andre/crewAI
1
0
Fork 0
mirror of https://github.com/crewAIInc/crewAI.git synced 2025-12-16 04:18:35 +00:00
Code Issues Actions 4 Packages Projects Releases Wiki Activity

Add support for retrieving user preferences and memories using Mem0 (#1209)

Browse Source 
* Integrate Mem0

* Update src/crewai/memory/contextual/contextual_memory.py

Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>

* pending commit for _fetch_user_memories

* update poetry.lock

* fixes mypy issues

* fix mypy checks

* New fixes for user_id

* remove memory_provider

* handle memory_provider

* checks for memory_config

* add mem0 to dependency

* Update pyproject.toml

Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>

* update docs

* update doc

* bump mem0 version

* fix api error msg and mypy issue

* mypy fix

* resolve comments

* fix memory usage without mem0

* mem0 version bump

* lazy import mem0

---------

Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>
Co-authored-by: João Moura <joaomdmoura@gmail.com>
Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com>
This commit is contained in:
Dev Khant
2024-11-15 00:29:24 +05:30
committed by GitHub
parent 9285ebf8a2
commit e70bc94ab6
17 changed files with 619 additions and 34 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
docs/concepts/crews.mdx
View File

@@ -22,7 +22,8 @@ A crew in crewAI represents a collaborative group of agents working together to
| **Max RPM** _(optional)_ | `max_rpm` | Maximum requests per minute the crew adheres to during execution. Defaults to `None`. |
| **Language** _(optional)_ | `language` | Language used for the crew, defaults to English. |
| **Language File** _(optional)_ | `language_file` | Path to the language file to be used for the crew. |
| **Memory** _(optional)_ | `memory` | Utilized for storing execution memories (short-term, long-term, entity memory). Defaults to `False`. |
| **Memory** _(optional)_ | `memory` | Utilized for storing execution memories (short-term, long-term, entity memory). |
| **Memory Config** _(optional)_ | `memory_config` | Configuration for the memory provider to be used by the crew. |
| **Cache** _(optional)_ | `cache` | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`. |
| **Embedder** _(optional)_ | `embedder` | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`. |
| **Full Output** _(optional)_ | `full_output` | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`. |

