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Rework README hero example and add ecosystem agent showcase (#226)
* Rework README hero example and add full ecosystem agent showcase
The previous hero example used GitHub's MCP server, which requires
authentication and ships tools without `outputSchema` -- triggering
CodeMode warnings and producing weakly-typed sandbox stubs. Replaced
with the open-source Hacker News MCP server (full output schemas, no
auth), and added `WebSearch` to make the prompt naturally exercise
parallel tool orchestration in a single `run_code` call.
`MCP(..., builtin=False)` is now explicit so the local toolset is used
and CodeMode actually wraps the tools (Anthropic supports MCP server
connectors as a builtin and would otherwise execute tools server-side,
bypassing the sandbox).
Added a "Full ecosystem agent" section above the capability matrix that
illustrates the breadth of the Pydantic AI ecosystem -- combining
capabilities from the harness, core, and the community packages we
endorse, with inline credits at each call site.
Made install requirements explicit above the hero block.
* Reword Quick start prompt to avoid datetime in generated code
Monty currently rejects `datetime` (pydantic/monty issue tracking),
so the previous prompt's "posted in the last 6 hours" filter caused
the agent to generate code that failed in the sandbox. Replaced with
a dedupe-by-id + score filter + parallel follow-up calls (thread,
user profile, web search) -- still exercises parallel orchestration,
dict manipulation, and multi-tool composition without any timestamps.
* Embed verbatim run output and soften 'single run_code' claim
Followed pydantic-ai docs convention of triple-quoted output after
print(). The included synthesis is the actual output from running
the example, not a fabrication.
Reworded the CodeMode explanation to match what Sonnet actually does:
two batched run_code calls each containing parallel tool calls, with
the dedupe/filter happening as plain Python in the sandbox -- not the
"all in a single run_code" claim from the previous draft.
* Surface monty timing restrictions in run_code description
Sonnet was occasionally generating `asyncio.sleep` between tool calls
(cargo-culted "polite rate-limiting" pattern) and `datetime.now()` for
recency filters -- both rejected by the monty sandbox. Adding an explicit
"no wall-clock or timing primitives" bullet to the `run_code` tool
description (and the matching code-mode README entry) keeps the prompt
free of sandbox-specific babysitting.
Switched the hero example to claude-opus-4-7 with a more natural prompt
("find the most-discussed story... summarize what you find in one
paragraph"), and refreshed the verbatim sample output to a clean run
that produced 3 run_code calls (3x parallel feed fetches, pure-Python
dedupe/filter, then 3x parallel follow-up calls) with no errors.
* Quick start: opt out of native WebSearch so CodeMode wraps it
`WebSearch()` defaults to `builtin=True`, and on providers like
Anthropic that natively support web search the local DDG fallback
is filtered out by `Model.prepare_request`. With `CodeMode` in the
mix that produced a split surface -- builtin web_search at top
level, duckduckgo_search inside `run_code` -- and the model
sometimes tried to call the builtin from inside the sandbox.
Passing `builtin=False` keeps every web call going through CodeMode
so it can be batched alongside the HN tools in a single `run_code`.
Same pattern we already use for `MCP(..., builtin=False)`.
The underlying flatten-prefer_builtin-into-run_code bug is filed
as #233 and is being fixed separately.
Refreshed the verbatim sample output to a clean run with this
configuration: 3 chats + 2 run_code calls, all parallel tool
batches, no errors.
* Quick start: link the public trace and refresh verbatim output
DouweM picked a run with substantively richer commentary (Simon
Willison's "Vibe coding and agentic engineering are getting closer
than I'd like" post on HN) and made the trace public, so we can
finally point readers at the actual run from the example.
* Add duckduckgo extra to install snippet for WebSearch(builtin=False)
* Move ecosystem example credits to dedicated comment lines
* Restructure README around the Quick start trace and an ecosystem agent
Quick start
- Move all per-capability commentary into inline comments at call sites,
so links sit next to the code they document. Drops the trailing
paragraph wall.
- Make the public Logfire trace the section's main visual: a screenshot
linking to the trace, then a one-liner explainer.
- Trim the verbatim sample output to ~5 sentences. Keeps the Willison
framing and the meta-substantive thread reference; drops the long
commenter-by-commenter paraphrase and the news-coverage paragraph
that previously took over half the section.
An ecosystem agent (formerly "Full ecosystem agent")
- Move below the capability matrix so readers meet the breadth before
the worked example.
- Renamed and rewritten intro: shorter, voice closer to the rest of
the README, no "upper bound of what's possible" phrasing.
- Long disclaimer paragraph moved below the code so readers see the
example before the caveats.
- Imports split into official (`pydantic_ai*` + harness) vs community
(alphabetical).
- Sections reorganized: Reasoning, Tools, Execution (CodeMode pulled
out of Tools), Context management, Memory, Orchestration, Safety.
- Each capability gets its own one-line comment. AnthropicCompaction
added as the official compaction option, with a comment pointing at
vstorm-co's `summarization-pydantic-ai` as a provider-agnostic alt.
- Agent gets a name (`brian` -- Monty Python) which `MemoryCapability`
picks up via its `agent_name` arg.
- Skills comment now flags @vstorm-co's pydantic-deep alongside Doug's,
noting the shape difference.
- Switched to `claude-opus-4-7` and `Thinking(effort='high')` to match
the Quick start.
