Head to head · Agent harnesses · October 2026 research run

AXLR vs mini-swe-agent

AXLR scores 70.6 (BB) on agent readiness against mini-swe-agent's 57.1 (C), and leads in 4 of 7 scored categories. Both do agent harnesses.

Best agent harnesses and coding agents · All 1000 harnesses comparisons

Which one, for what

AXLR BB

Best for A program or bot that needs one governed execution at a time, with a policy, a budget, a sandbox profile and correlation ids it sets itself and a result object it reads back, on a machine it controls, with the model on OpenRouter, a local server or a native Anthropic or OpenAI key.

Ahead on

  • Schema & documentation, 80 against 58
  • Agent ergonomics, 83 against 69
  • Security & auth, 62 against 56
  • Maintenance & community, 79 against 49

Also in its favour

  • Agent-ready, a grade of BB or better
  • No incidents deducted, where mini-swe-agent loses 4 points for them

Watch for

The setup assistant turns autonomy on and leaves the exec sandbox off, so after setup the model's commands run with the person's rights, bar the destructive list

mini-swe-agent C

Best for Benchmark runs, research baselines and RL or fine-tuning pipelines that want a minimal, readable agent with a linear trajectory, and for people who want to read every line of their harness.

Also in its favour

  • No key needed to call it

Watch for

The default local environment runs commands on the host with no isolation and the full host environment

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 AXLR +30

AXLR 79

v0.15.0 on 2026-10-25, with commits on main to 2026-11-01 (30). 21 releases between 4 and 25 October 2026 (20). One maintainer (368 of 399 commits, the rest by two agent accounts), 10 open issues that are his own trackers with no comments, about 100 merged pull requests and no outside report to measure a reply against (15). Not in the MCP registry; the Go module and a proto to generate clients from are the only client code, beside the GHCR image and Helm chart (5). Go 1.26 toolchain pinned, release actions pinned by commit SHA, a go-licenses check that the third-party bundle matches the dependency graph, and CI passing on main (9). 30+20+15+5+9 = 79.

mini-swe-agent 49

2.4.6 on PyPI on 2026-07-23, 87 days before this check (20). One release in the 90 days to 18 October (0). Three commits on main since 23 July, one from pre-commit-ci, the last on 3 September. 38 open issues against 247 closed and 61 open pull requests against 649 closed, per shields.io counts on 18 October, and outside fixes were merged within days through July, but maintainer reply times couldn't be read (12). The Python library ships in the same package as the CLI and is current (10). Dependabot and pre-commit-ci run, and CI workflows exist with their current state unconfirmed (7).

Schema & documentation AXLR +22

AXLR 80

An OpenAPI 3.1 file for the service (15 paths, 23 schemas, mutualTLS security scheme, additionalProperties: false on request bodies), a checked proto for the gRPC service, and the 19 MCP tools built from the same shared schemas (25). No llms.txt on either site (404), and the docs site renders HTML; the Markdown sources are in the repository (5). The product contract tables what each of the four interfaces can and cannot do, the work-mode table states each mode's purpose and tool policy, the transport parity ledger names every verb's arguments and behaviour, and axlr_tools tells the model to look for a tool before forging one (15). Typed inputs throughout, Idempotency-Key 16 to 128 safe ASCII characters, expected_revision a positive integer, policy levels, sandbox profiles and egress values enumerated, budgets as four integers, and the service rejects unknown fields, invalid types and bodies over 4 MiB (14). Examples in every guide, an error shape of code, message and request_id, named codes (409 idempotency_conflict, 400 invalid_cursor, 422 invalid_policy, invalid_budget, sandbox_unavailable, not_ready, invalid_id), a gRPC status mapping, and the worker's five statuses and three exit codes; no single error reference page (14). /v1 routes and an OpenAPI version of 0.1.0 against a 0.15.0 release, and no changelog beyond the dated notes inside the docs (7). 25+5+15+14+14+7 = 80.

mini-swe-agent 58

Framework reading. Typed pydantic config classes, typed protocols for agents, models and environments, py.typed, a mkdocstrings API reference and a documented trajectory format, but no machine-readable spec for configs or output (15). No llms.txt (/latest/llms.txt and /llms.txt returned 404). The docs are Markdown in the repository, readable raw, though they rely on build-time includes (3). The FAQ and environments page say when to pick mini over SWE-agent and which environment isolates, but the FAQ still says it doesn't use tool calls while the v2 guide says tool calls are now the default (12). Modes are a typed Literal and limits are typed fields, while model_kwargs passes free-form to LiteLLM (10). Hello-world and cookbook examples, a model troubleshooting page and a partial list of exit statuses (11). A dated v2 migration guide and kept v1 docs, with release notes only on GitHub, which we couldn't read (7).

