ContextPacker: Zero‑Infrastructure Retrieval for AI Agents

In the fast‑moving world of large language models, the ability to pull in the right context from a codebase can make the difference between a useful answer and a hallucination. ContextPacker, a new API from the team behind the name, promises to give LLMs the exact files they need without the overhead of building and maintaining a vector store.

How It Works

“Give an agent the right files, it answers correctly. Give it the wrong files, it hallucinates.” – Source: contextpacker.com

The service follows a three‑step pipeline:

1. On‑the‑fly cloning – A shallow clone of the target repository is fetched over HTTPS. The clone is discarded after the request, ensuring no data is stored.
2. Structural analysis – A lightweight Gemini Flash model reads the file tree and extracts symbols to understand the repository layout. This gives the system a sense of entry points, tests, and utilities that embeddings alone cannot capture.
3. File selection & packing – The model selects roughly eight files that are most relevant to the user’s query, truncating large files intelligently to fit within the user‑defined token budget.

The final output is a Markdown document that can be fed directly into any LLM.

import httpx

resp = httpx.post(
    "https://contextpacker.com/v1/packs",
    headers={"X-API-Key": KEY},
    json={
        "repo_url": "https://github.com/pallets/flask",
        "query": "Where is session handling?",
        "max_tokens": 6000,
    }
)
context = resp.json()["markdown"]

Benchmarks That Matter

The team evaluated the API on 177 questions across 14 repositories—including both open‑source stacks like Flask, FastAPI, and Gin, and nine private codebases. The key metrics were:

“Same answer quality as embeddings, +13 % better file ranking, zero infra.” – Source: contextpacker.com

The results show that ContextPacker’s structural awareness yields a higher relevance score (NDCG) while matching the answer quality of embedding‑based retrieval. Importantly, it does so without any vector database or pre‑indexing step.

Why Not Build Your Own RAG?

Many teams spend weeks building a custom RAG pipeline: indexing the repo, maintaining a vector store, and tuning similarity thresholds. ContextPacker eliminates these burdens:

• No pre‑indexing – The first call is instant; there’s no 5‑minute wait for embeddings.
• Zero vector DB – No Pinecone, Weaviate, or Chroma required.
• Structural insight – The API understands src/, tests/, and entry points, something plain cosine similarity cannot.

“We’re infrastructure, not a product. No dashboards, no onboarding. Just an API.” – Source: contextpacker.com

Private Repositories, No Compromise

Security is a top concern for many organizations. ContextPacker addresses this by:

• Using a read‑only personal access token (PAT) that expires after one hour.
• Fetching files over HTTPS, identical to an IDE’s workflow.
• Deleting the clone immediately after the request.

The API is designed to work with any LLM; the example above shows integration with GPT‑4o‑mini, but the same pattern applies to Claude, Gemini, or any other model.

Pricing & Availability

The service offers 100 free credits on signup and a simple credit‑based model: 1 $9 for 1,000 credits. Credits never expire, and each API call consumes one credit.

“Simple pricing, no subscriptions.” – Source: contextpacker.com

Takeaway

ContextPacker demonstrates that sophisticated retrieval for AI agents does not have to be coupled with heavyweight infrastructure. By combining on‑the‑fly cloning, structural parsing, and a fast LLM selection step, it delivers high‑quality, context‑aware answers with minimal operational overhead. For developers looking to bootstrap AI agents that need to reason over code, this API offers a compelling, low‑friction alternative to traditional RAG pipelines.

Source: contextpacker.com