42
docs/concepts/memory.mdx
View File

@@ -18,6 +18,7 @@ reason, and learn from past interactions.
| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
| **Entity Memory** | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
| **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
| **User Memory** | Stores user-specific information and preferences, enhancing personalization and user experience. |
## How Memory Systems Empower Agents
@@ -92,6 +93,47 @@ my_crew = Crew(
)
```
## Integrating Mem0 for Enhanced User Memory
[Mem0](https://mem0.ai/) is a self-improving memory layer for LLM applications, enabling personalized AI experiences.
To include user-specific memory you can get your API key [here](https://app.mem0.ai/dashboard/api-keys) and refer the [docs](https://docs.mem0.ai/platform/quickstart#4-1-create-memories) for adding user preferences.
```python Code
import os
from crewai import Crew, Process
from mem0 import MemoryClient
# Set environment variables for Mem0
os.environ["MEM0_API_KEY"] = "m0-xx"
# Step 1: Record preferences based on past conversation or user input
client = MemoryClient()
messages = [
{"role": "user", "content": "Hi there! I'm planning a vacation and could use some advice."},
{"role": "assistant", "content": "Hello! I'd be happy to help with your vacation planning. What kind of destination do you prefer?"},
{"role": "user", "content": "I am more of a beach person than a mountain person."},
{"role": "assistant", "content": "That's interesting. Do you like hotels or Airbnb?"},
{"role": "user", "content": "I like Airbnb more."},
]
client.add(messages, user_id="john")
# Step 2: Create a Crew with User Memory
crew = Crew(
agents=[...],
tasks=[...],
verbose=True,
process=Process.sequential,
memory=True,
memory_config={
"provider": "mem0",
"config": {"user_id": "john"},
},
)
```
## Additional Embedding Providers

6
poetry.lock generated
View File

@@ -1597,12 +1597,12 @@ files = [
google-auth = ">=2.14.1,<3.0.dev0"
googleapis-common-protos = ">=1.56.2,<2.0.dev0"
grpcio = [
{version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
{version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
{version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
]
grpcio-status = [
{version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
{version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
{version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
]
proto-plus = ">=1.22.3,<2.0.0dev"
protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0"
@@ -4286,8 +4286,8 @@ files = [
[package.dependencies]
numpy = [
{version = ">=1.23.2", markers = "python_version == \"3.11\""},
{version = ">=1.22.4", markers = "python_version < \"3.11\""},
{version = ">=1.23.2", markers = "python_version == \"3.11\""},
{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
]
python-dateutil = ">=2.8.2"

1
pyproject.toml
View File

@@ -39,6 +39,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
[project.optional-dependencies]
tools = ["crewai-tools>=0.14.0"]
agentops = ["agentops>=0.3.0"]
mem0 = ["mem0ai>=0.1.29"]
[tool.uv]
dev-dependencies = [

2
src/crewai/agent.py
View File

@@ -262,9 +262,11 @@ class Agent(BaseAgent):
if self.crew and self.crew.memory:
contextual_memory = ContextualMemory(
self.crew.memory_config,
self.crew._short_term_memory,
self.crew._long_term_memory,
self.crew._entity_memory,
self.crew._user_memory,
)
memory = contextual_memory.build_context_for_task(task, context)
if memory.strip() != "":

24
src/crewai/crew.py
View File

@@ -27,6 +27,7 @@ from crewai.llm import LLM
from crewai.memory.entity.entity_memory import EntityMemory
from crewai.memory.long_term.long_term_memory import LongTermMemory
from crewai.memory.short_term.short_term_memory import ShortTermMemory
from crewai.memory.user.user_memory import UserMemory
from crewai.process import Process
from crewai.task import Task
from crewai.tasks.conditional_task import ConditionalTask
@@ -71,6 +72,7 @@ class Crew(BaseModel):
manager_llm: The language model that will run manager agent.
