QriusAI Nonprofit Agent Harness

An open agent harness for the nonprofit sector. You write the agent. It gives you everything around the agent.

Built on Google's Agent Development Kit, and usable without it. The model layer is one swappable interface.

Status: alpha. Everything described below works and is covered by tests. The interfaces will still change. Pin a commit if you depend on it.

Why the Foundation built this

QriusAI Foundation works on AI capacity building for nonprofits. Across that work one pattern keeps repeating. A nonprofit team knows exactly what it needs an agent to do, because nobody understands their programme better than they do. What stops them is never the idea. It is the six weeks of scaffolding around the idea: sign-in, file handling, job state, cost control, an approval step, somewhere to put the output.

So they either do not build it, or they pay someone to build a single tool that only that someone can maintain.

We would rather give away the scaffolding.

We are not trying to build the tools the sector needs. We are building the blocks the sector can build them from. A harness is infrastructure. It is deliberately not a product, not a platform you sign up for, and not something that routes your work through us. You fork it, you run it, you own it, and the capability stays with you.

This is what we think AI capacity building has to mean eventually. Training helps. Tools help. But a sector that can only use what it is given stays dependent on whoever gives it. A sector with its own building blocks does not.

What we looked for, and did not find

Before writing this we searched for one already. Two angles: open-source agent projects scoped to the nonprofit sector, and agent projects built on Google's ADK for nonprofit use.

We found real, useful work in each direction separately. There are nonprofit-specific single-purpose tools, for grant writing and for board governance. There are ADK projects built by and for individual organisations. There are excellent general-purpose agent frameworks that any sector can build on.

What we did not find was something at the intersection: a reusable harness, scoped to this sector, that a nonprofit could fork and build several different agents on.

We want to be precise about what that is and is not. It is an absence of evidence from two search angles, not an exhaustive audit of everything that exists. So the honest claim is "we found no comparable project", not "this is the first in the world." If you know of one, please open an issue. We would genuinely like to know, and we would rather link to it than duplicate it.

What makes this a nonprofit harness

Plenty of frameworks will help you call a model. Very few assume the things this sector has to assume.

Someone is accountable for every output. A generated training handout, funder summary, or community-facing message cannot go out because a model produced it. Review is the default here, not an add-on, and every release records who approved it.

A claim has to be traceable to a source. An invented statistic in a funder report is the failure that costs an organisation its credibility. Cited quotes are checked against the source document before a reviewer ever sees them, and that check costs nothing to run.

The budget is fixed and small. Grant money does not stretch because a retry loop misbehaved. Every run carries a hard ceiling on cost, tokens, and calls.

There is no platform team. It has to run with no cloud account before it runs with one. A fresh clone works offline, for free, in one command.

If your agent does not need those things, use a general framework. This one is scoped on purpose.

Quickstart

No cloud account, no API key, no spend:

git clone https://github.com/QriusAI-Foundation/nonprofit-agent-harness
cd nonprofit-agent-harness
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev,documents]"

harness run examples.summarizer:SummarizerAgent path/to/report.pdf

That runs against a built-in offline provider that returns deterministic text, so the whole path works before you connect a real model. Continuous integration runs this exact flow on every commit, so the promise stays true.

Point it at Gemini when you are ready:

export HARNESS_PROVIDER=google
export GOOGLE_API_KEY=...          # or use Vertex AI with GOOGLE_CLOUD_PROJECT
harness run examples.summarizer:SummarizerAgent report.pdf

Writing an agent

One method. The harness owns the rest.

from nonprofit_harness import Agent, AgentResult, RunContext

class BoardBriefAgent(Agent):
    name = "board-brief"
    description = "Turns programme documents into a short brief for trustees."

    def run(self, ctx: RunContext) -> AgentResult:
        artifacts = []
        for document in ctx.inputs:
            response = ctx.provider.generate(
                f"Write a 200-word brief for trustees:\n\n{document.text}",
                system="Plain language. Short sentences. No invented facts.",
            )
            artifacts.append(self.artifact("brief", response.text, title=document.name))
        return AgentResult(artifacts=artifacts, summary=f"{len(artifacts)} brief(s)")

ctx is the only thing your agent touches. It never reaches for a database handle, a global client, or an environment variable of its own. That is why the same agent runs offline in a test and on Cloud Run in production without changing, and why it cannot skip a budget ceiling or approve its own output.

See docs/writing-an-agent.md for the full guide.

What you get around it

Verifying what an artifact claims

Attach claims to an artifact and the harness checks them before anyone reviews it:

from nonprofit_harness import Citation, Claim

self.artifact(
    "brief", text,
    claims=[
        Claim(
            statement="The programme reached twelve villages.",
            citations=[Citation(text="reached twelve villages", locator="p.3")],
        )
    ],
)

Layer one costs nothing. Every quoted span must actually appear in one of the run's own input documents. No model call, no network, no spend. This is what catches an invented citation, and it runs by default.

Matching accepts an exact quote, or a close one, because models do introduce small edits while quoting. It does not accept a changed number. "reached ninety villages" shares two words in three with "reached twelve villages", so plain word overlap would wave it through, and a swapped figure is precisely the fabrication that does damage. Any quantity in the quote that is missing from the source rejects the match.

