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MCP verified Apache-2.0 Self-run

Decision Trace Reconstructor

mcp-governance-evidence-decision-trace-reconstructor · by governance-evidence

Post-hoc trace reconstruction for AI agents: turns raw runtime traces into per-property reconstructability reports naming evidenced, missing, and opaque decision facts.

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Install

$ agentstack add mcp-governance-evidence-decision-trace-reconstructor

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

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Declared compatibility

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Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

Decision Trace Reconstructor

[](https://doi.org/10.5281/zenodo.19851574)

Decision Trace Reconstructor makes agent decisions auditable after the fact: it shows whether the available traces are enough to reconstruct what happened, and names every missing or opaque decision fact.

Instead of generating another narrative, it produces a per-property reconstructability matrix: which decision-event fields are evidenced, partial, structurally absent, or opaque. No synthetic rationale is invented.

Supported Adapters

Full adapter documentation lives in [docs/adapters/](docs/adapters/README.md). Each adapter supports offline file ingest; vendor-backed adapters also support live/network ingest when the optional extra and credentials are available.

| # | Adapter | Extra | Covers | |---|---|---|---| | 01 | [langsmith](docs/adapters/langsmith.md) | [langsmith] | LangChain / LangGraph ecosystem | | 02 | [otlp](docs/adapters/otlp.md) | [otlp] | OpenTelemetry GenAI — one adapter, many backends | | 03 | [bedrock](docs/adapters/bedrock.md) | [bedrock] | AWS Bedrock AgentCore | | 04 | [openai-agents](docs/adapters/openai-agents.md) | [openai-agents] | OpenAI Agents SDK + Traces dashboard exports | | 05 | [anthropic](docs/adapters/anthropic.md) | [anthropic] | Anthropic Messages API + Computer Use | | 06 | [mcp](docs/adapters/mcp.md) | [mcp] | Model Context Protocol transcripts | | 07 | [crewai](docs/adapters/crewai.md) | [crewai] | CrewAI multi-agent telemetry | | 08 | [agentframework](docs/adapters/agentframework.md) | [agentframework] | Microsoft Agent Framework / AutoGen v0.4 | | 09 | [pydantic-ai](docs/adapters/pydantic-ai.md) | [pydantic-ai] | Pydantic AI run records | | 10 | [generic-jsonl](docs/adapters/generic-jsonl.md) | none | Custom JSONL logs via mapping config |

Unsupported source systems should use [generic-jsonl](docs/adapters/generic-jsonl.md) first. If OpenTelemetry GenAI spans are available, [otlp](docs/adapters/otlp.md) is usually the better long-term integration path.

Quick Start

# 1. Install with the relevant adapter extra
pip install -e '.[langsmith]'   # or [otlp], [bedrock], [openai-agents], ...

# 2. Ingest a trace into a fragments manifest
decision-trace ingest langsmith --from-file traces/agent_run.json \
  --architecture single_agent --stack-tier within_stack \
  --state-mutation-tools "(write|exec|drop|delete|update)" \
  --out fragments.json

# 3. Reconstruct and emit evidence reports
decision-trace reconstruct fragments.json --out report/ --jsonld

Output:

  • report/feasibility.json: per-property reconstructability categories, gap descriptions, and completeness percentage
  • report/trace.jsonld: W3C PROV-O graph, queryable via SPARQL

Ingest can also be piped directly into reconstruction:

decision-trace ingest langsmith --from-file traces/run.json --out - | \
  decision-trace reconstruct /dev/stdin --out report/ --jsonld

Result Shape

The report is intentionally diagnostic, not narrative:

| Property | Category | Gap | |---|---|---| | inputs | fully_fillable | none | | policy_basis | structurally_unfillable | active policy was not recorded | | reasoning_trace | opaque | model reasoning is not externally observable |

Documentation

  • [Installation](docs/installation.md)
  • [Adapter documentation](docs/adapters/README.md)
  • [Reports and output artifacts](docs/reports.md)
  • [Reconstruction architecture](docs/architecture.md)
  • [Development](docs/development.md)
  • [Roadmap](docs/roadmap.md)

Examples

Worked examples live under examples/_basic_agent/ and are pinned by integration tests for bit-identical reproduction. The named incident example examples/replit_drop_database/ shows reconstruction from a public-record fragment manifest.

Related Work Status

  • Companion papers for the Decision Evidence Maturity Model and related evidence-regime concepts are in preparation. They are intentionally not cited as publications until public identifiers exist.
  • Conceptual dependencies: the Decision Event Schema, the upstream Evidence Collector SDK, and the downstream Governance Benchmark Dataset.

Citation

CITATION.cff, codemeta.json, and ro-crate-metadata.json ship with the package. Version v0.1.0 is archived on Zenodo at .

License

Apache-2.0. See [LICENSE](LICENSE).

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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