TokenPath

Token-level citation and attribution API for AI-generated answers

Freemium· 10M tokens free to start (no card), then $1 per 1M tokens pay-as-you-goRAGmodel-agnostic (works with any LLM output; uses in-house attribution model)

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Best for

Product and platform teams building document-grounded chat, RAG, or search where every AI claim needs a verifiable, click-through source at the sentence or phrase level.

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Teams that need a full RAG stack out of the box, ungrounded creative or chat use cases, or regulated workflows where sub-1.0 F1 attribution still requires the same human review it was meant to remove.

TokenPath is a citation and attribution API that pinpoints exactly which tokens in a source document an LLM actually drew from when producing a given answer. You POST the source document, the user's question, and the model's response; TokenPath returns a token-level mapping — with per-span confidence scores — showing which passages back each part of the answer. It is model-agnostic (OpenAI, Anthropic, or your own fine-tune) and works post-generation, so you do not have to re-run the model or rely on the model's own often-hallucinated citation tags. The team publishes a 0.815 F1 on citation-accuracy benchmarks and quotes sub-two-second latency on 20,000-token documents, which puts it in the practical range for interactive RAG chat.

The natural home for TokenPath is any RAG or grounded-QA stack where the downstream reader — a compliance officer, a support agent, a lawyer, a clinician, an internal knowledge-base user — needs to click a claim and land on the exact sentence it came from. Typical workflows: contract Q&A over a policy library, customer-support bots grounded on help-center docs, internal search over engineering wikis, and eval pipelines that want a mechanical faithfulness signal instead of an LLM-judge. Because it sits after generation and is decoupled from the retriever, teams can bolt it onto an existing LangChain, LlamaIndex, or custom pipeline without swapping their vector store or prompt template. The pay-as-you-go pricing plus a 10M-token free tier makes it cheap to prototype before committing budget.

Editor's take

A pleasingly focused piece of infrastructure — TokenPath does one thing (post-hoc, token-level attribution) that most RAG builders currently fake with brittle regex over model-emitted citation tags. If the published 0.815 F1 holds up on real corpora, it is a sensible drop-in for anyone shipping grounded chat where trust matters more than latency budget.

— The AI Tool Bible editorial team

Pros

✅ Model-agnostic — works with any LLM output, not tied to a single provider or fine-tune
✅ Runs post-generation, so no need to re-prompt or restructure existing RAG pipelines
✅ Token-level granularity with confidence scores rather than coarse chunk-level citations
✅ Fast enough for interactive use (sub-two-second on 20k-token documents)
✅ Published benchmark number (0.815 F1) gives a concrete accuracy baseline to reason about
✅ Cheap, transparent pricing ($1/M tokens) with a generous 10M-token free tier and no card required
✅ Solves a real, common RAG failure mode — hallucinated or drifting citations

Cons

⚠️ Narrow scope — only citation/attribution, not retrieval, generation, or a full RAG framework
⚠️ Adds an extra API round-trip and token cost on top of the underlying LLM call
⚠️ Public documentation is thin on SDKs, language coverage, and enterprise features like SSO/VPC deployment
⚠️ 0.815 F1 still means a non-trivial share of attributions are wrong, so it does not remove the need for human review in high-stakes domains
⚠️ Effectiveness depends on how well the source document was actually surfaced in-context — garbage retrieval in, garbage attribution out
⚠️ Young product with limited independent benchmarks or case studies from third-party users

Use cases

RAG chatbot citationcontract and policy Q&Acustomer support groundinginternal knowledge base searchcompliance and audit trails for AI answersfaithfulness evaluation in eval pipelinesclinical and legal document assistantsresearch assistant sourcing