trustcall
Tenacious & trustworthy tool calling built on LangGraph.
Decision gist · record as of 2026-08-14
Yes, if you're building LLM-driven extraction or tool-calling workflows with complex nested schemas. The patch-based approach genuinely solves real problems (validation failures on nested structures, information loss on updates) that naive regeneration doesn't handle well. Install friction is low and there are no known vulnerabilities. Monitor the aging maintenance status, but the last release is recent enough that the package is not abandoned.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later and a configured LLM provider with API credentials.
- Low install friction with a pure-Python wheel.
- Maintenance status is aging—last release was 2025-04-14 and the package is 487 days old—so monitor for updates, but no critical signals present.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute trustcall freely in commercial and private projects with minimal restrictions.
last release 2025-04-14 (487 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,007,549 downloads/mo, #4,522 on PyPI
Alternatives
Verify before relying
pip install trustcall
from trustcall import create_extractor
extractor = create_extractor(llm, tools=[YourSchema])
result = extractor.invoke("Extract data from: ...")- Whether the aging maintenance status (487 days since first release) signals active maintenance or dormancy.
- Performance characteristics when handling deeply nested schemas or very large JSON structures.
- Compatibility with LLM providers other than those shown in documentation examples.
What it is and what it does
Trustcall is a LangGraph-based tool for making LLM-driven structured data extraction and updates more reliable and cost-effective. It works by asking LLMs to generate JSON patch operations (RFC 6902) instead of full JSON blobs, which simplifies the generation task and enables iterative, resilient error recovery. The package handles complex, nested Pydantic schemas, schema dictionaries, and Python functions as validation targets, and automatically retries failed extractions by prompting the LLM to generate only the patches needed to fix validation errors.
The core use case is tool calling workflows—extraction, routing, and multi-step agent tasks—where naive LLM JSON generation often fails on complex nested structures or when updating existing objects. By using patches, trustcall reduces both failure rates and token costs: only the parts that failed are regenerated, not the entire output. It depends on langgraph, dydantic, and jsonpatch.
Use it for
- Extract complex nested user preferences or configuration objects from conversational input without validation errors.
- Update existing JSON documents (e.g., user profiles, memory records) when an LLM receives new information, avoiding unintended deletions.
- Build multi-step LLM agent workflows that route between tools or call multiple tools in sequence with reliable structured output.
- Reduce token costs in extraction tasks by retrying only the failed portions of a schema rather than regenerating the entire output.
- Handle LLM tool calling where the schema is too complex for strict mode or standard JSON schema parsers.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you're building LLM-driven extraction or tool-calling workflows with complex nested schemas.
The patch-based approach genuinely solves real problems (validation failures on nested structures, information loss on updates) that naive regeneration doesn't handle well. Install friction is low and there are no known vulnerabilities. Monitor the aging maintenance status, but the last release is recent enough that the package is not abandoned.
Install
trustcall on PyPI
Before you install
Low install friction with a pure-Python wheel. Maintenance status is aging—last release was 2025-04-14 and the package is 487 days old—so monitor for updates, but no critical signals present.
Requires Python 3.10 or later and a configured LLM provider with API credentials.
License in practice
MIT license is permissive; you can use, modify, and distribute trustcall freely in commercial and private projects with minimal restrictions.
Quickstart
pip install trustcall
from trustcall import create_extractor
extractor = create_extractor(llm, tools=[YourSchema])
result = extractor.invoke("Extract data from: ...")Verify before relying
- Whether the aging maintenance status (487 days since first release) signals active maintenance or dormancy.
- Performance characteristics when handling deeply nested schemas or very large JSON structures.
- Compatibility with LLM providers other than those shown in documentation examples.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packageslanggraphdydanticjsonpatch |
| Maintenance | Aging 487 days since the last release |
| First released | |
| Downloads | 1,007,549 / month, #4,522 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: trustcall-0.0.39-py3-none-any.whl
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