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# LiteLLM

Universal LLM memory integration via [LiteLLM](https://github.com/BerriAI/litellm). Add persistent memory to any LLM application with just a few lines of code.

[View Changelog →](/changelog/integrations/litellm)

## Features

- **Universal LLM Support** - Works with 100+ LLM providers via LiteLLM (OpenAI, Anthropic, Groq, Azure, AWS Bedrock, Google Vertex AI, and more)
- **Simple Integration** - Just configure, enable, and use `hindsight_litellm.completion()`
- **Automatic Memory Injection** - Relevant memories are injected into prompts before LLM calls
- **Automatic Conversation Storage** - Conversations are stored to Hindsight for future recall
- **Two Memory Modes** - Choose between `reflect` (synthesized context) or `recall` (raw memory retrieval)
- **Direct Memory APIs** - Query, synthesize, and store memories manually
- **Native Client Wrappers** - Alternative wrappers for OpenAI and Anthropic SDKs

## Installation

```bash
pip install hindsight-litellm
```

## Quick Start

```python
import hindsight_litellm

# Configure and enable memory integration
hindsight_litellm.configure(
    hindsight_api_url="http://localhost:8888",
    bank_id="my-agent",
)
hindsight_litellm.enable()

# Use the convenience wrapper - memory is automatically injected and stored
response = hindsight_litellm.completion(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "What did we discuss about AI?"}]
)
```

## How It Works

When you call `completion()`, the following happens automatically:

1. **Memory Retrieval** - Hindsight is queried for relevant memories based on the conversation
2. **Prompt Injection** - Memories are injected into the system message
3. **LLM Call** - The enriched prompt is sent to the LLM
4. **Conversation Storage** - The conversation is stored to Hindsight for future recall
5. **Response Returned** - You receive the response as normal

## Configuration Options

```python
hindsight_litellm.configure(
    # Required
    hindsight_api_url="http://localhost:8888",  # Hindsight API server URL
    bank_id="my-agent",                          # Memory bank ID

    api_key="your-api-key",        # Optional API key for authentication

    # Optional - Memory behavior
    store_conversations=True,      # Store conversations after LLM calls
    inject_memories=True,          # Inject relevant memories into prompts
    use_reflect=False,             # Use reflect API (synthesized) vs recall (raw memories)
    reflect_include_facts=False,   # Include source facts with reflect responses
    max_memories=None,             # Maximum memories to inject (None = unlimited)
    max_memory_tokens=4096,        # Maximum tokens for memory context
    recall_budget="mid",           # Recall budget: "low", "mid", "high"
    fact_types=["world", "agent"], # Filter fact types to inject

    # Optional - Bank Configuration
    bank_name="My Agent",          # Human-readable display name for the memory bank
    mission="This agent...",       # Instructions guiding what Hindsight should remember

    # Optional - Advanced
    injection_mode="system_message",  # or "prepend_user"
    excluded_models=["gpt-3.5*"],     # Exclude certain models
    verbose=True,                     # Enable verbose logging and debug info
)
```

### Bank Configuration

The `mission` and `bank_name` parameters configure the memory bank itself. When provided, `configure()` will automatically create or update the bank with these settings.

```python
hindsight_litellm.configure(
    hindsight_api_url="http://localhost:8888",
    bank_id="support-router",
    bank_name="Customer Support Router",
    mission="""You're a customer support router - keep track of which types of issues
    should go to which teams (billing, technical, sales), customer preferences for
    communication channels, and past issue resolutions.""",
)
```

### Memory Modes: Reflect vs Recall

- **Recall mode** (`use_reflect=False`, default): Retrieves raw memory facts and injects them as a numbered list. Best when you need precise, individual memories.
- **Reflect mode** (`use_reflect=True`): Synthesizes memories into a coherent context paragraph. Best for natural, conversational memory context.

```python
# Recall mode - raw memories
hindsight_litellm.configure(
    bank_id="my-agent",
    use_reflect=False,  # Default
)
# Injects: "1. [WORLD] User prefers Python\n2. [MENTAL MODEL] User prefers simple code..."

# Reflect mode - synthesized context
hindsight_litellm.configure(
    bank_id="my-agent",
    use_reflect=True,
)
# Injects: "Based on previous conversations, the user is a Python developer who..."
```

## Multi-Provider Support

Works with any LiteLLM-supported provider:

```python
import hindsight_litellm

hindsight_litellm.configure(
    hindsight_api_url="http://localhost:8888",
    bank_id="my-agent",
)
hindsight_litellm.enable()

# OpenAI
hindsight_litellm.completion(model="gpt-4o", messages=[...])

# Anthropic
hindsight_litellm.completion(model="claude-3-5-sonnet-20241022", messages=[...])

# Groq
hindsight_litellm.completion(model="groq/llama-3.1-70b-versatile", messages=[...])

# Azure OpenAI
hindsight_litellm.completion(model="azure/gpt-4", messages=[...])

# AWS Bedrock
hindsight_litellm.completion(model="bedrock/anthropic.claude-3", messages=[...])

# Google Vertex AI
hindsight_litellm.completion(model="vertex_ai/gemini-pro", messages=[...])
```

