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Recallr seamlessly integrates with Google Gemini by acting as a forward proxy. Configure your Gemini client to use our proxy URL and we’ll inject relevant context from user memory into each request.

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Quick Start

from google import genai
from google.genai import types

client = genai.Client(
    api_key='YOUR_GEMINI_API_KEY',  # Your Google Gemini API key
    http_options={
        'api_version': 'v1beta',
        'base_url': 'https://api.recallrai.com/api/v1/forward/https://generativelanguage.googleapis.com',
        'headers': {
            'X-Recallr-API-Key': 'rai-...',
            'X-Recallr-Project-Id': 'your-project-id',
            'X-Recallr-User-Id': 'user-123',
            'X-Recallr-Allow-New-User-Creation': 'true',
            'X-Recallr-Session-Timeout-Seconds': '600',
            'X-Recallr-Recall-Strategy': 'low_latency',  # Optional
        }
    }
)

# Use normally - memory is automatically injected
response = client.models.generate_content(
    model='gemini-2.5-pro',
    contents='My name is Alice',
    config=types.GenerateContentConfig(
        system_instruction='You are a helpful assistant.',
    )
)

print(response.text)

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Supported APIs

Generate Content

Standard text generation with non-streaming support

Generate Content Stream

Real-time streaming responses for interactive experiences

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Required Headers

These headers must be included via the http_options configuration:
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X-Recallr-API-Key
string
required
Your Recallr API key. Get it from the dashboard.
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X-Recallr-Project-Id
string
required
Your Recallr Project ID. Get it from the dashboard.
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X-Recallr-User-Id
string
required
Unique identifier for the user. Used to maintain separate memory graphs per user.
Must be passed in the headers configuration when initializing the Gemini client.

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Optional Headers

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Session Management

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X-Recallr-Allow-New-User-Creation
boolean
default:"false"
Automatically create a new user if the specified User-ID doesn’t exist. Set to true to avoid errors for new users.
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X-Recallr-Session-Timeout-Seconds
integer
default:"600"
Inactivity period (in seconds) before creating a new session. Minimum value is 600 (10 minutes).
Messages within a session are always passed directly to the LLM. Only memories from previous sessions are retrieved and injected as context.

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Recall Configuration

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X-Recallr-Recall-Strategy
string
default:"balanced"
Controls the recall method used for retrieving memories. Affects latency and accuracy.
  • low_latency
  • balanced
  • deep
Best for: Voice agents and real-time applications
  • Fastest response time
  • Retrieves more memories to compensate for reduced accuracy
  • Use when sub-second latency is critical
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X-Recallr-Min-Top-K
integer
default:"10"
Minimum number of memories to retrieve from the knowledge graph.
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X-Recallr-Max-Top-K
integer
default:"50"
Maximum number of memories to retrieve from the knowledge graph.
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X-Recallr-Memories-Threshold
float
default:"0.7"
Similarity threshold for retrieving individual memories (0.0 to 1.0). Lower values retrieve more memories.
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X-Recallr-Summaries-Threshold
float
default:"0.6"
Similarity threshold for retrieving session summaries (0.0 to 1.0). Lower values retrieve more summaries.
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X-Recallr-Last-N-User-Messages
integer
Include last N messages from past sessions when building context.
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X-Recallr-Last-N-Summaries
integer
Include last N session summaries when building context.
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X-Recallr-Timezone
string
User’s timezone for temporal context (e.g., “America/New_York”). Helps with time-based memories.
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X-Recallr-Include-System-Prompt
boolean
default:"true"
Whether to include Recallr AI’s system prompt (~ 3k tokens) in the context. This prompt includes instructions for how to use the injected memories. Set to false if you already have those instructions in your system prompt.

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Response Headers

Recallr returns these headers in the response for debugging and session tracking:
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X-Recallr-Session-Id
string
The internal session ID used by Recallr. Use this to continue the same session in future requests.
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X-Recallr-User-Id
string
Unique identifier for the user. Matches the X-Recallr-User-Id sent in the request.
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X-Recallr-Request-Id
string
Unique identifier for this request. Use for debugging and tracing.
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X-Recallr-Process-Time
string
Time taken to process the request on Recallr’s side (in milliseconds).

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Examples

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Generate Content - Non-Streaming

from google import genai
from google.genai import types

client = genai.Client(
    api_key='YOUR_GEMINI_API_KEY',  # Your Google Gemini API key
    http_options={
        'api_version': 'v1beta',
        'base_url': 'https://api.recallrai.com/api/v1/forward/https://generativelanguage.googleapis.com',
        'headers': {
            'X-Recallr-API-Key': 'rai-...',
            'X-Recallr-Project-Id': 'project-id',
            'X-Recallr-User-Id': 'alice-123',
            'X-Recallr-Allow-New-User-Creation': 'true',
            'X-Recallr-Session-Timeout-Seconds': '600',
            'X-Recallr-Recall-Strategy': 'low_latency',  # Optional
        }
    }
)

# Store user information in memory
response = client.models.generate_content(
    model='gemini-2.5-pro',
    contents='My name is Alice and I love Python programming.',
    config=types.GenerateContentConfig(
        system_instruction='You are a helpful assistant.',
    )
)

print(response.text)

​
Generate Content - Streaming

from google import genai
from google.genai import types

client = genai.Client(
    api_key='YOUR_GEMINI_API_KEY',  # Your Google Gemini API key
    http_options={
        'api_version': 'v1beta',
        'base_url': 'https://api.recallrai.com/api/v1/forward/https://generativelanguage.googleapis.com',
        'headers': {
            'X-Recallr-API-Key': 'rai-...',
            'X-Recallr-Project-Id': 'project-id',
            'X-Recallr-User-Id': 'alice-123',
            'X-Recallr-Allow-New-User-Creation': 'true',
            'X-Recallr-Session-Timeout-Seconds': '600',
            'X-Recallr-Recall-Strategy': 'low_latency',  # Optional
        }
    }
)

# Recall stored information about the user with streaming
response = client.models.generate_content_stream(
    model='gemini-2.5-pro',
    contents='What do you know about me and my interests?',
    config=types.GenerateContentConfig(
        system_instruction='You are a helpful assistant.',
    )
)

for chunk in response:
    if chunk.text:
        print(chunk.text, end='', flush=True)

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How It Works

Need Help?

Contact our support team for assistance with Gemini integration