Cheshire Cat AI 
Cheshire Cat AI is an open-source Python framework for learning and building conversational agents with tools, directives, memory and custom behavior. Developers should sandbox tools, restrict secrets and network access, validate model outputs, test failure modes, audit dependencies and retain human approval for consequential actions.
Starting price Free + custom services
Tool Information
Cheshire Cat AI is an open-source Python framework for learning and building conversational agents with tools, directives, memory and custom behavior. Developers should sandbox tools, restrict secrets and network access, validate model outputs, test failure modes, audit dependencies and retain human approval for consequential actions.
Use authorized inputs and grant connected accounts the least access required. Configure privacy, retention, sharing, quality, accessibility, disclosure, export, moderation and spending controls. Test representative cases, verify generated facts, calculations, citations, messages, code and media, preserve originals and version history, and retain accountable human approval before publication, outreach, deployment or operational action.
The open-source framework is available free. Users pay for their chosen models, infrastructure and maintenance, while professional implementation or support services may use custom pricing.
AI output can be inaccurate, biased, derivative, insecure, incomplete or misleading. Review consent, copyright, training and retention terms, renewals, refunds, platform rules and applicable law. Education, employment, health, finance, compliance, marketing and customer-facing workflows require qualified human review.
F.A.Q (3)
Pros and Cons
Pros
- Provides an open-source Python framework for building AI agents
- Uses a GPL-3.0 core that can be studied; modified; and redistributed
- Installs and starts with a short uv-based command sequence
- Includes a local multi-chat web interface
- Supports OpenAI; Anthropic; Gemini; Ollama; OpenRouter; and vLLM
- Lets developers switch language models with minimal changes
- Supports multiple agents in one conversation
- Allows agents to call one another
- Defines typed tools through Python decorators
- Provides directives for RAG; memory; skills; and guardrails
- Provides lifecycle hooks before and after agent runs
- Supports native Model Context Protocol servers
- Streams tokens and tool events through AG-UI
- Treats plugins as simple folders
- Supports custom API endpoints and role-based authentication
- Can ingest documents and URLs into vector memory
- Provides REST APIs
- Keeps the core free without a planned premium edition
- Has an active community and GitHub project
- Offers optional training and commercial support services
- Supports local models for more private deployments
- Can be extended by hand or by a coding agent
Cons
- It requires Python and software-development knowledge
- Self-hosters own deployment; security; monitoring; backups; and upgrades
- Using hosted models still requires external API keys and separate charges
- Agent tools can execute unsafe actions if permissions are too broad
- Function calling can choose the wrong tool or malformed arguments
- RAG can retrieve irrelevant chunks or miss critical context
- Vector memory may retain sensitive conversations and documents
- Plugins can introduce malicious code; vulnerable dependencies; or incompatible behavior
- GPL-3.0 obligations need review before embedding or distributing modified software
- Commercial plugins and adapters may have separate licenses or fees
- Local models require substantial RAM; storage; and sometimes GPU resources
- Older documentation still describes Docker and GPT-3.5 workflows
- Rapid framework changes can make tutorials and plugins stale
- Multi-agent orchestration increases loops; cost; latency; and debugging difficulty
- Model outputs remain nondeterministic and can hallucinate
- Role-based authentication must be configured and tested correctly
- MCP servers extend the trust boundary to additional tools and data sources
- No hosted enterprise SLA is inherent in the open-source package
- Community support is not the same as guaranteed production support
- Teams need evaluation; observability; rate limits; secrets management; and sandboxing
- Document ingestion must respect copyright; consent; deletion; and data residency
- Hooks and directives can conflict in complex agent pipelines
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Before you subscribe
- Check the currency, billing period, automatic renewal, usage limits and seats at checkout — prices and promotions change.
- App-store pricing can differ from website pricing by country, device and tax.
- Share only authorised data, and read the provider's encryption, retention and model-training terms first.
- AI output can be incomplete or wrong. Verify facts, citations, calculations and code before you publish or ship.
- Commercial usage rights vary by plan, provider terms and jurisdiction.
- None of these tools replaces a qualified professional for medical, legal or financial decisions.
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