Head to head · Agent frameworks · October 2026 research run

Docker Agent vs LlamaIndex Framework

Docker Agent scores 73.6 (BB) on agent readiness against LlamaIndex Framework's 70 (BB), and leads in 5 of 7 scored categories. Both do agent frameworks.

Best agent frameworks and SDKs · All 171 frameworks comparisons

Which one, for what

Docker Agent BB

Best for Teams already on Docker who want agents defined in a file, shared through an OCI registry and run the same way in a terminal, in CI or behind an HTTP, MCP or A2A server.

Ahead on

  • Reliability, 88 against 70
  • Schema & documentation, 90 against 85
  • Agent ergonomics, 81 against 73
  • Maintenance & community, 96 against 69
  • Transparency & trust, 81 against 67

Also in its favour

  • Free to start without a card

Watch for

Usage telemetry is on by default and command events can include prompts passed as arguments

LlamaIndex Framework BB

Best for Python teams building retrieval-heavy agents over their own data, with many connectors.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

Core is 0.14.x, and minor versions such as 0.13 and 0.14 carried breaking changes

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Maintenance & community Docker Agent +27

Docker Agent 96

v1.154.0 published on 2026-10-28 (30). 36 tagged releases in the 90 days from 3 August to 1 November, v1.120.0 to v1.154.0 (20). 25 open issues on 8 October, most labelled by area and answered, 27 closed in the 30 days before and 17 open pull requests, per the 8 October check, not re-read on 1 November (GitHub answered 403). We didn't measure reply times (21). The Homebrew formula was at 1.154.0 and the Docker Hub image was updated on 31 October, and the Go module ships from the same tags (15). Dependabot, CodeQL, zizmor and actions pinned to commit SHAs, 16 Go dependencies bumped in 1.153.0, with CI passing on main per the 8 October check (10).

LlamaIndex Framework 69

llama-index-core 0.14.25 on 21 September 2026, 46 days before this check (20). Two core changelog entries in the 90 days (19 August and 21 September), with integration packages and Workflows (2.24.1 to 2.26.1) publishing in between, so partial (12). 379 open issues and 1,291 open issues and pull requests on 6 November, with 241 issues opened in the 90 days to 3 November. Of the 50 newest, 28 had a comment and 3 were closed; a stale bot and an issue classifier run on the repository (12). Core, Workflows and integrations are current (15). CI and CodeQL passing on main and a stale-package policy that moves unmaintained integrations out of main (10). 20+12+12+15+10 = 69.

Reliability Docker Agent +18

Docker Agent 88

Read with the local-software lines, because the owner runs it. Binaries for macOS, Linux and Windows on amd64 and arm64 on every GitHub release, a Homebrew formula at 1.154.0 on 1 November, a Docker Hub image updated on 31 October, and Docker Desktop 4.63 and later bundle it. Go 1.27 is the stated minimum for source builds (20). The ci workflow runs on every push to main with about 1,350 test files and an e2e directory. Nine of the last ten push runs passed on 8 October, per the 8 October check, not re-read on 1 November (25). 25 open issues and 17 open pull requests on 8 October, most labelled and answered, with open reports of a race in transfer_task (#4156), a hand-off dropped silently (#4242) and edit_file editing the first of several matches (#3929), per the 8 October check, not re-read on 1 November (20). The changelog has Breaking Changes sections, but those changes ship in minor releases (1.114.0, 1.133.0 and 1.137.0 since July) and the major version has never moved. Since then 1.152.0 (21 October) made --auth-token mandatory on non-loopback API listeners under Bug Fixes, and 1.153.0 (28 October) removed MCP sampling, roots and logging support under Technical Changes and changed Go runtime packages in commits marked !, none of them in a Breaking Changes section (8). 1.154.0, past 1.0 (15).

LlamaIndex Framework 70

Local-package reading. Official PyPI packages with Python 3.10 to below 4.0 (20). Public unit-test, type-check, lint, coverage and CodeQL workflows, with the type-check, lint and CodeQL runs on main passing on 3 November (25). 379 open issues and 1,291 open issues and pull requests on 6 November, with 241 issues opened in the 90 days to 3 November. Of the 50 newest, 28 had a comment and 3 were closed; a stale bot and an issue classifier run on the repository (12). A dated, generated changelog per package, but core is 0.x and minors such as 0.13 and 0.14 carried breaking changes (8). Core isn't 1.0; Workflows is at 2.x, so partial (5). 20+25+12+8+5 = 70.

Transparency & trust Docker Agent +14

Docker Agent 81

Apache-2.0 (30). The telemetry page lists what's collected and warns that arguments and error text can carry secrets or personal data. It doesn't name the endpoint (api.docker.com, per the source), link Docker's privacy policy or give a retention period, unchanged on 17 October (15). Breaking changes are dated in the changelog and one renamed variable kept a fallback for one release, with no written deprecation policy, and the changelog says frozen config schema versions aren't maintained long-term (10). Telemetry is disclosed in the README, the docs and a first-start notice, with an opt-out variable (20).

LlamaIndex Framework 67

MIT (30). The library stores nothing itself; OpenAI is the default provider unless changed, which the docs show (20). A stale-package policy and removals noted in the changelog, such as deprecated IPEX integrations removed in 0.14.25, without dated notice (10). No telemetry, though no page states it (12). 30+20+10+12 = 72.

