infer

name	infer
description	Use the infer skill to generate speculative inference candidates from claims, persist them on branch-local inference graphs, and run the full mutation pipeline that coordinates active memory, claims, Terminus persistence, and speculative ranking.

Infer

Generate speculative knowledge from existing claims and persist it on isolated inference branches.

When to use

• Running the mutation pipeline end-to-end on new observations
• Generating inference candidates (abductive, heuristic, predictive, etc.)
• Producing facet relation candidates between claims
• Persisting ranked speculative output to a Terminus inference branch

Quick start

import pathlib
from src.active_memory.models import WorkingItem
from src.persistence.pipeline import MutationPipeline
from src.terminus.adapter import TerminusMemoryRepository
from src.manifold_sidecar import ManifoldRankingService

root = pathlib.Path(".agent-memory")
repo = TerminusMemoryRepository(url="http://localhost:6363")
ranker = ManifoldRankingService()

pipeline = MutationPipeline(
    root,
    enable_terminus=True,
    terminus_repo=repo,
    manifold_service=ranker,
    enable_inference=True,
)

item = WorkingItem(
    item_type="observation",
    content="The auth service failed after a certificate rotation.",
    session_id="sess-1",
)

result = pipeline.run(item, session_id="sess-1")

print(result.inference_candidates)        # number of candidates generated
print(result.ranked_inference_candidates)  # number ranked by manifold sidecar
print(result.inference_branch)             # e.g. "inference/sess-1"

Using the generator directly

from src.inference.generator import InferenceGenerator
from src.claims.extractor import ClaimExtractor

extractor = ClaimExtractor()
claims = extractor.extract(text="The API started returning 401 errors.")

generator = InferenceGenerator()
candidates = generator.generate_from_claims(
    claims,
    source_branch="inference/sess-1",
    source_commit="journal-event-abc",
    session_id="sess-1",
)
facets = generator.generate_facet_candidates(
    claims,
    provenance_commit="journal-event-abc",
    source_branch="inference/sess-1",
)

Key APIs

Class	Method	Purpose
MutationPipeline	run()	Full pipeline: active write → journal → claims → Terminus → inference → ranking
InferenceGenerator	generate_from_claims()	Produce InferenceNode candidates from a list of claims
InferenceGenerator	generate_facet_candidates()	Produce FacetRelation candidates between adjacent claims

Grounding

• src/persistence/pipeline.py — mutation pipeline orchestration
• src/inference/generator.py — rule-based inference and facet generation
• src/inference/models.py — InferenceNode and FacetRelation schemas
• src/claims/extractor.py — upstream claim extraction
• tests/integration/test_inference_flow.py — integration coverage
• notebooks/02_speculative_inference_and_facets.ipynb — executable walkthrough

Rules

1. Route orchestration through MutationPipeline in src/persistence/pipeline.py so all pipeline steps stay synchronized.
2. Reuse InferenceGenerator from src/inference/generator.py for candidate creation.
3. Speculative output must stay on inference/* branches; never write directly to trusted memory.
4. Validate with tests/integration/test_inference_flow.py.