- Drops `MCP(builtin=False)` here -- the ecosystem example isn't trying
to demo CodeMode-wrapped MCP, it's demoing breadth.
Drops the no-wall-clock bullet from `_toolset.py` and the code-mode
README -- now covered by the standalone PR for that change.
Trace screenshot reference uses a relative path that resolves on GitHub
once the asset is committed; ready to swap in once the file is in place.
* Ecosystem agent: rename to shrubbery, bump thinking to xhigh
* Add Logfire trace screenshots and surface them prominently
`docs/images/quick-start-trace.png` is the trace tree: it's the visual
the main README's Quick start now leads with -- click-through to the
public trace.
`docs/images/code-mode-trace.png` is the wider shot showing the actual
Python the model wrote (parallel `asyncio.gather` over three HN feeds,
dedupe-by-id, score filter). It's the centrepiece of a new "In practice"
section in the code-mode README that points back to the harness Quick
start as the more representative example, replacing the toy weather
demo as the leading visual of what code mode does in real use.
* Address review on PR #226
- Reorder ecosystem agent capabilities: CodeMode first (the headline),
then Thinking, Context management, Tools (MCP -> WebSearch -> Console),
then everything else. First-party leads each tier; ConsoleCapability
follows our two first-party tools.
- Drop the agent `name='shrubbery'` flourish.
- Use vstorm-co's `ContextManagerCapability` as the active compaction
capability and mention pydantic-ai's `AnthropicCompaction` /
`OpenAICompaction` in the comment instead of the other way around.
Drops the "tied to Anthropic's prompt caching" phrasing.
- `MemoryCapability(agent_name='harness-example')`.
- Code-mode README: rewrite the "In practice" prose to be clear that
CodeMode produces two `run_code` calls (parallel fetches + filter,
then parallel follow-ups), not one. Fix the screenshot alt text to
match.
* Add regression test for the README's Quick start example
The lead example in the main README is the most public surface of the
harness — if it stops working we need to know before the user tries it.
This test drives the example end-to-end with `FunctionModel` issuing the
two `run_code` calls the README's trace shows, and `CodeMode` actually
running the emitted Python through Monty.
What gets faked vs. what's real:
- The Hacker News MCP toolset is replaced with a `FunctionToolset` of
fake functions returning canned data from the README's linked public
trace. Avoids depending on an MCP package or hitting the network.
- `WebSearch(builtin=False, local=...)` skips the default DuckDuckGo
fallback so the test doesn't pull `ddgs`.
- `FunctionModel` drives the conversation through two `run_code` calls
and a final synthesis, mirroring the production trace.
- `CodeMode` itself is real; the `FunctionModel`'s emitted Python runs
through the Monty sandbox and dispatches calls back to the fakes.
Asserts on the call shape (each feed fetched once in parallel, follow-up
calls target the expected winner id) and on the final synthesis content,
so any future change in pydantic-ai or the harness that breaks how these
capabilities compose makes this test fail.
* Address review on PR #226
- Quick start: list `CodeMode` first in `capabilities=[]` to match the
ecosystem agent's ordering and to lead with the headline capability.
- Test: wire the fake HN tools through `MCP(local=hn_toolset, builtin=False)`
instead of bypassing via `toolsets=[hn_toolset]`. Same composition path
as production minus the network. Needs a narrow `# pyright: ignore`
because MCP's `__init__` signature narrows `local` to MCP-shaped types
but the parent `BuiltinOrLocalTool` accepts any `AbstractToolset` at
runtime.
- Test: have the second `run_code` call `web_search` (the WebSearch
capability's local fallback) instead of `hn_search_content`, so the
fake function is actually exercised. This fixes the coverage failure
on lines 231-232 and makes the test more representative of the README
example, which uses WebSearch.
* Use snapshot()+dirty-equals for README quick-start test
* Re-export dirty-equals via tests/conftest, restructure tests/
Two related cleanups based on review:
- Re-export `IsDatetime` / `IsNow` / `IsStr` / `IsPartialDict` from
`tests/conftest.py` with `TYPE_CHECKING` shims that pretend the
matchers return the concrete type (`-> str`, `-> datetime`, etc.).
Mirrors pydantic-ai's own conftest pattern and lets pyright strict
accept `tool_call_id=IsStr()`, `timestamp=IsDatetime()`, etc. without
the file-level `# pyright: reportArgumentType=false` we had before.
Tests now `from .conftest import ...` instead of importing directly
from `dirty_equals`.
- Rename `tests/_code_mode/` -> `tests/code_mode/` (drop the underscore
prefix; matches the source package `pydantic_ai_harness/code_mode/`).
Updates the convention note in AGENTS.md/CLAUDE.md.
- Move `test_readme_quick_start.py` up to `tests/` top level. It's an
end-to-end check on the README's flagship example -- it composes
`CodeMode` + `MCP` + `WebSearch` -- so it doesn't belong in any
single capability's test directory.
