debate.tellodb vs Emilia Protocol

debate.tellodb and Emilia Protocol are both inference engines & infra tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

debate.tellodb

The core mechanism is fact supersession: when a user moves from NYC to SF, TelloDB marks the old location as stale and filters it from active agent context — so the LLM never hallucinates a two-year-old truth. A hybrid HNSW vector plus BM25 search index handles recall, while a separate Metric Vault layer resolves numeric queries deterministically before they ever reach the LLM. The vendor reports p99 retrieval at 4.2ms and benchmarks recall precision above 95% on LongMemEval-S against 68% for standard RAG. The engine ships as a single Rust binary, self-hostable or deployable on the vendor's platform. At v0.1.0, the surface area is narrow — this is a memory layer, not a full agent runtime.

Emilia Protocol

EMILIA sits as a control layer between an agent's decision and the system of record, blocking any irreversible write until a named human has signed off on the exact action hash from their own device. The protocol's core guarantees — no replay, no self-approval, no bypassing the gate — are machine-checked as TLA+ invariants and Alloy facts on every commit, not asserted in a policy document. Every approved or rejected action produces a Merkle-anchored evidence receipt retrievable at a standard API endpoint, so your auditor gets a signed artifact, not a log you assembled after the fact. The control layer is passive: it does not plan or execute anything itself, which means there is no agentic surface area to compromise.

Attribute	debate.tellodb	Emilia Protocol
Pricing	Paid	Paid
Free trial	No	No
Open source	No	No
Has API	Yes	Yes
Self-hosted option	Yes	No
Platforms	Self-hosted binary, platform deployment	—
Pros	Fact supersession automatically marks prior user states as stale when contradicted by new input, so your agent stops confidently telling a user their old address is current. Deterministic aggregation in the Metric Vault resolves count and numeric queries before the LLM sees them, which means you stop relying on the model to do arithmetic over memory and stop getting wrong counts. Hybrid HNSW vector plus BM25 search runs in a single Rust binary, so you avoid stitching together a vector store and a keyword search service as separate infrastructure dependencies. Self-host path with an air-gapped proxy gateway option, so teams with data residency requirements can run the memory layer inside their own perimeter without routing user data through a third-party hosted service. Distillation pipeline extracts structured facts from raw conversational text rather than storing full transcripts, which means context windows stay narrow and you are not paying to re-embed every filler word.	Machine-checked formal proofs on every commit, so compliance teams can point auditors to published TLA+ invariants rather than internal policy documents that prove nothing under scrutiny. Signoff is cryptographically bound to the exact action hash, which means an agent or compromised session cannot reuse an approval for a different transaction — the replay and substitution attacks that make business email compromise so effective are closed at the protocol level. Merkle-anchored, publicly verifiable evidence receipts at a stable API endpoint, so your SOX audit trail is a signed artifact the auditor retrieves independently rather than a log your team assembles after an incident. Three independent verifier implementations — JS, Python, and Go — proven to agree, so receipt verification does not create a single point of failure or lock you into one runtime. Apache 2.0 open specification, which means a legal and security team can read exactly what they are deploying before any commercial agreement, reducing the procurement risk that opaque governance tools carry.
Cons	TelloDB is a memory substrate only — it provides no agent task planning, tool-calling scaffolding, or workflow logic. Teams that need a full agent runtime will integrate TelloDB as a dependency inside a separate framework (LangGraph, CrewAI, or similar), which means owning the glue code and debugging across two systems when memory retrieval and task execution diverge. The project is at v0.1.0 with the open-source release flagged as new. The knowledge graph engine and temporal truth decay subsystems are advertised but lack the community-tested surface area of established memory stores. Teams building production agents that cannot tolerate evolving APIs will hit breaking changes before the interface stabilizes. Fact supersession logic is deterministic by design, which works cleanly for discrete facts like location or ownership — but nuanced preference evolution ("I mostly still like coffee but only in the mornings now") requires the application layer to model partial invalidation explicitly. Teams handling ambiguous or graduated state changes find themselves writing conflict-resolution logic that the engine does not provide out of the box, at which point simpler alternatives backed by relational stores start looking more tractable.	Every irreversible action blocks until a named human approves it on their own device — there is no async or batch approval path described in the vendor docs. Teams running high-volume automated pipelines where human latency breaks throughput SLAs cannot use EMILIA as a gate without redesigning their pipeline around human review cycles, and most choose a different architecture rather than slow the pipeline. No self-hosted deployment option is documented, which means teams in air-gapped environments, strict data-residency jurisdictions, or FedRAMP-scoped infrastructure cannot route sensitive action context through an external control layer — those teams typically fall back to building internal approval workflows on their existing identity and audit stack. The formal verification scope is the authorization state machine only; the vendor states explicitly it does not prove anything about the AI model's behavior. Teams that conflate 'the protocol is safe' with 'the agent's decisions are safe' will find EMILIA prevents unauthorized execution but does nothing to catch an agent that requests plausible-but-wrong actions that a human approver rubber-stamps under time pressure.

Bottom line

debate.tellodb and Emilia Protocol are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Frequently asked questions

What is the difference between debate.tellodb and Emilia Protocol?

debate.tellodb is Paid, while Emilia Protocol is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is debate.tellodb better than Emilia Protocol?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.