Comparison
rag_api vs txtai
Verdict
Pick rag_api if key Insights for Using rag_api as an ID-based RAG FastAPI Tool with Langchain and PostgreSQL/pgvector Integration; pick txtai if txtai is an all-in-one AI framework that supports semantic search, LLM orchestration, and language model workflows, making it suitable for projects that require comprehensive AI capabilities in Python.
Markdown twin · rag_api alternatives · txtai alternatives
GraphCanon updated Sep 20, 2026
6views this month
Trust & integrity
| Signal | rag_api | txtai |
|---|---|---|
| Maintenance | Steady (35d since push) As of Sep 20, 2026 · github_public_v1 | Very active (2d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- rag_api
- ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector
- txtai
- All-in-one AI framework for semantic search, LLM orchestration and language model workflows
Stars
- rag_api
- 901
- txtai
- 13k
Forks
- rag_api
- 404
- txtai
- 891
Open issues
- rag_api
- 45
- txtai
- 10
Language
- rag_api
- Python
- txtai
- Python
Adopt for
- rag_api
- Key Insights for Using rag_api as an ID-based RAG FastAPI Tool with Langchain and PostgreSQL/pgvector Integration
- txtai
- txtai is an all-in-one AI framework that supports semantic search, LLM orchestration, and language model workflows, making it suitable for projects that require comprehensive AI capabilities in Python.
Persona
- rag_api
- -
- txtai
- -
Runtime
- rag_api
- -
- txtai
- -
License
- rag_api
- MIT
- txtai
- Apache-2.0
Last pushed
- rag_api
- Aug 15, 2026
- txtai
- Sep 15, 2026
Categories
- rag_api
- Data & Retrieval, Vector Databases
- txtai
- AI Agents, Data & Retrieval, Evaluation & Observability, Inference & Serving, Model Training, Vector Databases
Trust and health
Maintenance
- rag_api
- Steady (60%)
- txtai
- Very active (96%)
Days since push
- rag_api
- 35d
- txtai
- 2d
Open issues (now)
- rag_api
- 45
- txtai
- 10
Stars delta
- rag_api
- +35 (30d)
- txtai
- +69 (30d)
Open issues delta
- rag_api
- -2 (30d)
- txtai
- 0 (30d)
Owner type
- rag_api
- User
- txtai
- Organization
Full report
- rag_api
- Trust report
- txtai
- Trust report
Shared compatibility
- Python · rag_api: Python runtime · txtai: Python runtime
Choose rag_api if…
- License: rag_api is MIT, txtai is Apache-2.0.
- Tags unique to rag_api: api, api-rest, fastapi, langchain.
- rag_api ships Docker support for self-hosted deployment.
- When you need rapid integration of REST API services for Retrieval-Augmented Generation (RAG) with robust vector storage.
When NOT to use rag_api
- Avoid using if your project cannot leverage PostgreSQL/pgvector due to license or compatibility constraints.
- Not recommended for scenarios where high-level orchestration of multiple APIs and services is necessary without a direct need for FastAPI's simplicity.
Choose txtai if…
- License: txtai is Apache-2.0, rag_api is MIT.
- Tags unique to txtai: agents, ai, ai-agents, information retrieval.
- Also covers AI Agents, Evaluation & Observability, Inference & Serving, Model Training.
- When you need a comprehensive framework that integrates semantic search, LLM orchestration, and language model workflows in a single package.
When NOT to use txtai
- If your project strictly requires a framework that is not Python-based, as txtai is specifically designed for Python environments.
- When you need a tool that focuses solely on a specific aspect of AI, such as only semantic search or only LLM orchestration, as txtai's all-in-one approach might introduce unnecessary complexity.
- If your project cannot accommodate the Apache-2.0 license, as txtai is distributed under this license and may not be suitable for projects with different licensing requirements.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (danny-avila/rag_api) · observed Sep 20, 2026
- GitHub forks (danny-avila/rag_api) · observed Sep 20, 2026
- Last push (danny-avila/rag_api) · observed Aug 15, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (neuml/txtai) · observed Sep 20, 2026
- GitHub forks (neuml/txtai) · observed Sep 20, 2026
- Last push (neuml/txtai) · observed Sep 15, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: rag_api 901 · txtai 13k (synced Sep 20, 2026).
Common questions
- What is the difference between rag_api and txtai?
- rag_api: ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector. txtai: All-in-one AI framework for semantic search, LLM orchestration and language model workflows. See the comparison table for live GitHub stats and shared categories.
- When should I choose rag_api over txtai?
- Choose rag_api over txtai when License: rag_api is MIT, txtai is Apache-2.0; Tags unique to rag_api: api, api-rest, fastapi, langchain; rag_api ships Docker support for self-hosted deployment; When you need rapid integration of REST API services for Retrieval-Augmented Generation (RAG) with robust vector storage.
- When should I choose txtai over rag_api?
- Choose txtai over rag_api when License: txtai is Apache-2.0, rag_api is MIT; Tags unique to txtai: agents, ai, ai-agents, information retrieval; Also covers AI Agents, Evaluation & Observability, Inference & Serving, Model Training; When you need a comprehensive framework that integrates semantic search, LLM orchestration, and language model workflows in a single package.
- When should I avoid rag_api?
- Avoid using if your project cannot leverage PostgreSQL/pgvector due to license or compatibility constraints. Not recommended for scenarios where high-level orchestration of multiple APIs and services is necessary without a direct need for FastAPI's simplicity.
- When should I avoid txtai?
- If your project strictly requires a framework that is not Python-based, as txtai is specifically designed for Python environments. When you need a tool that focuses solely on a specific aspect of AI, such as only semantic search or only LLM orchestration, as txtai's all-in-one approach might introduce unnecessary complexity. If your project cannot accommodate the Apache-2.0 license, as txtai is distributed under this license and may not be suitable for projects with different licensing requirements.
- Is rag_api or txtai more popular on GitHub?
- txtai has more GitHub stars (12,959 vs 901). Stars measure visibility, not whether either tool fits your constraints.
- Are rag_api and txtai open source?
- Yes - both are open-source projects on GitHub (rag_api: MIT, txtai: Apache-2.0).
- Where can I find alternatives to rag_api or txtai?
- GraphCanon lists graph-backed alternatives at rag_api alternatives and txtai alternatives (rag_api markdown twin, txtai markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, rag_api or txtai?
- rag_api: Steady. txtai: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for rag_api and txtai?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: rag_api trust report; txtai trust report.