This commit is contained in:
Douwe Maan
@@ -80,7 +80,7 @@ pydantic_ai_harness/
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README.md # standalone docs for the capability
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tests/
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conftest.py # shared fixtures (TestModel, test_agent)
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_<capability>/ # tests mirror source packages
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<capability>/ # tests mirror source packages
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test_<capability>.py
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```
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@@ -15,7 +15,7 @@ Pydantic AI's [capabilities](https://ai.pydantic.dev/capabilities/) and [hooks](
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The [capability matrix](#capability-matrix) tracks where we are. [Tell us what to prioritize.](#help-us-prioritize)
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**Contents:** [Installation](#installation) · [Quick start](#quick-start) · [Capability matrix](#capability-matrix) · [Help us prioritize](#help-us-prioritize) · [Build your own](#build-your-own) · [Contributing](#contributing) · [Version policy](#version-policy) · [Pydantic AI references](#pydantic-ai-references) · [License](#license)
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**Contents:** [Installation](#installation) · [Quick start](#quick-start) · [Capability matrix](#capability-matrix) · [An ecosystem agent](#an-ecosystem-agent) · [Help us prioritize](#help-us-prioritize) · [Build your own](#build-your-own) · [Contributing](#contributing) · [Version policy](#version-policy) · [Pydantic AI references](#pydantic-ai-references) · [License](#license)
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## Installation
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@@ -35,32 +35,63 @@ Requires Python 3.10+ and `pydantic-ai-slim>=1.80.0`.
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## Quick start
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```bash
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uv add "pydantic-ai-slim[anthropic,mcp,duckduckgo,logfire]" "pydantic-ai-harness[code-mode]"
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```
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```python
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import logfire
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from pydantic_ai import Agent
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from pydantic_ai.capabilities import MCP # from the core pydantic-ai package
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from pydantic_ai.capabilities import MCP, WebSearch
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from pydantic_ai_harness import CodeMode
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# See https://ai.pydantic.dev/logfire/ for setup details.
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logfire.configure()
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logfire.instrument_pydantic_ai()
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agent = Agent(
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'anthropic:claude-sonnet-4-6',
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'anthropic:claude-opus-4-7',
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capabilities=[
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MCP('https://api.githubcopilot.com/mcp/'),
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# Wraps every tool into a single run_code tool, sandboxed by Monty
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# (https://github.com/pydantic/monty -- pulled in by the [code-mode] extra).
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# The model writes Python that calls multiple tools with loops, conditionals,
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# asyncio.gather, and local filtering -- one model round-trip for N tool calls.
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CodeMode(),
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# Connect to any MCP server -- here, the open-source Hacker News server
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# (https://github.com/cyanheads/hn-mcp-server). builtin=False forces the
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# local FastMCP toolset so CodeMode can wrap the tools; without it,
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# providers that natively support MCP server connectors execute the tools
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# server-side and bypass the sandbox.
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MCP('https://hn.caseyjhand.com/mcp', builtin=False),
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# Provider-adaptive web search; builtin=False routes through the local
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# DuckDuckGo fallback (the [duckduckgo] extra above) so CodeMode can batch
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# web searches alongside the HN calls in a single run_code.
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WebSearch(builtin=False),
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],
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)
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result = agent.run_sync('Rank the open PRs on pydantic/pydantic-ai-harness by thumbs-up reactions. Which 5 should we merge first?')
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result = agent.run_sync(
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"Across the top, best, and 'show HN' Hacker News feeds, find the most-discussed "
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"story with at least 100 points. Pull its comment thread, its submitter's profile, "
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"and any web coverage. Summarize what you find in one paragraph."
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)
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print(result.output)
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"""
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The most-discussed HN story across top/best/show clearing 100 points is "Vibe coding
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and agentic engineering are getting closer than I'd like" by Simon Willison (748 points,
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853 comments, on the Best feed), submitted by long-time HNer e12e. The piece argues
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that the two modes Willison once kept mentally separate -- throwaway "vibe coding" and
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disciplined "agentic engineering" -- are blurring, since agents like Claude Code now
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reliably handle non-trivial tasks like "build a JSON API endpoint that runs a SQL query"
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with tests and docs on the first pass. The HN thread is unusually substantive, with
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commenters debating whether LLMs created or merely *exposed* sloppy engineering
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practices and warning of a "normalization of deviance" as engineers stop reviewing diffs.
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"""
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```
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[`MCP`](https://ai.pydantic.dev/capabilities/#provider-adaptive-tools) (from the core `pydantic-ai` package) connects your agent to any MCP server -- here, [GitHub's official MCP server](https://github.com/github/github-mcp-server).
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[](https://logfire-us.pydantic.dev/public-trace/84bcf123-2106-49da-9f6f-5c26395339bb?spanId=7650806a0785b946)
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[`CodeMode`](pydantic_ai_harness/code_mode/) wraps all tools into a single `run_code` tool powered by our [Monty](https://github.com/pydantic/monty) sandbox, so the model can orchestrate multiple tool calls with Python code instead of one model round-trip per call.
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[`logfire`](https://pydantic.dev/logfire) gives you a trace for every agent run. With CodeMode, you can see the `run_code` span with each nested tool call as a child span -- making it easy to debug what the model's code actually did. See the [Pydantic AI Logfire docs](https://ai.pydantic.dev/logfire/) for setup details.
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**[See this run as a public Logfire trace →](https://logfire-us.pydantic.dev/public-trace/84bcf123-2106-49da-9f6f-5c26395339bb?spanId=7650806a0785b946)** Each `run_code` span fans out into the tool calls the model issued from inside the sandbox -- it's the easiest way to understand what code mode actually did.
