SA

Salesforce AgentForce CKG

io.github.Yarmoluk/ckg-agentforce

Salesforce AgentForce knowledge graph — 40 nodes, Einstein Trust Layer, 11x fewer tokens than RAG.

Yes, your AI can use this right now.Answered a live MCP handshake in 242 ms with no sign-in needed and exposes 10 tools.

Connect it

Settings → Connectors → Add custom connector
URL: https://ckg-agentforce.onrender.com/mcp

10 tools, verified live

Pulled straight from the server with tools/list on 2026-10-17.
list_concepts

List all 40 AgentForce concepts in this knowledge graph.

search_concepts

Find AgentForce concepts by keyword. Args: query: Search term — e.g. 'resolution', 'trust', 'grounding', 'action', 'NIM'.

query_ckg

Traverse the AgentForce knowledge graph from any concept. Returns prerequisites (what this concept needs) and dependents (what it enables). Every relationship traces to an authoritative Salesforce doc URL. Args: concept: Concept name — e.g. 'Autonomous Resolution', 'Einstein Trust Layer', 'Service Agent', 'Grounding', 'NVIDIA NIM'. depth: Traversal depth 1–5 (default 3

get_prerequisites

Return the full ordered prerequisite chain for an AgentForce concept. Shows everything the concept depends on — the complete upstream path. Args: concept: Target concept — e.g. 'Autonomous Resolution', 'Multi-LoRA Serving', 'Custom Actions', 'Semantic Retrieval'.

resolution_path

Trace the exact path that determines an AgentForce autonomous resolution event. This is the $2/resolution billing path — what the agent must traverse correctly to resolve autonomously without human handoff.

route_query

Route an AgentForce question to the optimal model and reasoning approach via graph depth. The CKG graph IS the router. AgentForce dependency chains (e.g. Einstein Trust Layer → Data Cloud → NVIDIA NIM → Resolution Criteria) have typed hops that signal reasoning complexity deterministically. No heuristic: the graph decides. Routing table: hop_depth 1 → haiku · direct (simple c

verify_source

Return the source URL and content hash for an AgentForce concept node. Audit chain: edge answer → graph commit → source_hash → source_url (fetch hint) Verification: curl -s <source_url> | sha256sum # compare output to source_hash Args: concept: Concept label (partial match supported). receipt: If True, also return a signed BoundaryAttest receipt envelope (

export_benchmark_receipt

Generate a signed BoundaryAttest receipt for the agentforce CKG benchmark result. Produces a `ckg.benchmark_result.exported` claim signed with this server's Ed25519 session key. Any party holding the public key can verify the signature over the canonical JSON claim. Implements GuardrailDecisionV1 · experimental-interop-v0.1. Result: F1 0.471 over 30 queries · ckg-benchmark v0.6.2 · 4× over R

evaluate_trust_chain

Return a machine-readable JSON trust chain + ROI analysis + payment recommendation. Designed for autonomous agent consumption. The agent reads the JSON, evaluates the ROI gate, selects the cheapest positive-ROI payment option, and purchases autonomously if budget is pre-authorized. Trust chain closes the audit gap that RAG cannot: source_hashes → SHA-256 of source docs (what was known at ex

query_intersect

Answer a conjunctive query: concepts reachable from EVERY anchor at once (A AND B). query_ckg walks outward from one concept. This intersects the reachable sets of two or more, which is the shape of most real questions — "the component that satisfies A AND applies to B". Neither anchor alone answers it; the answer lives in the overlap. Every branch is an exact set of declared edges, so the int

Alternatives in CRM & Sales

Compare →
Try “Notion”, “Stripe”, “create invoice”, “browser”