manager_agent: Custom agent that will be used as manager.
memory: Whether the crew should use memory to store memories of it's execution.
memory_config: Configuration for the memory to be used for the crew.
cache: Whether the crew should use a cache to store the results of the tools execution.
function_calling_llm: The language model that will run the tool calling for all the agents.
process: The process flow that the crew will follow (e.g., sequential, hierarchical).
@@ -94,6 +96,7 @@ class Crew(BaseModel):
_short_term_memory: Optional[InstanceOf[ShortTermMemory]] = PrivateAttr()
_long_term_memory: Optional[InstanceOf[LongTermMemory]] = PrivateAttr()
_entity_memory: Optional[InstanceOf[EntityMemory]] = PrivateAttr()
_user_memory: Optional[InstanceOf[UserMemory]] = PrivateAttr()
_train: Optional[bool] = PrivateAttr(default=False)
_train_iteration: Optional[int] = PrivateAttr()
_inputs: Optional[Dict[str, Any]] = PrivateAttr(default=None)
@@ -114,6 +117,10 @@ class Crew(BaseModel):
default=False,
description="Whether the crew should use memory to store memories of it's execution",
)
memory_config: Optional[Dict[str, Any]] = Field(
default=None,
description="Configuration for the memory to be used for the crew.",
)
short_term_memory: Optional[InstanceOf[ShortTermMemory]] = Field(
default=None,
description="An Instance of the ShortTermMemory to be used by the Crew",
@@ -126,7 +133,11 @@ class Crew(BaseModel):
default=None,
description="An Instance of the EntityMemory to be used by the Crew",
)
embedder: Optional[Any] = Field(
user_memory: Optional[InstanceOf[UserMemory]] = Field(
default=None,
description="An instance of the UserMemory to be used by the Crew to store/fetch memories of a specific user.",
)
embedder: Optional[dict] = Field(
default=None,
description="Configuration for the embedder to be used for the crew.",
)
@@ -238,13 +249,22 @@ class Crew(BaseModel):
self._short_term_memory = (
self.short_term_memory
if self.short_term_memory
else ShortTermMemory(crew=self, embedder_config=self.embedder)
else ShortTermMemory(
crew=self,
embedder_config=self.embedder,
)
)
self._entity_memory = (
self.entity_memory
if self.entity_memory
else EntityMemory(crew=self, embedder_config=self.embedder)
)
if hasattr(self, "memory_config") and self.memory_config is not None:
self._user_memory = (
self.user_memory if self.user_memory else UserMemory(crew=self)
)
else:
self._user_memory = None
return self
@model_validator(mode="after")

3
src/crewai/memory/__init__.py
View File

@@ -1,5 +1,6 @@
from .entity.entity_memory import EntityMemory
from .long_term.long_term_memory import LongTermMemory
from .short_term.short_term_memory import ShortTermMemory
from .user.user_memory import UserMemory
__all__ = ["EntityMemory", "LongTermMemory", "ShortTermMemory"]
__all__ = ["UserMemory", "EntityMemory", "LongTermMemory", "ShortTermMemory"]

47
src/crewai/memory/contextual/contextual_memory.py
View File

@@ -1,13 +1,25 @@
from typing import Optional
from typing import Optional, Dict, Any
from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory
from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory, UserMemory
class ContextualMemory:
def __init__(self, stm: ShortTermMemory, ltm: LongTermMemory, em: EntityMemory):
def __init__(
self,
memory_config: Optional[Dict[str, Any]],
stm: ShortTermMemory,
ltm: LongTermMemory,
em: EntityMemory,
um: UserMemory,
):
if memory_config is not None:
self.memory_provider = memory_config.get("provider")
else:
self.memory_provider = None
self.stm = stm
self.ltm = ltm
self.em = em
self.um = um
def build_context_for_task(self, task, context) -> str:
"""
@@ -23,6 +35,8 @@ class ContextualMemory:
context.append(self._fetch_ltm_context(task.description))
context.append(self._fetch_stm_context(query))
context.append(self._fetch_entity_context(query))
if self.memory_provider == "mem0":
context.append(self._fetch_user_context(query))
return "\n".join(filter(None, context))
def _fetch_stm_context(self, query) -> str:
@@ -32,7 +46,10 @@ class ContextualMemory:
"""
stm_results = self.stm.search(query)