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## Capability matrix
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@@ -105,6 +136,107 @@ We studied leading coding agents, agent frameworks, and Claw-style assistants to
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> Packages by [vstorm-co](https://github.com/vstorm-co) are endorsed by the Pydantic AI team. We're working with them to upstream some of their implementations into this repo.
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## An ecosystem agent
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The Quick start above is deliberately small. Here's the other end of the spectrum -- an agent wired up with capabilities drawn from across the Pydantic AI ecosystem: this repo, core `pydantic-ai`, and the community packages we vouch for in the matrix above.
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```python
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import logfire
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from pydantic_ai import Agent
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from pydantic_ai.capabilities import MCP, Thinking, WebSearch
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from pydantic_ai_harness import CodeMode
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# Community packages, alphabetical:
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from pydantic_ai_backends import ConsoleCapability
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from pydantic_ai_shields import CostTracking, InputGuard, SecretRedaction, ToolGuard
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from pydantic_ai_skills import SkillsCapability
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from pydantic_ai_summarization import ContextManagerCapability
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from pydantic_ai_todo import TodoCapability
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from pydantic_deep import MemoryCapability, StuckLoopDetection
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from subagents_pydantic_ai import SubAgentCapability, SubAgentConfig
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# See https://ai.pydantic.dev/logfire/ for setup details.
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logfire.configure()
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logfire.instrument_pydantic_ai()
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agent = Agent(
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'anthropic:claude-opus-4-7',
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capabilities=[
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# --- Execution ---
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# Wraps every tool into a single run_code, sandboxed by Monty.
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CodeMode(),
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# --- Reasoning ---
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# Provider-adaptive thinking; uses native extended thinking on supporting models.
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Thinking(effort='xhigh'),
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# --- Context management ---
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# Sliding window + LLM compaction. By @vstorm-co:
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# https://github.com/vstorm-co/summarization-pydantic-ai
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# Pydantic AI also ships `AnthropicCompaction` and `OpenAICompaction` for
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# provider-native compaction.
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ContextManagerCapability(max_tokens=180_000),
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# --- Tools ---
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# Connect to any MCP server -- here, the open-source Hacker News server
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# (https://github.com/cyanheads/hn-mcp-server).
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MCP('https://hn.caseyjhand.com/mcp'),
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# Provider-adaptive web search; falls back to a local DuckDuckGo implementation.
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WebSearch(),
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# Filesystem + shell. By @vstorm-co: https://github.com/vstorm-co/pydantic-ai-backend
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ConsoleCapability(),
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# --- Memory & persistence ---
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# Persistent ./MEMORY.md per agent name. By @vstorm-co:
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# https://github.com/vstorm-co/pydantic-deepagents
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MemoryCapability(agent_name='harness-example'),
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# --- Orchestration ---
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# Agent skills (Anthropic's spec) by @DougTrajano:
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# https://github.com/DougTrajano/pydantic-ai-skills
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# @vstorm-co's pydantic-deep also offers skills loading; the two have different
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# spec footprints (Doug's is closer to programmatic skills).
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SkillsCapability(directories=['./skills']),
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# Spawn sub-agents with their own toolsets and instructions. By @vstorm-co:
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# https://github.com/vstorm-co/subagents-pydantic-ai
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SubAgentCapability(subagents=[
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SubAgentConfig(
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name='researcher',
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description='Deep research on a topic',
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instructions='You are a thorough research assistant.',
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),
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]),
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# Track tasks and subtasks; in-memory by default, AsyncPostgresStorage available.
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# By @vstorm-co: https://github.com/vstorm-co/pydantic-ai-todo
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TodoCapability(enable_subtasks=True),
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# --- Safety & reliability ---
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# The next four are by @vstorm-co: https://github.com/vstorm-co/pydantic-ai-shields
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# Per-run cost cap with a callback hook.
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CostTracking(budget_usd=5.0),
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# Reject prompts that look like prompt-injection attempts.
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InputGuard(guard=lambda p: 'ignore previous instructions' not in p.lower()),
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# Block or require approval per tool name.
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ToolGuard(blocked=['rm'], require_approval=['write_file']),
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# Detect API keys/tokens in tool I/O and redact before they reach the model.
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SecretRedaction(),
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# Bail out if the agent gets stuck calling the same tools in a loop.
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# By @vstorm-co: https://github.com/vstorm-co/pydantic-deepagents
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StuckLoopDetection(),
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],
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)
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```
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This snippet is illustrative, not literally copy-pasteable: a few capabilities have setup requirements (a `./skills` directory, a Postgres database for `TodoCapability`'s persistent storage), and the community packages move independently of this one. The [capability matrix](#capability-matrix) tracks each one's status. As the harness ships first-party versions, the imports above will collapse onto fewer packages -- but the example will keep working, since the API surface is the same.
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## Help us prioritize
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**Vote on whatever is linked in the Status column above.** If there's a PR, vote on the PR -- it means we're actively building it. If there's only an issue, vote on the issue.
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@@ -51,6 +51,14 @@ tokyo_c = await convert_temp(fahrenheit=tokyo['temp_f'])
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{'paris': paris_c, 'tokyo': tokyo_c}
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```
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## In practice
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The [harness Quick start](../../README.md#quick-start) wires `CodeMode` up against an MCP server and a web search and asks it to find the most-discussed Hacker News story across three feeds, pull the comment thread and the submitter's profile, and search the web for follow-up coverage. CodeMode collapses that into two `run_code` calls: the first fetches all three feeds in parallel via `asyncio.gather`, dedupes by id, filters by score, and ranks by comment count -- in plain Python; the second batches the three follow-up calls (`hn_get_thread`, `hn_get_user`, `duckduckgo_search`) together.
