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GPTCache

Open-source semantic cache for LLM applications that stores and reuses model responses through embedding similarity to cut API cost and latency, with modular embedding, vector-store, cache-storage, and eviction components.

by Zilliz · submitted by jaytbarimbao-collab·added 2026-07-15·
CLI
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Canonical URL
https://heyclau.de/entry/tools/gptcache
Source URLs
https://gptcache.readthedocs.io/en/latest/, https://github.com/zilliztech/GPTCache, https://gptcache.readthedocs.io/
Brand
GPTCache
Brand domain
gptcache.readthedocs.io
Brand asset source
brandfetch
Safety notes
GPTCache returns previously stored answers for semantically similar prompts, so a loose similarity threshold can serve a cached response that does not actually match the new request., Cached answers bypass the live model, so freshness, correctness, and any per-request safety or policy checks must be re-evaluated rather than assumed from the original generation., Embedding functions, vector stores, and cache-storage backends can run locally or call external providers depending on configuration, which changes where prompt and response text travels., Distributed or networked cache-storage and vector-store deployments need explicit access, network-exposure, and resource-limit decisions., Eviction policies, similarity thresholds, and invalidation should be tuned and tested so stale or incorrect answers are not reused after upstream data or prompts change.
Privacy notes
GPTCache stores prompts, generated responses, embeddings, and metadata that can contain sensitive user or project data and should follow the same retention and deletion policy as the source conversations., Embeddings can encode information about the original prompts and should be access-controlled and expired like the underlying text., External embedding providers and hosted vector stores may process prompt or response text depending on the components chosen., Cache-storage and vector-store persistence, logs, and backups may retain user data beyond a single session unless retention and eviction are configured.
Author
Zilliz
Submitted by
jaytbarimbao-collab
Claim status
unclaimed
Last verified
2026-07-15

Decision playbook

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Current score

78

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Safety and privacy checks

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Package and install checks

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Setup at a glance

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Prerequisites

5 to clear

Platforms

1 listed

Install type

Copy & paste

Adoption plan

Balanced adoption plan

Current risk score 16/100. Use staged verification before broader rollout.

Risk 16

Pre-adoption checks

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Security checks

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  • Review privacy notesRequired

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Rollout

Adopt in controlled steps based on the selected plan.

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    Define rollback path and monitor errors after adoption.

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Evidence readiness

Evidence readiness matrix · balanced

Required evidence gates are covered (5/6 signals complete).

Risk 15

Source provenance

Present

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Required in this preset

Metadata review

Present

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Safety notes

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Privacy notes

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Optional in this preset

Package integrity

Missing

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Install payload

Present

Install payload is available.

Required in this preset

Required evidence gates are covered for this preset.

Decision timeline

Decision timeline · balanced

5/6 steps complete with no blocking gaps for this preset.

Risk 14

triage

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triage

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verify

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verify

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Pending

rollout

Verify install payload and commandsRequired

Install payload is available.

Done

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Prerequisite readiness

Prerequisite readiness

5 prerequisites to line up before setup. Includes a review or approval gate.

0/5 ready
Install & runtime2Review & approval1General2

Safety & privacy surface

Safety & privacy surface

5 safety and 4 privacy notes across 5 risk areas. Review closely: credentials & tokens, network access, third-party handling.