formatted_results = "\n".join(
[f"- {result['context']}" for result in stm_results]
[
f"- {result['memory'] if self.memory_provider == 'mem0' else result['context']}"
for result in stm_results
]
)
return f"Recent Insights:\n{formatted_results}" if stm_results else ""
@@ -62,6 +79,26 @@ class ContextualMemory:
"""
em_results = self.em.search(query)
formatted_results = "\n".join(
[f"- {result['context']}" for result in em_results] # type: ignore # Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
[
f"- {result['memory'] if self.memory_provider == 'mem0' else result['context']}"
for result in em_results
] # type: ignore # Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
)
return f"Entities:\n{formatted_results}" if em_results else ""
def _fetch_user_context(self, query: str) -> str:
"""
Fetches and formats relevant user information from User Memory.
Args:
query (str): The search query to find relevant user memories.
Returns:
str: Formatted user memories as bullet points, or an empty string if none found.
"""
user_memories = self.um.search(query)
if not user_memories:
return ""
formatted_memories = "\n".join(
f"- {result['memory']}" for result in user_memories
)
return f"User memories/preferences:\n{formatted_memories}"

42
src/crewai/memory/entity/entity_memory.py
View File

@@ -11,21 +11,43 @@ class EntityMemory(Memory):
"""
def __init__(self, crew=None, embedder_config=None, storage=None):
storage = (
storage
if storage
else RAGStorage(
type="entities",
allow_reset=True,
embedder_config=embedder_config,
crew=crew,
if hasattr(crew, "memory_config") and crew.memory_config is not None:
self.memory_provider = crew.memory_config.get("provider")
else:
self.memory_provider = None
if self.memory_provider == "mem0":
try:
from crewai.memory.storage.mem0_storage import Mem0Storage
except ImportError:
raise ImportError(
"Mem0 is not installed. Please install it with `pip install mem0ai`."
)
storage = Mem0Storage(type="entities", crew=crew)
else:
storage = (
storage
if storage
else RAGStorage(
type="entities",
allow_reset=False,
embedder_config=embedder_config,
crew=crew,
)
)
)
super().__init__(storage)
def save(self, item: EntityMemoryItem) -> None: # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
"""Saves an entity item into the SQLite storage."""
data = f"{item.name}({item.type}): {item.description}"
if self.memory_provider == "mem0":
data = f"""
Remember details about the following entity:
Name: {item.name}
Type: {item.type}
Entity Description: {item.description}
"""
else:
data = f"{item.name}({item.type}): {item.description}"
super().save(data, item.metadata)
def reset(self) -> None:

11
src/crewai/memory/memory.py
View File

@@ -23,5 +23,12 @@ class Memory:
self.storage.save(value, metadata)
def search(self, query: str) -> List[Dict[str, Any]]:
return self.storage.search(query)
def search(
self,
query: str,
limit: int = 3,
score_threshold: float = 0.35,
) -> List[Any]:
return self.storage.search(
query=query, limit=limit, score_threshold=score_threshold
)

39
src/crewai/memory/short_term/short_term_memory.py
View File

@@ -14,13 +14,27 @@ class ShortTermMemory(Memory):
"""
def __init__(self, crew=None, embedder_config=None, storage=None):
storage = (
storage
if storage
else RAGStorage(
type="short_term", embedder_config=embedder_config, crew=crew
if hasattr(crew, "memory_config") and crew.memory_config is not None:
self.memory_provider = crew.memory_config.get("provider")
else:
self.memory_provider = None
if self.memory_provider == "mem0":
try:
from crewai.memory.storage.mem0_storage import Mem0Storage
except ImportError:
raise ImportError(
"Mem0 is not installed. Please install it with `pip install mem0ai`."
)
storage = Mem0Storage(type="short_term", crew=crew)
else:
storage = (
storage
if storage
else RAGStorage(
type="short_term", embedder_config=embedder_config, crew=crew
)
)
)
super().__init__(storage)
def save(
@@ -30,11 +44,20 @@ class ShortTermMemory(Memory):
agent: Optional[str] = None,
) -> None:
item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent)
