Search & Discovery

Applies to SDK 0.16+ · Last updated: 2026-06-12

AgentNode search is designed for AI agent developers. Instead of keyword matching against package names, search queries are matched against capability descriptions, tool declarations, tags, and metadata. Results are ranked by relevance, trust level, and framework compatibility.

Basic search

terminal

$ agentnode search "web scraping"

Results for "web scraping":

  webpage-extractor-pack   v1.0.0  trusted   Extract clean text and metadata from any webpage
  browser-automation-pack  v1.1.0  verified  Automate browser interactions for data extraction
  web-search-pack          v1.0.0  trusted   Search the web and retrieve structured results

3 results found

Filtering results

Narrow results by framework, trust level, runtime, or capability ID.

terminal

# Only show packs compatible with LangChain
$ agentnode search "pdf" --framework langchain

# Only show trusted or curated packs
$ agentnode search "email" --trust trusted

# Filter by runtime
$ agentnode search "data analysis" --runtime python

# Filter by specific capability ID
$ agentnode search --capability pdf_extraction

# Combine filters
$ agentnode search "document processing" --framework crewai --trust verified

Search flags

Flag	Type	Description
--framework	string	Filter by framework compatibility: langchain, crewai, generic
--trust	string	Minimum trust level: unverified, verified, trusted, curated
--runtime	string	Filter by runtime: python
--capability	string	Filter by exact capability ID from the taxonomy
--limit	number	Maximum number of results to return (default: 20)
--json	boolean	Output results as JSON for programmatic consumption
--publisher	string	Filter by publisher namespace

Understanding results

Each result shows the package slug, current version, trust level, and a summary. Use agentnode info or agentnode explain for detailed information about a specific pack before installing.

terminal

$ agentnode explain pdf-reader-pack

pdf-reader-pack@1.2.0
  Publisher:    agentnode-official
  Trust:        trusted
  Runtime:      python >=3.10
  Frameworks:   langchain, crewai, generic

  Capabilities:
    - pdf_extraction: Extract text, tables, and metadata from PDF documents

  Permissions:
    Network:        none
    Filesystem:     read (reads input PDF files)
    Code Execution: none
    Data Access:    input_only

  Use Cases:
    - Extract text from PDF reports for summarization
    - Parse tables from financial PDFs
    - Read metadata and page counts from document archives

  Install: agentnode install pdf-reader-pack

Resolution Engine

Resolution is different from search. While search finds packages matching a text query, resolution takes a list of capability IDs your agent needs and returns the optimal packages to fill those gaps. The resolution engine scores candidates across multiple dimensions and respects policy constraints.

How resolution scoring works

Each candidate package receives a composite score from 0 to 1 based on five weighted factors:

Factor	Weight	Description
Capability match	40%	How well the pack's declared capabilities match your requested capability IDs
Framework compatibility	20%	Whether the pack supports your agent's framework (LangChain, CrewAI, etc.)
Runtime fit	15%	Whether the pack's runtime and version constraints match your environment
Trust level	15%	Higher trust levels (curated > trusted > verified > unverified) score higher
Permissions safety	10%	Packs requesting fewer permissions score higher (principle of least privilege)

CLI resolution

terminal

$ agentnode resolve pdf_extraction web_search --framework langchain

Resolving 2 capabilities for langchain...

  pdf_extraction:
    1. pdf-reader-pack       v1.0.0  score: 0.94  trusted
    2. pdf-extractor-pack    v1.0.0  score: 0.81  verified

  web_search:
    1. web-search-pack       v1.0.0  score: 0.92  trusted
    2. browser-automation-pack v1.1.0 score: 0.73  verified

Recommended: agentnode install pdf-reader-pack web-search-pack

SDK resolution

resolve.pypython

from agentnode_sdk import AgentNodeClient

client = AgentNodeClient(api_key="ank_live_abc123def456")

# Resolve multiple capability gaps at once
result = client.resolve(
    capabilities=["pdf_extraction", "web_search", "email_sending"],
    framework="langchain",
    limit=5,
)

for match in result.results:
    print(f"{match.matched_capabilities}: {match.slug} (score: {match.score})")
    print(f"  Trust: {match.trust_level}")
    print()

Policy constraints

The resolution engine accepts policy constraints that automatically filter out non-compliant packages. This is critical for production deployments where agents must operate within strict security boundaries.

terminal

# Only resolve packages that are trusted or curated
$ agentnode resolve pdf_extraction --trust trusted

# Only resolve packages with no network access
$ agentnode resolve pdf_extraction --policy-no-network

# Check if a specific package meets your policy
$ agentnode policy-check pdf-reader-pack --trust trusted --no-code-execution
Policy check for pdf-reader-pack@1.2.0:
  Trust level:     trusted     PASS
  Network:         none        PASS
  Filesystem:      read        PASS
  Code execution:  none        PASS
  Data access:     input_only  PASS

Package passes all policy constraints.