5 areas
  • SafetyNetwork accessGPTCache returns previously stored answers for semantically similar prompts, so a loose similarity threshold can serve a cached response that does not actually match the new request.
  • SafetyNetwork accessCached answers bypass the live model, so freshness, correctness, and any per-request safety or policy checks must be re-evaluated rather than assumed from the original generation.
  • SafetyThird-party handlingEmbedding functions, vector stores, and cache-storage backends can run locally or call external providers depending on configuration, which changes where prompt and response text travels.
  • SafetyNetwork accessDistributed or networked cache-storage and vector-store deployments need explicit access, network-exposure, and resource-limit decisions.
  • SafetyGeneralEviction policies, similarity thresholds, and invalidation should be tuned and tested so stale or incorrect answers are not reused after upstream data or prompts change.
  • PrivacyData retentionGPTCache stores prompts, generated responses, embeddings, and metadata that can contain sensitive user or project data and should follow the same retention and deletion policy as the source conversations.
  • PrivacyGeneralEmbeddings can encode information about the original prompts and should be access-controlled and expired like the underlying text.
  • PrivacyThird-party handlingExternal embedding providers and hosted vector stores may process prompt or response text depending on the components chosen.
  • PrivacyCredentials & tokensCache-storage and vector-store persistence, logs, and backups may retain user data beyond a single session unless retention and eviction are configured.

Disclosure: editorial

Safety notes

  • GPTCache returns previously stored answers for semantically similar prompts, so a loose similarity threshold can serve a cached response that does not actually match the new request.
  • Cached answers bypass the live model, so freshness, correctness, and any per-request safety or policy checks must be re-evaluated rather than assumed from the original generation.
  • Embedding functions, vector stores, and cache-storage backends can run locally or call external providers depending on configuration, which changes where prompt and response text travels.
  • Distributed or networked cache-storage and vector-store deployments need explicit access, network-exposure, and resource-limit decisions.
  • Eviction policies, similarity thresholds, and invalidation should be tuned and tested so stale or incorrect answers are not reused after upstream data or prompts change.

Privacy notes

  • GPTCache stores prompts, generated responses, embeddings, and metadata that can contain sensitive user or project data and should follow the same retention and deletion policy as the source conversations.
  • Embeddings can encode information about the original prompts and should be access-controlled and expired like the underlying text.
  • External embedding providers and hosted vector stores may process prompt or response text depending on the components chosen.
  • Cache-storage and vector-store persistence, logs, and backups may retain user data beyond a single session unless retention and eviction are configured.

Prerequisites

  • Python 3.8.1 or newer environment with GPTCache installed via pip and the LLM SDK being cached, for example the OpenAI client, available.
  • Chosen embedding function such as OpenAI, Hugging Face, ONNX, SentenceTransformers, or Cohere, with model license, embedding dimensions, and provider data handling reviewed.
  • Selected vector store such as FAISS, Milvus, Chroma, Weaviate, Qdrant, PGVector, or Hnswlib, plus a cache-storage backend such as SQLite, PostgreSQL, MySQL, or DuckDB, provisioned for the deployment.
  • Similarity threshold and evaluation strategy defined so near-miss queries do not return misleading cached answers.
  • Cache eviction, retention, and refresh policy decided before caching production LLM traffic.

Schema details

Install type
copy
Troubleshooting
No
Source repository stats
Scope
Source repo
Tool listing metadata
Pricing
open-source
Disclosure
editorial
Application category
DeveloperApplication
Operating system
macOS, Windows, Linux
Full copyable content
## Editorial notes

GPTCache is useful when Claude-adjacent teams want to reduce the cost and latency of repetitive LLM traffic in chat apps, agents, and pipelines. Instead of matching identical strings, it converts each query into an embedding and runs a vector similarity search so semantically similar prompts can reuse a stored answer. It is organized as pluggable components — an embedding generator, a cache-storage backend, a vector store, a similarity evaluator, a cache manager with eviction policies, and an LLM adapter — so each layer can be swapped for the provider a team already uses.

This is distinct from existing entries. Vector-database entries such as Weaviate and all-in-one frameworks such as txtai center on storing and retrieving embeddings as the primary product. GPTCache's center of gravity is a response-caching layer that happens to use an embedding function and a vector store internally to decide when a prior LLM answer can be reused; its purpose is cost and latency reduction for the model call itself, not general-purpose retrieval.