if self.memory_provider == "mem0":
item.data = f"Remember the following insights from Agent run: {item.data}"
super().save(value=item.data, metadata=item.metadata, agent=item.agent)
def search(self, query: str, score_threshold: float = 0.35):
return self.storage.search(query=query, score_threshold=score_threshold) # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters
def search(
self,
query: str,
limit: int = 3,
score_threshold: float = 0.35,
):
return self.storage.search(
query=query, limit=limit, score_threshold=score_threshold
) # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters
def reset(self) -> None:
try:

6
src/crewai/memory/storage/interface.py
View File

@@ -7,8 +7,10 @@ class Storage:
def save(self, value: Any, metadata: Dict[str, Any]) -> None:
pass
def search(self, key: str) -> List[Dict[str, Any]]: # type: ignore
pass
def search(
self, query: str, limit: int, score_threshold: float
) -> Dict[str, Any] | List[Any]:
return {}
def reset(self) -> None:
pass

104
src/crewai/memory/storage/mem0_storage.py Normal file
View File

@@ -0,0 +1,104 @@
import os
from typing import Any, Dict, List
from mem0 import MemoryClient
from crewai.memory.storage.interface import Storage
class Mem0Storage(Storage):
"""
Extends Storage to handle embedding and searching across entities using Mem0.
"""
def __init__(self, type, crew=None):
super().__init__()
if type not in ["user", "short_term", "long_term", "entities"]:
raise ValueError("Invalid type for Mem0Storage. Must be 'user' or 'agent'.")
self.memory_type = type
self.crew = crew
self.memory_config = crew.memory_config
# User ID is required for user memory type "user" since it's used as a unique identifier for the user.
user_id = self._get_user_id()
if type == "user" and not user_id:
raise ValueError("User ID is required for user memory type")
# API key in memory config overrides the environment variable
mem0_api_key = self.memory_config.get("config", {}).get("api_key") or os.getenv(
"MEM0_API_KEY"
)
self.memory = MemoryClient(api_key=mem0_api_key)
def _sanitize_role(self, role: str) -> str:
"""
Sanitizes agent roles to ensure valid directory names.
"""
return role.replace("\n", "").replace(" ", "_").replace("/", "_")
def save(self, value: Any, metadata: Dict[str, Any]) -> None:
user_id = self._get_user_id()
agent_name = self._get_agent_name()
if self.memory_type == "user":
self.memory.add(value, user_id=user_id, metadata={**metadata})
elif self.memory_type == "short_term":
agent_name = self._get_agent_name()
self.memory.add(
value, agent_id=agent_name, metadata={"type": "short_term", **metadata}
)
elif self.memory_type == "long_term":
agent_name = self._get_agent_name()
self.memory.add(
value,
agent_id=agent_name,
infer=False,
metadata={"type": "long_term", **metadata},
)
elif self.memory_type == "entities":
entity_name = None
self.memory.add(
value, user_id=entity_name, metadata={"type": "entity", **metadata}
)
def search(
self,
query: str,
limit: int = 3,
score_threshold: float = 0.35,
) -> List[Any]:
params = {"query": query, "limit": limit}
if self.memory_type == "user":
user_id = self._get_user_id()
params["user_id"] = user_id
elif self.memory_type == "short_term":
agent_name = self._get_agent_name()
params["agent_id"] = agent_name
params["metadata"] = {"type": "short_term"}
elif self.memory_type == "long_term":
agent_name = self._get_agent_name()
params["agent_id"] = agent_name
params["metadata"] = {"type": "long_term"}
elif self.memory_type == "entities":
agent_name = self._get_agent_name()
params["agent_id"] = agent_name
params["metadata"] = {"type": "entity"}
# Discard the filters for now since we create the filters
# automatically when the crew is created.
results = self.memory.search(**params)
return [r for r in results if r["score"] >= score_threshold]
def _get_user_id(self):
if self.memory_type == "user":
if hasattr(self, "memory_config") and self.memory_config is not None:
return self.memory_config.get("config", {}).get("user_id")
else:
return None
return None
def _get_agent_name(self):
