foghorn-ai 0.1.1

Decision staleness alerts for AI agents

foghorn

Decision staleness alerts for AI agents.

Quick Start · How It Works · CLI Reference · GitHub Action · vs. Alternatives · Contributing

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Why

AI agents make decisions. Those decisions depend on facts about the world. The world changes.

When the facts an agent depended on are no longer true, its conclusions become stale — but nothing tells you which ones, or how much to worry. You either re-run everything (expensive) or trust outdated conclusions (dangerous).

foghorn solves this by treating agent knowledge like source code: every fact and decision is version-controlled, content-addressed, and diff-able. When facts change, foghorn tells you exactly which decisions are affected and how confident you should be about the impact.

foghorn stale --exit-code   # Fails CI if any agent decision is based on stale facts

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How It Works

flowchart LR
    A[Agent records Fact\nsubject · predicate · object] --> B[Agent records Decision\ndepends_on Fact IDs]
    B --> C[foghorn commit\nWorldCommit snapshot]
    C --> D{Fact changes\nnew triple added}
    D --> E[diff_commits\ndetects added/removed]
    E --> F[compute_staleness\nfinds affected decisions]
    F --> G[StalenessAlert\nimpact_score ranked]

Core primitives:

• Fact — an immutable, content-addressed triple (subject, predicate, object). ID = SHA-256[:16] of the triple. Two agents recording the same fact always get the same ID.
• Decision — a named agent conclusion that records which Fact IDs it depended on.
• WorldCommit — a snapshot of all facts and decisions at a point in time.
• StalenessAlert — emitted when a decision's upstream facts have changed, ranked by impact_score (confidence-weighted).

Facts and decisions are staged, then committed in batches — exactly like git. diff_commits() computes the fact-level delta between two commits, and compute_staleness() propagates that delta through the dependency graph in O(changed_facts × avg_decisions_per_fact).

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Features

Feature	Details
Content-addressed facts	Same triple always produces the same ID — no duplicates
Decision dependency graph	Decisions explicitly declare which facts they relied on
Staleness propagation	compute_staleness() finds all affected decisions in one pass
Confidence-weighted impact	impact_score reflects how certain the now-stale facts were
Offline / local-first	Single SQLite file, no server required
CI exit code	--exit-code makes foghorn stale fail CI if anything is stale
JSON output	Machine-readable output for downstream automation
Markdown output	Ready-to-paste GitHub PR comment
FastAPI REST server	/fact, /decide, /commit, /stale, /log endpoints
MCP server	Model Context Protocol integration for Claude and other agents
114 tests	Comprehensive test suite covering all layers

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Quick Start

pip install foghorn-ai

from foghorn.repo import WorldRepo

repo = WorldRepo.init(".foghorn/world.db")

# Record facts your agent is relying on
f = repo.add_fact("Redis", "is-appropriate-for", "rate-limiting", confidence=0.95)
pg = repo.add_fact("Postgres", "is-primary-db", "yes")

# Record a decision that depends on those facts
repo.decide(
    "chose-redis-for-rate-limiting",
    "Redis is fast enough for our rate-limiting needs at current scale.",
    depends_on=[f.id],
)

commit = repo.commit("Initial architecture decisions")
print(commit.id)  # e.g. "a3f8b2c1d4e5f6a7"

# Later — the world changed: retract the old fact, add the replacement
repo.retract_fact(f.id)
repo.add_fact("Redis", "replaced-by", "Valkey")
repo.commit("Redis EOL notice")

# Which decisions are now stale?
alerts = repo.stale()
for alert in alerts:
    print(f"STALE: {alert.decision_label} (impact: {alert.impact_score:.0%})")

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CLI Reference

foghorn [--db PATH] COMMAND [OPTIONS]

Command	Description	Key options
fact SUBJECT PREDICATE OBJECT	Stage a new fact triple	--confidence FLOAT
decide LABEL CONTENT	Stage a decision	--on FACT_ID (repeatable)
commit	Commit all staged items	-m MESSAGE (required)
stale	Show stale decisions	--since COMMIT_ID, --format {rich,json,markdown}, --exit-code
diff	Show fact changes between HEAD and parent	--format {rich,json,markdown}
log	Show commit history	—
status	Show staged item count and HEAD	—
recommend	Show actionable recommendations for all stale decisions	—

Global options:

Option	Default	Env var
--db PATH	.foghorn/world.db	FOGHORN_DB

Examples:

# Stage facts
foghorn fact Redis is-appropriate-for rate-limiting
foghorn fact Postgres is-primary-db yes --confidence 0.9

# Stage a decision that depends on the Redis fact
foghorn decide chose-redis "Redis fits our rate-limiter requirements" \
    --on a3f8b2c1d4e5f6a7

# Commit
foghorn commit -m "Initial architecture decisions"

# Check for staleness (machine-readable)
foghorn stale --format json

# Fail CI if anything is stale
foghorn stale --exit-code

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GitHub Action

Add foghorn staleness checks to your CI pipeline:

# .github/workflows/foghorn.yml
name: foghorn staleness check
on: [push, pull_request]

jobs:
  stale:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: sandeep-alluru/foghorn@main
        with:
          db: .foghorn/world.db
          fail-on-stale: "true"

The action installs foghorn, runs foghorn stale --exit-code, and fails the job if any decisions are stale. See docs/github-action.md for full documentation.

