Quickstart
1. Create a venv and install the current PyPI prerelease
py -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install --pre "ecp-runtime==0.2.9b0" "ecp-sdk[langchain]==0.2.9b0" langchain-openai
2. Run the LangChain demo
python -m ecp_runtime.cli run --manifest .\examples\langchain_demo\manifest.yaml
3. Generate an HTML report
python -m ecp_runtime.cli run --manifest .\examples\langchain_demo\manifest.yaml --report .\report.html
4. JSON output (for CI)
Print a JSON report to stdout:
python -m ecp_runtime.cli run --manifest .\examples\langchain_demo\manifest.yaml --json
Save a JSON report to a file:
python -m ecp_runtime.cli run --manifest .\examples\langchain_demo\manifest.yaml --json-out .\report.json
5. Run the CrewAI demo
Install CrewAI support:
pip install --pre "ecp-sdk[crewai]==0.2.9b0" crewai
Run CrewAI manifest:
python -m ecp_runtime.cli run --manifest .\examples\crewai_demo\manifest.yaml
6. Run the PydanticAI demo
Install PydanticAI support:
pip install --pre "ecp-sdk[pydanticai]==0.2.9b0" pydantic-ai
Run PydanticAI manifest:
python -m ecp_runtime.cli run --manifest .\examples\pydantic_ai_demo\manifest.yaml
7. Run the LlamaIndex demo
Install LlamaIndex support:
pip install "ecp-sdk[llamaindex]" llama-index llama-index-llms-openai llama-index-tools-yahoo-finance
Run LlamaIndex manifest:
python -m ecp_runtime.cli run --manifest .\examples\llamaindex_demo\manifest.yaml
8. Optional: enable LLM judge
If your manifest uses llm_judge, set the API key:
$env:OPENAI_API_KEY="your_key_here"
$env:ECP_LLM_JUDGE_MODEL="gpt-4o-mini"
$env:ECP_LLM_JUDGE_TEMPERATURE="0"
Notes
- The latest stable packages on PyPI are still
0.2.4. This docs site currently matches the0.2.9-betarelease line, so use the prerelease install command above if you want the published packages to match the repo and GitHub release. - The runtime launches your agent via the
targetcommand in the manifest. - The agent responds over JSON-RPC 2.0 on stdio.
- Use
ECP_RPC_TIMEOUTto control step timeouts (default 30s).