Mirascope
A unified interface for calling frontier models and building tool-using agents with structured outputs.
- Source repo
- Mirascope/mirascope
- Stars
- ★ 1.5k
- Last updated
- 1d ago
- License
- MIT
- Primary language
- Python
- FA score
- 55/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platformClaude API
- Cost
- Free software; you pay for model usage
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- A Python developer who wants one decorator-based interface for model calls instead of embedding provider-specific SDK calls throughout business functions.
- Not a fit if
- Users wanting a ready-made chat UI instead of writing code
- Teams needing built-in tool sandboxing and permission isolation
- Source review
- 55/100 · Major gaps 2 safety controls not found
What does this agent do, and when should you use it?
Mirascope is an MIT-licensed Python and TypeScript monorepo that presents a unified programming interface for frontier LLMs. Its Python API turns ordinary functions into model calls with @llm.call and can constrain and parse responses through Pydantic models. Developers register callable functions with @llm.tool, then drive tool execution through response.tool_calls, execute_tools(), and resume(). The project also states support for streaming, async execution, and multi-turn conversations, although those workflows are not detailed in the supplied quick start. It is an application library rather than a documented standalone chat deployment, and the repository also contains a website and unified cross-language documentation.
An application defines a Python function decorated with @llm.call("provider/model"); the string returned by that function becomes the model request, and invoking the function returns a response object. Supplying a Pydantic type such as format=Book lets the application call .parse() to obtain a typed structured result. A function decorated with @llm.tool can be passed through tools=[...]; the application inspects response.tool_calls, runs local functions with response.execute_tools(), and submits their results back through response.resume(...) until no tool calls remain. The monorepo provides Python and TypeScript implementations and examples. For repository development, bun run website:dev launches the documentation site, while bun run ci runs spelling checks, Python and TypeScript linting and type-checking, and the documentation build.
- A Python developer who wants one decorator-based interface for model calls instead of embedding provider-specific SDK calls throughout business functions.
- A backend team that needs model responses parsed into Pydantic entities such as books, tickets, or other domain records.
- An agent developer who wants to expose existing Python functions as model tools while retaining explicit control over execution and continuation.
- A team evaluating streaming, asynchronous, or multi-turn model interactions through an application library.
- An engineering organization with both Python and TypeScript services that wants to evaluate implementations in both languages with shared documentation.
How do you install or deploy this agent?
Run this in a Python project:
uv add "mirascope[all]"This requires Python and uv. Calling a hosted model also requires network access and credentials for the selected provider, but the supplied material does not specify credential names, environment variables, or supported Python versions. Bun is explicitly required only for developing the documentation website.
How do you use this agent?
Start with:
from mirascope import llm@llm.call("anthropic/claude-sonnet-4-5")
def recommend_book(genre: str):
return f"Recommend a {genre} book."response = recommend_book("fantasy")
print(response.text())For structured output, define a Pydantic BaseModel, pass it as format, and call .parse(). For tools, decorate a function with @llm.tool, register it through tools=[get_available_books], and process while response.tool_calls: with response.resume(response.execute_tools()). Provider credentials must be configured before the first request, but the source does not document the exact Anthropic credential setup.
What are this agent's strengths and limitations?
- The
@llm.callabstraction provides a single model-call interface, and the project explicitly describes support for any frontier LLM. - Structured outputs integrate directly with Pydantic and expose a clear
.parse()step. - The application retains visible control of the tool loop through
tool_calls,execute_tools(), andresume(). - The monorepo contains both Python and TypeScript implementations plus unified cross-language documentation.
- The documented scope extends beyond basic calls to streaming, async, and multi-turn conversations.
- The quick start shows only an Anthropic model identifier and does not enumerate other provider adapters or their configuration.
- The supplied material does not document credential variable names, retries, timeouts, rate limits, or failure handling.
- Tools execute through application functions, leaving permissions, isolation, and side-effect controls to the adopter.
- The Python command installs the
[all]extra, but its contents and a minimal dependency option are not explained. - A TypeScript implementation is present, but no TypeScript installation command or first invocation is included.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Mirascope This agent | 55 · Major gaps | Library / SDKFree + model costs | ★ 1.5k | 1d ago | Python | Claude API |
| TanStack AI | 76 · Some gaps | Library / SDKFree + model costs | ★ 3.2k | 4d ago | TypeScript | OpenAI API · Claude API |
| Strands Agents | 30 · Major gaps | Library / SDKFree + model costs | ★ 8.9k | 2d ago | Python | OpenAI API · Claude API |
| Composio | 51 · Major gaps | Library / SDKFree + model costs | ★ 31k | 2d ago | TypeScript | Claude Code · OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
The CI workflow limits default permissions to contents: read, while the release job separately grants only id-token: write. Test cassettes filter authorization headers, API keys, cookies, and organization identifiers, providing concrete evidence for constrained automation and sensitive-data hygiene. Deductions apply because no product-level tool sandbox, pre-execution user confirmation, provider data-retention disclosure, side-effect classification, recovery, or rollback mechanism is shown. Repository, website, copyright, and license attribution are clear, but individual maintainers are not identified and publisher identity remains unknown.
