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Tessera
Pricing
- Model
- Free
Summary
Agent hallucinations are invisible until they're not — until the wrong fact ships in a customer email, a support ticket cites a policy that doesn't exist, or an audit trail comes back empty. Tessera exists for that exact failure mode: it refuses to let an agent say anything it cannot trace to a source record.
Tessera operates as a deterministic evidence layer that sits between your agent and its outputs. Every claim the agent surfaces is linked to a specific source record; claims without that linkage are refused outright, not softened or hedged. Before any action executes, the agent drafts it from verified claims only and surfaces it for your review. The architecture is open-source under MIT and built to integrate with MCP-based agent setups. Where it breaks: teams that need the agent to synthesize across sources where no single record covers the answer will hit refusals that require data-model work to resolve.
Bottom line: Use Tessera when auditability and deterministic refusal of ungrounded claims are non-negotiable — it holds for enterprise deployments where every answer must trace to evidence; it will block your agent entirely in open-ended synthesis tasks until you restructure the underlying data.
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Pros
Sign in to edit- Claim-level source tracing on every agent output, which means you can hand an auditor a specific record behind any statement the agent made rather than a probability score and a shrug.
- Deterministic refusal of ungrounded claims, so hallucinated facts cannot surface as softened hedges — the agent stays silent instead of guessing, which matters when wrong answers carry compliance risk.
- Human review gate before action execution, which means the agent cannot act on an unverified claim without a sign-off — removing the failure mode where an agent takes a consequential action based on a fabricated premise.
- Quantitative faithfulness scoring, so teams can track grounding quality as a metric over time rather than relying on spot-checks or manual audits after something goes wrong.
- MIT-licensed and self-hosted via Dockerfile, which means no vendor lock-in and no data leaving your infrastructure — a requirement in regulated industries where SaaS data routing is blocked.
Cons
Sign in to edit- Agents that need to synthesize an answer spanning multiple records — where no single source fully covers the claim — will trigger refusals until the underlying data is restructured into discrete, citable units; teams handling broad knowledge-base queries spend significant time on data modeling before the agent becomes useful.
- The refusal-first design produces dead ends in open-domain or exploratory use cases where partial answers have value; teams building research assistants or general-purpose copilots will find the strict evidence gate too aggressive and move to a retrieval-augmented framework with configurable confidence thresholds rather than binary refusal.
- With two GitHub stars and no visible community issues or pull requests at the time of the scrape, the contributor base is minimal — teams that hit an integration edge case have the MIT source code to work from but no active community forum or documented support path to resolve it quickly.
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About
- Platforms
- Python, Docker
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-07-08T13:16:43.330Z
Best For
Who it's for
- Enterprise AI agent deployments requiring auditability
- Teams needing deterministic refusal of ungrounded claims
- Environments with heterogeneous data sources
- MCP-based agent architectures
What it does well
- Grounding enterprise AI agent responses in verifiable evidence
- Preventing hallucinated facts in agent outputs
- Auditing agent actions before execution
- Measuring faithfulness of agent answers quantitatively
Integrations
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Frequently Asked Questions
- Is Tessera free?
- Yes — Tessera is fully free to use. There is no paid tier.
- Is Tessera open source?
- Yes. Tessera is open source.
- Does Tessera have an API?
- Yes. Tessera exposes a developer API. See the official documentation at https://github.com/robert-vetter/tessera for details.
- Can I self-host Tessera?
- Yes. Tessera supports self-hosting on your own infrastructure.
- What platforms does Tessera support?
- Tessera is available on: Python, Docker.
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Curated lists that include this category
Enterprise AI agents fail silently — a grounded-sounding answer with no source behind it, an action taken before anyone reviewed it, a faithfulness score nobody can compute after the fact. Tessera inserts itself into that gap. The core workflow is claim-level: every statement an agent would make is decomposed into discrete claims, each of which must trace back to an exact source record. What cannot be verified against evidence is refused, not approximated. Actions are drafted from those verified claims, previewed in full, and withheld from execution until a human approves.
The differentiating design choice is determinism at the refusal layer. Most retrieval-augmented approaches soften low-confidence answers with hedging language; Tessera, per the repo README, refuses outright when the evidence threshold is not met. That makes auditability a structural property rather than a prompt engineering goal. Faithfulness scores are computed quantitatively, so teams can measure drift over time rather than spot-check outputs manually.
Tessera fits cleanly into environments running MCP-based agent architectures and heterogeneous data sources where traceability across source types matters more than response coverage. It is self-hosted, ships with a Dockerfile and pyproject.toml, and exposes an API — which means it drops into existing pipelines rather than requiring a platform migration. The friction appears when agents need to synthesize answers that no single source record fully supports: the refusal mechanism that makes Tessera trustworthy in compliance contexts becomes a blocker in exploratory or open-domain tasks. Teams in those scenarios either restructure their data to produce discrete, citable claims or move to a less strict grounding layer.
A live Hugging Face Space demo is available for evaluation. The GitHub repository includes an eval directory, suggesting the scoring mechanism is testable against your own data before production deployment.