agents = self.crew.agents if self.crew else []
agents = [self._sanitize_role(agent.role) for agent in agents]
agents = "_".join(agents)
return agents

0
src/crewai/memory/user/__init__.py Normal file
View File

45
src/crewai/memory/user/user_memory.py Normal file
View File

@@ -0,0 +1,45 @@
from typing import Any, Dict, Optional
from crewai.memory.memory import Memory
class UserMemory(Memory):
"""
UserMemory class for handling user memory storage and retrieval.
Inherits from the Memory class and utilizes an instance of a class that
adheres to the Storage for data storage, specifically working with
MemoryItem instances.
"""
def __init__(self, crew=None):
try:
from crewai.memory.storage.mem0_storage import Mem0Storage
except ImportError:
raise ImportError(
"Mem0 is not installed. Please install it with `pip install mem0ai`."
)
storage = Mem0Storage(type="user", crew=crew)
super().__init__(storage)
def save(
self,
value,
metadata: Optional[Dict[str, Any]] = None,
agent: Optional[str] = None,
) -> None:
# TODO: Change this function since we want to take care of the case where we save memories for the usr
data = f"Remember the details about the user: {value}"
super().save(data, metadata)
def search(
self,
query: str,
limit: int = 3,
score_threshold: float = 0.35,
):
results = super().search(
query=query,
limit=limit,
score_threshold=score_threshold,
)
return results

8
src/crewai/memory/user/user_memory_item.py Normal file
View File

@@ -0,0 +1,8 @@
from typing import Any, Dict, Optional
class UserMemoryItem:
def __init__(self, data: Any, user: str, metadata: Optional[Dict[str, Any]] = None):
self.data = data
self.user = user
self.metadata = metadata if metadata is not None else {}

270
tests/memory/cassettes/test_save_and_search_with_provider.yaml Normal file
View File

@@ -0,0 +1,270 @@
interactions:
- request:
body: ''
headers:
accept:
- '*/*'
accept-encoding:
- gzip, deflate
connection:
- keep-alive
host:
- api.mem0.ai
user-agent:
- python-httpx/0.27.0
method: GET
uri: https://api.mem0.ai/v1/memories/?user_id=test
response:
body:
string: '[]'
headers:
CF-Cache-Status:
- DYNAMIC
CF-RAY:
- 8b477138bad847b9-BOM
Connection:
- keep-alive
Content-Length:
- '2'
Content-Type:
- application/json
Date:
- Sat, 17 Aug 2024 06:00:11 GMT
NEL:
- '{"success_fraction":0,"report_to":"cf-nel","max_age":604800}'
Report-To:
- '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v4?s=uuyH2foMJVDpV%2FH52g1q%2FnvXKe3dBKVzvsK0mqmSNezkiszNR9OgrEJfVqmkX%2FlPFRP2sH4zrOuzGo6k%2FjzsjYJczqSWJUZHN2pPujiwnr1E9W%2BdLGKmG6%2FqPrGYAy2SBRWkkJVWsTO3OQ%3D%3D"}],"group":"cf-nel","max_age":604800}'
Server:
- cloudflare
allow:
- GET, POST, DELETE, OPTIONS
alt-svc:
- h3=":443"; ma=86400
cross-origin-opener-policy:
- same-origin
referrer-policy:
- same-origin
vary:
- Accept, origin, Cookie
x-content-type-options:
- nosniff
x-frame-options:
- DENY
status:
code: 200
message: OK
- request:
body: '{"batch": [{"properties": {"python_version": "3.12.4 (v3.12.4:8e8a4baf65,
Jun 6 2024, 17:33:18) [Clang 13.0.0 (clang-1300.0.29.30)]", "os": "darwin",
"os_version": "Darwin Kernel Version 23.4.0: Wed Feb 21 21:44:54 PST 2024; root:xnu-10063.101.15~2/RELEASE_ARM64_T6030",
"os_release": "23.4.0", "processor": "arm", "machine": "arm64", "function":
"mem0.client.main.MemoryClient", "$lib": "posthog-python", "$lib_version": "3.5.0",
"$geoip_disable": true}, "timestamp": "2024-08-17T06:00:11.526640+00:00", "context":
{}, "distinct_id": "fd411bd3-99a2-42d6-acd7-9fca8ad09580", "event": "client.init"}],
"historical_migration": false, "sentAt": "2024-08-17T06:00:11.701621+00:00",
"api_key": "phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX"}'
headers:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate
Connection:
- keep-alive
Content-Length:
- '740'
Content-Type:
- application/json
User-Agent:
- posthog-python/3.5.0
method: POST
uri: https://us.i.posthog.com/batch/
response:
body:
string: '{"status":"Ok"}'
headers:
Connection:
- keep-alive
Content-Length:
- '15'
Content-Type:
- application/json
Date:
- Sat, 17 Aug 2024 06:00:12 GMT
