Merge branch 'main' into documentation-updates

README.md

@@ -212,6 +212,35 @@ agentops.end_session('Success')
 </details>
 
 
+### LiteLLM
+
+AgentOps provides support for LiteLLM(>=1.3.1), allowing you to call 100+ LLMs using the same Input/Output Format. 
+
+- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/litellm)
+- [Official LiteLLM documentation](https://docs.litellm.ai/docs/providers)
+
+<details>
+  <summary>Installation</summary>
+
+```bash
+pip install litellm
+```
+
+```python python
+# Do not use LiteLLM like this
+# from litellm import completion
+# ...
+# response = completion(model="claude-3", messages=messages)
+
+# Use LiteLLM like this
+import litellm
+...
+response = litellm.completion(model="claude-3", messages=messages)
+# or
+response = await litellm.acompletion(model="claude-3", messages=messages)
+```
+</details>
+
 ### LlamaIndex 🦙
 
 (Coming Soon)
@@ -261,4 +290,4 @@ AgentOps is designed to make agent observability, testing, and monitoring easy.
 
 Check out our growth in the community:
 
-<img src="https://api.star-history.com/svg?repos=AgentOps-AI/agentops&type=Date" style="max-width: 500px" width="50%" alt="Logo">
+<img src="https://api.star-history.com/svg?repos=AgentOps-AI/agentops&type=Date" style="max-width: 500px" width="50%" alt="Logo">

agentops/__init__.py

@@ -6,7 +6,6 @@
 from .client import Client
 from .config import Configuration
 from .event import Event, ActionEvent, LLMEvent, ToolEvent, ErrorEvent
-from .enums import Models
 from .decorators import record_function
 from .agent import track_agent
 from .log_config import logger
@@ -184,16 +183,6 @@ def set_tags(tags: List[str]):
     Client().set_tags(tags)
 
 
-@check_init
-def record_function(event_name: str):
-    return decorators.record_function(event_name)
-
-
-@check_init
-def track_agent(name: Union[str, None] = None):
-    return agent.track_agent(name)
-
-
 def get_api_key() -> str:
     return Client().api_key
 

agentops/client.py

@@ -1,8 +1,8 @@
 """
-    AgentOps client module that provides a client class with public interfaces and configuration.
+AgentOps client module that provides a client class with public interfaces and configuration.
 
-    Classes:
-        Client: Provides methods to interact with the AgentOps service.
+Classes:
+    Client: Provides methods to interact with the AgentOps service.
 """
 
 import os
@@ -80,7 +80,6 @@ def __init__(
         inherited_session_id: Optional[str] = None,
         skip_auto_end_session: Optional[bool] = False,
     ):
-
         if override is not None:
             logger.warning(
                 "The 'override' parameter is deprecated. Use 'instrument_llm_calls' instead.",
@@ -389,7 +388,7 @@ def end_session(
         end_state_reason: Optional[str] = None,
         video: Optional[str] = None,
         is_auto_end: Optional[bool] = None,
-    ):
+    ) -> Decimal:
         """
         End the current session with the AgentOps service.
 
@@ -398,6 +397,9 @@ def end_session(
             end_state_reason (str, optional): The reason for ending the session.
             video (str, optional): The video screen recording of the session
             is_auto_end (bool, optional): is this an automatic use of end_session and should be skipped with skip_auto_end_session
+
+        Returns:
+            Decimal: The token cost of the session. Returns 0 if the cost is unknown.
         """
 
         if is_auto_end and self.config.skip_auto_end_session:
@@ -420,6 +422,7 @@ def end_session(
 
         if token_cost is None or token_cost == "unknown":
             logger.info("Could not determine cost of run.")
+            token_cost_d = Decimal(0)
         else:
             token_cost_d = Decimal(token_cost)
             logger.info(
@@ -439,6 +442,7 @@ def end_session(
 
         self._session = None
         self._worker = None
+        return token_cost_d
 
     def create_agent(self, name: str, agent_id: Optional[str] = None):
         if agent_id is None:

agentops/enums.py

@@ -9,20 +9,6 @@ class EventType(Enum):
     ERROR = "errors"
 
 
-class Models(Enum):
-    GPT_3_5_TURBO = "gpt-3.5-turbo"
-    GPT_3_5_TURBO_0301 = "gpt-3.5-turbo-0301"
-    GPT_3_5_TURBO_0613 = "gpt-3.5-turbo-0613"
-    GPT_3_5_TURBO_16K = "gpt-3.5-turbo-16k"
-    GPT_3_5_TURBO_16K_0613 = "gpt-3.5-turbo-16k-0613"
-    GPT_4_0314 = "gpt-4-0314"
-    GPT_4 = "gpt-4"
-    GPT_4_32K = "gpt-4-32k"
-    GPT_4_32K_0314 = "gpt-4-32k-0314"
-    GPT_4_0613 = "gpt-4-0613"
-    TEXT_EMBEDDING_ADA_002 = "text-embedding-ada-002"
-
-
 class EndState(Enum):
     SUCCESS = "Success"
     FAIL = "Fail"

agentops/event.py

@@ -5,10 +5,10 @@
     Event: Represents discrete events to be recorded.
 """
 
