weakincentives 0.5.0

Tools for developing and optimizing side effect free background agents

Weak Incentives

Lean, typed building blocks for side-effect-free background agents. Compose deterministic prompts, run typed tools, and parse strict JSON replies without heavy dependencies. Optional adapters snap in when you need a model provider.

Why now?

This library was built out of frustration with LangGraph and DSPy to explore better ways to do state and context management when building apps with LLMs while allowing the prompts to be automatically optimized.

Highlights

• Namespaced prompt trees with deterministic Markdown renders, placeholder verification, and tool-aware versioning metadata.
• Stdlib-only dataclass serde (parse, dump, clone, schema) keeps request and response types honest end-to-end.
• Session state container and event bus collect prompt and tool telemetry for downstream automation.
• Built-in planning and virtual filesystem tool suites give agents durable plans and sandboxed edits backed by reducers and selectors.
• Optional OpenAI and LiteLLM adapters integrate structured output parsing, tool orchestration, and telemetry hooks.

Requirements

• Python 3.12+ (the repository pins 3.14 in .python-version for development)
• uv CLI

Install

uv add weakincentives
# optional provider adapters
uv add "weakincentives[openai]"
uv add "weakincentives[litellm]"
# cloning the repo? use: uv sync --extra openai --extra litellm

Tutorial: Build a Stateful Code-Reviewing Agent

Use Weak Incentives to assemble a reproducible reviewer that tracks every decision, stages file edits in a sandbox, and evaluates quick calculations when the diff raises questions. Compared to LangGraph you do not need to bolt on a custom state store—the Session captures prompt and tool telemetry out of the box. Unlike DSPy, prompt sections already expose versioning and override hooks so optimizers can swap instructions without rewriting the runtime.

1. Model review data and expected outputs

Typed dataclasses keep inputs and outputs honest so adapters can emit consistent telemetry and structured responses stay predictable. Dive into the Dataclass Serde Utilities and Structured Output via Prompt[OutputT] specs for the validation rules and JSON-return guarantees that back this example.

from dataclasses import dataclass


@dataclass
class PullRequestContext:
    repository: str
    title: str
    body: str
    files_summary: str


@dataclass
class ReviewComment:
    file_path: str
    line: int
    severity: str
    summary: str
    rationale: str


@dataclass
class ReviewBundle:
    comments: tuple[ReviewComment, ...]
    overall_assessment: str

2. Create a session, surface built-in tool suites, and mount diffs

Planning, virtual filesystem, and Python-evaluation sections register reducers on the provided session. Introducing them early keeps every evaluation capable of multi-step plans, staged edits, and quick calculations. Host mounts feed the reviewer precomputed diffs before the run begins so it can read them through the virtual filesystem tools without calling back to your orchestrator. Because each tool suite records its activity on the session, selectors later in the tutorial can recover audit logs without extra plumbing. See the Session State Specification, Prompt Event Emission, Virtual Filesystem Tool Suite, Planning Tool Suite, and Asteval Integration for the reducer contracts and tool capabilities these snippets rely on.

from pathlib import Path

from weakincentives.events import InProcessEventBus, PromptExecuted
from weakincentives.session import Session
from weakincentives.tools import (
    AstevalSection,
    HostMount,
    PlanningToolsSection,
    VfsPath,
    VfsToolsSection,
)


bus = InProcessEventBus()
session = Session(bus=bus)


diff_root = Path("/srv/agent-mounts")
diff_root.mkdir(parents=True, exist_ok=True)
vfs_section = VfsToolsSection(
    session=session,
    allowed_host_roots=(diff_root,),
    mounts=(
        HostMount(
            host_path="octo_widgets/cache-layer.diff",
            mount_path=VfsPath(("diffs", "cache-layer.diff")),
        ),
    ),
)
planning_section = PlanningToolsSection(session=session)
asteval_section = AstevalSection(session=session)


def log_prompt(event: PromptExecuted) -> None:
    print(
        f"Prompt {event.prompt_name} completed with "
        f"{len(event.result.tool_results)} tool calls"
    )


bus.subscribe(PromptExecuted, log_prompt)

Copy unified diff files into /srv/agent-mounts before launching the run. The host mount resolves octo_widgets/cache-layer.diff relative to that directory and exposes it to the agent as diffs/cache-layer.diff inside the virtual filesystem snapshot.