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[](https://logfire-us.pydantic.dev/public-trace/84bcf123-2106-49da-9f6f-5c26395339bb?spanId=7650806a0785b946)
|
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|
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**[See the full Logfire trace →](https://logfire-us.pydantic.dev/public-trace/84bcf123-2106-49da-9f6f-5c26395339bb?spanId=7650806a0785b946)** Each `run_code` span fans out into the tool calls the model issued from inside the sandbox -- the easiest way to understand what code mode actually did. See the [Pydantic AI Logfire docs](https://ai.pydantic.dev/logfire/) for setup details.
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## Installation
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Code mode requires the Monty sandbox:
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@@ -63,6 +63,8 @@ dev = [
|
||||
'pytest-anyio',
|
||||
'coverage>=7.10.7',
|
||||
'logfire[httpx]>=4.31.0',
|
||||
"dirty-equals>=0.9.0",
|
||||
"inline-snapshot>=0.32.5",
|
||||
]
|
||||
lint = [
|
||||
'ruff>=0.14',
|
||||
|
||||
@@ -1,13 +1,32 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Iterator
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Any
|
||||
|
||||
import pydantic_ai.models
|
||||
import pytest
|
||||
from pydantic_ai import Agent
|
||||
from pydantic_ai.models.test import TestModel
|
||||
|
||||
# `dirty-equals` matchers are typed as `DirtyEquals[T]`, not `T`, so passing
|
||||
# them where pydantic-ai expects concrete `str`/`datetime`/etc. fails pyright
|
||||
# strict. Following pydantic-ai's own conftest, re-export with TYPE_CHECKING
|
||||
# stubs that pretend the matchers return the concrete type. Tests should
|
||||
# `from tests.conftest import IsStr, IsDatetime, ...` instead of importing
|
||||
# from `dirty_equals` directly.
|
||||
if TYPE_CHECKING:
|
||||
|
||||
def IsDatetime(*args: Any, **kwargs: Any) -> datetime: ...
|
||||
def IsNow(*args: Any, **kwargs: Any) -> datetime: ...
|
||||
def IsStr(*args: Any, **kwargs: Any) -> str: ...
|
||||
def IsPartialDict(*args: Any, **kwargs: Any) -> dict[Any, Any]: ...
|
||||
else:
|
||||
from dirty_equals import IsDatetime, IsNow, IsPartialDict, IsStr
|
||||
|
||||
__all__ = ('IsDatetime', 'IsNow', 'IsPartialDict', 'IsStr')
|
||||
|
||||
# Prevent accidental real model requests during tests.
|
||||
pydantic_ai.models.ALLOW_MODEL_REQUESTS = False
|
||||
|
||||
|
||||
@@ -0,0 +1,510 @@
|
||||
"""Regression test for the harness README's Quick start example.
|
||||
|
||||
The README ships a Hacker News + web-search agent wrapped in `CodeMode` and
|
||||
asks it to find the most-discussed HN story across three feeds, then pull
|
||||
the comment thread, the submitter's profile, and follow-up coverage. We
|
||||
fake everything that talks to the network so the test runs in CI without
|
||||
`ddgs`, an MCP package, or any HTTP traffic:
|
||||
|
||||
- A `FunctionModel` drives the conversation through the same shape the
|
||||
example produces in production -- two `run_code` calls (parallel feed
|
||||
fetches + dedupe + filter, then parallel follow-ups) and a final summary.
|
||||
- The Hacker News MCP toolset is replaced with a `FunctionToolset` of
|
||||
fake functions whose return values come from the public Logfire trace
|
||||
linked in the README.
|
||||
- `WebSearch(builtin=False, local=...)` skips the default DuckDuckGo
|
||||
fallback so the test doesn't pull `ddgs` and the harness doesn't depend
|
||||
on it in CI.
|
||||
|
||||
`CodeMode` itself is real -- the `FunctionModel`'s emitted Python code
|
||||
runs through the Monty sandbox, dispatches the calls back through
|
||||
pydantic-ai's tool machinery to our fakes, and the return values flow
|
||||
back into the model loop. Any future change in pydantic-ai or the harness
|
||||
that breaks how these capabilities compose makes this test fail.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import textwrap
|
||||
from typing import Any
|
||||
|
||||
import pytest
|
||||
from inline_snapshot import snapshot
|
||||
from pydantic_ai import Agent, Tool
|
||||
from pydantic_ai.capabilities import MCP, WebSearch
|
||||
from pydantic_ai.messages import (
|
||||
ModelMessage,
|
||||
ModelRequest,
|
||||
ModelResponse,
|
||||
TextPart,
|
||||
ToolCallPart,
|
||||
ToolReturnPart,
|
||||
UserPromptPart,
|
||||
)
|
||||
from pydantic_ai.models.function import AgentInfo, FunctionModel
|
||||
from pydantic_ai.toolsets.function import FunctionToolset
|
||||
from pydantic_ai.usage import RequestUsage
|
||||
|
||||
from pydantic_ai_harness import CodeMode
|
||||
|
||||
from .conftest import IsDatetime, IsPartialDict, IsStr
|
||||
|
||||
pytestmark = pytest.mark.anyio
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def anyio_backend() -> str:
|
||||
"""Run async tests on the asyncio backend (pydantic-ai uses asyncio.create_task internally)."""