## Source notes

- The PyPI summary describes GPTCache as "a powerful caching library that can be used to speed up and lower the cost of chat applications that rely on the LLM service."
- The project describes itself as a semantic cache that can reduce LLM API expense and improve response speed by storing and reusing results rather than making repeated calls.
- The README says GPTCache converts queries into embeddings and uses a vector store for similarity search on those embeddings, so related queries already in the cache can be reused.
- The README lists modular components including an embedding generator, cache storage, a vector store, a similarity evaluator, a cache manager with eviction policies, and an LLM adapter.
- The README lists integrations across LLMs and frameworks such as OpenAI, LangChain, llama_index, Llamacpp, MiniGPT-4, and Dolly.
- The README lists embedding options such as OpenAI, ONNX, Hugging Face, Cohere, and SentenceTransformers, and vector stores such as Milvus, Zilliz Cloud, FAISS, Hnswlib, PGVector, Chroma, Weaviate, and Qdrant.
- The README lists cache-storage backends such as SQLite, PostgreSQL, MySQL, and DuckDB.
- The package installs with `pip install gptcache`, requires Python 3.8.1 or newer, and the repository `zilliztech/GPTCache` is MIT licensed.

## Duplicate check

Checked current `content/tools/`, `content/mcp/`, agents, hooks, rules, skills, commands, guides, open pull requests, and repository-wide content for `GPTCache`, `gptcache`, `zilliztech/GPTCache`, `semantic cache`, and `LLM caching`. No dedicated GPTCache entry, GPTCache source URL, or open duplicate PR was found; existing vector-database and retrieval entries (for example Weaviate and txtai) cover storage and retrieval rather than LLM response caching.

## Disclosure

Editorial listing. No paid placement or affiliate link is used. GPTCache is an MIT-licensed open-source library maintained under the `zilliztech` organization.

About this resource

Editorial notes

GPTCache is useful when Claude-adjacent teams want to reduce the cost and latency of repetitive LLM traffic in chat apps, agents, and pipelines. Instead of matching identical strings, it converts each query into an embedding and runs a vector similarity search so semantically similar prompts can reuse a stored answer. It is organized as pluggable components — an embedding generator, a cache-storage backend, a vector store, a similarity evaluator, a cache manager with eviction policies, and an LLM adapter — so each layer can be swapped for the provider a team already uses.

This is distinct from existing entries. Vector-database entries such as Weaviate and all-in-one frameworks such as txtai center on storing and retrieving embeddings as the primary product. GPTCache's center of gravity is a response-caching layer that happens to use an embedding function and a vector store internally to decide when a prior LLM answer can be reused; its purpose is cost and latency reduction for the model call itself, not general-purpose retrieval.

Source notes

  • The PyPI summary describes GPTCache as "a powerful caching library that can be used to speed up and lower the cost of chat applications that rely on the LLM service."
  • The project describes itself as a semantic cache that can reduce LLM API expense and improve response speed by storing and reusing results rather than making repeated calls.
  • The README says GPTCache converts queries into embeddings and uses a vector store for similarity search on those embeddings, so related queries already in the cache can be reused.
  • The README lists modular components including an embedding generator, cache storage, a vector store, a similarity evaluator, a cache manager with eviction policies, and an LLM adapter.
  • The README lists integrations across LLMs and frameworks such as OpenAI, LangChain, llama_index, Llamacpp, MiniGPT-4, and Dolly.
  • The README lists embedding options such as OpenAI, ONNX, Hugging Face, Cohere, and SentenceTransformers, and vector stores such as Milvus, Zilliz Cloud, FAISS, Hnswlib, PGVector, Chroma, Weaviate, and Qdrant.
  • The README lists cache-storage backends such as SQLite, PostgreSQL, MySQL, and DuckDB.
  • The package installs with pip install gptcache, requires Python 3.8.1 or newer, and the repository zilliztech/GPTCache is MIT licensed.