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vs. Alternatives

	foghorn	Graphiti / Zep	Letta / Mem0	Memoria	LangGraph checkpointing
Decision-dependency tracking	Yes — explicit fact IDs per decision	No	No	No	No
Staleness alerts	Yes — ranked by impact_score	No	No	No	No
Content-addressed facts	Yes — SHA-256[:16]	No	No	No	No
Offline / local	Yes — single SQLite file	Requires Neo4j/Redis	Requires server	No	Partial
CI exit code	Yes — --exit-code flag	No	No	No	No
Primary purpose	Decision staleness tracking	Long-term agent memory	Personalized memory	In-context memory	State persistence
Graph storage	Dependency edges only	Full knowledge graph	Vector + metadata	In-context only	State snapshots
Open source	MIT	Open core	Open core	MIT	Apache 2.0

foghorn is not a general-purpose agent memory system. It is specifically designed to answer: "Given that these facts changed, which agent decisions are now invalid?"

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Claude / MCP integration

foghorn ships a Model Context Protocol server that lets Claude and other MCP-compatible agents record facts and decisions directly:

# Start the MCP server
python -m foghorn.mcp_server

# In your Claude Code project's .claude/settings.json:
{
  "mcpServers": {
    "foghorn": {
      "command": "python",
      "args": ["-m", "foghorn.mcp_server"]
    }
  }
}

Once connected, Claude can call foghorn/fact, foghorn/decide, foghorn/commit, and foghorn/stale as tools. See docs/mcp.md for the full tool schema.

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OpenAI integration

foghorn exposes a FastAPI REST server compatible with OpenAI's function-calling format. The tool definitions are in tools/openai-tools.json and the full API spec is in openapi.yaml.

# Start the REST server
uvicorn foghorn.api:app --reload

# Pass to Codex CLI or any OpenAI-compatible agent
codex --tools tools/openai-tools.json "Check which architecture decisions are stale"

Endpoints: GET /health, POST /fact, POST /decide, POST /commit, GET /stale, GET /log. See docs/openai.md for details.

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Case Studies

See how teams are using foghorn in production:

• Preventing Stale Architecture Decisions in a Coding Assistant
• Eliminating Cross-Agent Data Inconsistency in a Multi-Agent Research Pipeline

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Repository structure

foghorn/
├── src/
│   └── foghorn/
│       ├── fact.py           # Fact, Decision, StalenessAlert dataclasses
│       ├── store.py          # SQLite-backed WorldStore + WorldCommit
│       ├── staleness.py      # DiffResult, diff_commits(), compute_staleness()
│       ├── repo.py           # WorldRepo high-level API
│       ├── report.py         # print_stale(), print_diff(), to_json(), to_markdown()
│       ├── export.py         # export_json(), import_json(), export_graphviz()
│       ├── propagate.py      # propagate_staleness(), PropagationResult
│       ├── recommend.py      # recommend(), Recommendation
│       ├── cli.py            # Click CLI (fact, decide, commit, stale, diff, log, status, recommend)
│       ├── api.py            # FastAPI REST server
│       └── mcp_server.py     # MCP server (list_facts, record_decision, commit, check_stale)
├── tests/
│   ├── test_fact.py          # Fact, Decision, StalenessAlert unit tests
│   ├── test_store.py         # WorldStore + WorldCommit tests
│   ├── test_staleness.py     # Staleness propagation tests
│   ├── test_repo.py          # WorldRepo integration tests
│   └── test_cli.py           # CLI subprocess integration tests
├── examples/
│   └── demo.py               # Standalone demo script
├── docs/                     # MkDocs documentation
├── tools/
│   └── openai-tools.json     # OpenAI function-calling tool definitions
├── assets/
│   ├── hero.png              # README hero image
│   └── logo.png              # Project logo
├── action.yml                # GitHub Action
├── openapi.yaml              # OpenAPI 3.1 spec
├── pyproject.toml            # Package metadata + dependencies
└── CONTRIBUTING.md           # Contribution guide

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GitHub Topics

Suggested topics for discoverability:

ai-agents decision-tracking staleness-detection knowledge-graph sqlite mcp openai langchain llm-tools agent-memory fact-tracking ci-cd python

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