|
||||
return 'asyncio'
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Canned tool responses -- shapes mirror what the cyanheads HN MCP server
|
||||
# actually returns, with values from the run captured in the README's
|
||||
# linked public trace.
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
_WINNER_ID = 48037128
|
||||
_WINNER_USER = 'e12e'
|
||||
|
||||
_TOP_FEED = {
|
||||
'stories': [
|
||||
{
|
||||
'id': 48037555,
|
||||
'type': 'story',
|
||||
'title': 'Valve releases Steam Controller CAD files under Creative Commons license',
|
||||
'score': 1687,
|
||||
'by': 'haunter',
|
||||
'time': 1778082253,
|
||||
'descendants': 572,
|
||||
'url': 'https://www.digitalfoundry.net/news/2026/05/valve-releases-steam-controller-cad-files',
|
||||
},
|
||||
{
|
||||
'id': 48050499,
|
||||
'type': 'story',
|
||||
'title': 'I want to live like Costco people',
|
||||
'score': 235,
|
||||
'by': 'speckx',
|
||||
'time': 1778167167,
|
||||
'descendants': 495,
|
||||
'url': 'https://tastecooking.com/i-want-to-live-like-costco-people/',
|
||||
},
|
||||
],
|
||||
}
|
||||
|
||||
_BEST_FEED = {
|
||||
'stories': [
|
||||
{
|
||||
'id': _WINNER_ID,
|
||||
'type': 'story',
|
||||
'title': "Vibe coding and agentic engineering are getting closer than I'd like",
|
||||
'score': 748,
|
||||
'by': _WINNER_USER,
|
||||
'time': 1778079997,
|
||||
'descendants': 853,
|
||||
'url': 'https://simonwillison.net/2026/May/6/vibe-coding-and-agentic-engineering/',
|
||||
},
|
||||
{
|
||||
'id': 48038001,
|
||||
'type': 'story',
|
||||
'title': 'Appearing productive in the workplace',
|
||||
'score': 1534,
|
||||
'by': 'diebillionaires',
|
||||
'time': 1778084309,
|
||||
'descendants': 629,
|
||||
'url': 'https://nooneshappy.com/article/appearing-productive-in-the-workplace/',
|
||||
},
|
||||
{
|
||||
'id': 48037555,
|
||||
'type': 'story',
|
||||
'title': 'Valve releases Steam Controller CAD files under Creative Commons license',
|
||||
'score': 1687,
|
||||
'by': 'haunter',
|
||||
'time': 1778082253,
|
||||
'descendants': 572,
|
||||
'url': 'https://www.digitalfoundry.net/news/2026/05/valve-releases-steam-controller-cad-files',
|
||||
},
|
||||
],
|
||||
}
|
||||
|
||||
_SHOW_FEED: dict[str, list[dict[str, Any]]] = {'stories': []}
|
||||
|
||||
_THREAD = {
|
||||
'item': {
|
||||
'id': _WINNER_ID,
|
||||
'title': "Vibe coding and agentic engineering are getting closer than I'd like",
|
||||
'url': 'https://simonwillison.net/2026/May/6/vibe-coding-and-agentic-engineering/',
|
||||
'score': 748,
|
||||
'by': _WINNER_USER,
|
||||
'descendants': 853,
|
||||
},
|
||||
'comments': [
|
||||
{'by': 'etothet', 'depth': 0, 'text': 'LLMs exposed sloppy practices, not created them.'},
|
||||
{'by': 'kelnos', 'depth': 0, 'text': 'Normalization of deviance as engineers stop reviewing diffs.'},
|
||||
],
|
||||
'totalLoaded': 2,
|
||||
'totalAvailable': 853,
|
||||
}
|
||||
|
||||
_USER_PROFILE: dict[str, Any] = {
|
||||
'user': {
|
||||
'id': _WINNER_USER,
|
||||
'created': 1331059200,
|
||||
'karma': 15024,
|
||||
'submitted': 9700,
|
||||
'about': 'perpetual student and sometimes developer based in Tromsø, Norway',
|
||||
},
|
||||
'submissions': [],
|
||||
}
|
||||
|
||||
_WEB_RESULTS: dict[str, list[dict[str, Any]]] = {
|
||||
'results': [
|
||||
{
|
||||
'title': 'GLM-5: From Vibe Coding to Agentic Engineering',
|
||||
'url': 'https://simonwillison.net/2026/Feb/11/glm-5/',
|
||||
'snippet': 'Earlier piece by the same author tracing the same arc.',
|
||||
},
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Fake tool implementations. The model's tool dispatch is captured in each
|
||||
# `run_code` ToolReturnPart's metadata, so the snapshot assertion below
|
||||
# already records every call -- no separate recorder needed.
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _make_fake_hn_toolset() -> FunctionToolset[None]:
|
||||
feeds: dict[str, dict[str, list[dict[str, Any]]]] = {
|
||||
'top': _TOP_FEED,
|
||||
'best': _BEST_FEED,
|
||||
'show': _SHOW_FEED,
|
||||
}
|
||||
|
||||
def hn_get_stories(*, feed: str, count: int = 50) -> dict[str, Any]:
|
||||
"""Fetch a Hacker News feed (top, best, or show)."""