Duplicate check

Checked current content/tools/, content/mcp/, agents, hooks, rules, skills, commands, guides, open pull requests, and repository-wide content for GPTCache, gptcache, zilliztech/GPTCache, semantic cache, and LLM caching. No dedicated GPTCache entry, GPTCache source URL, or open duplicate PR was found; existing vector-database and retrieval entries (for example Weaviate and txtai) cover storage and retrieval rather than LLM response caching.

Disclosure

Editorial listing. No paid placement or affiliate link is used. GPTCache is an MIT-licensed open-source library maintained under the zilliztech organization.

#llm-caching#semantic-cache#cost-optimization

Source citations

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How it compares

GPTCache side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.

1 trust signal differ across this comparison (Submitter).

Field
GPTCache logoGPTCache

Open-source semantic cache for LLM applications that stores and reuses model responses through embedding similarity to cut API cost and latency, with modular embedding, vector-store, cache-storage, and eviction components.

Open dossier
Instructor logoInstructor

Open-source Python library for structured LLM outputs using Pydantic response models, validation, retries, streaming, and provider adapters.

Open dossier
txtai logotxtai

Open-source all-in-one AI framework for semantic search, LLM orchestration, and language-model workflows, built around an embeddings database that unions sparse and dense vector indexes, graph networks, and relational databases, with pipelines, workflows, agents, and web and MCP APIs.

Open dossier
Agenta logoAgenta

Open-source LLMOps platform for prompt management, prompt versioning, evaluation, and observability across LLM applications.

