Parallel AI Search

Get API Key: https://www.parallel.ai

LiteLLM Python SDK

Parallel AI Search

import os
from litellm import search

os.environ["PARALLEL_AI_API_KEY"] = "..."

response = search(
    query="latest AI developments",
    search_provider="parallel_ai",
    max_results=5
)

LiteLLM AI Gateway

1. Setup config.yaml

config.yaml

model_list:
  - model_name: gpt-5.6-terra
    litellm_params:
      model: gpt-5.6-terra
      api_key: os.environ/OPENAI_API_KEY

search_tools:
  - search_tool_name: parallel-search
    litellm_params:
      search_provider: parallel_ai
      api_key: os.environ/PARALLEL_AI_API_KEY

2. Start the proxy

litellm --config /path/to/config.yaml

# RUNNING on http://0.0.0.0:4000

3. Test the search endpoint

Test Request

curl http://0.0.0.0:4000/v1/search/parallel-search \
  -H "Authorization: Bearer $LITELLM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "latest AI developments",
    "max_results": 5
  }'

Provider-specific Parameters

Parallel AI Search with Provider-specific Parameters

import os
from litellm import search

os.environ["PARALLEL_AI_API_KEY"] = "..."

response = search(
    query="latest developments in quantum computing",
    search_provider="parallel_ai",
    max_results=5,
    # Parallel AI-specific parameters
    processor="pro",                 # 'base' or 'pro'
    max_chars_per_result=500         # Max characters per result
)

Web Search Interception

When a Parallel AI search tool backs web search interception, the intercepted litellm_web_search tool's optional objective and search_queries fields are forwarded to Parallel's v1 Search API as objective and search_queries, so one tool call from the model searches several keyword angles at once. Parallel AI is the only search provider that receives this richer shape today; every other provider gets the tool call's single query.

config.yaml

model_list:
  - model_name: claude
    litellm_params:
      model: anthropic/claude-sonnet-5
      api_key: os.environ/ANTHROPIC_API_KEY

search_tools:
  - search_tool_name: parallel-search
    litellm_params:
      search_provider: parallel_ai
      api_key: os.environ/PARALLEL_AI_API_KEY
      max_results: 5

litellm_settings:
  callbacks: ["websearch_interception"]
  websearch_interception_params:
    enabled_providers: ["anthropic"]
    search_tool_name: parallel-search

Request

curl http://0.0.0.0:4000/v1/messages \
  -H "Authorization: Bearer $LITELLM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude",
    "max_tokens": 1024,
    "messages": [{"role": "user", "content": "What is the latest stable Node.js release and what changed in it?"}],
    "tools": [{"type": "web_search_20250305", "name": "web_search"}]
  }'

The model sees the three-field schema and typically fills all of them. LiteLLM then sends Parallel the model's objective and up to five of its search_queries; queries past the fifth are dropped before the request goes out, matching Parallel's cap.

Outbound Parallel AI request

{
  "objective": "Find the most current stable Node.js release version and what changes were included in that release",
  "search_queries": ["latest stable Node.js release", "Node.js newest version changelog", "current Node.js LTS release"],
  "mode": "basic",
  "advanced_settings": {"max_results": 5}
}

When the model fills only query, or you call /v1/search/parallel-search with a single query string, that string is sent as both a one-item search_queries list and the objective. A list of query strings on a direct search call maps to search_queries the same way, with objective passed through only when you supply it. An objective set in the search tool's litellm_params is kept on every request and the model's is dropped.