Cognify

Cognify (Hermes skill)

Give yourself a knowledge graph. ingest documents, recall facts plus how they connect. Backed by ChromaDB + networkx locally (no external services), or TurboVec + Neo4j for a shared graph.

Setup (once per box)

pip install 'cognify-kg[local]'          # or [claude] to also use Claude as extractor
export ANTHROPIC_API_KEY=...             # Claude extractor (auto-detected)
# or: export OPENROUTER_API_KEY=...      # any OpenAI-compatible model
export COGNIFY_DATA_DIR="$HOME/.cognify" # where the graph lives

Use it from the shell (simplest)

# ingest a file, a folder, or piped text — pick a tenant to isolate this agent's data
cognify ingest /path/to/handbook.pdf --tenant myagent --namespace docs
cognify ingest-dir ~/notes --glob '**/*.md' --tenant myagent --cache
echo "free text to remember" | cognify ingest - --tenant myagent

# recall: returns chunks + connected entities/relations as grounded context
cognify recall "who owns onboarding and what tool do they use?" --tenant myagent
cognify stats --tenant myagent

Parse the JSON from recall and use the entities/relations/chunks as context for your answer.

Use it over HTTP (for a shared graph or a long-running agent)

cognify-serve &      # 127.0.0.1:8799  (set COGNIFY_BACKEND=neo4j for a shared graph)
curl -s localhost:8799/ingest -d '{"path":"/docs/policy.md","tenant":"myagent"}' -H 'content-type: application/json'
curl -s localhost:8799/recall -d '{"query":"refund policy?","tenant":"myagent"}' -H 'content-type: application/json'

Rules

• Always pass a stable --tenant for this agent so your memory stays isolated

from other agents on the box.

• Ingesting calls the LLM once per chunk (cost). Use --cache on ingest-dir so

re-runs skip unchanged files.

• For a fleet-shared graph use COGNIFY_BACKEND=neo4j with NEO4J_* set; for a

private per-box graph use the default local backend.

Source & license

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

• Author: S3YED
• Source: S3YED/appie-kit
• License: MIT
• Homepage: https://weblyfe.ai

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