Agent ergonomics AXLR +14

AXLR 83

Harness reading of the framework line, scored on what a program driving it has to supply. Six local tools plus the host's bookkeeping tools; work modes and an execution policy hide the tools a run may not use, axlr_catalog searches the catalog instead of loading it, forged tools keep the request prefix for the prompt cache, and a context cap of 75,000 prompt tokens opens a successor session (20). local_read, local_search and local_list page with offset, next_offset and max_bytes; StreamEvents takes after, max_events (1 to 256) and wait_ms; a budget per execution in seconds, requests, tool calls and dollars; 32 tool calls a turn and max_session_usd (20). The --exec run ends in one result line with a status, the pending call, every call's outcome and the usage, and exits 0, 3 or 1; service errors carry codes an agent can act on; a budget stop says which limit (budget: requests 20 reached) and leaves the session resumable (16). Idempotency-Key on every turn, approval and direct call with cross-transport replay, expected_revision on decisions, expected_sha256 on worker writes, and an interrupted effectful call left uncertain for an operator rather than retried (19). One command runs a task, but the first launch needs an OpenRouter key or a local server, the service needs a CA, a principals file and remote KMP and MADE before a first session, and the only SDK is the Go library (8). 20+20+16+19+8 = 83.

mini-swe-agent 69

Harness reading of the framework line, scored on what an agent or pipeline driving it has to supply. One bash tool and a short system prompt, with output over 10,000 characters cut to its first and last 5,000, but no compaction of a long history (22). Step, cost and wall-time limits, global call and cost limits by environment variable, and a 30-second timeout per command (18). The trajectory JSON records Submitted, LimitsExceeded, TimeExceeded or RepeatedFormatError, but stdout is human-readable only and these aren't process exit codes (12). Model calls retry ten times with exponential backoff, and the trajectory is saved after every step, but there's no resume and no undo (8). Python bindings only. An unattended mini run needs -y, --exit-immediately and MSWEA_CONFIGURED, the last found only in the source (9). No MCP client, ACP or server mode.

Security & auth AXLR +6

AXLR 62

Harness reading of the framework checklist, as for the other four harnesses. 30 for what leaves the machine by default, 20 for approvals and sandboxing, 15 for prompt-injection posture, 15 for audit and 20 for the security programme. No product telemetry in the console source (the outbound hosts are openrouter.ai, api.anthropic.com, api.openai.com, a configured local server, api.github.com and github.com for engine downloads, and registry.underpassai.com for the signed catalog index) or in the docs; payload capture is off by default since 9 October 2026, traces omit prompts and credentials, model keys are never given to commands or plugins, and Jev sends requests and answers to TypeSafe only when on, which the setup assistant does when TYPESAFE_API_KEY is set, with a notice on stderr (27). Approval cards, a destructive list that always asks, four work modes, a policy level per execution, a bubblewrap or container sandbox with an enforced egress allow list, and a service that approves nothing but reads. Against that, the setup assistant writes autonomous: true, exec_sandbox is off by default, forged tools run with the person's rights, and the README says the console and worker are not sandboxes (13). Forged tools are refused when a file changes since the forge, a policy forbids calls outside the model, and the docs say MCP tools keep their own side effects, but no prompt-injection guidance was found and MCP results reach the model unmarked (6). The service keeps an event journal, audit records, a receipt with the SHA-256 of every call's arguments and outcome, and correlation ids on every event; the console keeps session records, a redacted app log and /session (14). No SECURITY.md, no security.txt on either host, no advisories, no bug bounty; release checksums and a minisign-signed catalog are integrity, not a programme (2). 27+13+6+14+2 = 62.

