PyAgentHound

Evidence-based debugging and root-cause analysis for AI agents and LLM applications — trace, analyze, replay, and explain why a run produced its result.

Status: Phase 1–9 MVP (Pre-Alpha). Trace capture, storage, inspection, a queryable execution graph, a deterministic rule engine (5 built-in rules), root-cause ranking, historical baseline comparison, safe replay, file-based evaluation/regression testing, a server-rendered web UI, and an optional LLM-powered explanation layer (local Ollama, evidence-grounded) work end to end and are tested — see ROADMAP_HONEST.md for the exact built-vs-not line.

Not to be confused with PyVectorHound: PyAgentHound traces and root-causes AI agent failures; PyVectorHound diagnoses RAG retrieval failures.

Which project do I need?

Part of the Mullassery open-source family. These tools have similar names — here is the short version of who does what:

If you need to…	Use
Build a repository's dependency graph and match it against vulnerability advisories (Python, Rust, Node, containers)	PyDeepMesh
Understand why a Python dependency exists, who added it, and whether it is actually used	PyDependencyCheck
Statically analyze a dbt project (risky tests, test blast radius, cost)	PyDBTGuard
Statically analyze Apache Beam / Dataflow pipelines (cost, hot keys, reliability)	PyBeamGuard
Validate Airflow DAGs and dbt runs	PyAirflowTester
Simulate cascading-failure blast radius across infrastructure, data pipelines, and AI systems	PyBlastRadius
Check the quality of the data itself at runtime (schema, drift, anomalies)	StatGuardian
Discover devices, services, and vulnerabilities on a network	PyNetworkIntel
Discover and score API security risk (real traffic, agent/MCP, Cedar policy)	PyAPICheck
Trace and root-cause AI agent failures	PyAgentHound
Diagnose RAG retrieval failures	PyVectorHound
Orchestrate local, dependency-aware tasks (DAG scheduler + content-addressed cache)	PyTaskWireCheck

Browse all projects →

What it is

PyAgentHound answers a specific question conventional observability does not:

Why did my AI application produce this result?

not just "what happened during this request?" It captures execution evidence across the full path (request → agent → prompt → retriever → vector DB → tools/MCP → model → response), assembles it into a causal execution graph, and runs deterministic analysis over that graph. Every finding distinguishes observed facts from detected anomalies from inferred causes from recommendations, and every conclusion carries an evidence reference — an inference is never presented as a fact.

It is local-first: traces write straight to a local SQLite database, and an optional local Ollama model can narrate findings. No cloud dependency.

Features / Highlights

• OpenTelemetry-compatible tracing model — traces, spans with AI-specific SpanTypes (LLM, RETRIEVAL, TOOL, MCP, ...), events, attributes, input/output, and error capture, via the pyagenthound Python SDK.
• Local-first capture — no server required; traces write to a local SQLite database. An optional API server (pyagenthound serve) + REST ingestion centralizes traces from multiple processes.
• Queryable execution graph — spans linked by parent/child structure, plus retrieved documents as their own nodes for basic data lineage.
• Deterministic rule engine — 5 built-in rules covering retrieval, tool, and configuration failures; each returns evidence-backed findings, with no LLM involved.
• Root-cause ranking — turns findings into likely_cause / contributing_factor hypotheses with a confidence score decomposed into visible components (evidence strength, causal proximity, temporal correlation, historical frequency).
• Historical baseline comparison — diffs a trace against the most recent prior successful execution of the same named workflow; never claims causality, only states what changed.
• Safe replay — clone a trace, override span attributes, and check whether a finding clears; a READ_ONLY/WRITE/DESTRUCTIVE/UNKNOWN safety classification refuses to override a non-READ_ONLY span without --allow-unsafe.
• Evaluation / regression testing — file-based JSON test cases asserting properties of a captured trace, CI-gateable via a non-zero exit on failure.
• Web UI — server-rendered (FastAPI + Jinja2, no npm/build step): requests list, request detail with an interactive execution graph, findings, root-cause hypotheses, and a replay form.
• Optional LLM explanation — a local Ollama model narrates existing findings in plain English; it never invents evidence, and any unsupported citation is a hard rejection.

