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Add support for procedural memory (#2460)

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This commit is contained in:
Deshraj Yadav
2025-03-29 15:58:12 -07:00
committed by GitHub
parent 2bf9286071
commit 72bb631bb5
10 changed files with 158 additions and 20 deletions
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2
mem0/__init__.py
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@@ -2,5 +2,5 @@ import importlib.metadata
__version__ = importlib.metadata.version("mem0ai")
from mem0.client.main import MemoryClient, AsyncMemoryClient # noqa
from mem0.client.main import AsyncMemoryClient, MemoryClient # noqa
from mem0.memory.main import Memory # noqa

7
mem0/configs/enums.py Normal file
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@@ -0,0 +1,7 @@
from enum import Enum
class MemoryType(Enum):
SEMANTIC = "semantic_memory"
EPISODIC = "episodic_memory"
PROCEDURAL = "procedural_memory"

77
mem0/configs/prompts.py
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@@ -208,6 +208,83 @@ Please note to return the IDs in the output from the input IDs only and do not g
}
"""
PROCEDURAL_MEMORY_SYSTEM_PROMPT = """
You are a memory summarization system that records and preserves the complete interaction history between a human and an AI agent. You are provided with the agent’s execution history over the past N steps. Your task is to produce a comprehensive summary of the agent's output history that contains every detail necessary for the agent to continue the task without ambiguity. **Every output produced by the agent must be recorded verbatim as part of the summary.**
### Overall Structure:
- **Overview (Global Metadata):**
- **Task Objective**: The overall goal the agent is working to accomplish.
- **Progress Status**: The current completion percentage and summary of specific milestones or steps completed.
- **Sequential Agent Actions (Numbered Steps):**
Each numbered step must be a self-contained entry that includes all of the following elements:
1. **Agent Action**:
- Precisely describe what the agent did (e.g., "Clicked on the 'Blog' link", "Called API to fetch content", "Scraped page data").
- Include all parameters, target elements, or methods involved.
2. **Action Result (Mandatory, Unmodified)**:
- Immediately follow the agent action with its exact, unaltered output.
- Record all returned data, responses, HTML snippets, JSON content, or error messages exactly as received. This is critical for constructing the final output later.
3. **Embedded Metadata**:
For the same numbered step, include additional context such as:
- **Key Findings**: Any important information discovered (e.g., URLs, data points, search results).
- **Navigation History**: For browser agents, detail which pages were visited, including their URLs and relevance.
- **Errors & Challenges**: Document any error messages, exceptions, or challenges encountered along with any attempted recovery or troubleshooting.
- **Current Context**: Describe the state after the action (e.g., "Agent is on the blog detail page" or "JSON data stored for further processing") and what the agent plans to do next.
### Guidelines:
1. **Preserve Every Output**: The exact output of each agent action is essential. Do not paraphrase or summarize the output. It must be stored as is for later use.
2. **Chronological Order**: Number the agent actions sequentially in the order they occurred. Each numbered step is a complete record of that action.
3. **Detail and Precision**:
- Use exact data: Include URLs, element indexes, error messages, JSON responses, and any other concrete values.
- Preserve numeric counts and metrics (e.g., "3 out of 5 items processed").
- For any errors, include the full error message and, if applicable, the stack trace or cause.
4. **Output Only the Summary**: The final output must consist solely of the structured summary with no additional commentary or preamble.
### Example Template:
```
**Task Objective**: Scrape blog post titles and full content from the OpenAI blog.
**Progress Status**: 10% complete — 5 out of 50 blog posts processed.
1. **Agent Action**: Opened URL "https://openai.com"
**Action Result**:
"HTML Content of the homepage including navigation bar with links: 'Blog', 'API', 'ChatGPT', etc."
**Key Findings**: Navigation bar loaded correctly.
**Navigation History**: Visited homepage: "https://openai.com"
**Current Context**: Homepage loaded; ready to click on the 'Blog' link.
2. **Agent Action**: Clicked on the "Blog" link in the navigation bar.
**Action Result**:
"Navigated to 'https://openai.com/blog/' with the blog listing fully rendered."
**Key Findings**: Blog listing shows 10 blog previews.
**Navigation History**: Transitioned from homepage to blog listing page.
**Current Context**: Blog listing page displayed.
3. **Agent Action**: Extracted the first 5 blog post links from the blog listing page.
**Action Result**:
"[ '/blog/chatgpt-updates', '/blog/ai-and-education', '/blog/openai-api-announcement', '/blog/gpt-4-release', '/blog/safety-and-alignment' ]"
**Key Findings**: Identified 5 valid blog post URLs.
**Current Context**: URLs stored in memory for further processing.
4. **Agent Action**: Visited URL "https://openai.com/blog/chatgpt-updates"
**Action Result**:
"HTML content loaded for the blog post including full article text."
**Key Findings**: Extracted blog title "ChatGPT Updates – March 2025" and article content excerpt.
**Current Context**: Blog post content extracted and stored.
5. **Agent Action**: Extracted blog title and full article content from "https://openai.com/blog/chatgpt-updates"
**Action Result**:
"{ 'title': 'ChatGPT Updates – March 2025', 'content': 'We\'re introducing new updates to ChatGPT, including improved browsing capabilities and memory recall... (full content)' }"
**Key Findings**: Full content captured for later summarization.
**Current Context**: Data stored; ready to proceed to next blog post.
... (Additional numbered steps for subsequent actions)
```
"""
def get_update_memory_messages(retrieved_old_memory_dict, response_content, custom_update_memory_prompt=None):
if custom_update_memory_prompt is None:

1
mem0/configs/vector_stores/azure_ai_search.py
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@@ -1,4 +1,5 @@
from typing import Any, Dict, Optional
from pydantic import BaseModel, Field, model_validator

2
mem0/configs/vector_stores/supabase.py
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@@ -1,5 +1,5 @@
from typing import Any, Dict, Optional
from enum import Enum
from typing import Any, Dict, Optional
from pydantic import BaseModel, Field, model_validator

1
mem0/configs/vector_stores/weaviate.py
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@@ -1,4 +1,5 @@
from typing import Any, ClassVar, Dict, Optional
from pydantic import BaseModel, Field, model_validator

74
mem0/memory/main.py
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@@ -11,17 +11,15 @@ import pytz
from pydantic import ValidationError
from mem0.configs.base import MemoryConfig, MemoryItem
from mem0.configs.prompts import get_update_memory_messages
from mem0.configs.enums import MemoryType
from mem0.configs.prompts import (PROCEDURAL_MEMORY_SYSTEM_PROMPT,
get_update_memory_messages)
from mem0.memory.base import MemoryBase
from mem0.memory.setup import setup_config
from mem0.memory.storage import SQLiteManager
from mem0.memory.telemetry import capture_event
from mem0.memory.utils import (
get_fact_retrieval_messages,
parse_messages,
parse_vision_messages,
remove_code_blocks,
)
from mem0.memory.utils import (get_fact_retrieval_messages, parse_messages,
parse_vision_messages, remove_code_blocks)
from mem0.utils.factory import EmbedderFactory, LlmFactory, VectorStoreFactory
# Setup user config
@@ -89,6 +87,7 @@ class Memory(MemoryBase):
metadata=None,
filters=None,
infer=True,
memory_type=None,
prompt=None,
):
"""
@@ -102,8 +101,8 @@ class Memory(MemoryBase):
metadata (dict, optional): Metadata to store with the memory. Defaults to None.
filters (dict, optional): Filters to apply to the search. Defaults to None.
infer (bool, optional): Whether to infer the memories. Defaults to True.
prompt (str, optional): Prompt to use for memory deduction. Defaults to None.
memory_type (str, optional): Type of memory to create. Defaults to None. By default, it creates the short term memories and long term (semantic and episodic) memories. Pass "procedural_memory" to create procedural memories.
prompt (str, optional): Prompt to use for the memory creation. Defaults to None.
Returns:
dict: A dictionary containing the result of the memory addition operation.
result: dict of affected events with each dict has the following key:
@@ -131,9 +130,18 @@ class Memory(MemoryBase):
if not any(key in filters for key in ("user_id", "agent_id", "run_id")):
raise ValueError("One of the filters: user_id, agent_id or run_id is required!")
if memory_type is not None and memory_type != MemoryType.PROCEDURAL.value:
raise ValueError(
f"Invalid 'memory_type'. Please pass {MemoryType.PROCEDURAL.value} to create procedural memories."
)
if isinstance(messages, str):
messages = [{"role": "user", "content": messages}]
if agent_id is not None and memory_type == MemoryType.PROCEDURAL.value:
results = self._create_procedural_memory(messages, metadata, prompt)
return results
if self.config.llm.config.get("enable_vision"):