## Direct Memory APIs

### Recall - Query raw memories

```python
from hindsight_litellm import configure, recall

configure(bank_id="my-agent", hindsight_api_url="http://localhost:8888")

memories = recall("what projects am I working on?", budget="mid")
for m in memories:
    print(f"- [{m.fact_type}] {m.text}")
```

### Reflect - Get synthesized context

```python
from hindsight_litellm import configure, reflect

configure(bank_id="my-agent", hindsight_api_url="http://localhost:8888")

result = reflect("what do you know about the user's preferences?")
print(result.text)
```

### Retain - Store memories

```python
from hindsight_litellm import configure, retain

configure(bank_id="my-agent", hindsight_api_url="http://localhost:8888")

result = retain(
    content="User mentioned they're working on a machine learning project",
    context="Discussion about current projects",
)
```

### Async APIs

```python
from hindsight_litellm import arecall, areflect, aretain

# Async versions of all memory APIs
memories = await arecall("what do you know about me?")
context = await areflect("summarize user preferences")
result = await aretain(content="New information to remember")
```

## Native Client Wrappers

Alternative to LiteLLM callbacks for direct SDK integration.

### OpenAI Wrapper

```python
from openai import OpenAI
from hindsight_litellm import wrap_openai

client = OpenAI()
wrapped = wrap_openai(
    client,
    bank_id="my-agent",
    hindsight_api_url="http://localhost:8888",
)

response = wrapped.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "What do you know about me?"}]
)
```

### Anthropic Wrapper

```python
from anthropic import Anthropic
from hindsight_litellm import wrap_anthropic

client = Anthropic()
wrapped = wrap_anthropic(
    client,
    bank_id="my-agent",
    hindsight_api_url="http://localhost:8888",
)

response = wrapped.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}]
)
```

## Debug Mode

When `verbose=True`, you can inspect exactly what memories are being injected:

```python
from hindsight_litellm import configure, enable, completion, get_last_injection_debug

configure(
    bank_id="my-agent",
    hindsight_api_url="http://localhost:8888",
    verbose=True,
)
enable()

response = completion(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "What's my favorite color?"}]
)

# Inspect what was injected
debug = get_last_injection_debug()
if debug:
    print(f"Mode: {debug.mode}")           # "reflect" or "recall"
    print(f"Injected: {debug.injected}")   # True/False
    print(f"Results: {debug.results_count}")
    print(f"Memory context:\n{debug.memory_context}")
```

## Context Manager

```python
from hindsight_litellm import hindsight_memory
import litellm

with hindsight_memory(bank_id="user-123"):
    response = litellm.completion(model="gpt-4", messages=[...])
# Memory integration automatically disabled after context
```

## Disabling and Cleanup

```python
from hindsight_litellm import disable, cleanup

# Temporarily disable memory integration
disable()

# Clean up all resources (call when shutting down)
cleanup()
```

## API Reference

### Main Functions

| Function | Description |
|----------|-------------|
| `configure(...)` | Configure global Hindsight settings |
| `enable()` | Enable memory integration with LiteLLM |
| `disable()` | Disable memory integration |
| `is_enabled()` | Check if memory integration is enabled |
| `cleanup()` | Clean up all resources |

### Configuration Functions

| Function | Description |
|----------|-------------|
| `get_config()` | Get current configuration |
| `is_configured()` | Check if Hindsight is configured |
| `reset_config()` | Reset configuration to defaults |

### Memory Functions

| Function | Description |
|----------|-------------|
| `recall(query, ...)` | Synchronously query raw memories |
| `arecall(query, ...)` | Asynchronously query raw memories |
| `reflect(query, ...)` | Synchronously get synthesized memory context |
| `areflect(query, ...)` | Asynchronously get synthesized memory context |
| `retain(content, ...)` | Synchronously store a memory |
| `aretain(content, ...)` | Asynchronously store a memory |

### Debug Functions

| Function | Description |
|----------|-------------|
| `get_last_injection_debug()` | Get debug info from last memory injection |
| `clear_injection_debug()` | Clear stored debug info |

### Client Wrappers

| Function | Description |
|----------|-------------|
| `wrap_openai(client, ...)` | Wrap OpenAI client with memory |
| `wrap_anthropic(client, ...)` | Wrap Anthropic client with memory |

## Requirements

- Python >= 3.10
- litellm >= 1.40.0
- A running Hindsight API server