-from dataclasses import asdict, dataclass, field
+from dataclasses import dataclass, field
 from typing import Any, Dict, List, Optional, Sequence, Union
 from .helpers import get_ISO_time, check_call_stack_for_agent_id
-from .enums import EventType, Models
+from .enums import EventType
 from uuid import UUID, uuid4
 import traceback
 
@@ -72,7 +72,7 @@ class LLMEvent(Event):
     prompt_tokens(int, optional): The number of tokens in the prompt message.
     completion(str, object, optional): The message or returned by the LLM. Preferably in ChatML format which is more fully supported by AgentOps.
     completion_tokens(int, optional): The number of tokens in the completion message.
-    model(Models, str, optional): LLM model e.g. "gpt-4". Models defined in enums.Models are more fully supported by AgentOps e.g. extra features in dashboard.
+    model(str, optional): LLM model e.g. "gpt-4", "gpt-3.5-turbo".
 
     """
 
@@ -82,7 +82,7 @@ class LLMEvent(Event):
     prompt_tokens: Optional[int] = None
     completion: Union[str, object] = None
     completion_tokens: Optional[int] = None
-    model: Optional[Union[Models, str]] = None
+    model: Optional[str] = None
 
 
 @dataclass

docs/mint.json

@@ -84,6 +84,7 @@
       "pages": [
         "v1/integrations/crewai",
         "v1/integrations/autogen",
+        "v1/integrations/langchain",
         "v1/integrations/cohere",
         "v1/integrations/litellm"
       ]

docs/v1/integrations/autogen.mdx

@@ -19,9 +19,7 @@ Autogen has comprehensive [documentation](https://microsoft.github.io/autogen/do
 			poetry add agentops
 			```
 		</CodeGroup>
-		<Check>
-			[Give us a star](https://github.com/AgentOps-AI/agentops) on GitHub while you're at it (you may be our <span id="stars-text">2,000th</span> 😊)
-		</Check>
+		<Check>Give us a star](https://github.com/AgentOps-AI/agentops) on GitHub while you're at it (you may be our <span id="stars-text">2,000th</span> 😊)</Check>
 	</Step>
 	<Step title="Install Autogen">
 		<CodeGroup>

docs/v1/integrations/crewai.mdx

@@ -24,9 +24,7 @@ Crew has comprehensive [documentation](https://docs.crewai.com) available as wel
 			poetry add agentops
 			```
 		</CodeGroup>
-		<Check>
-			[Give us a star](https://github.com/AgentOps-AI/agentops) on GitHub while you're at it (you may be our <span id="stars-text">2,000th</span> 😊)
-		</Check>
+		<Check>[Give us a star](https://github.com/AgentOps-AI/agentops) on GitHub while you're at it (you may be our <span id="stars-text">2,000th</span> 😊)</Check>
 	</Step>
 	<Step title="Install Crew from the AgentOps fork">
 		<Warning>