Who it's for / Use cases

• Agent/LLM developers debugging an AI-shaped failure — stale retrieval, the wrong tool selected, a silently changed prompt, truncated context.
• Teams adding AI regression tests that assert a captured trace still behaves (status, output content, absence of a finding, tools not called) in CI.
• Anyone who needs a local, private, single-machine debugging loop — the whole pipeline runs on your machine.
• Practitioners who want to test a hypothesized fix without re-invoking live models or tools, using replay.

What it is not

• Not a full observability platform. It is early-stage, CLI/web-UI, and single-machine — not a replacement for a mature, widely-deployed tracing backend at scale.
• Not cloud-provider supported for explanations. The explanation layer is local Ollama only; cloud providers (OpenAI, Anthropic, etc.) are deliberately not built rather than built-and-unverified. LLMProvider is a small Protocol.
• No multi-hop failure chains yet. A chain like stale_document → incorrect_context → incorrect_output is not implemented.
• Not a live re-runner. Replay clones a captured trace and overrides attributes; it never re-invokes a real model or tool.
• Not yet production-hardened. Pre-Alpha: expect rough edges and evolving interfaces.

Project status

Pre-Alpha (v0.1.0). Phases 1–9 are implemented and tested end to end. What's still missing (cloud LLM providers for the explanation layer, multi-hop failure chains) is documented rather than stubbed. See ROADMAP_HONEST.md for the current built-vs-planned line and docs/architecture.md for the full design.

Installation

Python 3.10+:

pip install pyagenthound
pyagenthound init

For development, clone and install the editable development dependencies:

git clone https://github.com/Mullassery/PyAgentHound.git
cd PyAgentHound
pip install -e ".[dev]"

Quick start

from pyagenthound import AgentHound, SpanType

hound = AgentHound()  # local-only, writes to ~/.pyagenthound/pyagenthound.db

with hound.trace("customer-support-request") as trace:
    with trace.span("retrieval", span_type=SpanType.RETRIEVAL) as span:
        span.set_attribute("query", "cancellation policy")
        span.set_attribute("documents", ["policy-2024", "policy-2026"])

    with trace.span("llm", span_type=SpanType.LLM) as span:
        span.set_attribute("model", "gpt-4o")
        span.set_output("You can cancel within 30 days...")

pyagenthound inspect <trace_id>
pyagenthound analyze <trace_id>
pyagenthound replay <trace_id> --set 'retrieval.documents=[]'   # try a fix
pyagenthound analyze <trace_id> --explain   # optional: local-LLM narrative (needs Ollama)
pyagenthound serve   # then open http://localhost:8787 for the web UI

Usage

CLI commands:

Command	Purpose
pyagenthound init	Initialize local storage/config.
pyagenthound inspect <trace_id>	Inspect a captured trace.
pyagenthound analyze <trace_id>	Run the rule engine; add --explain for the optional local-LLM narrative.
pyagenthound replay <trace_id> --set '<attr>=<value>'	Clone and override a span; refuses non-READ_ONLY spans unless --allow-unsafe.
pyagenthound test <tests_dir>	Run file-based JSON evaluation/regression cases (non-zero exit on failure).
pyagenthound serve	Start the API server and web UI (http://localhost:8787).

Evaluation test case example:

{
  "name": "cancellation_policy_mentions_window",
  "trace_id": "cf8f6b96514d4e1e89c946e7140b2d5b",
  "assertions": [
    {"type": "STATUS_OK", "target": null},
    {"type": "OUTPUT_CONTAINS", "target": "cancel"},
    {"type": "NO_FINDING", "target": "stale_retrieval_documents"},
    {"type": "NO_TOOL_CALLED", "target": "charge_card"}
  ]
}

pyagenthound test ./tests

Documentation

• docs/quickstart.md — full walkthrough, including the API server.
• docs/architecture.md — execution graph, evidence model, and the root-cause confidence design.
• ROADMAP_HONEST.md — what's shipped vs. planned, with the honest limitations spelled out.
• CHANGELOG.md — release history.
• CONTRIBUTING.md — contribution guidelines.
• SECURITY.md — how to report a vulnerability.

Contributing

Contributions are welcome — see CONTRIBUTING.md. Run the same checks CI runs:

pip install -e ".[dev]"
ruff check pyagenthound tests examples
mypy pyagenthound
pytest -q

License

Apache-2.0 — see LICENSE.