|
||||
return feeds[feed]
|
||||
|
||||
def hn_get_thread(*, itemId: int, depth: int = 2, maxComments: int = 60) -> dict[str, Any]:
|
||||
"""Fetch the comment thread for a story id."""
|
||||
return _THREAD
|
||||
|
||||
def hn_get_user(
|
||||
*,
|
||||
username: str,
|
||||
includeSubmissions: bool = False,
|
||||
submissionCount: int = 5,
|
||||
) -> dict[str, Any]:
|
||||
"""Fetch a Hacker News user's profile."""
|
||||
return _USER_PROFILE
|
||||
|
||||
return FunctionToolset[None](
|
||||
tools=[
|
||||
Tool(hn_get_stories),
|
||||
Tool(hn_get_thread),
|
||||
Tool(hn_get_user),
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
def _fake_web_search(*, query: str) -> dict[str, Any]:
|
||||
"""Stand-in for the WebSearch capability's local DDG fallback."""
|
||||
return _WEB_RESULTS
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# FunctionModel state machine -- two run_code calls, then a final synthesis,
|
||||
# matching the trace in the README.
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
# First run_code: parallel feed fetches, dedupe by id, score filter, rank by descendants.
|
||||
_FIRST_RUN_CODE = textwrap.dedent(
|
||||
"""
|
||||
import asyncio
|
||||
top, best, show = await asyncio.gather(
|
||||
hn_get_stories(feed='top', count=50),
|
||||
hn_get_stories(feed='best', count=50),
|
||||
hn_get_stories(feed='show', count=50),
|
||||
)
|
||||
seen = {}
|
||||
for feed_name, data in [('top', top), ('best', best), ('show', show)]:
|
||||
for s in data['stories']:
|
||||
if s.get('score', 0) >= 100:
|
||||
if s['id'] not in seen:
|
||||
entry = dict(s)
|
||||
entry['feeds'] = []
|
||||
seen[s['id']] = entry
|
||||
seen[s['id']]['feeds'].append(feed_name)
|
||||
ranked = sorted(seen.values(), key=lambda x: x.get('descendants', 0), reverse=True)
|
||||
ranked[:5]
|
||||
"""
|
||||
).strip()
|
||||
|
||||
# Second run_code: parallel follow-up calls on the winner. Uses the WebSearch
|
||||
# capability's local fallback (`web_search`) and the MCP-served HN tools side by
|
||||
# side, mirroring the README example's "HN tools + web search" composition.
|
||||
_SECOND_RUN_CODE = textwrap.dedent(
|
||||
f"""
|
||||
import asyncio
|
||||
thread, user, coverage = await asyncio.gather(
|
||||
hn_get_thread(itemId={_WINNER_ID}, depth=2, maxComments=60),
|
||||
hn_get_user(username='{_WINNER_USER}', includeSubmissions=True, submissionCount=5),
|
||||
web_search(query='vibe coding agentic engineering simonwillison'),
|
||||
)
|
||||
(thread['item'], user['user'], coverage['results'][:5])
|
||||
"""
|
||||
).strip()
|
||||
|
||||
_FINAL_SYNTHESIS = (
|
||||
'The most-discussed HN story across top/best/show clearing 100 points is '
|
||||
'"Vibe coding and agentic engineering are getting closer than I\'d like" '
|
||||
'by Simon Willison (748 points, 853 comments), submitted by e12e.'
|
||||
)
|
||||
|
||||
|
||||
def _model_fn(messages: list[ModelMessage], info: AgentInfo) -> ModelResponse:
|
||||
completed_run_codes = [
|
||||
p
|
||||
for m in messages
|
||||
if isinstance(m, ModelRequest)
|
||||
for p in m.parts
|
||||
if isinstance(p, ToolReturnPart) and p.tool_name == 'run_code'
|
||||
]
|
||||
if not completed_run_codes:
|
||||
return ModelResponse(parts=[ToolCallPart(tool_name='run_code', args={'code': _FIRST_RUN_CODE})])
|
||||
if len(completed_run_codes) == 1:
|
||||
return ModelResponse(
|
||||
parts=[
|
||||
TextPart('The winner is the Simon Willison post; pulling thread, user, and coverage in parallel.'),
|
||||
ToolCallPart(tool_name='run_code', args={'code': _SECOND_RUN_CODE}),
|
||||
]
|
||||
)
|
||||
return ModelResponse(parts=[TextPart(_FINAL_SYNTHESIS)])
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# The test
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestReadmeQuickStart:
|
||||
"""End-to-end check that the README's Quick start example still works."""
