awprism

Docs · Source · pip install awprism · The Aither World

The Aither World is an operating system for agents — a Linux you can hand to one, the runtimes it works in, and the tools it works with. awnix is the Linux underneath it; awprism is one of its 67 bricks — each installs on its own, runs offline, and needs no account.

Start here: Hand it a failure and get back ranked candidate causes, each with the one observation that would rule it in or out.

Turn a failure into ranked hypotheses — and say what would confirm each one.

```bash
pip install awprism

```python
from awprism import Prism

prism = Prism()
diagnosis = prism.diagnose(
    "the database connection timed out",
    context="this happens only on the replica, not primary",
)

for hyp in diagnosis:
    print(f"• {hyp.claim}")
    print(f"  Test: {hyp.falsifier}")
    print()

```bash
awprism diagnose "the API returns 500"
awprism diagnose "service is slow" --context config.txt --markdown
awprism diagnose "network timeout" -k 3 --json
awprism health
awprism --self-test

What this is

When debugging with an agent, the conversation collapses onto the first plausible story. That's not wrong, but it's systematically biased: the loudest error in the logs often names an innocent service, so the first plausible explanation leads away from the root cause.

Prism breaks the first-story bias by generating multiple ranked hypotheses for every failure, each with a falsifier — the single observation that would confirm or rule it out. Instead of picking a theory and defending it, you now know what to check first.

Prism ranks candidates by the evidence at hand, degrades gracefully without an LLM, and works offline.

The bug this package exists to prevent

Debugging produces a narrative. A narrative is a story that sounds good, which is the exact opposite of what you want. The story you pick is the one you'll defend, which is the story you won't question.

Prism forces the question: What if I'm wrong about this? It doesn't answer for you — it just hands you the test that would tell.

Core API

Prism(complete=None, registry=None)

Create a diagnostic engine.

Arguments: - complete — Optional LLM completion callable: (prompt: str) -> str. If omitted, Prism operates in heuristic mode using structural patterns. - registry — Optional StrategyRegistry. If omitted, a default registry with built-in strategies is created.

diagnosis = prism.diagnose(symptom, context="", k=5)

Generate ranked hypotheses for a failure.

Arguments: - symptom — Description of the failure (required, non-empty). - context — Additional background (optional). - k — Maximum number of hypotheses to return (default 5).

Returns: - Diagnosis object with .hypotheses (sorted by score) and .to_dict() / .to_markdown().

Raises: - ValueError if symptom is empty.

Hypothesis

```python
Hypothesis(
    claim="the service is down",
    score=0.7,                                     # 0.0 to 1.0
    falsifier="check the service status endpoint", # the one test
    evidence_for=["timeout occurred"],             # supporting observations
    evidence_against=[],                           # contradicting observations
    rationale="timeouts are common when services are overloaded",
)

• claim — The hypothesis statement (required).
• score — Confidence from 0.0 (ruled out) to 1.0 (certain). Always in [0, 1].
• falsifier — The single observation that would confirm or rule this out (required, non-empty).
• evidence_for — List of supporting observations (default: []).
• evidence_against — List of contradicting observations (default: []).
• rationale — Explanation of the score (default: "").

A hypothesis without a falsifier is an opinion. The package enforces non-empty falsifiers.

Diagnosis

```python
diagnosis.hypotheses       # list of Hypothesis, sorted by score (descending)
diagnosis.symptom          # the original symptom string
diagnosis.context          # the context, if provided

len(diagnosis)             # number of hypotheses
for h in diagnosis:        # iterate hypotheses
    ...
diagnosis[0]               # first hypothesis (highest score)

diagnosis.to_dict()        # serialize to dict (for JSON)
diagnosis.to_markdown()    # render as markdown (for human reading)

Degraded mode (no LLM)

Prism generates hypotheses even without a completion backend, using a registry of reusable diagnostic patterns:

• timeout — service overload, network throughput, crash+restart
• auth — wrong credentials, missing permissions, auth system down
• resource — disk full, out of memory, quota hit, memory leak
• unknown — fallback for anything else

Each pattern comes with ranked hypotheses backed by structural reasoning. You can add your own with registry.register(DiagnosticStrategy(...)).

CLI

awprism diagnose SYMPTOM [OPTIONS]

Analyze a failure.

Options: - --context FILE — Path to a file with additional context. - -k N — Max hypotheses to return (default 5). - --json — Output as JSON. - --markdown — Output as Markdown.

Examples:

```bash
awprism diagnose "API returns 500"
awprism diagnose "database timed out" --context logs.txt --markdown
awprism diagnose "network unreachable" -k 3 --json

awprism health

Run the self-test to verify Prism is working.

awprism --self-test

Prove Prism holds its core contracts offline (no network, no LLM).

awprism mcp -- for a coding agent

Serves diagnose to any MCP client over stdio. Stdlib only, starts in under a second, read-only.

```json
{"mcpServers": {"awprism": {"command": "awprism", "args": ["mcp"]}}}

Python tracebacks and pytest output are parsed: exception class, message and innermost file:line. A chained traceback (raise ... from) is diagnosed at its FIRST exception, not the wrapper printed last. context_paths are read (tail 64 KiB each, max 8 files), never written; a path that could not be read is listed under context_skipped.

What this does NOT do

It does not execute checks. Prism tells you what to measure, not how to measure it or whether the measurement passes. You decide.

It does not fix anything. It diagnoses and ranks; fixing is your call.

It does not replace a monitoring system. Alerts tell you something broke; Prism tells you what might have broken it.

It does not substitute for domain knowledge. Prism works with what you tell it. If you know the system well, use that — Prism augments it, not replaces it.

Adding diagnostic strategies

A DiagnosticStrategy is a pattern that applies to a class of failures and generates relevant hypotheses.

```python
from awprism import Prism, DiagnosticStrategy, Hypothesis

def my_pattern_applies(symptom: str) -> bool:
    return "crash" in symptom.lower()

def my_hypotheses(symptom: str, context: str) -> list[Hypothesis]:
    return [
        Hypothesis(
            claim="the process ran out of memory",
            score=0.8,
            falsifier="check memory usage at the crash time",
        ),
        Hypothesis(
            claim="a signal was sent to terminate the process",
            score=0.5,
            falsifier="check system logs for SIGKILL or administrative actions",
        ),
    ]

strategy = DiagnosticStrategy(
    name="crash",
    description="Diagnoses process crashes",
    pattern_check=my_pattern_applies,
    generate_hypotheses=my_hypotheses,
)

prism = Prism()
prism.registry.register(strategy)

diagnosis = prism.diagnose("the worker process crashed")

--self-test

Every install can prove Prism still holds its contracts, offline:

```console
$ awprism --self-test
  PASS  Hypothesis requires claim, score, and falsifier
  PASS  Diagnosis sorts by score; Prism generates >= 2 hypotheses
  PASS  Every hypothesis has a non-empty falsifier
  PASS  Registry includes timeout, auth, resource, and unknown strategies
SELF-TEST: awprism ok

The key assertions: - A Hypothesis without a falsifier is rejected. - Prism.diagnose() always generates at least 2 hypotheses (or none). - Every hypothesis has a non-empty, non-whitespace falsifier. - The StrategyRegistry includes built-in patterns for common failure classes.

The aw family

Standalone tools that share one idea: replace something you would otherwise have to trust with something you can check.

Each installs on its own, works offline, and needs no account.

awnix is the ground floor — A Linux you can hand to an agent — immutable base, capabilities included.

The Aitherium ecosystem

Every repository here is public. Each publishes an aither-manifest.json beside its page, so any surface can read every sibling's — the network is browsable from any node in it.

Built on llama.cpp · vLLM · ComfyUI · CentOS Stream · Podman · Docker · LanceDB · WireGuard · FFmpeg · Blender + Rigify · headroom · SANA · Hunyuan3D · repowise · Playwright · Chromium · Next.js · React.

License

Apache 2.0. See LICENSE file.