* implements agentops with a langchain handler, agent tracking and tool call recording
* track tool usage
* end session after completion
* track tool usage time
* better tool and llm tracking
* code cleanup
* make agentops optional
* optional dependency usage
* remove telemetry code
* optional agentops
* agentops version bump
* remove org key
* true dependency
* add crew org key to agentops
* cleanup
* Update pyproject.toml
* Revert "true dependency"
This reverts commit e52e8e9568.
* Revert "cleanup"
This reverts commit 7f5635fb9e.
* optional parent key
* agentops 0.1.5
* Revert "Revert "cleanup""
This reverts commit cea33d9a5d.
* Revert "Revert "true dependency""
This reverts commit 4d1b460b
* cleanup
* Forcing version 0.1.5
* Update pyproject.toml
* agentops update
* noop
* add crew tag
* black formatting
* use langchain callback handler to support all LLMs
* agentops version bump
* track task evaluator
* merge upstream
* Fix typo in instruction en.json (#676)
* Enable search in docs (#663)
* Clarify text in docstring (#662)
* Update agent.py (#655)
Changed default model value from gpt-4 to gpt-4o.
Reasoning.
gpt-4 costs 30$ per million tokens while gpt-4o costs 5$.
This is more cost friendly for default option.
* Update README.md (#652)
Rework example so that if you use a custom LLM it doesn't throw code errors by uncommenting.
* Update BrowserbaseLoadTool.md (#647)
* Update crew.py (#644)
Fixed Type on line 53
* fixes#665 (#666)
* Added timestamp to logger (#646)
* Added timestamp to logger
Updated the logger.py file to include timestamps when logging output. For example:
[2024-05-20 15:32:48][DEBUG]: == Working Agent: Researcher
[2024-05-20 15:32:48][INFO]: == Starting Task: Research the topic
[2024-05-20 15:33:22][DEBUG]: == [Researcher] Task output:
* Update tool_usage.py
* Revert "Update tool_usage.py"
This reverts commit 95d18d5b6f.
incorrect bramch for this commit
* support skip auto end session
* conditional protect agentops use
* fix crew logger bug
* fix crew logger bug
* Update crew.py
* Update tool_usage.py
---------
Co-authored-by: João Moura <joaomdmoura@gmail.com>
Co-authored-by: Howard Gil <howardbgil@gmail.com>
Co-authored-by: Olivier Roberdet <niox5199@gmail.com>
Co-authored-by: Paul Sanders <psanders1@gmail.com>
Co-authored-by: Anudeep Kolluri <50168940+Anudeep-Kolluri@users.noreply.github.com>
Co-authored-by: Mike Heavers <heaversm@users.noreply.github.com>
Co-authored-by: Mish Ushakov <10400064+mishushakov@users.noreply.github.com>
Co-authored-by: theCyberTech - Rip&Tear <84775494+theCyberTech@users.noreply.github.com>
Co-authored-by: Saif Mahmud <60409889+vmsaif@users.noreply.github.com>
To Resolve :
pydantic_core._pydantic_core.ValidationError: 1 validation error for Task
expected_output
Field required [type=missing, input_value=, input_type=dict]
For further information visit https://errors.pydantic.dev/2.6/v/missing
"Expected Output" is mandatory now as it forces people to be specific about the expected result and get better result
refer : https://github.com/joaomdmoura/crewAI/issues/308
- Added a "Parameters" column to attribute tables. Improved overall document formatting for enhanced readability and ease of use.
Thank you to the author for the great project and the excellent foundation provided!
Revised to utilize Ollama from langchain.llms instead as the functionality from the other method simply doesn't work when delegating.
Co-authored-by: João Moura <joaomdmoura@gmail.com>
fixed error for some cases with Pandas DataFrame:
ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
* better spacing
* works with llama index
* works on langchain custom just need delegation to work
* cleanup for custom_agent class
* works with different argument expectations for agent_executor
* cleanup for hierarchial process, better agent_executor args handler and added to the crew agent doc page
* removed code examples for langchain + llama index, added to docs instead
* added key output if return is not a str for and added some tests
* added hinting for CustomAgent class
* removed pass as it was not needed
* closer just need to figuire ou agentTools
* running agents - llamaindex and langchain with base agent
* some cleanup on baseAgent
* minimum for agent to run for base class and ensure it works with hierarchical process
* cleanup for original agent to take on BaseAgent class
* Agent takes on langchainagent and cleanup across
* token handling working for usage_metrics to continue working
* installed llama-index, updated docs and added better name
* fixed some type errors
* base agent holds token_process
* heirarchail process uses proper tools and no longer relies on hasattr for token_processes
* removal of test_custom_agent_executions
* this fixes copying agents
* leveraging an executor class for trigger llamaindex agent
* llama index now has ask_human
* executor mixins added
* added output converter base class
* type listed
* cleanup for output conversions and tokenprocess eliminated redundancy
* properly handling tokens
* simplified token calc handling
* original agent with base agent builder structure setup
* better docs
* no more llama-index dep
* cleaner docs
* test fixes
* poetry reverts and better docs
* base_agent_tools set for third party agents
* updated task and test fix
* feat: add CodeInterpreterTool to run when enable code execution is allowed on agent
* feat: change to allow_code_execution
* feat: add readme for CodeInterpreterTool
* feat: add training logic to agent and crew
* feat: add training logic to agent executor
* feat: add input parameter to cli command
* feat: add utilities for the training logic
* feat: polish code, logic and add private variables
* feat: add docstring and type hinting to executor
* feat: add constant file, add constant to code
* feat: fix name of training handler function
* feat: remove unused var
* feat: change file handler file name
* feat: Add training handler file, class and change on the code
* feat: fix name error from file
* fix: change import to adapt to logic
* feat: add training handler test
* feat: add tests for file and training_handler
* feat: add test for task evaluator function
* feat: change text to fit in-screen
* feat: add test for train function
* feat: add test for agent training_handler function
* feat: add test for agent._use_trained_data