3. Define a symbol search helper tool

Weak Incentives tools are typed functions that return structured results. Use them to expose deterministic helpers alongside the built-in suites. A reviewer benefits from a lightweight code-search utility that surfaces the context around symbols referenced in a diff. Mount a checkout of the repository under /srv/agent-repo before launching the run so the tool can read from it. Refer to the Tool Registration Specification and Error Handling Revamp to mirror the expected handler contract and ToolResult semantics.

from dataclasses import dataclass
from pathlib import Path

from weakincentives.prompt.tool import Tool, ToolResult


@dataclass
class SymbolSearchRequest:
    query: str
    file_glob: str = "*.py"
    max_results: int = 5


@dataclass
class SymbolMatch:
    file_path: str
    line: int
    snippet: str


@dataclass
class SymbolSearchResult:
    matches: tuple[SymbolMatch, ...]


repo_root = Path("/srv/agent-repo")


def find_symbol(params: SymbolSearchRequest) -> ToolResult[SymbolSearchResult]:
    if not repo_root.exists():
        raise FileNotFoundError(
            "Mount a repository checkout at /srv/agent-repo before running the agent."
        )

    matches: list[SymbolMatch] = []
    for file_path in repo_root.rglob(params.file_glob):
        if not file_path.is_file():
            continue
        with file_path.open("r", encoding="utf-8") as handle:
            for line_number, line in enumerate(handle, start=1):
                if params.query in line:
                    matches.append(
                        SymbolMatch(
                            file_path=str(file_path.relative_to(repo_root)),
                            line=line_number,
                            snippet=line.strip(),
                        )
                    )
                    if len(matches) >= params.max_results:
                        break
        if len(matches) >= params.max_results:
            break

    return ToolResult(
        message=f"Found {len(matches)} matching snippets.",
        value=SymbolSearchResult(matches=tuple(matches)),
    )


symbol_search_tool = Tool[SymbolSearchRequest, SymbolSearchResult](
    name="symbol_search",
    description=(
        "Search the repository checkout for a symbol and return file snippets."
    ),
    handler=find_symbol,
)

Attach custom tools to sections (next step) so the adapter can call them and record their outputs on the session alongside built-in reducers. The prompt can now chase suspicious references without delegating work back to the orchestrator.

4. Compose the prompt with deterministic sections

Sections rely on string.Template, so prepare readable placeholders up front. Combine your review instructions with the built-in tool suites to publish a single, auditable prompt tree. The Prompt Class Specification and Prompt Versioning & Persistence detail the rendering rules and hashing metadata that keep this structure stable.

from weakincentives import MarkdownSection, Prompt


@dataclass
class ReviewGuidance:
    severity_scale: str = "minor | major | critical"
    output_schema: str = "ReviewBundle with comments[] and overall_assessment"
    focus_areas: str = (
        "Security regressions, concurrency bugs, test coverage gaps, and"
        " ambiguous logic should be escalated."
    )


overview_section = MarkdownSection[PullRequestContext](
    title="Repository Overview",
    key="review.overview",
    template="""
    You are a principal engineer reviewing a pull request.
    Repository: ${repository}
    Title: ${title}

    Pull request summary:
    ${body}

    Files touched: ${files_summary}
    """,
)


analysis_section = MarkdownSection[ReviewGuidance](
    title="Review Directives",
    key="review.directives",
    template="""
    - Classify findings using this severity scale: ${severity_scale}.
    - Emit output that matches ${output_schema}; missing fields fail the run.
    - Investigation focus:
      ${focus_areas}
    - Inspect mounted diffs under `diffs/` with `vfs_read_file` before
      commenting on unfamiliar hunks.
    - Reach for `symbol_search` when you need surrounding context from the
      repository checkout.
    """,
    tools=(symbol_search_tool,),
    default_params=ReviewGuidance(),
)


review_prompt = Prompt[ReviewBundle](
    ns="tutorial/code_review",
    key="review.generate",
    name="code_review_agent",
    sections=(
        overview_section,
        planning_section,
        vfs_section,
        asteval_section,
        analysis_section,
    ),
)


rendered = review_prompt.render(
    PullRequestContext(
        repository="octo/widgets",
        title="Add caching layer",
        body="Introduces memoization to reduce redundant IO while preserving correctness.",
        files_summary="loader.py, cache.py",
    ),
    ReviewGuidance(),
)


print(rendered.text)
print([tool.name for tool in rendered.tools])

5. Evaluate the prompt with an adapter

Adapters send the rendered prompt to a provider and publish telemetry to the event bus. The session subscribed above automatically ingests each PromptExecuted and ToolInvoked event. Review the Adapter Evaluation Specification and Native OpenAI Structured Outputs for provider payload expectations and parsing guarantees.

from weakincentives.adapters.openai import OpenAIAdapter


adapter = OpenAIAdapter(
    model="gpt-4o-mini",
    client_kwargs={"api_key": "sk-..."},
)


response = adapter.evaluate(
    review_prompt,
    PullRequestContext(
        repository="octo/widgets",
        title="Add caching layer",
        body="Introduces memoization to reduce redundant IO while preserving correctness.",
        files_summary="loader.py, cache.py",
    ),
    bus=bus,
)


bundle = response.output
if bundle is None:
    raise RuntimeError("Structured parsing failed")


for comment in bundle.comments:
    print(f"{comment.file_path}:{comment.line} → {comment.summary}")

If the model omits a required field, OpenAIAdapter raises PromptEvaluationError with provider context rather than silently degrading.

