Tools API

The Tools Library provides agentic capabilities for your AI applications. Add web search, document retrieval (RAG), and conversation memory with a single API.

Available Tools

Tool	Description	Availability
Web Search	Real-time web search with AI-optimized results	All tiers
Documents/RAG	Upload and query documents with semantic search	Pro+
Memory	Persistent conversation context across sessions	Pro+

Web Search

Search the web in real-time with AI-optimized results.

Endpoint

POST https://api.withperf.pro/v1/tools/search

Request

curl -X POST https://api.withperf.pro/v1/tools/search \
  -H "Authorization: Bearer pk_live_abc123" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "latest developments in AI agents",
    "max_results": 5,
    "search_depth": "basic"
  }'

Parameter	Type	Default	Description
`query`	string	required	Search query
`max_results`	integer	5	Maximum results to return (1-10)
`search_depth`	string	”basic”	Search depth: `basic` or `advanced`
`include_domains`	string[]	-	Only include results from these domains
`exclude_domains`	string[]	-	Exclude results from these domains

Response

{
  "results": [
    {
      "title": "AI Agents Are Transforming Software Development",
      "url": "https://example.com/ai-agents-2024",
      "content": "Recent advances in AI agents have enabled autonomous coding, debugging, and deployment...",
      "relevance_score": 0.95
    }
  ],
  "metadata": {
    "provider": "perf",
    "latency_ms": 1234,
    "cost_usd": 0.01
  }
}

Pricing

Tier	Cost per Search
Starter	$0.015
Pro	$0.012
Growth	$0.011
Team	$0.01

Documents / RAG

Upload documents and query them with semantic search. Use documents as context in chat requests for Q&A, structured extraction, and more.

Upload Document

POST https://api.withperf.pro/v1/documents

Upload a file directly or send base64-encoded content. Perf will automatically chunk the document, generate embeddings, and make it available for search and chat context.

File Upload (Recommended)

Send the file directly using multipart/form-data:

curl -X POST https://api.withperf.pro/v1/documents \
  -H "Authorization: Bearer pk_live_abc123" \
  -F "file=@product-datasheet.pdf"

Python:

import requests

response = requests.post(
    "https://api.withperf.pro/v1/documents",
    headers={"Authorization": "Bearer pk_live_abc123"},
    files={"file": open("product-datasheet.pdf", "rb")}
)
document_id = response.json()["id"]  # Save this to use with /v1/chat

Node.js:

const form = new FormData();
form.append("file", fs.createReadStream("product-datasheet.pdf"));

const response = await fetch("https://api.withperf.pro/v1/documents", {
  method: "POST",
  headers: { "Authorization": "Bearer pk_live_abc123" },
  body: form
});
const { id: documentId } = await response.json();

You can include optional fields as form fields alongside the file:

curl -X POST https://api.withperf.pro/v1/documents \
  -H "Authorization: Bearer pk_live_abc123" \
  -F "file=@product-datasheet.pdf" \
  -F "collection_id=solar-datasheets" \
  -F 'metadata={"manufacturer": "Vikram Solar"}'

JSON Upload (Base64)

Alternatively, send base64-encoded content in a JSON body:

curl -X POST https://api.withperf.pro/v1/documents \
  -H "Authorization: Bearer pk_live_abc123" \
  -H "Content-Type: application/json" \
  -d '{
    "filename": "product-datasheet.pdf",
    "content": "<base64-encoded-file-content>",
    "content_type": "application/pdf"
  }'

Parameters

File upload (multipart/form-data):

Field	Type	Required	Description
`file`	file	Yes	The file to upload (PDF, TXT, MD). Max 20 MB.
`collection_id`	string	No	Collection to organize the document into
`metadata`	string	No	JSON string of custom key-value metadata

JSON upload (application/json):

Parameter	Type	Required	Description
`filename`	string	Yes	Original filename (used for display and format detection)
`content`	string	Yes	Base64-encoded file content, or plain text for `.txt`/`.md` files
`content_type`	string	No	MIME type: `application/pdf`, `text/plain`, `text/markdown`
`collection_id`	string	No	Collection to organize the document into
`metadata`	object	No	Custom key-value metadata for filtering

Response

{
  "id": "doc_abc123",
  "filename": "product-datasheet.pdf",
  "content_type": "application/pdf",
  "status": "processing",
  "file_size_bytes": 125000,
  "collection_id": null,
  "created_at": "2024-01-15T10:30:00Z"
}

Save the id from the response — you’ll need it to reference this document in /v1/chat requests. Document Status:

processing - Being chunked and embedded
ready - Available for search
failed - Processing failed (check error_message)

Use with Chat API

Once a document is ready, pass its id as document_id in a /v1/chat request to use it as context:

curl -X POST https://api.withperf.pro/v1/chat \
  -H "Authorization: Bearer pk_live_abc123" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "Summarize the key points from this document"}
    ],
    "document_id": "doc_abc123"
  }'

You can also combine document_id with schema_id for structured data extraction. See Document Context in Chat API for full details.