The README, package scripts, CI, and tests consistently describe unified calls, structured output, and tool loops, with coverage across Python 3.10–3.13, TypeScript, and several runtimes. Lockfile checks, frozen installs, and recorded HTTP playback support dependency availability, although real model use still depends on external providers and credentials. Named error types such as ToolExecutionError and ToolNotFoundError are visible, but their user-facing messages and recovery guidance are not. The README's claim of four CI jobs also does not fully match the larger workflow.
Examples address basic calls, Pydantic-structured results, tool-using agents, and pointers to async and streaming usage. The sources also show Python, TypeScript, Node, Bun, Deno, and a macOS-specific MLX condition. Decorators, an explicit tools list, and the tool_calls loop make activation reasonably precise. Deductions apply because the claim to support any frontier LLM is not bounded by a complete support matrix, and provider differences, network requirements, and unsuitable scenarios are not systematically documented.
The README clearly separates the Python, TypeScript, website, and documentation areas and points to an architecture document. Installation, development and CI commands, semantic versioning, and the complete MIT license are clearly presented. The examples are useful, but no FAQ is included in the supplied evidence. Limitations are limited to scattered Bun and Apple Silicon notes. A version, SemVer statement, and release workflow exist, but no changelog is shown. Mirascope, Inc. and the release workflow establish organizational responsibility, while contacts, support ownership, and a security-reporting path are absent.
Unified model calls, typed structured output, tool registration, and response-resumption loops provide outputs that applications can directly consume and offer meaningful integration value. Deductions apply because advanced capabilities are mostly referred to external documentation, and the supplied files do not demonstrate complex agent orchestration, governance benefits, or comparisons with alternatives. Token usage, latency, provider charges, and operational costs are not discussed, so cost-benefit support is thin.
Installation, examples, versioning, licensing, and CI claims can largely be traced and cross-checked across package.json, workflows, and test configuration; cross-source corroboration is particularly strong. Deductions apply because broad statements such as support for any frontier LLM lack a complete compatibility table, while streaming, async, and multi-turn support are only referenced through external documentation. The mismatch between the README's CI-job count and the workflow also weakens strict separation between verified facts and summary claims.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Application-defined tools are executed through execute_tools. Add permission isolation, argument review, and per-action confirmation before connecting tools that can write, delete, pay, or send messages.
- Calls depend on external model providers and API keys, but the supplied material does not explain provider retention, residency, telemetry, or privacy behavior.
- Filtering sensitive VCR headers is helpful, but request bodies and URIs may also contain sensitive material; the configuration matches on URI and body and may record them.
- Do not infer that CI or coverage passed at this revision solely from README badges; no tests were executed for this assessment.
- Installing the all extra can broaden the dependency surface. Review language-specific lockfiles, transitive dependencies, and only the provider extras actually required before deployment.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
-
Release notes excerpt
What's Changed * feat(providers): add XAIProvider for Grok via Responses API by @NishchayMahor in https://github.com/Mirascope/mirascope/pull/2865 * chore: bump minor version number by @willbakst in https://github.com/Mirascope/mirascope/pull/2867 New Contributors * @NishchayMahor made their first contribution in https://github.com/Mirascope/mirascope/pull/2865 Full Changelog: https://github.com/Mirascope/mirascope/compare/v2.4.0...v2.5.0View on GitHub ↗ -
Release notes excerpt
BREAKING We're unfortunately discontinuing Mirascope Cloud. The api module and anything else related to our cloud backend has been ripped out in this version so that it's a pure LLM SDK from now on. We will continue to build and maintain Mirascope the Python/TypeScript LLM library, but please find an alternative for your OTEL backend. What's Changed * chore: discontinue cloud by @willbakst in https://github.com/Mirascope/mirascope/pull/2856 Full Changelog: https://github.com/Mirascope/mirascope/compare/v2.3.0...v2.4.0View on GitHub ↗ -
Release notes excerpt
What's Changed * fix: resolve dark mode styling for span signatures (#2164) by @vnkaralkar in https://github.com/Mirascope/mirascope/pull/2844 New Contributors * @sazed-verity made their first contribution in https://github.com/Mirascope/mirascope/pull/2751 * @vnkaralkar made their first contribution in https://github.com/Mirascope/mirascope/pull/2844 Full Changelog: https://github.com/Mirascope/mirascope/compare/v2.2.2...v2.3.0View on GitHub ↗
FAQ
Does Mirascope include the cost of model usage?
Which credentials are required?
anthropic/claude-sonnet-4-5, but the supplied material does not name the credential variable or configuration procedure.Are tool calls completed automatically?
response.tool_calls, invokes execute_tools(), and calls resume() until the tool loop ends.