access-control-allow-credentials:
- 'true'
server:
- envoy
vary:
- origin, access-control-request-method, access-control-request-headers
x-envoy-upstream-service-time:
- '69'
status:
code: 200
message: OK
- request:
body: '{"messages": [{"role": "user", "content": "Remember the following insights
from Agent run: test value with provider"}], "metadata": {"task": "test_task_provider",
"agent": "test_agent_provider"}, "app_id": "Researcher"}'
headers:
accept:
- '*/*'
accept-encoding:
- gzip, deflate
connection:
- keep-alive
content-length:
- '219'
content-type:
- application/json
host:
- api.mem0.ai
user-agent:
- python-httpx/0.27.0
method: POST
uri: https://api.mem0.ai/v1/memories/
response:
body:
string: '{"message":"ok"}'
headers:
CF-Cache-Status:
- DYNAMIC
CF-RAY:
- 8b477140282547b9-BOM
Connection:
- keep-alive
Content-Length:
- '16'
Content-Type:
- application/json
Date:
- Sat, 17 Aug 2024 06:00:13 GMT
NEL:
- '{"success_fraction":0,"report_to":"cf-nel","max_age":604800}'
Report-To:
- '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v4?s=FRjJKSk3YxVj03wA7S05H8ts35KnWfqS3wb6Rfy4kVZ4BgXfw7nJbm92wI6vEv5fWcAcHVnOlkJDggs11B01BMuB2k3a9RqlBi0dJNiMuk%2Bgm5xE%2BODMPWJctYNRwQMjNVbteUpS%2Fad8YA%3D%3D"}],"group":"cf-nel","max_age":604800}'
Server:
- cloudflare
allow:
- GET, POST, DELETE, OPTIONS
alt-svc:
- h3=":443"; ma=86400
cross-origin-opener-policy:
- same-origin
referrer-policy:
- same-origin
vary:
- Accept, origin, Cookie
x-content-type-options:
- nosniff
x-frame-options:
- DENY
status:
code: 200
message: OK
- request:
body: '{"query": "test value with provider", "limit": 3, "app_id": "Researcher"}'
headers:
accept:
- '*/*'
accept-encoding:
- gzip, deflate
connection:
- keep-alive
content-length:
- '73'
content-type:
- application/json
host:
- api.mem0.ai
user-agent:
- python-httpx/0.27.0
method: POST
uri: https://api.mem0.ai/v1/memories/search/
response:
body:
string: '[]'
headers:
CF-Cache-Status:
- DYNAMIC
CF-RAY:
- 8b47714d083b47b9-BOM
Connection:
- keep-alive
Content-Length:
- '2'
Content-Type:
- application/json
Date:
- Sat, 17 Aug 2024 06:00:14 GMT
NEL:
- '{"success_fraction":0,"report_to":"cf-nel","max_age":604800}'
Report-To:
- '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v4?s=2DRWL1cdKdMvnE8vx1fPUGeTITOgSGl3N5g84PS6w30GRqpfz79BtSx6REhpnOiFV8kM6KGqln0iCZ5yoHc2jBVVJXhPJhQ5t0uerD9JFnkphjISrJOU1MJjZWneT9PlNABddxvVNCmluA%3D%3D"}],"group":"cf-nel","max_age":604800}'
Server:
- cloudflare
allow:
- POST, OPTIONS
alt-svc:
- h3=":443"; ma=86400
cross-origin-opener-policy:
- same-origin
referrer-policy:
- same-origin
vary:
- Accept, origin, Cookie
x-content-type-options:
- nosniff
x-frame-options:
- DENY
status:
code: 200
message: OK
- request:
body: '{"batch": [{"properties": {"python_version": "3.12.4 (v3.12.4:8e8a4baf65,
Jun 6 2024, 17:33:18) [Clang 13.0.0 (clang-1300.0.29.30)]", "os": "darwin",
"os_version": "Darwin Kernel Version 23.4.0: Wed Feb 21 21:44:54 PST 2024; root:xnu-10063.101.15~2/RELEASE_ARM64_T6030",
"os_release": "23.4.0", "processor": "arm", "machine": "arm64", "function":
"mem0.client.main.MemoryClient", "$lib": "posthog-python", "$lib_version": "3.5.0",
"$geoip_disable": true}, "timestamp": "2024-08-17T06:00:13.593952+00:00", "context":
{}, "distinct_id": "fd411bd3-99a2-42d6-acd7-9fca8ad09580", "event": "client.add"}],
"historical_migration": false, "sentAt": "2024-08-17T06:00:13.858277+00:00",
"api_key": "phc_hgJkUVJFYtmaJqrvf6CYN67TIQ8yhXAkWzUn9AMU4yX"}'
headers:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate
Connection:
- keep-alive
Content-Length:
- '739'
Content-Type:
- application/json
User-Agent:
- posthog-python/3.5.0
method: POST
uri: https://us.i.posthog.com/batch/
response:
body:
string: '{"status":"Ok"}'
headers:
Connection:
- keep-alive
Content-Length:
- '15'
Content-Type:
- application/json
Date:
- Sat, 17 Aug 2024 06:00:13 GMT
access-control-allow-credentials:
- 'true'
server:
- envoy
vary:
- origin, access-control-request-method, access-control-request-headers
x-envoy-upstream-service-time:
- '33'
status:
code: 200
message: OK
version: 1