docs/v1/integrations/langchain.mdx

@@ -0,0 +1,110 @@
+---
+title: Langchain
+description: "AgentOps provides first class support for Lanchain applications"
+---
+
+AgentOps works seamlessly with applications built using Langchain.
+
+## Adding AgentOps to Langchain applications
+
+<Steps>
+	<Step title="Install the AgentOps SDK and the additional Langchain dependency">
+		<CodeGroup>
+			```bash pip
+			pip install agentops
+			pip install agentops[langchain]
+			```
+			```bash poetry
+			poetry add agentops
+			poetry add agentops[langchain]
+			```
+		</CodeGroup>
+		<Check>
+			[Give us a star](https://github.com/AgentOps-AI/agentops) on GitHub while you're at it (you may be our 2,000th 😊)
+		</Check>
+	</Step>
+	<Step title="Set up your import statements">
+		Import the following Langchain and AgentOps dependencies
+		<CodeGroup>
+			```python python
+			import os
+			from langchain.chat_models import ChatOpenAI
+			from langchain.agents import initialize_agent, AgentType
+			from agentops.langchain_callback_handler import LangchainCallbackHandler
+			```
+		</CodeGroup>
+		<Tip>
+			For more features see our [Usage](/v1/usage) section.
+		</Tip>
+	</Step>
+	<Step title="Set up your Langchain handler to make the calls">
+		Set up your Langchain agent with the AgentOps callback handler and AgentOps will automatically record your Langchain sessions.
+		<CodeGroup>
+			```python python
+			handler = LangchainCallbackHandler(api_key=AGENTOPS_API_KEY, tags=['Langchain Example'])
+
+			llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY,
+				callbacks=[handler],
+				model='gpt-3.5-turbo')
+
+			agent = initialize_agent(tools,
+				llm,
+				agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
+				verbose=True,
+				callbacks=[handler], # You must pass in a callback handler to record your agent
+				handle_parsing_errors=True)
+			```
+		</CodeGroup>
+		<Tip>
+			Note that you don't need to set up a separate agentops.init() call, as the Langchain callback handler will automatically initialize the AgentOps client for you.
+		</Tip>
+	</Step>
+	<Step title="Set your API key">
+		Retrieve an API Key from your Settings > [Projects & API Keys](https://app.agentops.ai/settings/projects) page.
+		<Frame type="glass" caption="Settings > Projects & API Keys">
+			<img height="200" src="/images/api-keys.png" />
+		</Frame>
+		<Info>
+			API keys are tied to individual projects.<br></br>
+			A Default Project has been created for you, so just click Copy API Key
+		</Info>
+		Set this API Key in your [environment variables](/v1/usage/environment-variables)
+		```python .env
+		AGENTOPS_API_KEY=<YOUR API KEY>
+		```
+	</Step>
+    <Step title="Run your agent">
+		Execute your program and visit [app.agentops.ai/drilldown](https://app.agentops.ai/drilldown) to observe your Langchain Agent! 🕵️
+		<Tip>
+			After your run, AgentOps prints a clickable url to console linking directly to your session in the Dashboard
+		</Tip>
+		<div/>{/* Intentionally blank div for newline */}
+		<Frame type="glass" caption="Clickable link to session">
+			<img height="200" src="https://github.com/AgentOps-AI/agentops/blob/cf67191f13e0e2a09446a61b7393e1810b3eee95/docs/images/link-to-session.gif?raw=true" />
+		</Frame>
+	</Step>
+</Steps>
+
+## Full Examples
+
+<CodeGroup>
+	```python python
+	import os
+	from langchain.chat_models import ChatOpenAI
+	from langchain.agents import initialize_agent, AgentType
+	from agentops.langchain_callback_handler import LangchainCallbackHandler
+
+	handler = LangchainCallbackHandler(api_key=AGENTOPS_API_KEY, tags=['Langchain Example'])
+
+	llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY,
+		callbacks=[handler],
+		model='gpt-3.5-turbo')
+
+	agent = initialize_agent(tools,
+		llm,
+		agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
+		verbose=True,
+		callbacks=[handler], # You must pass in a callback handler to record your agent
+		handle_parsing_errors=True)
+	```
+</CodeGroup>

docs/v1/introduction.mdx

@@ -6,21 +6,29 @@ mode: "wide"
 
 <Check>[Give us a star](https://github.com/AgentOps-AI/agentops) on GitHub if you find AgentOps helpful (you may be our <span id="stars-text">2,000th</span> 😊)</Check>
 
-# AgentOps is your new terminal
-
-<Card title="Terminals suck" icon="trash" iconType="solid" color="#d6483e">
-	- • Lose track of what your agents did in between executions
-	- • Parsing through terminal output searching for LLM completions
-	- • Printing "tool called"
-</Card>
-<Card title="AgentOps doesn't" icon="face-smile-halo" iconType="regular" color="#2bd600">
-	- • Visual dashboard so you can see what your agents did in human-readable format
-	- • LLM calls are magically recorded - prompt, completion, timestamps for each - with one line of code
-	- • Agents and their events (including tool calls) are recorded with one more line of code
-	- • Errors are magically associated to its causal event
-	- • Record any other events to your session with two more lines of code
-	- • Tons of other useful data if you're developing with supported agent frameworks: SDK version
-</Card>
+# AgentOps solves what Terminal can't
+
+<CardGroup cols={2}>
+	<Card title="Terminal CAN'T..." icon="trash" iconType="solid" color="#d6483e">
+		- • Track agents across executions
+		- • Parse out LLM completions from output logs
+		- • Give you insight into what your agents did
+	</Card>
+	<Card title="AgentOps does it all and more..." icon="face-smile-halo" iconType="regular" color="#2bd600">
+		- • Record LLM prompts, completions, & timestamps
+		- • Log events, calls, and any other agent activity
+		- • Link agent errors back to their causal event
+	</Card>
+</CardGroup>
+
+And we do it all in just two lines of code...
+<CodeGroup>
+	```python python
+	import agentops
+	agentops.init(<INSERT YOUR API KEY HERE>)
+	```
+</CodeGroup>
+... that logs everything back to your AgentOps Dashboard.
 
 ## The AgentOps Dashboard
 

docs/v1/quickstart.mdx

@@ -60,10 +60,7 @@ import SupportedModels from '/snippets/supported-models.mdx'
     </Frame>
   </Step>
 </Steps>
-
 <Check>[Give us a star](https://github.com/AgentOps-AI/agentops) if you liked AgentOps! (you may be our <span id="stars-text">2,000th</span> 😊)</Check>
-<Test />
-
 
 ## More basic functionality