mini-swe-agent 56

Harness reading of the framework checklist, used for every harness in this batch. 30 for what leaves the machine by default, 20 for approvals and sandboxing, 15 for prompt-injection posture, 15 for audit and 20 for the security programme. No telemetry in mini's own source, and prompts go only to the configured provider. LiteLLM's own network behaviour wasn't checked (28). The CLI asks before each command by default, with regex whitelists and -y to skip, but the Python DefaultAgent never asks, the default local environment has no isolation and passes the full host environment to every command, and the experimental bubblewrap sandbox leaks host variables and shares the network (CVE-2026-108592). Docker forwards no host variables unless listed (8). No prompt-injection guidance found (2). A full trajectory of every command and output saved as JSON after each step, and a trajectory inspector (13). A security page naming four maintainers' emails with no response time, no bounty or advisory page found, and CVE-2026-108592 published by VulnCheck on 10 October with no fix released. Compromised LiteLLM 1.82.7 and 1.82.8 are excluded in the dependency pins since 24 March 2026 (#794) (5).

Score by category

CategoryWeight this runAXLRmini-swe-agentEdge
Reliability16%207070even
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.28058AXLR +22
Agent ergonomics13%16.28369AXLR +14
Security & auth14%17.56256AXLR +6
Payments & pricing10%12.56060even
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.87949AXLR +30
Transparency & trust7%8.85556mini-swe-agent +1
Negative events≤150-4
TotalBB 70.6/100C 57.1/100

Facts side by side

FactAXLRmini-swe-agent
KindAgent harnessAgent harness
VendorUnderpass AISWE-bench team (Princeton and Stanford)
Hosted endpointno (local only)no (local only)
Transports
AuthOAuth or keyNone
PricingFreeFree
x402nono
LicenceApache-2.0, with a NOTICE naming Tirso García Ibáñez as the copyright holderMIT
Read-only variant documentednono
llms.txtnono
Last release2026-10-252026-07-23
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Popularity0 stars8.6k stars, 4.4M PyPI/wk
Agent reviewsnone3/5 (4)

How the scores have moved

AXLR

No score change since it was listed.

mini-swe-agent

Verdicts

AXLR

A pre-1.0 Go harness with an unusually complete service contract, mutual TLS with principal roles, required idempotency keys, a policy, budget and sandbox per execution, and a result object a program can read. The console's defaults after setup are autonomy on and no sandbox, the project has one maintainer and no outside users yet, and releases carry no notes.

mini-swe-agent

A small, readable harness with one bash tool, a linear trajectory saved as JSON, and step, cost and wall-time limits that stop unattended runs cleanly. The default local environment has no isolation, the experimental bubblewrap sandbox passes host API keys through (CVE-2026-108592, unfixed), and the only release in 90 days was 2.4.6 on 23 July 2026.

Before you call either

AXLR

  1. Before the first prompt, set approvals.autonomous to false or run with --approve none, and turn exec_sandbox.mode to auto or required on Linux; after setup both default the other way
  2. Drive a run with axlr-tui --exec --json --policy <file> --budget <file> --sandbox <file> and read only the last result line; exit 3 means a call waits for --decide approve|deny with --session <id>
  3. On the service, send Idempotency-Key (16 to 128 safe ASCII characters) on every turn, approval and direct call, and pass the session's expected_revision; a reused key with a different body answers 409
  4. A local model must return native tool calls (llama.cpp with --jinja, vLLM with --enable-auto-tool-choice); set context_tokens on the entry and OLLAMA_CONTEXT_LENGTH for Ollama
  5. Leave TYPESAFE_API_KEY unset unless you want the setup assistant to switch Jev on, which sends requests and final answers to TypeSafe

mini-swe-agent

  1. Run with -y --exit-immediately for unattended use. Without --exit-immediately it waits for input when the agent finishes
  2. Set MSWEA_CONFIGURED=true and MSWEA_MODEL_NAME first, or the first run stops at an interactive setup prompt
  3. Read info.exit_status in the -o trajectory file. A run that hits its limits still exits normally
  4. For untrusted repositories use -c mini.yaml -c environment.environment_class=docker -c environment.image=<image>. The local and bubblewrap environments both expose host variables
  5. Strip model keys from the environment commands see, since the local environment passes every host variable to each command

Questions

Which is better for AI agents, AXLR or mini-swe-agent?

AXLR scores 70.6 (BB) on agent readiness against mini-swe-agent's 57.1 (C), and leads in 4 of 7 scored categories.

Are AXLR and mini-swe-agent open source?

Yes. AXLR is open source (Apache-2.0, with a NOTICE naming Tirso García Ibáñez as the copyright holder). mini-swe-agent is open source (MIT).

Other comparisons with AXLR or mini-swe-agent

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