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westey
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* Add initial harness console for python * Add textual to project * Add planning and approval flows with list selector * Address PR comments * Fix list selection bug * Fix PR #6312 round 2 review comments - Escape untrusted agent text with rich.markup.escape() in observers (text_output, planning_output, reasoning_display) to prevent markup injection - Remove non-functional 'Always approve' choices from tool_approval.py (framework lacks CreateAlwaysApproveToolResponse support) - Remove textual from root pyproject.toml dev deps (sample-specific) - Add PEP 723 inline script metadata to harness_research.py - Narrow except Exception to except NoMatches in list_selection.py Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix build error * Fix build errors --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
116 lines
4.3 KiB
Python
116 lines
4.3 KiB
Python
# /// script
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# requires-python = ">=3.10"
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# dependencies = [
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# "agent-framework",
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# "textual>=6.2.1",
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# "rich>=13.7.1",
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# "azure-identity",
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# "python-dotenv",
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# ]
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# ///
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# Run with any PEP 723 compatible runner, e.g.:
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# uv run samples/02-agents/harness/harness_research.py
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# Copyright (c) Microsoft. All rights reserved.
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"""Harness Research Assistant with Console UI.
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Demonstrates ``create_harness_agent`` — a factory function that builds a
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pre-configured agent with batteries included, automatically wiring up function
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invocation, per-service-call history persistence, compaction, and a rich set of
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context providers:
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- **TodoProvider** — the agent can create, track, and complete work items
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- **AgentModeProvider** — plan/execute mode tracking (interactive vs. autonomous)
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- **SkillsProvider** — file-based skill discovery and progressive loading
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- **CompactionProvider** — automatic context-window management
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- **InMemoryHistoryProvider** — session history with per-service-call persistence
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- **OpenTelemetry** — built-in observability via AgentTelemetryLayer
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- **Web Search** — real-time web search via ``get_web_search_tool()``
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The sample creates a research-focused agent with web search capability and runs
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it inside the Textual-based harness console. The agent will plan research tasks
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using todos, switch between plan and execute modes, search the web for current
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information, and track its progress.
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Environment variables:
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FOUNDRY_PROJECT_ENDPOINT — Azure AI Foundry project endpoint URL
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FOUNDRY_MODEL — Model deployment name
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Authentication:
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Run ``az login`` before running this sample.
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"""
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import asyncio
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from agent_framework import create_harness_agent
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from agent_framework.foundry import FoundryChatClient
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from azure.identity import AzureCliCredential
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from console import build_observers_with_planning, run_agent_async
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from dotenv import load_dotenv
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RESEARCH_INSTRUCTIONS = """\
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## Research Assistant Instructions
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You are a research assistant. When given a research topic, research it
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thoroughly using web search and web browsing. Use your knowledge to form good
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search queries and hypotheses, but always verify claims with the tools
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available to you rather than relying on memory alone.
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### Research quality
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Consult multiple sources when possible and cross-reference key claims.
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When sources disagree, note the discrepancy and explain which source you
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consider more reliable and why.
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If a web page fails to load or a search returns irrelevant results, try
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alternative search queries or sources before moving on.
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Track your sources — you will need them when presenting results.
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### Presenting results
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When presenting your final findings:
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- Use Markdown formatting for clarity.
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- Use clear sections with headings for each major topic or sub-question.
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- Cite your sources inline (e.g., "According to [source name](URL), ...").
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- End with a brief summary of key takeaways.
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- In addition to returning the results to the user, save the final research
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report to file memory so it survives compaction and can be referenced later.
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"""
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async def main() -> None:
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load_dotenv()
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# Create the chat client.
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# For authentication, run `az login` in terminal or replace AzureCliCredential
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# with your preferred authentication option.
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client = FoundryChatClient(credential=AzureCliCredential())
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# Create a harness agent with research-specific instructions.
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# All other features (todo, mode, compaction, skills, telemetry, web search) are
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# automatically configured with sensible defaults.
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agent = create_harness_agent(
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client=client,
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max_context_window_tokens=128_000,
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max_output_tokens=16_384,
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name="ResearchAgent",
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description="A research assistant that plans and executes research tasks.",
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agent_instructions=RESEARCH_INSTRUCTIONS,
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)
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# Run the harness console with the research agent.
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await run_agent_async(
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agent,
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session=agent.create_session(),
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observers=build_observers_with_planning(agent),
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initial_mode="plan",
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title="🔬 Research Assistant",
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placeholder="Enter a research topic...",
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max_context_window_tokens=128_000,
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max_output_tokens=16_384,
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)
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if __name__ == "__main__":
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asyncio.run(main())
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