* 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().