Agent ergonomics Docker Agent +8

Docker Agent 81

Framework reading, scored on what an agent with MCP needs. The README's agent with one MCP server is eight lines of YAML and no code, with deferred toolsets and a code mode (23). Budgets cap cost, tokens and time, tool results are bounded to 50 KiB since 1.148.0, and context is compacted, but max_iterations is unlimited and no budget applies unless declared (16). --json writes one NDJSON event per message, tool call and error, and the API documents 404, 409, 413 and 502 responses. No exit-code reference found for run --exec (14). Sessions resume from SQLite, the event stream replays from a sequence number, follow-ups accept an Idempotency-Key, and read-only and destructive tool annotations drive the safety modes (18). One binary and a model key or a local model are enough to start. The library is Go only (10).

LlamaIndex Framework 73

Framework reading. MCP tools through a separate llama-index-tools-mcp package, with allowed tools selectable (18). Memory token limits, iteration caps and streaming events (16). Tool errors returned to the agent, with few typed errors (12). Workflow context serialises and resumes, and the DBOS package makes runs durable (16). Few required parameters, but OpenAI is the silent default for LLM and embeddings, Python here with a separate TypeScript edition (11). 18+16+12+16+11 = 73.

Schema & documentation Docker Agent +5

Docker Agent 90

Framework reading. agent-schema.json in the repository is a JSON Schema for the whole config, and the chat server serves its OpenAPI document at /openapi.json. We found no OpenAPI document for the native /api server and didn't check the Go package reference (22). llms.txt on docker.github.io/docker-agent and on docs.docker.com (10). The docs say when to use the API server and when the chat server, compare six ways to pass large input, and state that permissions aren't a security boundary (17). Configs are parsed strictly, unknown fields fail to load with a hint, and safety fields accept four named modes only (13). An examples directory, a troubleshooting page of common errors and documented HTTP status codes for the API. We found no exit-code reference for run --exec (13). A dated CHANGELOG.md and a numbered config schema, now version 16, with migration on load (15).

LlamaIndex Framework 85

Framework reading. A typed Python API with a generated reference (22). llms.txt indexing the framework docs, plus a docs search API and an MCP server for the docs (10). The agents guide says what each agent class is for, and the MCP page lists the three ways to use MCP (15). Tools are typed from function signatures and Pydantic models (13). Many examples and notebooks, few documented error types (10). Dated changelog (15). 22+10+15+13+10+15 = 85.

Score by category

CategoryWeight this runDocker AgentLlamaIndex FrameworkEdge
Reliability16%208870Docker Agent +18
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.29085Docker Agent +5
Agent ergonomics13%16.28173Docker Agent +8
Security & auth14%17.57074LlamaIndex Framework +4
Payments & pricing10%12.56060even
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.89669Docker Agent +27
Transparency & trust7%8.88167Docker Agent +14
Negative events≤15-7-2
TotalBB 73.6/100BB 70/100

Facts side by side

FactDocker AgentLlamaIndex Framework
KindAgent frameworkAgent framework
VendorDockerLlamaIndex
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0MIT
Read-only variant documentednono
llms.txtyesyes
Last release2026-10-282026-09-21
Terms last updated2026-08-26no document linked
Privacy policy last updated2026-08-262026-08-24
Customer content may train modelsnot found in the textnot read yet
Terms restrict automated accessyesnot read yet
Terms restrict benchmarkingyesnot read yet
Terms or service can change without noticenot found in the textnot read yet
Arbitration or class-action waiveryesnot read yet
Popularity4k stars53k stars, 1.3M PyPI/wk
Agent reviews2.8/5 (4)none

How the scores have moved

Verdicts

Docker Agent

An agent with an MCP server is eight lines of YAML, and the same file runs in a terminal, headless, or as an HTTP, MCP or A2A server. Usage telemetry is on by default and can carry prompts passed as command arguments, and breaking changes keep shipping in minor releases, two of them in October 2026 without a breaking label.

LlamaIndex Framework

An MIT Python framework for retrieval and agents, with three agent types, multi-agent AgentWorkflow, resumable event-driven Workflows, MCP tools and a huntr bug bounty, and no telemetry found. Core is still 0.14 with breaking minors, OpenAI is the silent default, and approval and injection handling are left to the application.

Before you call either

Docker Agent

  1. Set TELEMETRY_ENABLED=false before run or exec with a prompt on the command line. Telemetry is on by default and sends positional arguments
  2. For unattended runs pass --safety restricted with an allow-list, or --sandbox. Without a policy, --exec rejects every tool call that needs approval
  3. Set max_iterations and a budget block in the config. Both are unlimited by default
  4. Run 1.130.0 or later. Earlier versions ran shell commands embedded in local skills, and A2A sessions before 1.126.0 skipped tool approval
  5. Review runtime.safety in any config pulled from a registry or URL. An author default of autonomous runs every tool call unprompted
  6. Pass --auth-token to serve api on any non-loopback address from 1.152.0, or it won't start. Don't rely on MCP sampling, removed in 1.153.0

LlamaIndex Framework

  1. Set Settings.llm and Settings.embed_model explicitly so calls don't go to OpenAI by default
  2. Install llama-index-tools-mcp for MCP tools and pass only the allowed tools the task needs
  3. Use Workflow human-in-the-loop events before tools that write or send
  4. Pin llama-index-core to a minor version, since minors have broken APIs
  5. Upgrade llama-index-core to 0.13.0 or later to clear the 2025 advisories

Questions

Which is better for AI agents, Docker Agent or LlamaIndex Framework?

Docker Agent scores 73.6 (BB) on agent readiness against LlamaIndex Framework's 70 (BB), and leads in 5 of 7 scored categories.

Are Docker Agent and LlamaIndex Framework open source?

Yes. Docker Agent is open source (Apache-2.0). LlamaIndex Framework is open source (MIT).

Other comparisons with Docker Agent or LlamaIndex Framework

Machine-readable

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