired with an LLM and a list of FunctionTool objects produced by a ToolSpec. The Wikipedia integration illustrates the exact shape used across the codebase:

from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
from llama_index.tools.wikipedia import WikipediaToolSpec

tool_spec = WikipediaToolSpec()
agent = FunctionAgent(
    tools=tool_spec.to_tool_list(),
    llm=OpenAI(model="gpt-4.1"),
)
print(await agent.run("Who is Ben Affleck's spouse?"))

Source: llama-index-integrations/tools/llama-index-tools-wikipedia/README.md:1-30

The same three-argument pattern — tools=..., llm=..., and a call to agent.run(...) — recurs in nearly every ToolSpec README. The Seltz integration uses OpenAI(model="gpt-4o") and SeltzToolSpec(api_key=...) Source: [llama-index-integrations/tools/llama-index-tools-seltz/README.md:1-40], the Linkup integration composes LinkupToolSpec with depth, output_type, and structured_output_schema parameters Source: [llama-index-integrations/tools/llama-index-tools-linkup-research/README.md:1-50], and the AgentQL integration uses AgentQLRestAPIToolSpec to expose extract_web_data_with_rest_api Source: [llama-index-integrations/tools/llama-index-tools-agentql/README.md:1-60].

2. Tools and ToolSpecs

A ToolSpec is a thin wrapper that exposes one or more backend capabilities as a list of FunctionTool objects via .to_tool_list(). The Agent itself is tool-agnostic — it can mix Wikipedia search, web research, REST extraction, and local retrievers in the same call. The Parallel Web Systems tool demonstrates the contract clearly: every spec is created with credentials, converted via to_tool_list(), and attached to a FunctionAgent Source: [llama-index-integrations/tools/llama-index-tools-parallel-web-systems/README.md:1-60].

Wikipedia exposes two tools, load_data (load a page) and search_data (search and load matches) Source: [llama-index-integrations/tools/llama-index-tools-wikipedia/README.md:18-22]. Seltz exposes a search function with parameters query, max_documents, context, and profile Source: [llama-index-integrations/tools/llama-index-tools-seltz/README.md:32-38]. The tool parameters are surfaced directly into the LLM tool-calling schema, which is why the Agent can reason about them without manual mapping.

3. Reader-Driven Agents

While ToolSpec is the modern path, several BaseReader integrations also show how reader output feeds into an Agent pipeline. The Apify reader illustrates the pattern: load documents with a dataset_mapping_function that returns Document objects, then index and query them. The reader is "designed to be used as a way to load data as a Tool in an Agent" Source: [llama-index-integrations/readers/llama-index-readers-apify/README.md:1-25].

The Obsidian reader, ServiceNow reader, and Confluence reader all follow the same recipe: produce a List[Document], optionally enrich metadata, and let downstream agent tooling (RAG, query engines) consume them Source: [llama-index-integrations/readers/llama-index-readers-obsidian/README.md:1-30]; Source: [llama-index-integrations/readers/llama-index-readers-service-now/README.md:1-60]; Source: [llama-index-integrations/readers/llama-index-readers-confluence/README.md:1-60].

4. Execution Flow

flowchart LR
    U[User Query] --> A[FunctionAgent]
    A -->|tool_call| T[ToolSpec.to_tool_list]
    T --> W[WikipediaToolSpec]
    T --> S[SeltzToolSpec]
    T --> L[LinkupToolSpec]
    T --> R[BaseReader Documents]
    W --> A
    S --> A
    L --> A
    R --> A
    A -->|final| O[Synthesized Answer]

The Agent loop is naturally asynchronous — every integration example uses await agent.run(...). This matches the v0.14.12 release note that added async tool spec support across llama-index-core and llama-index-callbacks-agentops [Source: community context, v0.14.12]. Recent v0.14.x releases have also added governance instrumentation hooks (TealTiger) for evaluating security policies before tool calls and query execution [Source: community context, issue #21882], an early_stopping_method parameter for agent workflows [Source: community context, v0.14.13], and trust-layer integrations such as llama-index-agent-agentmesh (added in v0.14.15, updated through v0.14.22) [Source: community context, v0.14.15–v0.14.22].

5. Common Failure Modes

A few patterns recur in the integration READMEs and should be treated as known contracts:

• Missing credentials — most specs require an API key, e.g. LinkupToolSpec(api_key=...), SeltzToolSpec(api_key=...), and the Apify reader requires an Apify API token Source: [llama-index-integrations/tools/llama-index-tools-seltz/README.md:18-22]; Source: [llama-index-integrations/readers/llama-index-readers-apify/README.md:1-10].
• Required custom parsers — the ServiceNow reader requires the caller to provide a custom_parsers dict with at minimum an HTML parser; the built-in tool does not include parsers Source: [llama-index-integrations/readers/llama-index-readers-service-now/README.md:30-60].
• Async-only tools — AgentQL's browser tools are async-only and require a Playwright browser instance to be created first Source: [llama-index-integrations/tools/llama-index-tools-agentql/README.md:1-40].
• Pydantic v1 vs v2 — the long-standing community issue #13477 flags that LlamaIndex default models are still Pydantic v1, which can break mixing with apps on Pydantic v2 [Source: community context, issue #13477].
• Off-document answers — issue #1321 reports that a SimpleVectorIndex returns answers even when the indexed documents have no relevant context; in agent workflows this is mitigated by grounding tool outputs through the tool-calling loop rather than a direct chat completion.

