Agent tooling for cost, context and memory
Not agents themselves, but the layers you add to one: cut token spend, give it memory, route and meter model calls, see what it did, and connect it to tools and the web.
By weekly package downloads, the leaders are LiteLLM (23.1M), Langfuse (8.7M), E2B (3.7M). By GitHub stars, the leaders are Firecrawl (190K), Crawl4AI (85K), Headroom (74.8K). The fastest grower over the last 30 days is Composio, with downloads up 34%. As of Oct 8, 2026.
| # | Agent | Gid Score | Downloads 7d | 7d | 30d | VS Code installs | Stars | Latest release | Price | ||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 8 |
|
73 | 23.1M | up 1.8% | — | — | 60.4K+60/day | 1.104.2today | Free (OSS) | ||
| 14 |
|
63 | 2.7M | down 3.5% | down 5.6% | — | 190K+368/day | 2.11.03mo ago | Free + $16/mo | ||
| 15 |
|
63 | 8.7M | up 11.2% | down 1.9% | — | 35.5K+39/day | 4.55.0today | Free (OSS) | ||
| 21 |
|
60 | 3.7M | up 2.6% | up 13.7% | — | 14.2K+24/day | 2.53.12d ago | Free + $150/mo | ||
| 34 |
|
55 | 1.7M | up 8.4% | up 33.7% | — | 30.5K+16/day | 0.13.09d ago | Free + $29/mo | ||
| 35 |
|
55 | 351K | up 5.9% | down 6.5% | — | 85K+61/day | 0.9.415d ago | Free (OSS) | ||
| 63 |
|
47 | 692K | up 4.8% | down 29.9% | — | 66.8K+63/day | 3.3.113d ago | Free (OSS) | ||
| 66 |
|
46 | 96.9K | down 21.1% | up 14.4% | — | 28.8K+20/day | 1.18.117d ago | Free (OSS) | ||
| 73 |
|
44 | 480K | up 5.6% | down 32.9% | — | 62.8K+38/day | 0.5.15today | Free + $10/mo | ||
| 75 |
|
44 | 160K | up 12.3% | — | — | 11.8K+12/day | 3.9.1today | Free (OSS) | ||
| 91 |
|
42 | 143K | up 25.5% | down 20.1% | — | 74.8K+76/day | 0.40.02d ago | Free (OSS) | ||
| 109 |
|
38 | 156K | up 1.3% | down 29.1% | — | 31.6K+34/day | 0.30.230d ago | Free (OSS) | ||
| No agents match that filter. | |||||||||||
Ranks are overall positions by Gid Score. Click a column to sort by what matters to you. Methodology.
What these tools add to an agent
- Fewer tokens. Context compressors such as Headroom shrink tool outputs, logs, JSON and files before they reach the model; Repomix packs a repository into one file; Context7 feeds current library docs instead of whole pages.
- Memory. Mem0 and Graphiti keep facts across sessions so the agent does not relearn the same things every time.
- One gateway for every model. LiteLLM puts 100+ model APIs behind one interface, with spend tracking, budgets and fallbacks.
- Visibility. Langfuse and Phoenix trace every model and tool call, show cost per run and run evaluations.
- Tools, sandboxes and web data. Composio connects agents to apps with managed sign-in, E2B runs agent code in isolated sandboxes, and Firecrawl and Crawl4AI turn websites into clean text for models.
Where to start
If the bill is the problem, measure first: put a gateway or tracing in front of the agent to see which calls and tool outputs use the most tokens, then add compression or tighter context where the waste is. If the agent repeats mistakes or asks the same questions, add memory. If it cannot reach the data it needs, add tools or web data.
Reading the numbers
Most of these are libraries, so downloads count installs in CI and as dependencies of other packages. Savings figures are the vendors' own and depend heavily on your workload: repetitive JSON and logs compress far more than prose or code.
Head-to-head comparisons
Related guides
Questions about agent tooling for cost, context and memory
How can I reduce the token usage of an AI coding agent?
Start by measuring which calls use the most tokens, with a gateway such as LiteLLM or tracing such as Langfuse. Then shrink what the agent reads: compress large tool outputs and logs (Headroom), pack only the files that matter (Repomix), and give it current docs instead of whole web pages (Context7). Shorter project instructions and starting new sessions for unrelated tasks also cut the context the model re-reads on every turn.
Does compressing context make the model's answers worse?
It can if important details are dropped. Tools such as Headroom keep the originals locally so the model can fetch the full text when it needs it, and publish their own accuracy benchmarks; test on your own tasks before relying on vendor savings figures.
Which agent tooling is the most downloaded?
LiteLLM, with 23.1M downloads in the last 7 days, then Langfuse (8.7M) and E2B (3.7M). Counts include installs as a dependency of other packages.