messages = parse_vision_messages(messages, self.llm, self.config.llm.config.get("vision_details"))
else:
@@ -595,11 +603,11 @@ class Memory(MemoryBase):
return self.db.get_history(memory_id)
def _create_memory(self, data, existing_embeddings, metadata=None):
logging.info(f"Creating memory with {data=}")
logging.debug(f"Creating memory with {data=}")
if data in existing_embeddings:
embeddings = existing_embeddings[data]
else:
embeddings = self.embedding_model.embed(data, "add")
embeddings = self.embedding_model.embed(data, memory_action="add")
memory_id = str(uuid.uuid4())
metadata = metadata or {}
metadata["data"] = data
@@ -615,6 +623,50 @@ class Memory(MemoryBase):
capture_event("mem0._create_memory", self, {"memory_id": memory_id})
return memory_id
def _create_procedural_memory(self, messages, metadata, llm=None, prompt=None):
"""
Create a procedural memory
"""
try:
from langchain_core.messages.utils import convert_to_messages # type: ignore
except Exception:
logger.error("Import error while loading langchain-core. Please install 'langchain-core' to use procedural memory.")
raise
logger.info("Creating procedural memory")
parsed_messages = [
{"role": "system", "content": prompt or PROCEDURAL_MEMORY_SYSTEM_PROMPT},
*messages,
{"role": "user", "content": "Create procedural memory of the above conversation."},
]
try:
if llm is not None:
parsed_messages = convert_to_messages(parsed_messages)
response = llm.invoke(messages=parsed_messages)
procedural_memory = response.content
else:
procedural_memory = self.llm.generate_response(messages=parsed_messages)
except Exception as e:
logger.error(f"Error generating procedural memory summary: {e}")
raise
if metadata is None:
raise ValueError("Metadata cannot be done for procedural memory.")
metadata["memory_type"] = MemoryType.PROCEDURAL.value
# Generate embeddings for the summary
embeddings = self.embedding_model.embed(procedural_memory, memory_action="add")
# Create the memory
memory_id = self._create_memory(procedural_memory, {procedural_memory: embeddings}, metadata=metadata)
capture_event("mem0._create_procedural_memory", self, {"memory_id": memory_id})
# Return results in the same format as add()
result = {"results": [{"id": memory_id, "memory": procedural_memory, "event": "ADD"}]}
return result
def _update_memory(self, memory_id, data, existing_embeddings, metadata=None):
logger.info(f"Updating memory with {data=}")

2
mem0/memory/storage.py
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@@ -1,6 +1,6 @@
import sqlite3
import uuid
import threading
import uuid
class SQLiteManager:

2
mem0/vector_stores/supabase.py
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@@ -9,8 +9,8 @@ try:
except ImportError:
raise ImportError("The 'vecs' library is required. Please install it using 'pip install vecs'.")
from mem0.configs.vector_stores.supabase import IndexMeasure, IndexMethod
from mem0.vector_stores.base import VectorStoreBase
from mem0.configs.vector_stores.supabase import IndexMethod, IndexMeasure
logger = logging.getLogger(__name__)

2
pyproject.toml
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@@ -1,6 +1,6 @@
[tool.poetry]
name = "mem0ai"
version = "0.1.79"
version = "0.1.80"
description = "Long-term memory for AI Agents"
authors = ["Mem0 <founders@mem0.ai>"]
exclude = [