Layer two costs model calls and is opt-in. Several reviewers re-derive the claim from its evidence alone and have to agree:

export HARNESS_VERIFY_PASSES=3
export HARNESS_VERIFY_MODELS=model-a,model-b,model-c

Use different models rather than the same one several times. Reviewers that share an architecture tend to share its blind spots, which makes their agreement much weaker evidence than it looks. A reviewer that cannot decide abstains rather than guessing.

Results reach the person doing the review, not a silent filter:

{ "ok": false, "summary": "3 claim(s), 2 verified, 1 citation not found" }

A failed claim never discards work and never fails the run. It flags the artifact so a reviewer knows where to look. One consequence worth knowing: an agent that sets requires_review = False still cannot auto-release an artifact whose claims failed. Opting out of review says the output is routine. A citation that does not resolve is evidence that it is not.

Reading the sector's own data

Funders and implementing organisations publish activity data to the IATI standard. The harness reads it and turns it into ordinary harness input:

from nonprofit_harness.datasources import IatiClient

client = IatiClient(cache={})                                  # IATI_API_KEY, free
activities = client.search_activities(reporting_org="XM-DAC-41114", rows=5)

run = runner.start("my-agent", org_id="org_1",
                   inputs=[a.to_document() for a in activities])

Fetching happens on the caller's side, never inside the agent. An agent that could fetch its own data would reach past its inputs, could not be tested offline, and could not have its claims checked against a known set of sources.

The free IATI tier allows 100 calls a week, so pass a cache. See docs/datasources.md.

Readiness scoring

The engine is open. The questionnaire is data you supply.

from nonprofit_harness.readiness import Instrument, score

instrument = Instrument.from_json("my-instrument.json")
result = score(instrument, {"d1": "spreadsheets", "d2": "dont_know"})

result.overall            # 0.0 to 1.0
result.dimension_scores   # per dimension
result.tier               # whichever band the overall lands in
result.excluded_questions # what was left out of scoring, and why

Two design choices worth knowing about, because they change the numbers:

• "Don't know" removes a question from scoring rather than scoring it zero. In this sector, "don't know" usually means a thin back office, not low capability. Scoring it as zero systematically marks down the smallest organisations.
• Dimensions combine with a geometric mean. An organisation with excellent tooling and no data governance is not "average". It is blocked on data governance, and an arithmetic mean hides that.

A small example instrument ships in src/nonprofit_harness/readiness/instruments/. It is a demonstration, not a validated index. Bring your own.

The Foundation authors and stewards a nonprofit AI readiness index of its own. That index is meant as a neutral standard for the sector rather than a QriusAI product, which is exactly why the scoring engine is here in the open and the questionnaire is just data.

Run it as a service

uvicorn nonprofit_harness.main:app --reload

HARNESS_AGENTS=examples.summarizer:SummarizerAgent uvicorn nonprofit_harness.main:app

GET  /healthz
GET  /v1/agents
POST /v1/uploads
POST /v1/runs
GET  /v1/runs/{id}
GET  /v1/review/queue
POST /v1/review/{run_id}/artifacts/{artifact_id}/approve
POST /v1/review/{run_id}/artifacts/{artifact_id}/reject
GET  /v1/review/{run_id}/released
POST /v1/readiness/score
POST /v1/webhooks/{source}

Interactive docs at /docs.

Configuration

Everything is environment variables. Defaults are offline, free, and reviewed. See docs/configuration.md for the full reference.

Ceilings accept none, unlimited, or off to switch off. 0 means zero, not "unset". Run harness check to print what is actually in force.

Deploying

deployment/ has a Dockerfile and Terraform for Cloud Run, configured to scale to zero so an idle deployment costs nothing. See docs/deployment.md, and read docs/security.md before exposing an instance publicly.

Use it as a starter template

This repo doubles as an agent-starter-pack remote template:

uvx agent-starter-pack create my-agent -a github.com/QriusAI-Foundation/nonprofit-agent-harness

Documentation

Project status

Honest about where this is:

Works and is tested. The agent contract, review gate, claim verification, budget ceilings, readiness engine, document extraction, in-memory and GCP storage, auth, the HTTP API, and the CLI. 119 tests, all offline, running on Python 3.11 through 3.13.

Known gaps, tracked in issues. Per-run ceilings bound a single run rather than a total, so a deployment open to the public needs quota and rate limiting on top. Runs execute in-process, so an instance restart can strand a run. Firestore queries need a composite index before the GCP backend is used in anger.

Not started. Published package on PyPI. Provider adapters beyond Gemini.

Contributing

See CONTRIBUTING.md. Issues and pull requests are welcome, especially from people running agents in actual nonprofit settings. That is the feedback this is hardest to get and the most useful to have.

License and credit

Apache License 2.0. You may fork it, change it, and use it commercially.

Attribution is required, and the license enforces it rather than asking. Section 4(d) says any derivative work must carry forward the notice in NOTICE. Keep that file and you are compliant.

About QriusAI Foundation

QriusAI Foundation is the public brand of KRIAAJEE ENVIRONMENTAL FOUNDATION, a Section 8 nonprofit. The Foundation works on AI literacy and AI capacity building with nonprofits, schools, and colleges, including in places where the digital divide is widest.

This harness is part of that work. Software is one of the ways capability transfers, and open source is the version of it that does not depend on us still being here.