Open dossier
Next steps
Open dossierAPI JSONOpen LLMOpen sourceNewsletterClaim listing
Open dossierAPI JSONOpen LLMOpen sourceNewsletterClaim listing
Open dossierAPI JSONOpen LLMOpen sourceNewsletterClaim listing
Open dossierAPI JSONOpen LLMOpen sourceNewsletterClaim listing
Trust
Review statusReviewedMaintainer reviewedReviewedMaintainer reviewedReviewedMaintainer reviewedReviewedMaintainer reviewed
Package trustPackage not verifiedPackage not verifiedPackage not verifiedPackage not verified
Source provenanceSource-backedSource-backedSource-backedSource-backed
SubmitterDiffersjaytbarimbao-collaboktofeesh1davion-knightoktofeesh1
Install riskReview firstReview firstReview firstReview first
Notes Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓
BrandGPTCache logoGPTCacheInstructor logoInstructortxtai logotxtaiAgenta logoAgenta
Categorytoolstoolstoolstools
Sourcesource-backedsource-backedsource-backedsource-backed
AuthorZilliz567 LabsneumlAgenta
Added2026-07-152026-06-032026-07-102026-06-03
Platforms
CLI
CLI
CLI
CLI
Source repo————
Safety notes✓GPTCache returns previously stored answers for semantically similar prompts, so a loose similarity threshold can serve a cached response that does not actually match the new request. Cached answers bypass the live model, so freshness, correctness, and any per-request safety or policy checks must be re-evaluated rather than assumed from the original generation. Embedding functions, vector stores, and cache-storage backends can run locally or call external providers depending on configuration, which changes where prompt and response text travels. Distributed or networked cache-storage and vector-store deployments need explicit access, network-exposure, and resource-limit decisions. Eviction policies, similarity thresholds, and invalidation should be tuned and tested so stale or incorrect answers are not reused after upstream data or prompts change.✓Instructor validates structure and field constraints, but schema-valid LLM output can still be factually wrong, hallucinated, incomplete, biased, or unsafe for automated decisions. Automatic retries can increase model cost, latency, rate-limit pressure, and repeated exposure of prompts or failed outputs, so retries should be bounded and observable. Do not let successful parsing directly trigger irreversible writes, approvals, billing changes, or user-facing actions without domain checks, provenance, and fallback review.✓txtai can run language-model pipelines and agents that call tools and execute multi-step workflows, so review what a pipeline, workflow, or agent does before running it on untrusted input. When you expose the web or MCP API, run it on a trusted network or behind authentication, and do not expose an unauthenticated endpoint publicly. Local models keep inference on your machine, while hosted model APIs receive your prompts and data; scope any provider credentials to the minimum needed and keep them out of source control. Treat indexed content and model outputs as untrusted input for downstream actions, and gate any workflow step that writes data or calls external services. Keep production indexes, pipelines, and permissions narrower than notebook or example configurations.✓Agenta can manage and deploy prompt or configuration changes, so production updates should go through review and rollback controls. Webhooks and GitHub automations tied to prompt or deployment changes should be scoped to trusted repositories and guarded workflows. Evaluation and online monitoring results should support, not replace, domain review for high-risk application behavior.
Privacy notes✓GPTCache stores prompts, generated responses, embeddings, and metadata that can contain sensitive user or project data and should follow the same retention and deletion policy as the source conversations. Embeddings can encode information about the original prompts and should be access-controlled and expired like the underlying text. External embedding providers and hosted vector stores may process prompt or response text depending on the components chosen. Cache-storage and vector-store persistence, logs, and backups may retain user data beyond a single session unless retention and eviction are configured.✓Prompts, source text, extracted fields, validation errors, failed outputs, retry messages, and typed response objects may contain sensitive user or business data. Provider calls send extraction inputs and retry context to the configured LLM backend unless a local model path is used. Application logs, traces, eval datasets, examples, and debugging output can retain structured records that are easier to query and exfiltrate than raw prose.✓The embeddings database stores your indexed content and vectors, which can include personal or proprietary data, so apply retention and access-control policies to that store. Embedding and language-model pipelines send content to the models you configure; hosted APIs process it under their terms, while local models keep it on your machine. Multimodal indexing can include documents, audio, images, and video, so treat those inputs and any derived embeddings as sensitive where appropriate. Model-provider keys, index data, and any exports should be kept out of version control and access-controlled like other operational data.✓Prompt records, variants, test sets, traces, model inputs and outputs, feedback, annotations, and evaluation results may be stored in Agenta. Hosted Agenta use sends that data to Agenta Cloud; self-hosted deployments still require retention, access-control, and backup policies. Review Agenta's sensitive-data redaction and retention guidance before sending production, customer, or regulated data.
Prerequisites
  • Python 3.8.1 or newer environment with GPTCache installed via pip and the LLM SDK being cached, for example the OpenAI client, available.
  • Chosen embedding function such as OpenAI, Hugging Face, ONNX, SentenceTransformers, or Cohere, with model license, embedding dimensions, and provider data handling reviewed.
  • Selected vector store such as FAISS, Milvus, Chroma, Weaviate, Qdrant, PGVector, or Hnswlib, plus a cache-storage backend such as SQLite, PostgreSQL, MySQL, or DuckDB, provisioned for the deployment.
  • Similarity threshold and evaluation strategy defined so near-miss queries do not return misleading cached answers.
  • Python LLM application or extraction pipeline that needs typed structured outputs rather than free-form text parsing.
  • Pydantic response models, validation rules, retry policy, and downstream error-handling behavior reviewed before production use.
  • Model provider credentials and provider-specific configuration for OpenAI, Anthropic, Google, Ollama, Groq, or another supported backend.
  • Python 3.10+ project and a dependency manager to install `txtai` from PyPI (bindings for JavaScript, Java, Rust, and Go are also available).
  • A model source for embeddings and language-model pipelines, either local models (via Hugging Face Transformers and Sentence Transformers) or hosted APIs.
  • Enough local compute for the models you run, or container orchestration if you scale out.
  • The data you want to index (text, documents, audio, images, or video) and a place to store the embeddings database.
  • LLM application, prompt workflow, or agent workflow whose prompts and configurations need shared management.
  • Access to Agenta Cloud or a reviewed self-hosted Agenta deployment.
  • Provider credentials and a release policy for test sets, traces, prompt versions, and production deployment approvals.
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