See Also

• Wikipedia Tool integration
• Seltz, Linkup, AgentQL, and Parallel Web Systems ToolSpecs
• Apify, Obsidian, ServiceNow, and Confluence Readers
• AgentMesh (llama-index-agent-agentmesh) — trust layer for agents
• AgentOps callbacks (llama-index-callbacks-agentops) — async tool spec instrumentation

Doramagic Pitfall Log

Found 29 structured pitfall item(s), including 2 high/blocking item(s). Top priority: Installation risk - Installation risk requires verification.

1. Installation risk: Installation risk requires verification

• Severity: high
• Finding: Project evidence flags a installation risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/run-llama/llama_index/issues/20543

2. Configuration risk: Configuration risk requires verification

• Severity: high
• Finding: Project evidence flags a configuration risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/run-llama/llama_index/issues/21888

3. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Developers should check this installation risk before relying on the project: feat: Governance instrumentation handler for tool/query security (TealTiger integration)
• User impact: Developers may fail before the first successful local run: feat: Governance instrumentation handler for tool/query security (TealTiger integration)
• Recommended check: Before packaging this project, run the relevant install/config/quickstart check for: feat: Governance instrumentation handler for tool/query security (TealTiger integration). Context: Observed when using node, python
• Evidence: failure_mode_cluster:github_issue | https://github.com/run-llama/llama_index/issues/21882

4. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Developers should check this installation risk before relying on the project: v0.14.12
• User impact: Upgrade or migration may change expected behavior: v0.14.12
• Recommended check: Before packaging this project, run the relevant install/config/quickstart check for: v0.14.12. Context: Observed when using node, python
• Evidence: failure_mode_cluster:github_release | https://github.com/run-llama/llama_index/releases/tag/v0.14.12

5. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Developers should check this installation risk before relying on the project: v0.14.15
• User impact: Upgrade or migration may change expected behavior: v0.14.15
• Recommended check: Before packaging this project, run the relevant install/config/quickstart check for: v0.14.15. Context: Observed when using node, python
• Evidence: failure_mode_cluster:github_release | https://github.com/run-llama/llama_index/releases/tag/v0.14.15

6. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Developers should check this installation risk before relying on the project: v0.14.19
• User impact: Upgrade or migration may change expected behavior: v0.14.19
• Recommended check: Before packaging this project, run the relevant install/config/quickstart check for: v0.14.19. Context: Observed when using node, python
• Evidence: failure_mode_cluster:github_release | https://github.com/run-llama/llama_index/releases/tag/v0.14.19

7. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Developers should check this installation risk before relying on the project: v0.14.21
• User impact: Upgrade or migration may change expected behavior: v0.14.21
• Recommended check: Before packaging this project, run the relevant install/config/quickstart check for: v0.14.21. Context: Observed when using node, python
• Evidence: failure_mode_cluster:github_release | https://github.com/run-llama/llama_index/releases/tag/v0.14.21

8. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Developers should check this installation risk before relying on the project: v0.14.22
• User impact: Upgrade or migration may change expected behavior: v0.14.22
• Recommended check: Before packaging this project, run the relevant install/config/quickstart check for: v0.14.22. Context: Observed when using python
• Evidence: failure_mode_cluster:github_release | https://github.com/run-llama/llama_index/releases/tag/v0.14.22

9. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Project evidence flags a installation risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/run-llama/llama_index/issues/21923

10. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Project evidence flags a installation risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/run-llama/llama_index/issues/21695

11. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Project evidence flags a installation risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/run-llama/llama_index/issues/21880

12. Configuration risk: Configuration risk requires verification

• Severity: medium
• Finding: Developers should check this configuration risk before relying on the project: External Bocha Web Search ToolSpec package for LlamaIndex
• User impact: Developers may misconfigure credentials, environment, or host setup: External Bocha Web Search ToolSpec package for LlamaIndex
• Recommended check: Before packaging this project, run the relevant install/config/quickstart check for: External Bocha Web Search ToolSpec package for LlamaIndex. Context: Observed when using python
• Evidence: failure_mode_cluster:github_issue | https://github.com/run-llama/llama_index/issues/21883

Community Discussion Evidence

Doramagic exposes project-level community discussion separately from official documentation. Review these links before using llama_index with real data or production workflows.

• [[Integration] llama-index-readers-azure-tax-forms — IRS tax form extract](https://github.com/run-llama/llama_index/issues/21928) - github / github_issue
• SunfishLoop: An agent social network — connect LlamaIndex agents with th - github / github_issue
• Feature: Screenpipe data connector for screen/audio context - github / github_issue
• Feature: Add FunASR speech-to-text reader for audio/video ingestion - github / github_issue
• [[Question]: How to make multi_agents have separate Memoreis while share](https://github.com/run-llama/llama_index/issues/21888) - github / github_issue
• [[Feature Request]: Memory should accept AsyncDBChatStore instead of SQLA](https://github.com/run-llama/llama_index/issues/19763) - github / github_issue
• External Bocha Web Search ToolSpec package for LlamaIndex - github / github_issue
• feat: Governance instrumentation handler for tool/query security (TealTi - github / github_issue
• [[Feature Request]: Add Agent Magnet as a self-learning memory integratio](https://github.com/run-llama/llama_index/issues/21880) - github / github_issue
• v0.14.22 - github / github_release
• v0.14.21 - github / github_release
• v0.14.20 - github / github_release