Foghorn
foghorn
Decision staleness alerts for AI agents.

Quick Start · How It Works · CLI Reference · GitHub Action · vs. Alternatives · Contributing
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 You either re-run everything (expensive) or trust outdated conclusions (dangerous).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 factsRelated MCP server: adr-skills
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).
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 |
|
Confidence-weighted impact |
|
Offline / local-first | Single SQLite file, no server required |
CI exit code |
|
JSON output | Machine-readable output for downstream automation |
Markdown output | Ready-to-paste GitHub PR comment |
FastAPI REST server |
|
MCP server | Model Context Protocol integration for Claude and other agents |
114 tests | Comprehensive test suite covering all layers |
Quick Start
pip install foghorn-aifrom 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%})")CLI Reference
foghorn [--db PATH] COMMAND [OPTIONS]Command | Description | Key options |
| Stage a new fact triple |
|
| Stage a decision |
|
| Commit all staged items |
|
| Show stale decisions |
|
| Show fact changes between HEAD and parent |
|
| Show commit history | — |
| Show staged item count and HEAD | — |
| Show actionable recommendations for all stale decisions | — |
Global options:
Option | Default | Env var |
|
|
|
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-codeGitHub 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.
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 — | 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?"
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.
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.
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
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 guideGitHub 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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