|
||||
|
||||
async def test_quick_start_runs_through_codemode_with_faked_io(self) -> None:
|
||||
agent: Agent[None, str] = Agent(
|
||||
FunctionModel(_model_fn),
|
||||
capabilities=[
|
||||
# Wire the fake HN tools through the `MCP` capability the same way
|
||||
# the README does -- `local=` overrides the default FastMCP HTTP
|
||||
# toolset with our in-process fake, so the test exercises the same
|
||||
# capability composition path as production without any network.
|
||||
# MCP's `__init__` narrows `local` to MCP-specific types, but the
|
||||
# parent `BuiltinOrLocalTool` accepts any `AbstractToolset` at runtime.
|
||||
MCP[None](
|
||||
'https://hn.caseyjhand.com/mcp',
|
||||
builtin=False,
|
||||
local=_make_fake_hn_toolset(), # pyright: ignore[reportArgumentType]
|
||||
),
|
||||
# The auto-wrapped Tool would take its name from the function
|
||||
# (`_fake_web_search`); pass `name='web_search'` so the sandbox
|
||||
# exposes it under the same name the model uses.
|
||||
WebSearch[None](builtin=False, local=Tool(_fake_web_search, name='web_search')),
|
||||
CodeMode[None](),
|
||||
],
|
||||
)
|
||||
|
||||
result = await agent.run(
|
||||
"Across the top, best, and 'show HN' Hacker News feeds, find the most-discussed "
|
||||
'story with at least 100 points. Pull its comment thread, its submitter profile, '
|
||||
'and any web coverage. Summarize what you find in one paragraph.'
|
||||
)
|
||||
|
||||
# The full message tree -- two `run_code` calls, each with parallel tool
|
||||
# dispatches captured in the return metadata, plus the final synthesis.
|
||||
# Run with `--inline-snapshot=fix` to update if the example legitimately
|
||||
# changes shape.
|
||||
assert result.all_messages() == snapshot(
|
||||
[
|
||||
ModelRequest(
|
||||
parts=[
|
||||
UserPromptPart(
|
||||
content="Across the top, best, and 'show HN' Hacker News feeds, find the most-discussed story with at least 100 points. Pull its comment thread, its submitter profile, and any web coverage. Summarize what you find in one paragraph.",
|
||||
timestamp=IsDatetime(),
|
||||
)
|
||||
],
|
||||
timestamp=IsDatetime(),
|
||||
run_id=IsStr(),
|
||||
conversation_id=IsStr(),
|
||||
),
|
||||
ModelResponse(
|
||||
parts=[
|
||||
ToolCallPart(
|
||||
tool_name='run_code',
|
||||
args={
|
||||
'code': """\
|
||||
import asyncio
|
||||
top, best, show = await asyncio.gather(
|
||||
hn_get_stories(feed='top', count=50),
|
||||
hn_get_stories(feed='best', count=50),
|
||||
hn_get_stories(feed='show', count=50),
|
||||
)
|
||||
seen = {}
|
||||
for feed_name, data in [('top', top), ('best', best), ('show', show)]:
|
||||
for s in data['stories']:
|
||||
if s.get('score', 0) >= 100:
|
||||
if s['id'] not in seen:
|
||||
entry = dict(s)
|
||||
entry['feeds'] = []
|
||||
seen[s['id']] = entry
|
||||
seen[s['id']]['feeds'].append(feed_name)
|
||||
ranked = sorted(seen.values(), key=lambda x: x.get('descendants', 0), reverse=True)
|
||||
ranked[:5]\
|
||||
"""
|
||||
},
|
||||
tool_call_id=IsStr(),
|
||||
)
|
||||
],
|
||||
usage=RequestUsage(input_tokens=88, output_tokens=72),
|
||||
model_name='function:_model_fn:',
|
||||
timestamp=IsDatetime(),
|
||||
run_id=IsStr(),
|
||||
conversation_id=IsStr(),
|
||||
),
|
||||
ModelRequest(
|
||||
parts=[
|
||||
ToolReturnPart(
|
||||
tool_name='run_code',
|
||||
content=[
|
||||
{
|
||||
'id': 48037128,
|
||||
'type': 'story',
|
||||
'title': "Vibe coding and agentic engineering are getting closer than I'd like",
|
||||
'score': 748,
|
||||
'by': 'e12e',
|
||||
'time': 1778079997,
|
||||
'descendants': 853,
|
||||
'url': 'https://simonwillison.net/2026/May/6/vibe-coding-and-agentic-engineering/',
|
||||
'feeds': ['best'],
|
||||
},
|
||||
{
|
||||
'id': 48038001,
|
||||
'type': 'story',
|
||||
'title': 'Appearing productive in the workplace',
|
||||
'score': 1534,
|
||||
'by': 'diebillionaires',
|
||||
'time': 1778084309,
|
||||
'descendants': 629,
|
||||
'url': 'https://nooneshappy.com/article/appearing-productive-in-the-workplace/',
|
||||
'feeds': ['best'],
|
||||
},
|
||||
{
|
||||
'id': 48037555,
|
||||
'type': 'story',
|
||||
'title': 'Valve releases Steam Controller CAD files under Creative Commons license',
|
||||
'score': 1687,
|
||||
'by': 'haunter',
|
||||
'time': 1778082253,
|
||||
'descendants': 572,
|
||||
'url': 'https://www.digitalfoundry.net/news/2026/05/valve-releases-steam-controller-cad-files',
|
||||
'feeds': ['top', 'best'],
|
||||
},
|
||||
{
|
||||
'id': 48050499,
|
||||
'type': 'story',
|
||||
'title': 'I want to live like Costco people',
|
||||
'score': 235,
|
||||
'by': 'speckx',
|
||||
'time': 1778167167,
|
||||
'descendants': 495,
|
||||
'url': 'https://tastecooking.com/i-want-to-live-like-costco-people/',
|
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[[package]]
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[[package]]
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Reference in New Issue
Block a user