6. Mine session state for downstream automation

Built-in selectors expose the data collected by reducers that each tool suite registered. This gives you ready-to-ship audit logs without building LangGraph callbacks or DSPy side channels. Planning reducers keep only the latest Plan snapshot; register your own reducer before instantiating PlanningToolsSection if you need to retain a historical ledger alongside the current state. Consult the Session State Specification and Session Snapshots Specification to understand how selectors and rollbacks behave in production runs.

from weakincentives.session import select_latest
from weakincentives.tools import Plan, VirtualFileSystem


latest_plan = select_latest(session, Plan)
vfs_snapshot = select_latest(session, VirtualFileSystem)


if latest_plan:
    print(f"Plan objective: {latest_plan.objective}")
    for step in latest_plan.steps:
        print(f"- [{step.status}] {step.title}")
else:
    print("No plan recorded yet.")


if vfs_snapshot:
    for file in vfs_snapshot.files:
        print(f"Staged file {file.path.segments} (version {file.version})")

7. Override sections with an overrides store

DSPy-style optimizers can persist improved instructions and let the runtime swap them in without redeploying code. For most projects the LocalPromptOverridesStore is the recommended implementation—it discovers the workspace root, enforces descriptor metadata, and reads JSON overrides from the .weakincentives/prompts/overrides/ tree described in the Local Prompt Overrides Store Specification. Pair it with the Prompt Versioning & Persistence guidance to understand how namespace, prompt key, and tag hashing keep overrides pinned to the right sections and tools.

from pathlib import Path

from weakincentives.prompt.local_prompt_overrides_store import (
    LocalPromptOverridesStore,
)
from weakincentives.prompt.versioning import (
    PromptDescriptor,
    PromptOverride,
    SectionOverride,
)


workspace_root = Path("/srv/agent-workspace")
overrides_store = LocalPromptOverridesStore(root_path=workspace_root)

descriptor = PromptDescriptor.from_prompt(review_prompt)
seed_override = overrides_store.seed_if_necessary(
    review_prompt, tag="assertive-feedback"
)

section_path = ("review", "directives")
section_descriptor = next(
    section
    for section in descriptor.sections
    if section.path == section_path
)

custom_override = PromptOverride(
    ns=descriptor.ns,
    prompt_key=descriptor.key,
    tag="assertive-feedback",
    sections={
        **seed_override.sections,
        section_path: SectionOverride(
            expected_hash=section_descriptor.content_hash,
            body="\n".join(
                (
                    "- Classify findings using this severity scale: minor | major | critical.",
                    "- Always cite the exact diff hunk when raising a major or critical issue.",
                    "- Respond with ReviewBundle JSON. Missing fields terminate the run.",
                )
            ),
        ),
    },
    tool_overrides=seed_override.tool_overrides,
)

persisted_override = overrides_store.upsert(descriptor, custom_override)

rendered_with_override = review_prompt.render_with_overrides(
    PullRequestContext(
        repository="octo/widgets",
        title="Add caching layer",
        body="Introduces memoization to reduce redundant IO while preserving correctness.",
        files_summary="loader.py, cache.py",
    ),
    overrides_store=overrides_store,
    tag=persisted_override.tag,
)


print(rendered_with_override.text)

The overrides store writes atomically to .weakincentives/prompts/overrides/{ns}/{prompt_key}/{tag}.json inside the workspace described in the Local Prompt Overrides Store Specification. Optimizers and prompt engineers can still drop JSON overrides into that tree by hand—checked into source control or generated during evaluations—without subclassing PromptOverridesStore. Because sections expose stable (ns, key, path) identifiers, overrides stay scoped to the intended content so teams can iterate on directives without risking accidental drift elsewhere in the tree.

8. Ship it

You now have a deterministic reviewer that:

1. Enforces typed contracts for inputs, tools, and outputs.
2. Persists multi-step plans, VFS edits, and evaluation transcripts inside a session without custom plumbing.
3. Supports optimizer-driven overrides that slot cleanly into CI, evaluation harnesses, or on-call tuning workflows.

Drop the agent into a queue worker, Slack bot, or scheduled job. Every evaluation is replayable thanks to the captured session state, so postmortems start with facts—not speculation. For long-lived deployments, pair these patterns with the Tool-Aware Prompt Versioning guidance to keep overrides and tooling descriptions aligned as the code evolves.

Development Setup

1. Install Python 3.14 (for example with pyenv install 3.14.0).

2. Install uv, then bootstrap the environment and hooks:

   uv sync
   ./install-hooks.sh
   

3. Run checks with uv run so everything shares the managed virtualenv:

   • make format / make format-check
   • make lint / make lint-fix
   • make typecheck (Ty + Pyright, warnings fail the build)
   • make test (pytest via build/run_pytest.py, 100% coverage enforced)
   • make check (aggregates the quiet checks above plus Bandit, Deptry, pip-audit, and markdown linting)

Documentation

• AGENTS.md — operational handbook and contributor workflow.
• specs/ — design docs for prompts, planning tools, and adapters.
• ROADMAP.md — upcoming feature sketches.
• docs/api/ — API reference material.

License

Apache 2.0 • Status: Alpha (APIs may change between releases)