Query Documents

POST https://api.withperf.pro/v1/documents/query

curl -X POST https://api.withperf.pro/v1/documents/query \
  -H "Authorization: Bearer pk_live_abc123" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What is the vacation policy?",
    "collection_id": "col_abc123",
    "top_k": 5,
    "threshold": 0.3
  }'

Parameter	Type	Default	Description
`query`	string	required	Search query
`collection_id`	string	none	Filter to a specific collection
`top_k`	integer	5	Maximum results
`threshold`	number	0.3	Minimum similarity score (0-1)

Response:

{
  "query": "What is the vacation policy?",
  "results": [
    {
      "chunk_id": "chunk_xyz789",
      "document_id": "doc_abc123",
      "filename": "company-handbook.pdf",
      "content": "Employees receive 20 days of paid vacation per year, accrued monthly...",
      "score": 0.94,
      "chunk_index": 15,
      "metadata": {
        "department": "HR"
      }
    }
  ],
  "metadata": {
    "latency_ms": 234,
    "cost_usd": 0.0001
  }
}

List Documents

GET https://api.withperf.pro/v1/documents?collection_id=col_abc123

Delete Document

DELETE https://api.withperf.pro/v1/documents/{documentId}

Document Limits by Tier

Tier	Documents	Storage	Collections
Pro	500	1 GB	10
Growth	5,000	10 GB	50
Team	50,000	100 GB	500
Enterprise	500,000	1 TB	5,000

Memory / Context

Persist conversation context across sessions. The AI remembers previous interactions without you managing conversation history.

Using Memory with Chat

Add session_id to your chat requests to enable memory:

curl -X POST https://api.withperf.pro/v1/chat \
  -H "Authorization: Bearer pk_live_abc123" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "Hi, my name is Sarah"}
    ],
    "session_id": "user_123_conversation",
    "memory": {
      "enabled": true,
      "include_last_n": 10
    }
  }'

Parameter	Type	Description
`session_id`	string	Unique session identifier
`memory.enabled`	boolean	Enable memory for this request
`memory.include_last_n`	integer	Include last N messages from memory

Now in a subsequent request:

curl -X POST https://api.withperf.pro/v1/chat \
  -H "Authorization: Bearer pk_live_abc123" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "What is my name?"}
    ],
    "session_id": "user_123_conversation",
    "memory": {"enabled": true}
  }'

The AI will remember: “Your name is Sarah, as you mentioned earlier.”

Memory Management

List Sessions:

GET https://api.withperf.pro/v1/memory

Get Session:

GET https://api.withperf.pro/v1/memory/{sessionId}

Clear Session:

POST https://api.withperf.pro/v1/memory/{sessionId}/clear

Delete Session:

DELETE https://api.withperf.pro/v1/memory/{sessionId}

Memory Limits by Tier

Tier	Memory Tokens	Sessions	Retention
Pro	100,000	100	30 days
Growth	500,000	1,000	90 days
Team	2,000,000	10,000	1 year
Enterprise	10,000,000	100,000	2 years

Auto-Summarization

When a session approaches its token limit, Perf automatically:

Summarizes older messages
Preserves recent messages intact
Continues conversation with summary + recent context

This ensures conversations can continue indefinitely within your tier limits.

Combining Tools

Tools work together seamlessly. For example, search the web, store relevant information in documents, and use memory to maintain context:

# 1. Search for current information
curl -X POST https://api.withperf.pro/v1/tools/search \
  -H "Authorization: Bearer pk_live_abc123" \
  -d '{"query": "latest AI news today"}'

# 2. Chat with memory and context
curl -X POST https://api.withperf.pro/v1/chat \
  -H "Authorization: Bearer pk_live_abc123" \
  -d '{
    "messages": [{"role": "user", "content": "Summarize the AI news I just searched"}],
    "session_id": "research_session",
    "memory": {"enabled": true}
  }'

Pricing Summary

Tool	Pricing Model
Web Search	Per search ($0.01-0.015)
Documents	Per embedding token
Memory	Included in tier

Plan Availability

Feature	Starter	Pro	Growth	Team	Enterprise
Web Search	Yes	Yes	Yes	Yes	Yes
Documents/RAG	-	Yes	Yes	Yes	Yes
Memory	-	Yes	Yes	Yes	Yes

Chat API - Core chat endpoint with tool integration
Streaming API - Real-time responses with tools
Policies API - Control tool usage with policies

Getting Started

Voice Agents

SDKs

API Documentation

Platform

Advanced

Resources

Tools API

Tools API

Available Tools

Web Search

Endpoint

Request

Response

Pricing

Documents / RAG

Upload Document

File Upload (Recommended)

JSON Upload (Base64)

Parameters

Response

Use with Chat API

Query Documents

List Documents

Delete Document

Document Limits by Tier

Memory / Context

Using Memory with Chat

Memory Management

Memory Limits by Tier

Auto-Summarization

Combining Tools

Pricing Summary

Plan Availability

​Tools API

​Available Tools

​Web Search

​Endpoint

​Request

​Response

​Pricing

​Documents / RAG

​Upload Document

​File Upload (Recommended)

​JSON Upload (Base64)

​Parameters

​Response

​Use with Chat API

​Query Documents

​List Documents

​Delete Document

​Document Limits by Tier

​Memory / Context

​Using Memory with Chat

​Memory Management

​Memory Limits by Tier

​Auto-Summarization

​Combining Tools

​Pricing Summary

​Plan Availability

​Related

Tools API

Available Tools

Web Search

Endpoint

Request

Response

Pricing

Documents / RAG

Upload Document

File Upload (Recommended)

JSON Upload (Base64)

Parameters

Response

Use with Chat API

Query Documents

List Documents

Delete Document

Document Limits by Tier

Memory / Context

Using Memory with Chat

Memory Management

Memory Limits by Tier

Auto-Summarization

Combining Tools

Pricing Summary

Plan Availability

Related